Subjects -> MATHEMATICS (Total: 1075 journals)
    - APPLIED MATHEMATICS (86 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (794 journals)
    - MATHEMATICS (GENERAL) (43 journals)
    - NUMERICAL ANALYSIS (23 journals)
    - PROBABILITIES AND MATH STATISTICS (106 journals)

MATHEMATICS (794 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 5)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 7)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 39)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 12)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 23)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 8)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 11)
Advances in Materials Science     Open Access   (Followers: 19)
Advances in Mathematical Physics     Open Access   (Followers: 8)
Advances in Mathematics     Full-text available via subscription   (Followers: 17)
Advances in Nonlinear Analysis     Open Access   (Followers: 1)
Advances in Numerical Analysis     Open Access   (Followers: 9)
Advances in Operations Research     Open Access   (Followers: 13)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 10)
Advances in Pure Mathematics     Open Access   (Followers: 10)
Advances in Science and Research (ASR)     Open Access   (Followers: 9)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 7)
Afrika Matematika     Hybrid Journal   (Followers: 3)
Air, Soil & Water Research     Open Access   (Followers: 13)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 3)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access   (Followers: 1)
Al-Qadisiyah Journal for Computer Science and Mathematics     Open Access   (Followers: 1)
AL-Rafidain Journal of Computer Sciences and Mathematics     Open Access   (Followers: 5)
Algebra and Logic     Hybrid Journal   (Followers: 7)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Open Access   (Followers: 5)
Algorithms     Open Access   (Followers: 12)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 10)
American Journal of Mathematical Analysis     Open Access   (Followers: 2)
American Journal of Mathematical and Management Sciences     Hybrid Journal  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 8)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 11)
Anadol University Journal of Science and Technology B : Theoritical Sciences     Open Access  
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 6)
Analysis Mathematica     Full-text available via subscription  
Analysis. International mathematical journal of analysis and its applications     Hybrid Journal   (Followers: 5)
Annales Mathematicae Silesianae     Open Access   (Followers: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 13)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 7)
Annals of Mathematics     Full-text available via subscription   (Followers: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 14)
Annals of PDE     Hybrid Journal  
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 2)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applications of Mathematics     Hybrid Journal   (Followers: 3)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics     Open Access   (Followers: 8)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 10)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 4)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 4)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Armenian Journal of Mathematics     Open Access   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 25)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Research Journal of Mathematics     Open Access   (Followers: 1)
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 3)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 5)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 2)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 2)
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 2)
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal   (Followers: 1)
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 2)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 13)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 3)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Cadernos do IME : Série Matemática     Open Access   (Followers: 1)
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 22)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access  
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Ciencia     Open Access   (Followers: 1)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CODEE Journal     Open Access  
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 15)
Commentarii Mathematici Helvetici     Hybrid Journal  
Communications in Advanced Mathematical Sciences     Open Access  
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 4)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 4)
Complex Analysis and its Synergies     Open Access   (Followers: 3)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Compositio Mathematica     Full-text available via subscription  
Comptes Rendus Mathematique     Full-text available via subscription  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Methods     Hybrid Journal  
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 9)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 11)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
Contributions to Game Theory and Management     Open Access  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 13)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 8)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 15)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 31)
Desimal : Jurnal Matematika     Open Access   (Followers: 1)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 4)

        1 2 3 4 | Last

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Journal Cover
Algorithms
Journal Prestige (SJR): 0.217
Citation Impact (citeScore): 1
Number of Followers: 12  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1999-4893
Published by MDPI Homepage  [222 journals]
  • Algorithms, Vol. 13, Pages 50: Fractional Sliding Mode Nonlinear Procedure
           for Robust Control of an Eutrophying Microalgae Photobioreactor

    • Authors: Abraham Efraim Rodríguez-Mata, Ricardo Luna, Jose Ricardo Pérez-Correa, Alejandro Gonzalez-Huitrón, Rafael Castro-Linares, Manuel A. Duarte-Mermoud
      First page: 50
      Abstract: This paper proposes a fractional-order sliding mode controller (FOSMC) for the robust control of a nonlinear process subjected to unknown parametric disturbances. The controller aims to ensure optimal growth in photobioreactors of native microalgae involved in eutrophication of the Sinaloa rivers in Mexico. The controller design is based on the Caputo fractional integral-order derivative and on the convergence properties of a sliding surface. For nonlinear systems, the proposed FOSMC guarantees convergence to the sliding surface even in the presence of model disturbances. The proposed controller is compared to an Internal Model Control (IMC) through numerical simulations.
      Citation: Algorithms
      PubDate: 2020-02-26
      DOI: 10.3390/a13030050
      Issue No: Vol. 13, No. 3 (2020)
       
  • Algorithms, Vol. 13, Pages 51: Approximation and Uncertainty
           Quantification of Systems with Arbitrary Parameter Distributions using
           Weighted Leja Interpolation

    • Authors: Dimitrios Loukrezis , De Gersem
      First page: 51
      Abstract: Approximation and uncertainty quantification methods based on Lagrange interpolation are typically abandoned in cases where the probability distributions of one or more system parameters are not normal, uniform, or closely related distributions, due to the computational issues that arise when one wishes to define interpolation nodes for general distributions. This paper examines the use of the recently introduced weighted Leja nodes for that purpose. Weighted Leja interpolation rules are presented, along with a dimension-adaptive sparse interpolation algorithm, to be employed in the case of high-dimensional input uncertainty. The performance and reliability of the suggested approach is verified by four numerical experiments, where the respective models feature extreme value and truncated normal parameter distributions. Furthermore, the suggested approach is compared with a well-established polynomial chaos method and found to be either comparable or superior in terms of approximation and statistics estimation accuracy.
      Citation: Algorithms
      PubDate: 2020-02-26
      DOI: 10.3390/a13030051
      Issue No: Vol. 13, No. 3 (2020)
       
  • Algorithms, Vol. 13, Pages 30: Learning Manifolds from Dynamic Process
           Data

    • Authors: Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola
      First page: 30
      Abstract: Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understanding of the underlying processes requires mapping such data to a low-dimensional manifold where the dynamics of the latent processes are evident. While nonlinear spectral dimensionality reduction methods, e.g., Isomap, and their scalable variants, are conceptually fit candidates for obtaining such a mapping, the presence of the strong temporal correlation in the data can significantly impact these methods. In this paper, we first show why such methods fail when dealing with dynamic process data. A novel method, Entropy-Isomap, is proposed to handle this shortcoming. We demonstrate the effectiveness of the proposed method in the context of understanding the fabrication process of organic materials. The resulting low-dimensional representation correctly characterizes the process control variables and allows for informative visualization of the material morphology evolution.
      Citation: Algorithms
      PubDate: 2020-01-21
      DOI: 10.3390/a13020030
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 31: Constrained Connectivity in Bounded X-Width
           Multi-Interface Networks

    • Authors: Alessandro Aloisio, Alfredo Navarra
      First page: 31
      Abstract: As technology advances and the spreading of wireless devices grows, the establishment of interconnection networks is becoming crucial. Main activities that involve most of the people concern retrieving and sharing information from everywhere. In heterogeneous networks, devices can communicate by means of multiple interfaces. The choice of the most suitable interfaces to activate (switch-on) at each device results in the establishment of different connections. A connection is established when at its endpoints the devices activate at least one common interface. Each interface is assumed to consume a specific percentage of energy for its activation. This is referred to as the cost of an interface. Due to energy consumption issues, and the fact that most of the devices are battery powered, special effort must be devoted to suitable solutions that prolong the network lifetime. In this paper, we consider the so-called p-Coverage problem where each device can activate at most p of its available interfaces in order to establish all the desired connections of a given network of devices. As the problem has been shown to be NP -hard even for p = 2 and unitary costs of the interfaces, algorithmic design activities have focused in particular topologies where the problem is optimally solvable. Following this trend, we first show that the problem is polynomially solvable for graphs (modeling the underlying network) of bounded treewidth by means of the Courcelle’s theorem. Then, we provide two optimal polynomial time algorithms to solve the problem in two subclasses of graphs with bounded treewidth that are graphs of bounded pathwidth and graphs of bounded carvingwidth. The two solutions are obtained by means of dynamic programming techniques.
      Citation: Algorithms
      PubDate: 2020-01-26
      DOI: 10.3390/a13020031
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 32: Latency-bounded Target Set Selection in
           Signed Networks

    • Authors: Miriam Di Ianni, Giovanna Varricchio
      First page: 32
      Abstract: It is well-documented that social networks play a considerable role in information spreading. The dynamic processes governing the diffusion of information have been studied in many fields, including epidemiology, sociology, economics, and computer science. A widely studied problem in the area of viral marketing is the target set selection: in order to market a new product, hoping it will be adopted by a large fraction of individuals in the network, which set of individuals should we “target” (for instance, by offering them free samples of the product)' In this paper, we introduce a diffusion model in which some of the neighbors of a node have a negative influence on that node, namely, they induce the node to reject the feature that is supposed to be spread. We study the target set selection problem within this model, first proving a strong inapproximability result holding also when the diffusion process is required to reach all the nodes in a couple of rounds. Then, we consider a set of restrictions under which the problem is approximable to some extent.
      Citation: Algorithms
      PubDate: 2020-01-29
      DOI: 10.3390/a13020032
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 33: GA-Adaptive Template Matching for Offline
           Shape Motion Tracking Based on Edge Detection: IAS Estimation from the
           SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes

    • Authors: Daga, Garibaldi
      First page: 33
      Abstract: The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone's pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.
      Citation: Algorithms
      PubDate: 2020-01-29
      DOI: 10.3390/a13020033
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 34: Using Biased-Randomized Algorithms for the
           Multi-Period Product Display Problem with Dynamic Attractiveness

    • Authors: Mage Marmol, Leandro do C. Martins, Sara Hatami, Angel A. Juan, Vicenc Fernandez
      First page: 34
      Abstract: From brick-and-mortar stores to omnichannel retail, the efficient selection of products to be displayed on store tables, advertising brochures, or online front pages has become a critical issue. One possible goal is to maximize the overall `attractiveness’ level of the displayed items, i.e., to enhance the shopping experience of our potential customers as a way to increase sales and revenue. With the goal of maximizing the total attractiveness value for the visiting customers over a multi-period time horizon, this paper studies how to configure an assortment of products to be included in limited display spaces, either physical or online. In order to define a realistic scenario, several constraints are considered for each period and display table: (i) the inclusion of both expensive and non-expensive products on the display tables; (ii) the diversification of product collections; and (iii) the achievement of a minimum profit margin. Moreover, the attractiveness level of each product is assumed to be dynamic, i.e., it is reduced if the product has been displayed in a previous period (loss of novelty) and vice versa. This generates dependencies across periods. Likewise, correlations across items are also considered to account for complementary or substitute products. In the case of brick-and-mortar stores, for instance, solving this rich multi-period product display problem enables them to provide an exciting experience to their customers. As a consequence, an increase in sales revenue should be expected. In order to deal with the underlying optimization problem, which contains a quadratic objective function in its simplest version and a non-smooth one in its complete version, two biased-randomized metaheuristic algorithms are proposed. A set of new instances has been generated to test our approach and compare its performance with that of non-linear solvers.
      Citation: Algorithms
      PubDate: 2020-02-01
      DOI: 10.3390/a13020034
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 35: Adaptive Scatter Search to Solve the
           Minimum Connected Dominating Set Problem for Efficient Management of
           Wireless Networks

    • Authors: Abdel-Rahman Hedar, Shada N. Abdulaziz, Adel A. Sewisy, Gamal A. El-Sayed
      First page: 35
      Abstract: An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality.
      Citation: Algorithms
      PubDate: 2020-02-04
      DOI: 10.3390/a13020035
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 36: Approximation Algorithm for Shortest Path
           in Large Social Networks

    • Authors: Dennis Nii Ayeh Mensah, Hui Gao, Liang Wei Yang
      First page: 36
      Abstract: Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall’s algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.
      Citation: Algorithms
      PubDate: 2020-02-06
      DOI: 10.3390/a13020036
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 37: New Numerical Treatment for a Family of
           Two-Dimensional Fractional Fredholm Integro-Differential Equations

    • Authors: Amer Darweesh, Marwan Alquran, Khawla Aghzawi
      First page: 37
      Abstract: In this paper, we present a robust algorithm to solve numerically a family of two-dimensional fractional integro differential equations. The Haar wavelet method is upgraded to include in its construction the Laplace transform step. This modification has proven to reduce the accumulative errors that will be obtained in case of using the regular Haar wavelet technique. Different examples are discussed to serve two goals, the methodology and the accuracy of our new approach.
      Citation: Algorithms
      PubDate: 2020-02-09
      DOI: 10.3390/a13020037
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 38: Multi-Loop Model Reference Proportional
           Integral Derivative Controls: Design and Performance Evaluations

    • Authors: Baris Baykant Alagoz, Aleksei Tepljakov, Eduard Petlenkov, Celaleddin Yeroglu
      First page: 38
      Abstract: Due to unpredictable and fluctuating conditions in real-world control system applications, disturbance rejection is a substantial factor in robust control performance. The inherent disturbance rejection capacity of classical closed loop control systems is limited, and an increase in disturbance rejection performance of single-loop control systems affects the set-point control performance. Multi-loop control structures, which involve model reference control loops, can enhance the inherent disturbance rejection capacity of classical control loops without degrading set-point control performance; while the classical closed Proportional Integral Derivative (PID) control loop deals with stability and set-point control, the additional model reference control loop performs disturbance rejection control. This adaptive disturbance rejection, which does not influence set-point control performance, is achieved by selecting reference models as transfer functions of real control systems. This study investigates six types of multi-loop model reference (ML-MR) control structures for PID control loops and presents straightforward design schemes to enhance the disturbance rejection control performance of existing PID control loops. For this purpose, linear and non-linear ML-MR control structures are introduced, and their control performance improvements and certain inherent drawbacks of these structures are discussed. Design examples demonstrate the benefits of the ML-MR control structures for disturbance rejection performance improvement of PID control loops without severely deteriorating their set-point performance.
      Citation: Algorithms
      PubDate: 2020-02-13
      DOI: 10.3390/a13020038
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 39: Transfer Learning: Video Prediction and
           Spatiotemporal Urban Traffic Forecasting

    • Authors: Pavlyuk
      First page: 39
      Abstract: Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.
      Citation: Algorithms
      PubDate: 2020-02-13
      DOI: 10.3390/a13020039
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 40: Neural PD Controller for an Unmanned Aerial
           Vehicle Trained with Extended Kalman Filter

    • Authors: Javier Gomez-Avila, Carlos Villaseñor, Jesus Hernandez-Barragan, Nancy Arana-Daniel, Alma Y. Alanis, Carlos Lopez-Franco
      First page: 40
      Abstract: Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness.
      Citation: Algorithms
      PubDate: 2020-02-18
      DOI: 10.3390/a13020040
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 41: Designing the Uniform Stochastic
           Photomatrix Therapeutic Systems

    • Authors: Oleg K. Karaduta, Aleksei F. Deon, Yulian A. Menyaev
      First page: 41
      Abstract: Photomatrix therapeutic systems (PMTS) are widely used for the tasks of preventive, stimulating and rehabilitation medicine. They consist of low-intensity light-emitting diodes (LEDs) having the quasi-monochromatic irradiation properties. Depending on the LED matrix structures, PMTS are intended to be used for local and large areas of bio-objects. However, in the case of non-uniform irradiation of biological tissues, there is a risk of an inadequate physiological response to this type of exposure. The proposed approach considers a novel technique for designing this type of biomedical technical systems, which use the capabilities of stochastic algorithms for LED switching. As a result, the use of stochastic photomatrix systems based on the technology of uniform twisting generation of random variables significantly expands the possibilities of their medical application.
      Citation: Algorithms
      PubDate: 2020-02-18
      DOI: 10.3390/a13020041
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 42: Lower and Upper Bounds for the Discrete
           Bi-Directional Preemptive Conversion Problem with a Constant Price
           Interval

    • Authors: Michael Schwarz
      First page: 42
      Abstract: In the conversion problem, wealth has to be distributed between two assets with the objective to maximize the wealth at the end of the investment horizon. The bi-directional preemptive conversion problem with a constant price interval is the only problem, of the four main variants of the conversion problem, that has not yet been optimally solved by competitive analysis. Assuming a given number of monotonous price trends called runs, lower and upper bounds for the competitive ratio are given. In this work, the assumption of a given number of runs is rejected and lower and upper bounds for the bi-directional preemptive conversion problem with a constant price interval are given. Furthermore, an algorithm based on error balancing is given which at minimum achieves the given upper bound. It can also be shown that this algorithm is optimal for the single-period model.
      Citation: Algorithms
      PubDate: 2020-02-18
      DOI: 10.3390/a13020042
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 43: Optimal Model for Carsharing Station
           Location Based on Multi-Factor Constraints

    • Authors: Sai, Bi, Chai
      First page: 43
      Abstract: The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing companies. On the basis of the current status of carsharing development, this research considers multiple influencing factors of carsharing to meet the maximum user demand. Meanwhile, the constraint of the limited cost of the company is considered to establish a nonlinear integer programming model for station location of carsharing. A genetic algorithm is designed to solve the problem by analyzing the location model of the carsharing network. Finally, the results of a case study of Lanzhou, China show the effectiveness of the establishment and solution of the station location model.
      Citation: Algorithms
      PubDate: 2020-02-18
      DOI: 10.3390/a13020043
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 44: Modified Migrating Birds Optimization for
           Energy-Aware Flexible Job Shop Scheduling Problem

    • Authors: Li, Zhu, Jiang
      First page: 44
      Abstract: In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.
      Citation: Algorithms
      PubDate: 2020-02-20
      DOI: 10.3390/a13020044
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 45: Adaptive Tolerance Dehazing Algorithm Based
           on Dark Channel Prior

    • Authors: Yang, Tang
      First page: 45
      Abstract: The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.
      Citation: Algorithms
      PubDate: 2020-02-20
      DOI: 10.3390/a13020045
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 46: FADIT: Fast Document Image Thresholding

    • Authors: Yufang Min, Yaonan Zhang
      First page: 46
      Abstract: We propose a fast document image thresholding method (FADIT) and evaluations of the two classic methods for demonstrating the effectiveness of FADIT. We put forward two assumptions: (1) the probability of the occurrence of grayscale text and background is ideally two constants, and (2) a pixel with a low grayscale has a high probability of being classified as text and a pixel with a high grayscale has a high probability of being classified as background. With the two assumptions, a new criterion function is applied to document image thresholding in the Bayesian framework. The effectiveness of the method has been borne of a quantitative metric as well as qualitative comparisons with the state-of-the-art methods.
      Citation: Algorithms
      PubDate: 2020-02-21
      DOI: 10.3390/a13020046
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 47: Accelerating Binary String Comparisons with
           a Scalable, Streaming-Based System Architecture based on FPGAs

    • Authors: Sarah Pilz, Florian Porrmann, Martin Kaiser, Jens Hagemeyer, James M. Hogan, Ulrich Rückert
      First page: 47
      Abstract: This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m * n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.
      Citation: Algorithms
      PubDate: 2020-02-21
      DOI: 10.3390/a13020047
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 48: PD Steering Controller Utilizing the
           Predicted Position on Track for Autonomous Vehicles Driven on Slippery
           Roads

    • Authors: Natalia Alekseeva, Ivan Tanev, Katsunori Shimohara
      First page: 48
      Abstract: Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known to provide a good quality of steering on non-slippery (dry) roads. However, on slippery roads, due to the poor yaw controllability of the vehicle (suffering from understeering and oversteering), the quality of control of such controllers deteriorates. The proposed predicted PD controller (PPD controller) overcomes the main drawback of PD controllers, namely, the reactiveness of their steering behavior. The latter implies that steering output is a direct result of the currently perceived lateral- and angular deviation of the vehicle from its intended, ideal trajectory, which is the center of the lane. This reactiveness, combined with the tardiness of the yaw control of the vehicle on slippery roads, results in a significant lag in the control loop that could not be compensated completely by the predictive (derivative) component of these controllers. In our approach, keeping the controller efforts at the same level as in PD controllers by avoiding (i) complex computations and (ii) adding additional variables, the PPD controller shows better quality of steering than that of the evolved (via genetic programming) models.
      Citation: Algorithms
      PubDate: 2020-02-21
      DOI: 10.3390/a13020048
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 49: Optimization of Constrained Stochastic
           Linear-Quadratic Control on an Infinite Horizon: A Direct-Comparison Based
           Approach

    • Authors: Ruobing Xue, Xiangshen Ye, Weiping Wu
      First page: 49
      Abstract: In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov Decision Problems (MDPs) with continuous state spaces and therefore we can apply the direct-comparison based optimization approach to solve the problem. We first derive the performance difference formula for the LQ problem by utilizing the state separation property of the system structure. Based on this, we successfully derive the optimality conditions and the stationary optimal feedback control. By introducing the optimization, we establish a general framework for infinite horizon stochastic control problems. The direct-comparison based approach is applicable to both linear and nonlinear systems. Our work provides a new perspective in LQ control problems; based on this approach, learning based algorithms can be developed without identifying all of the system parameters.
      Citation: Algorithms
      PubDate: 2020-02-24
      DOI: 10.3390/a13020049
      Issue No: Vol. 13, No. 2 (2020)
       
  • Algorithms, Vol. 13, Pages 15: A Visual Object Tracking Algorithm Based on
           Improved TLD

    • Authors: Zhen, Fei, Wang, Du
      First page: 15
      Abstract: Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built‐in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments wereconducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes.Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.
      Citation: Algorithms
      PubDate: 2020-01-01
      DOI: 10.3390/a13010015
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 16: A Comparative Study of Four Metaheuristic
           Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

    • Authors: Olive Niyomubyeyi, Tome Eduardo Sicuaio, José Ignacio Díaz González, Petter Pilesjö, Ali Mansourian
      First page: 16
      Abstract: Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.
      Citation: Algorithms
      PubDate: 2020-01-03
      DOI: 10.3390/a13010016
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 17: A Grey-Box Ensemble Model Exploiting
           Black-Box Accuracy and White-Box Intrinsic Interpretability

    • Authors: Emmanuel Pintelas, Ioannis E. Livieris, Panagiotis Pintelas
      First page: 17
      Abstract: Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.
      Citation: Algorithms
      PubDate: 2020-01-05
      DOI: 10.3390/a13010017
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 18: Top Position Sensitive Ordinal Relation
           Preserving Bitwise Weight for Image Retrieval

    • Authors: Zhen Wang, Fuzhen Sun, Longbo Zhang, Lei Wang, Pingping Liu
      First page: 18
      Abstract: In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the importance of each binary bit as the same and treat training samples at different positions equally, which causes many data pairs to share the same Hamming distance and a larger retrieval loss at the top position. To handle these problems, we propose a novel method dubbed by the top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method. The core idea is to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom and assign different weight values to their binary bits according to the distribution of query data. Specifically, we design an iterative optimization mechanism to simultaneously learn binary codes and bitwise weights, which makes their learning processes related to each other. When the iterative procedure converges, the binary codes and bitwise weights are effectively adapted to each other. To reduce the training complexity, we relax the discrete constraints of both the binary codes and the indicator function. Furthermore, we pretrain a tensor ordinal graph to decrease the time consumption of computing a relative similarity relationship among data points. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed TORBW method can achieve superior performance over state-of-the-art approaches.
      Citation: Algorithms
      PubDate: 2020-01-06
      DOI: 10.3390/a13010018
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 19: Computing Persistent Homology of Directed
           Flag Complexes

    • Authors: Daniel Lütgehetmann, Dejan Govc, Jason P. Smith, Ran Levi
      First page: 19
      Abstract: We present a new computing package FLAGSERmathsizesmall, designed to construct the directed flag complex of a finite directed graph, and compute persistent homology for flexibly defined filtrations on the graph and the resulting complex. The persistent homology computation part of FLAGSERmathsizesmall is based on the program RIPSERmathsizesmall by U. Bauer, but is optimised specifically for large computations. The construction of the directed flag complex is done in a way that allows easy parallelisation by arbitrarily many cores. FLAGSERmathsizesmall also has the option of working with undirected graphs. For homology computations FLAGSERmathsizesmall has an approximate option, which shortens compute time with remarkable accuracy. We demonstrate the power of FLAGSERmathsizesmall by applying it to the construction of the directed flag complex of digital reconstructions of brain microcircuitry by the Blue Brain Project and several other examples. In some instances we perform computation of homology. For a more complete performance analysis, we also apply FLAGSERmathsizesmall to some other data collections. In all cases the hardware used in the computation, the use of memory and the compute time are recorded.
      Citation: Algorithms
      PubDate: 2020-01-07
      DOI: 10.3390/a13010019
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 20: Citywide Cellular Traffic Prediction Based
           on a Hybrid Spatiotemporal Network

    • Authors: Dehai Zhang, Linan Liu, Cheng Xie, Bing Yang, Qing Liu
      First page: 20
      Abstract: With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.
      Citation: Algorithms
      PubDate: 2020-01-08
      DOI: 10.3390/a13010020
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 21: Markov Chain Monte Carlo Based Energy Use
           Behaviors Prediction of Office Occupants

    • Authors: Qiao Yan, Xiaoqian Liu, Xiaoping Deng, Wei Peng, Guiqing Zhang
      First page: 21
      Abstract: Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been studied as intensively as residential occupants. This paper proposes a method based on Markov chain Monte Carlo (MCMC) to predict the energy use behaviors of office occupants. Firstly, an indoor electrical Internet of Things system (IEIoTS) for the office scenario is developed to collect the switching state time series data of selected user electrical equipment (desktop computer, water dispenser, light) and the historical environment parameters. Then, the Metropolis–Hastings (MH) algorithm is used to sample and obtain the optimal solution of the parameters for the office occupants’ behavior function, the model of which includes the energy action model, energy working hours model, and air-conditioner energy use behavior model. Finally, comparative experiments are carried out to evaluate the performance of the proposed method. The experimental results show that while the mean value performs similarly in estimating the energy use model, the proposed method outperforms the Maximum Likelihood Estimation (MLE) method on uncertainty quantification with relatively narrower confidence intervals.
      Citation: Algorithms
      PubDate: 2020-01-09
      DOI: 10.3390/a13010021
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 22: Non Data-Aided SNR Estimation for UAV OFDM
           Systems

    • Authors: Junfang Li, Mingqian Liu, Ningjie Tang, Bodong Shang
      First page: 22
      Abstract: Signal-to-noise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel non-data-aided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.
      Citation: Algorithms
      PubDate: 2020-01-10
      DOI: 10.3390/a13010022
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 23: Optimal Learning and Self-Awareness Versus
           PDI

    • Authors: Smeresky, Rizzo, Sands
      First page: 23
      Abstract: This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system.
      Citation: Algorithms
      PubDate: 2020-01-11
      DOI: 10.3390/a13010023
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 24: Unstructured Uncertainty Based Modeling and
           Robust Stability Analysis of Textile-Reinforced Composites with Embedded
           Shape Memory Alloys

    • Authors: Najmeh Keshtkar, Klaus Röbenack
      First page: 24
      Abstract: This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The mathematical model of the system is derived using the identification method and an unstructured uncertainty approach. Based on this model and a robust stability analysis, a robust proportional–integral controller is designed for controlling the deflection of the composite. We showed that the robust controller depends significantly on the modeling of the uncertainty. The performance of the proposed controller is compared with a classical one through experimental analysis. Experimental results show that the proposed controller has a better performance as it reduces the overshoot and provide robustness to uncertainty. Due to the robust design, the controller also has a wide operating range, which is advantageous for practical applications.
      Citation: Algorithms
      PubDate: 2020-01-15
      DOI: 10.3390/a13010024
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 25: Local Convergence of an Efficient
           Multipoint Iterative Method in Banach Space

    • Authors: Janak Raj Sharma, Sunil Kumar, Ioannis K. Argyros
      First page: 25
      Abstract: We discuss the local convergence of a derivative-free eighth order method in a Banach space setting. The present study provides the radius of convergence and bounds on errors under the hypothesis based on the first Fréchet-derivative only. The approaches of using Taylor expansions, containing higher order derivatives, do not provide such estimates since the derivatives may be nonexistent or costly to compute. By using only first derivative, the method can be applied to a wider class of functions and hence its applications are expanded. Numerical experiments show that the present results are applicable to the cases wherein previous results cannot be applied.
      Citation: Algorithms
      PubDate: 2020-01-15
      DOI: 10.3390/a13010025
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 26: A Soft-Voting Ensemble Based Co-Training
           Scheme Using Static Selection for Binary Classification Problems

    • Authors: Karlos, Kostopoulos, Kotsiantis
      First page: 26
      Abstract: In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.
      Citation: Algorithms
      PubDate: 2020-01-16
      DOI: 10.3390/a13010026
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 27: A Matheuristic for Joint Optimal Power and
           Scheduling Assignment in DVB-T2 Networks

    • Authors: Fabio D’Andreagiovanni, Hicham Lakhlef, Antonella Nardin
      First page: 27
      Abstract: Because of the introduction and spread of the second generation of the Digital Video Broadcasting—Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.
      Citation: Algorithms
      PubDate: 2020-01-16
      DOI: 10.3390/a13010027
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 28: Editorial: Special Issue on Data
           Compression Algorithms and their Applications

    • Authors: Bille
      First page: 28
      Abstract: This Special Issue of Algorithms is focused on data compression algorithms and theirapplications.
      Citation: Algorithms
      PubDate: 2020-01-17
      DOI: 10.3390/a13010028
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 29: Acknowledgement to Reviewers of Algorithms
           in 2019

    • Authors: Algorithms Editorial Office
      First page: 29
      Abstract: The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or no [...]
      Citation: Algorithms
      PubDate: 2020-01-20
      DOI: 10.3390/a13010029
      Issue No: Vol. 13, No. 1 (2020)
       
  • Algorithms, Vol. 13, Pages 1: Image Restoration Using a Fixed-Point Method
           for a TVL2 Regularization Problem

    • Authors: Kyoum Sun Kim, Jae Heon Yun
      First page: 1
      Abstract: In this paper, we first propose a new TVL2 regularization model for image restoration, and then we propose two iterative methods, which are fixed-point and fixed-point-like methods, using CGLS (Conjugate Gradient Least Squares method) for solving the new proposed TVL2 problem. We also provide convergence analysis for the fixed-point method. Lastly, numerical experiments for several test problems are provided to evaluate the effectiveness of the proposed two iterative methods. Numerical results show that the new proposed TVL2 model is preferred over an existing TVL2 model and the proposed fixed-point-like method is well suited for the new TVL2 model.
      Citation: Algorithms
      PubDate: 2019-12-18
      DOI: 10.3390/a13010001
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 2: Journey Planning Algorithms for
           Massive Delay-Prone Transit Networks

    • Authors: D'Emidio, Khan, Frigioni
      First page: 2
      Abstract: This paper studies the journey planning problem in the context of transit networks. Giventhe timetable of a schedule-based transportation system (consisting, e.g., of trains, buses, etc.),the problem seeks journeys optimizing some criteria. Specifically, it seeks to answer natural queriessuch as, for example, “find a journey starting from a source stop and arriving at a target stop as earlyas possible”. The fastest approach for answering to these queries, yielding the smallest average querytime even on very large networks, is the Public Transit Labeling framework, proposed for the firsttime in Delling et al., SEA 2015. This method combines three main ingredients: (i) a graph-basedrepresentation of the schedule of the transit network; (ii) a labeling of such graph encoding itstransitive closure (computed via a time-consuming pre-processing); (iii) an efficient query algorithmexploiting both (i) and (ii) to answer quickly to queries of interest at runtime. Unfortunately, whiletransit networks’ timetables are inherently dynamic (they are often subject to delays or disruptions),PTL is not natively designed to handle updates in the schedule—even after a single change,precomputed data may become outdated and queries can return incorrect results. This is a majorlimitation, especially when dealing with massively sized inputs (e.g., metropolitan or continentalsized networks), as recomputing the labeling from scratch, after each change, yields unsustainabletime overheads that are not compatible with interactive applications. In this work, we introduce a newframework that extends PTL to function in delay-prone transit networks. In particular, we providea new set of algorithms able to update both the graph and the precomputed labeling whenevera delay affects the network, without performing any recomputation from scratch. We demonstratethe effectiveness of our solution through an extensive experimental evaluation conducted onreal-world networks. Our experiments show that: (i) the update time required by the new algorithmsis, on average, orders of magnitude smaller than that required by the recomputation from scratch viaPTL; (ii) the updated graph and labeling induce both query time performance and space overhead thatare equivalent to those that are obtained by the recomputation from scratch via PTL. This suggests thatour new solution is an effective approach to handling the journey planning problem in delay-pronetransit networks.
      Citation: Algorithms
      PubDate: 2019-12-18
      DOI: 10.3390/a13010002
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 3: Parameterized Optimization in Uncertain
           Graphs—A Survey and Some Results

    • Authors: N. S. Narayanaswamy, R. Vijayaragunathan
      First page: 3
      Abstract: We present a detailed survey of results and two new results on graphical models of uncertainty and associated optimization problems. We focus on two well-studied models, namely, the Random Failure (RF) model and the Linear Reliability Ordering (LRO) model. We present an FPT algorithm parameterized by the product of treewidth and max-degree for maximizing expected coverage in an uncertain graph under the RF model. We then consider the problem of finding the maximal core in a graph, which is known to be polynomial time solvable. We show that the Probabilistic-Core problem is polynomial time solvable in uncertain graphs under the LRO model. On the other hand, under the RF model, we show that the Probabilistic-Core problem is W[1]-hard for the parameter d, where d is the minimum degree of the core. We then design an FPT algorithm for the parameter treewidth.
      Citation: Algorithms
      PubDate: 2019-12-19
      DOI: 10.3390/a13010003
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 4: Two-Machine Job-Shop Scheduling Problem to
           Minimize the Makespan with Uncertain Job Durations

    • Authors: Yuri N. Sotskov, Natalja M. Matsveichuk, Vadzim D. Hatsura
      First page: 4
      Abstract: We study two-machine shop-scheduling problems provided that lower and upper bounds on durations of n jobs are given before scheduling. An exact value of the job duration remains unknown until completing the job. The objective is to minimize the makespan (schedule length). We address the issue of how to best execute a schedule if the job duration may take any real value from the given segment. Scheduling decisions may consist of two phases: an off-line phase and an on-line phase. Using information on the lower and upper bounds for each job duration available at the off-line phase, a scheduler can determine a minimal dominant set of schedules (DS) based on sufficient conditions for schedule domination. The DS optimally covers all possible realizations (scenarios) of the uncertain job durations in the sense that, for each possible scenario, there exists at least one schedule in the DS which is optimal. The DS enables a scheduler to quickly make an on-line scheduling decision whenever additional information on completing jobs is available. A scheduler can choose a schedule which is optimal for the most possible scenarios. We developed algorithms for testing a set of conditions for a schedule dominance. These algorithms are polynomial in the number of jobs. Their time complexity does not exceed O ( n 2 ) . Computational experiments have shown the effectiveness of the developed algorithms. If there were no more than 600 jobs, then all 1000 instances in each tested series were solved in one second at most. An instance with 10,000 jobs was solved in 0.4 s on average. The most instances from nine tested classes were optimally solved. If the maximum relative error of the job duration was not greater than 20 % , then more than 80 % of the tested instances were optimally solved. If the maximum relative error was equal to 50 % , then 45 % of the tested instances from the nine classes were optimally solved.
      Citation: Algorithms
      PubDate: 2019-12-20
      DOI: 10.3390/a13010004
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 5: Simple Constructive, Insertion, and
           Improvement Heuristics Based on the Girding Polygon for the Euclidean
           Traveling Salesman Problem

    • Authors: Víctor Pacheco-Valencia, José Alberto Hernández, José María Sigarreta, Nodari Vakhania
      First page: 5
      Abstract: The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.
      Citation: Algorithms
      PubDate: 2019-12-21
      DOI: 10.3390/a13010005
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 6: A Generalized MILP Formulation for the
           Period-Aggregated Resource Leveling Problem with Variable Job Duration

    • Authors: Ilia Tarasov, Alain Haït, Olga Battaïa
      First page: 6
      Abstract: We study a resource leveling problem with variable job duration. The considered problem includes both scheduling and resource management decisions. The planning horizon is fixed and separated into a set of time periods of equal length. There are several types of resources and their amount varies from one period to another. There is a set of jobs. For each job, a fixed volume of work has to be completed without any preemption while using different resources. If necessary, extra resources can be used at additional costs during each time period. The optimization goal is to minimize the total overload costs required for the execution of all jobs by the given deadline. The decision variables specify the starting time of each job, the duration of the job and the resource amount assigned to the job during each period (it may vary over periods). We propose a new generalized mathematical formulation for this optimization problem. The formulation is compared with existing approaches from the literature. Theoretical study and computational experiments show that our approach provides more flexible resource allocation resulting in better final solutions.
      Citation: Algorithms
      PubDate: 2019-12-23
      DOI: 10.3390/a13010006
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 7: Detection of Suicide Ideation in Social
           Media Forums Using Deep Learning

    • Authors: Michael Mesfin Tadesse, Hongfei Lin, Bo Xu, Liang Yang
      First page: 7
      Abstract: Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.
      Citation: Algorithms
      PubDate: 2019-12-24
      DOI: 10.3390/a13010007
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 8: On the Use of Biased-Randomized Algorithms
           for Solving Non-Smooth Optimization Problems

    • Authors: Angel Alejandro Juan, Canan Gunes Corlu, Rafael David Tordecilla, Rocio de la Torre, Albert Ferrer
      First page: 8
      Abstract: Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines.
      Citation: Algorithms
      PubDate: 2019-12-25
      DOI: 10.3390/a13010008
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 9: Special Issue on Exact and Heuristic
           Scheduling Algorithms

    • Authors: Frank Werner, Larysa Burtseva, Yuri N. Sotskov
      First page: 9
      Abstract: This special issue of Algorithms is a follow-up issue of an earlier one, entitled ‘Algorithms for Scheduling Problems’. In particular, the new issue is devoted to the development of exact and heuristic scheduling algorithms. Submissions were welcome both for traditional scheduling problems as well as for new practical applications. In the Call for Papers, we mentioned topics such as single-criterion and multi-criteria scheduling problems with additional constraints including setup times (costs), precedence constraints, batching (lot sizing), resource constraints as well as scheduling problems arising in emerging applications.
      Citation: Algorithms
      PubDate: 2019-12-25
      DOI: 10.3390/a13010009
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 10: Comparative Analysis of Different
           Model-Based Controllers Using Active Vehicle Suspension System

    • Authors: Yumna Shahid, Minxiang Wei
      First page: 10
      Abstract: This paper deals with the active vibration control of a quarter-vehicle suspension system. Damping control methods investigated in this paper are: higher-order sliding mode control (HOSMC) based on super twisting algorithm (STA), first-order sliding mode control (FOSMC), integral sliding mode control (ISMC), proportional integral derivative (PID), linear quadratic regulator (LQR) and passive suspension system. Performance comparison of different active controllers are analyzed in terms of vertical displacement, suspension travel and wheel deflection. The theoretical, quantitative and qualitative analysis verify that the STA-based HOSMC exhibits better performance as well as negate the undesired disturbances with respect to FOSMC, ISMC, PID, LQR and passive suspension system. Furthermore, it is also robust to intrinsic bounded uncertain dynamics of the model.
      Citation: Algorithms
      PubDate: 2019-12-26
      DOI: 10.3390/a13010010
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 11: A Numerical Approach for the Filtered
           Generalized Čech Complex

    • Authors: Jesús F. Espinoza, Rosalía Hernández-Amador, Héctor A. Hernández-Hernández, Beatriz Ramonetti-Valencia
      First page: 11
      Abstract: In this paper, we present an algorithm to compute the filtered generalized Čech complex for a finite collection of disks in the plane, which do not necessarily have the same radius. The key step behind the algorithm is to calculate the minimum scale factor needed to ensure rescaled disks have a nonempty intersection, through a numerical approach, whose convergence is guaranteed by a generalization of the well-known Vietoris–Rips Lemma, which we also prove in an alternative way, using elementary geometric arguments. We give an algorithm for computing the 2-dimensional filtered generalized Čech complex of a finite collection of d-dimensional disks in R d , and we show the performance of our algorithm.
      Citation: Algorithms
      PubDate: 2019-12-30
      DOI: 10.3390/a13010011
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 12: Optimal Prefix Free Codes with Partial
           Sorting

    • Authors: Jérémy Barbay
      First page: 12
      Abstract: We describe an algorithm computing an optimal prefix free code for n unsorted positive weights in time within O ( n ( 1 + lg α ) ) ⊆ O ( n lg n ) , where the alternation α ∈ [ 1 . . n − 1 ] approximates the minimal amount of sorting required by the computation. This asymptotical complexity is within a constant factor of the optimal in the algebraic decision tree computational model, in the worst case over all instances of size n and alternation α . Such results refine the state of the art complexity of Θ ( n lg n ) in the worst case over instances of size n in the same computational model, a landmark in compression and coding since 1952. Beside the new analysis technique, such improvement is obtained by combining a new algorithm, inspired by van Leeuwen’s algorithm to compute optimal prefix free codes from sorted weights (known since 1976), with a relatively minor extension of Karp et al.’s deferred data structure to partially sort a multiset accordingly to the queries performed on it (known since 1988). Preliminary experimental results on text compression by words show α to be polynomially smaller than n, which suggests improvements by at most a constant multiplicative factor in the running time for such applications.
      Citation: Algorithms
      PubDate: 2019-12-31
      DOI: 10.3390/a13010012
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 13: An Effective and Efficient Genetic-Fuzzy
           Algorithm for Supporting Advanced Human-Machine Interfaces in Big Data
           Settings

    • Authors: Alfredo Cuzzocrea, Enzo Mumolo, Giorgio Mario Grasso
      First page: 13
      Abstract: In this paper we describe a novel algorithm, inspired by the mirror neuron discovery, to support automatic learning oriented to advanced man-machine interfaces. The algorithm introduces several points of innovation, based on complex metrics of similarity that involve different characteristics of the entire learning process. In more detail, the proposed approach deals with an humanoid robot algorithm suited for automatic vocalization acquisition from a human tutor. The learned vocalization can be used to multi-modal reproduction of speech, as the articulatory and acoustic parameters that compose the vocalization database can be used to synthesize unrestricted speech utterances and reproduce the articulatory and facial movements of the humanoid talking face automatically synchronized. The algorithm uses fuzzy articulatory rules, which describe transitions between phonemes derived from the International Phonetic Alphabet (IPA), to allow simpler adaptation to different languages, and genetic optimization of the membership degrees. Large experimental evaluation and analysis of the proposed algorithm on synthetic and real data sets confirms the benefits of our proposal. Indeed, experimental results show that the vocalization acquired respects the basic phonetic rules of Italian languages and that subjective results show the effectiveness of multi-modal speech production with automatic synchronization between facial movements and speech emissions. The algorithm has been applied to a virtual speaking face but it may also be used in mechanical vocalization systems as well.
      Citation: Algorithms
      PubDate: 2019-12-31
      DOI: 10.3390/a13010013
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 13, Pages 14: Image Completion with Large or Edge-Missing
           Areas

    • Authors: Jianjian Ji, Gang Yang
      First page: 14
      Abstract: Existing image completion methods are mostly based on missing regions that are small or located in the middle of the images. When regions to be completed are large or near the edge of the images, due to the lack of context information, the completion results tend to be blurred or distorted, and there will be a large blank area in the final results. In addition, the unstable training of the generative adversarial network is also prone to cause pseudo-color in the completion results. Aiming at the two above-mentioned problems, a method of image completion with large or edge-missing areas is proposed; also, the network structures have been improved. On the one hand, it overcomes the problem of lacking context information, which thereby ensures the reality of generated texture details; on the other hand, it suppresses the generation of pseudo-color, which guarantees the consistency of the whole image both in vision and content. The experimental results show that the proposed method achieves better completion results in completing large or edge-missing areas.
      Citation: Algorithms
      PubDate: 2019-12-31
      DOI: 10.3390/a13010014
      Issue No: Vol. 13, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 245: Observations on the Computation of
           Eigenvalue and Eigenvector Jacobians

    • Authors: Andrew J. Liounis, John A. Christian, Shane B. Robinson
      First page: 245
      Abstract: Many scientific and engineering problems benefit from analytic expressions for eigenvalue and eigenvector derivatives with respect to the elements of the parent matrix. While there exists extensive literature on the calculation of these derivatives, which take the form of Jacobian matrices, there are a variety of deficiencies that have yet to be addressed—including the need for both left and right eigenvectors, limitations on the matrix structure, and issues with complex eigenvalues and eigenvectors. This work addresses these deficiencies by proposing a new analytic solution for the eigenvalue and eigenvector derivatives. The resulting analytic Jacobian matrices are numerically efficient to compute and are valid for the general complex case. It is further shown that this new general result collapses to previously known relations for the special cases of real symmetric matrices and real diagonal matrices. Finally, the new Jacobian expressions are validated using forward finite differencing and performance is compared with another technique.
      Citation: Algorithms
      PubDate: 2019-11-20
      DOI: 10.3390/a12120245
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 246: Estimation of Reliability in a
           Multicomponent Stress–Strength System for the Exponentiated Moment-Based
           Exponential Distribution

    • Authors: Rao, Bhatti, Aslam, Albassam
      First page: 246
      Abstract: A multicomponent system of k components with independent and identically distributed random strengths, with each component undergoing random stress, is in working condition if and only if at least s out of k strengths exceed the subjected stress. Reliability is measured while strength and stress are obtained through a process following an exponentiated moment-based exponential distribution with different shape parameters. Reliability is gauged from the samples using maximum likelihood (ML) on the computed distributions of strength and stress. Asymptotic estimates of reliability are compared using Monte Carlo simulation. Application to forest data and to breaking strengths of jute fiber shows the usefulness of the model.
      Citation: Algorithms
      PubDate: 2019-11-21
      DOI: 10.3390/a12120246
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 247: Modeling and Solving Scheduling Problem
           with m Uniform Parallel Machines Subject to Unavailability Constraints

    • Authors: Jihene Kaabi
      First page: 247
      Abstract: The problem investigated in this paper is scheduling on uniform parallel machines, taking into account that machines can be periodically unavailable during the planning horizon. The objective is to determine planning for job processing so that the makespan is minimal. The problem is known to be NP-hard. A new quadratic model was developed. Because of the limitation of the aforementioned model in terms of problem sizes, a novel algorithm was developed to tackle big-sized instances. This consists of mainly two phases. The first phase generates schedules using a modified Largest Processing Time ( L P T )-based procedure. Then, theses schedules are subject to further improvement during the second phase. This improvement is obtained by simultaneously applying pairwise job interchanges between machines. The proposed algorithm and the quadratic model were implemented and tested on variously sized problems. Computational results showed that the developed quadratic model could optimally solve small- to medium-sized problem instances. However, the proposed algorithm was able to optimally solve large-sized problems in a reasonable time.
      Citation: Algorithms
      PubDate: 2019-11-21
      DOI: 10.3390/a12120247
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 248: Solving Integer Linear Programs by
           Exploiting Variable-Constraint Interactions: A Survey

    • Authors: Robert Ganian, Sebastian Ordyniak
      First page: 248
      Abstract: Integer Linear Programming (ILP) is among the most successful and general paradigms for solving computationally intractable optimization problems in computer science. ILP is NP-complete, and until recently we have lacked a systematic study of the complexity of ILP through the lens of variable-constraint interactions. This changed drastically in recent years thanks to a series of results that together lay out a detailed complexity landscape for the problem centered around the structure of graphical representations of instances. The aim of this survey is to summarize these recent developments, put them into context and a unified format, and make them more approachable for experts from many diverse backgrounds.
      Citation: Algorithms
      PubDate: 2019-11-22
      DOI: 10.3390/a12120248
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 249: SVM-Based Multiple Instance Classification
           via DC Optimization

    • Authors: Annabella Astorino, Antonio Fuduli, Giovanni Giallombardo, Giovanna Miglionico
      First page: 249
      Abstract: A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported.
      Citation: Algorithms
      PubDate: 2019-11-23
      DOI: 10.3390/a12120249
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 250: Some Results on Shop Scheduling with
           S-Precedence Constraints among Job Tasks

    • Authors: Alessandro Agnetis, Fabrizio Rossi, Stefano Smriglio
      First page: 250
      Abstract: We address some special cases of job shop and flow shop scheduling problems with s-precedence constraints. Unlike the classical setting, in which precedence constraints among the tasks of a job are finish–start, here the task of a job cannot start before the task preceding it has started. We give polynomial exact algorithms for the following problems: a two-machine job shop with two jobs when recirculation is allowed (i.e., jobs can visit the same machine many times), a two-machine flow shop, and an m-machine flow shop with two jobs. We also point out some special cases whose complexity status is open.
      Citation: Algorithms
      PubDate: 2019-11-25
      DOI: 10.3390/a12120250
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 251: Target Image Mask Correction Based on
           Skeleton Divergence

    • Authors: Yaming Wang, Zhengheng Xu, Wenqing Huang, Yonghua Han, Mingfeng Jiang
      First page: 251
      Abstract: Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms.
      Citation: Algorithms
      PubDate: 2019-11-25
      DOI: 10.3390/a12120251
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 252: Planning the Schedule for the Disposal of
           the Spent Nuclear Fuel with Interactive Multiobjective Optimization

    • Authors: Outi Montonen, Timo Ranta, Marko M. Mäkelä
      First page: 252
      Abstract: Several countries utilize nuclear power and face the problem of what to do with the spent nuclear fuel. One possibility, which is under the scope in this paper, is to dispose of the fuel assemblies in the disposal facility. Before the assemblies can be disposed of, they must cool down their decay heat power in the interim storage. Next, they are loaded into canisters in the encapsulation facility, and finally, the canisters are placed in the disposal facility. In this paper, we model this process as a nonsmooth multiobjective mixed-integer nonlinear optimization problem with the minimization of nine objectives: the maximum number of assemblies in the storage, maximum storage time, average storage time, total number of canisters, end time of the encapsulation, operation time of the encapsulation facility, the lengths of disposal and central tunnels, and total costs. As a result, we obtain the disposal schedule i.e., amount of canisters disposed of periodically. We introduce the interactive multiobjective optimization method using the two-slope parameterized achievement scalarizing functions which enables us to obtain systematically several different Pareto optimal solutions from the same preference information. Finally, a case study adapting the disposal in Finland is given. The results obtained are analyzed in terms of the objective values and disposal schedules.
      Citation: Algorithms
      PubDate: 2019-11-25
      DOI: 10.3390/a12120252
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 253: Walking Gait Phase Detection Based on
           Acceleration Signals Using LSTM-DNN Algorithm

    • Authors: Tao Zhen, Lei Yan, Peng Yuan
      First page: 253
      Abstract: Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.
      Citation: Algorithms
      PubDate: 2019-11-26
      DOI: 10.3390/a12120253
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 254: FPT Algorithms for Diverse Collections of
           Hitting Sets

    • Authors: Julien Baste, Lars Jaffke, Tomáš Masařík, Geevarghese Philip, Günter Rote
      First page: 254
      Abstract: In this work, we study the d-Hitting Set and Feedback Vertex Set problems through the paradigm of finding diverse collections of r solutions of size at most k each, which has recently been introduced to the field of parameterized complexity. This paradigm is aimed at addressing the loss of important side information which typically occurs during the abstraction process that models real-world problems as computational problems. We use two measures for the diversity of such a collection: the sum of all pairwise Hamming distances, and the minimum pairwise Hamming distance. We show that both problems are fixed-parameter tractable in k + r for both diversity measures. A key ingredient in our algorithms is a (problem independent) network flow formulation that, given a set of `base’ solutions, computes a maximally diverse collection of solutions. We believe that this could be of independent interest.
      Citation: Algorithms
      PubDate: 2019-11-27
      DOI: 10.3390/a12120254
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 255: Pre and Postprocessing for JPEG to Handle
           Large Monochrome Images

    • Authors: Walaa Khalaf, Abeer Al Gburi, Dhafer Zaghar
      First page: 255
      Abstract: Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.
      Citation: Algorithms
      PubDate: 2019-12-01
      DOI: 10.3390/a12120255
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 256: Parameterized Algorithms in
           Bioinformatics: An Overview

    • Authors: Laurent Bulteau, Mathias Weller
      First page: 256
      Abstract: Bioinformatics regularly poses new challenges to algorithm engineers and theoretical computer scientists. This work surveys recent developments of parameterized algorithms and complexity for important NP-hard problems in bioinformatics. We cover sequence assembly and analysis, genome comparison and completion, and haplotyping and phylogenetics. Aside from reporting the state of the art, we give challenges and open problems for each topic.
      Citation: Algorithms
      PubDate: 2019-12-01
      DOI: 10.3390/a12120256
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 257: A General Integrated Method for Design
           Analysis and Optimization of Missile Structure

    • Authors: Wang, Yang, Guo, Guo
      First page: 257
      Abstract: In the demonstration phase of a missile scheme, to obtain the optimum proposal, designers need to modify the parameters of the overall structure frequently and significantly, and perform the structural analysis repeatedly. In order to reduce the manual workload and improve the efficiency of research and development, a general integrated method of missile structure modeling, analysis and optimization was proposed. First, CST (Class and Shape transformation functions) parametric method was used to describe the general structure of the missile. The corresponding software geometric modeling and FEM (Finite Element Method) analyzing of the missile were developed in C/C++ language on the basis of the CST parametric method and UG (Unigraphics) secondary development technology. Subsequently, a novel surrogate model-based optimation strategy was proposed to obtain a relatively light mass missile structure under existing shape size. Eventually, different missile models were used to verify the validity of the method. After executing the structure modeling, analysis and optimization modules, satisfactory results can be obtained that demonstrated the stability and adaptability of the proposed method. The method presented saves plenty of time comparing to the traditional manual modeling and analysis method, which provides a valuable technique to improve the efficiency of research and development.
      Citation: Algorithms
      PubDate: 2019-12-01
      DOI: 10.3390/a12120257
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 258: Improved Bilateral Filtering for a
           Gaussian Pyramid Structure-Based Image Enhancement Algorithm

    • Authors: Lin, Zhou, Chen
      First page: 258
      Abstract: To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.
      Citation: Algorithms
      PubDate: 2019-12-01
      DOI: 10.3390/a12120258
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 259: The Research of Improved Active
           Disturbance Rejection Control Algorithm for Particleboard Glue System
           Based on Neural Network State Observer

    • Authors: Peiyu Wang, Chunrui Zhang, Liangkuan Zhu, Chengcheng Wang
      First page: 259
      Abstract: For achieving high-performance control for a particleboard glue mixing and dosing control system, which is a time-delay system in low frequency working conditions, an improved active disturbance rejection controller is proposed. In order to reduce overshoot caused by a given large change between the actual output and expected value of the control object, a tracking differentiator (TD) is used to arrange the appropriate excesses. Through the first-order approximation of the time-delay link, the time-delay system is transformed into an output feedback problem with unknown function. Using the neural network state observer (NNSO), a sliding mode control law is used to achieve the accurate and fast tracking of the output signal. Finally, the numerical simulation results verify the effectiveness and feasibility of the proposed method.
      Citation: Algorithms
      PubDate: 2019-12-03
      DOI: 10.3390/a12120259
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 260: Damage Diagnosis of Reactive Powder
           Concrete under Fatigue Loading Using 3D Laser Scanning Technology

    • Authors: Kexin Li, Jun Wang, Dawei Qi
      First page: 260
      Abstract: Damage mechanisms of Reactive Powder Concrete (RPC) under fatigue loading are investigated using the 3D laser scanning technology. An independently configured 3D laser scanning system is used to monitor the damaging procedure. Texture analysis technique is also applied to enhance the understanding of the damage mechanisms of RPC under fatigue loading. In order to obtain the characteristic parameters of point cloud data, a point clouds projection algorithm is proposed. Damage evolution is described by the change of point cloud data of the damage in the 2D plane and 3D space during fatigue loading. The Gray Level Co-occurrence Matrix (GLCM) method is used to extract the characteristic parameters to evaluate the statue of the structural. Angular Second Moment and Cluster Shadow of typical sensitive characteristic indexes is screened by using the Digital Feature Screening. The reliability of the damage index was verified by image texture analysis and data expansion. Indexes extracted in this paper can be used as a new structural health monitoring indicator to assess health condition.
      Citation: Algorithms
      PubDate: 2019-12-04
      DOI: 10.3390/a12120260
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 261: A Pareto-Based Hybrid Whale Optimization
           Algorithm with Tabu Search for Multi-Objective Optimization

    • Authors: Amr Mohamed AbdelAziz, Taysir Hassan A. Soliman, Kareem Kamal A. Ghany, Adel Abu El-Magd Sewisy
      First page: 261
      Abstract: Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.
      Citation: Algorithms
      PubDate: 2019-12-04
      DOI: 10.3390/a12120261
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 262: Using Interval Analysis to Compute the
           Invariant Set of a Nonlinear Closed-Loop Control System

    • Authors: Swantje Romig, Luc Jaulin, Andreas Rauh
      First page: 262
      Abstract: In recent years, many applications, as well as theoretical properties of interval analysis have been investigated. Without any claim for completeness, such applications and methodologies range from enclosing the effect of round-off errors in highly accurate numerical computations over simulating guaranteed enclosures of all reachable states of a dynamic system model with bounded uncertainty in parameters and initial conditions, to the solution of global optimization tasks. By exploiting the fundamental enclosure properties of interval analysis, this paper aims at computing invariant sets of nonlinear closed-loop control systems. For that purpose, Lyapunov-like functions and interval analysis are combined in a novel manner. To demonstrate the proposed techniques for enclosing invariant sets, the systems examined in this paper are controlled via sliding mode techniques with subsequently enclosing the invariant sets by an interval based set inversion technique. The applied methods for the control synthesis make use of a suitably chosen Gröbner basis, which is employed to solve Bézout’s identity. Illustrating simulation results conclude this paper to visualize the novel combination of sliding mode control with an interval based computation of invariant sets.
      Citation: Algorithms
      PubDate: 2019-12-06
      DOI: 10.3390/a12120262
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 263: Linking Scheduling Criteria to Shop Floor
           Performance in Permutation Flowshops

    • Authors: Jose M. Framinan, Rainer Leisten
      First page: 263
      Abstract: The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing.
      Citation: Algorithms
      PubDate: 2019-12-07
      DOI: 10.3390/a12120263
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 264: Exploring Travelers’ Characteristics
           Affecting their Intention to Shift to Bike-Sharing Systems due to a
           Sophisticated Mobile App

    • Authors: Andreas Nikiforiadis, Katerina Chrysostomou, Georgia Aifadopoulou
      First page: 264
      Abstract: Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app.
      Citation: Algorithms
      PubDate: 2019-12-07
      DOI: 10.3390/a12120264
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 265: Enhanced Knowledge Graph Embedding by
           Jointly Learning Soft Rules and Facts

    • Authors: Jindou Zhang, Jing Li
      First page: 265
      Abstract: Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced Embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.
      Citation: Algorithms
      PubDate: 2019-12-10
      DOI: 10.3390/a12120265
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 266: Storage Efficient Trajectory Clustering
           and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

    • Authors: Elias Dritsas, Andreas Kanavos, Maria Trigka, Spyros Sioutas, Athanasios Tsakalidis
      First page: 266
      Abstract: The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1 . To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work.
      Citation: Algorithms
      PubDate: 2019-12-11
      DOI: 10.3390/a12120266
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 267: Finding Patterns in Signals Using Lossy
           Text Compression

    • Authors: Liat Rozenberg, Sagi Lotan, Dan Feldman
      First page: 267
      Abstract: Whether the source is autonomous car, robotic vacuum cleaner, or a quadcopter, signals from sensors tend to have some hidden patterns that repeat themselves. For example, typical GPS traces from a smartphone contain periodic trajectories such as “home, work, home, work, ⋯”. Our goal in this study was to automatically reverse engineer such signals, identify their periodicity, and then use it to compress and de-noise these signals. To do so, we present a novel method of using algorithms from the field of pattern matching and text compression to represent the “language” in such signals. Common text compression algorithms are less tailored to handle such strings. Moreover, they are lossless, and cannot be used to recover noisy signals. To this end, we define the recursive run-length encoding (RRLE) method, which is a generalization of the well known run-length encoding (RLE) method. Then, we suggest lossy and lossless algorithms to compress and de-noise such signals. Unlike previous results, running time and optimality guarantees are proved for each algorithm. Experimental results on synthetic and real data sets are provided. We demonstrate our system by showing how it can be used to turn commercial micro air-vehicles into autonomous robots. This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them. Our open source code may be useful for both the community of millions of toy robots users, as well as for researchers that may extend it for further protocols.
      Citation: Algorithms
      PubDate: 2019-12-11
      DOI: 10.3390/a12120267
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 268: Construction Method of Probabilistic
           Boolean Networks Based on Imperfect Information

    • Authors: Katsuaki Umiji, Koichi Kobayashi, Yuh Yamashita
      First page: 268
      Abstract: A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example.
      Citation: Algorithms
      PubDate: 2019-12-12
      DOI: 10.3390/a12120268
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 269: Application and Evaluation of Surrogate
           Models for Radiation Source Search

    • Authors: Jared A. Cook, Ralph C. Smith, Jason M. Hite, Razvan Stefanescu, John Mattingly
      First page: 269
      Abstract: Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.
      Citation: Algorithms
      PubDate: 2019-12-12
      DOI: 10.3390/a12120269
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 270: Graph Theory Approach to the Vulnerability
           of Transportation Networks

    • Authors: Sambor Guze
      First page: 270
      Abstract: Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal dominating set and minimal (maximal) weighted dominating sets are proposed. This way, the exemplary academic transportation network was analyzed in two cases: stationary and dynamic. Some conclusions are presented. The main one is the fact that the methods given in this article are universal and applicable to both small and large-scale networks. Moreover, the approach can support the dynamic analysis of bottlenecks in transport networks.
      Citation: Algorithms
      PubDate: 2019-12-12
      DOI: 10.3390/a12120270
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 271: An Unknown Radar Emitter Identification
           Method Based on Semi-Supervised and Transfer Learning

    • Authors: Yuntian Feng, Guoliang Wang, Zhipeng Liu, Runming Feng, Xiang Chen, Ning Tai
      First page: 271
      Abstract: Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.
      Citation: Algorithms
      PubDate: 2019-12-16
      DOI: 10.3390/a12120271
      Issue No: Vol. 12, No. 12 (2019)
       
  • Algorithms, Vol. 12, Pages 223: (Hyper)Graph Embedding and Classification
           via Simplicial Complexes

    • Authors: Alessio Martino, Alessandro Giuliani, Antonello Rizzi
      First page: 223
      Abstract: This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy.
      Citation: Algorithms
      PubDate: 2019-10-25
      DOI: 10.3390/a12110223
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 224: A QUBO Model for the Traveling Salesman
           Problem with Time Windows

    • Authors: Christos Papalitsas, Theodore Andronikos, Konstantinos Giannakis, Georgia Theocharopoulou, Sofia Fanarioti
      First page: 224
      Abstract: This work focuses on expressing the TSP with Time Windows (TSPTW for short) as a quadratic unconstrained binary optimization (QUBO) problem. The time windows impose time constraints that a feasible solution must satisfy. These take the form of inequality constraints, which are known to be particularly difficult to articulate within the QUBO framework. This is, we believe, the first time this major obstacle is overcome and the TSPTW is cast in the QUBO formulation. We have every reason to anticipate that this development will lead to the actual execution of small scale TSPTW instances on the D-Wave platform.
      Citation: Algorithms
      PubDate: 2019-10-25
      DOI: 10.3390/a12110224
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 225: Enhancing Backtracking Search Algorithm
           using Reflection Mutation Strategy Based on Sine Cosine

    • Authors: Chong Zhou, Shengjie Li, Yuhe Zhang, Zhikun Chen, Cuijun Zhang
      First page: 225
      Abstract: Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.
      Citation: Algorithms
      PubDate: 2019-10-28
      DOI: 10.3390/a12110225
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 226: Multiple-Attribute Decision Making ELECTRE
           II Method under Bipolar Fuzzy Model

    • Authors: Shumaiza, Muhammad Akram, Ahmad N. Al-Kenani
      First page: 226
      Abstract: The core aim of this paper is to provide a new multiple-criteria decision making (MCDM) model, namely bipolar fuzzy ELimination and Choice Translating REality (ELECTRE) II method, by combining the bipolar fuzzy set with ELECTRE II technique. It can be used to solve the problems having bipolar uncertainty. The proposed method is established by defining the concept of bipolar fuzzy strong, median and weak concordance as well as discordance sets and indifferent set to define two types of outranking relations, namely strong outranking relation and weak outranking relation. The normalized weights of criteria, which may be partly or completely unknown for decision makers, are calculated by using an optimization technique, which is based on maximizing deviation method. A systematic iterative procedure is applied to strongly outrank as well as weakly outrank graphs to determine the ranking of favorable actions or alternatives or to choose the best possible solution. The implementation of the proposed method is presented by numerical examples such as selection of business location and to chose the best supplier. A comparative analysis of proposed ELECTRE II method is also presented with already existing multiple-attribute decision making methods, including Technique for the Order of Preference by Similarity to an Ideal Solution (TOPSIS) and ELECTRE I under bipolar fuzzy environment by solving the problem of business location.
      Citation: Algorithms
      PubDate: 2019-10-29
      DOI: 10.3390/a12110226
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 227: Facial Expression Recognition Based on
           Auxiliary Models

    • Authors: Yingying Wang, Yibin Li, Yong Song, Xuewen Rong
      First page: 227
      Abstract: In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.
      Citation: Algorithms
      PubDate: 2019-10-31
      DOI: 10.3390/a12110227
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 228: Blended Filter-Based Detection for
           Thruster Valve Failure and Control Recovery Evaluation for RLV

    • Authors: Hongqiang Sun, Shuguang Zhang
      First page: 228
      Abstract: Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance.
      Citation: Algorithms
      PubDate: 2019-11-01
      DOI: 10.3390/a12110228
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 229: Special Issue on “Algorithm Engineering:
           Towards Practically Efficient Solutions to Combinatorial Problems”

    • Authors: Mattia D’Emidio, Daniele Frigioni
      First page: 229
      Abstract: The purpose of this special issue of Algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues and standards in algorithmic experimentation were encouraged. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing were also welcome. In this regard, we solicited contributions from all most prominent areas of applied algorithmic research, which include but are not limited to graphs, databases, computational geometry, big data, networking, combinatorial aspects of scientific computing, and computational problems in the natural sciences or engineering.
      Citation: Algorithms
      PubDate: 2019-11-03
      DOI: 10.3390/a12110229
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 230: The Inapproximability of k-DominatingSet
           for Parameterized AC 0 Circuits †

    • Authors: Wenxing Lai
      First page: 230
      Abstract: Chen and Flum showed that any FPT-approximation of the k-Clique problem is not in para- AC 0 and the k-DominatingSet (k-DomSet) problem could not be computed by para- AC 0 circuits. It is natural to ask whether the f ( k ) -approximation of the k-DomSet problem is in para- AC 0 for some computable function f. Very recently it was proved that assuming W [ 1 ] ≠ FPT , the k-DomSet problem cannot be f ( k ) -approximated by FPT algorithms for any computable function f by S., Laekhanukit and Manurangsi and Lin, seperately. We observe that the constructions used in Lin’s work can be carried out using constant-depth circuits, and thus we prove that para- AC 0 circuits could not approximate this problem with ratio f ( k ) for any computable function f. Moreover, under the hypothesis that the 3-CNF-SAT problem cannot be computed by constant-depth circuits of size 2 ε n for some ε > 0 , we show that constant-depth circuits of size n o ( k ) cannot distinguish graphs whose dominating numbers are either ≤k or > log n 3 log log n 1 / k . However, we find that the hypothesis may be hard to settle by showing that it implies NP ⊈ NC 1 .
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110230
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 231: A GA-SA Hybrid Planning Algorithm Combined
           with Improved Clustering for LEO Observation Satellite Missions

    • Authors: Xiangyu Long, Shufan Wu, Xiaofeng Wu, Yixin Huang, Zhongcheng Mu
      First page: 231
      Abstract: This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110231
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 232: Comparison and Interpretation Methods for
           Predictive Control of Mechanics

    • Authors: Timothy Sands
      First page: 232
      Abstract: Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110232
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 233: Comparison of Satellite Repeat Shift Time
           for GPS, BDS, and Galileo Navigation Systems by Three Methods

    • Authors: Yanxi Yang, Jinguang Jiang, Mingkun Su
      First page: 233
      Abstract: The characteristic of the satellite repeat shift time can reflect the status of the satellite operation, and is also one of the key factors of the sidereal filtering multipath correction. Although some methods have been developed to calculate the repeat shift time, few efforts have been made to analyze and compare the performance of this feature for the GPS (Global Positioning System), BDS (BeiDou System), and Galileo in depth. Hence, three methods used for calculating the repeat shift time are presented, and used to compare and analyze the three global systems in depth, named the broadcast ephemeris method (BEM), correlation coefficient method (CCM), and aspect repeat time method (ARTM). The experiment results show that the repeat shift time of each satellite is different. Also, the difference between the maximum and minimum varies from different systems. The maximum difference is about 25 s for the BDS IGSO (Inclined Geosynchronous Orbit) and the minimum is merely 10 s for the GPS system. Furthermore, for the same satellite, the shift time calculated by the three methods is almost identical, and the maximum difference is only about 7 s between the CCM and the ARTM method for the BDS MEO (Medium Earth Orbit) satellite. Although the repeat shift time is different daily for the same satellite and the same method, the changes are very small. Moreover, in terms of the STD (Standard Deviation) of the BS (between satellites) and MS (mean shift for the same satellite), the GPS system is the best, the performance of the BDS system is medium, and the Galileo performs slightly worse than the GPS and BDS.
      Citation: Algorithms
      PubDate: 2019-11-05
      DOI: 10.3390/a12110233
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 234: Complex Neutrosophic Hypergraphs: New
           Social Network Models

    • Authors: Anam Luqman, Muhammad Akram, Florentin Smarandache
      First page: 234
      Abstract: A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110234
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 235: Exploring an Ensemble of Methods that
           Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time
           Series Prediction Problem of Gas Consumption in Greece

    • Authors: Konstantinos I. Papageorgiou, Katarzyna Poczeta, Elpiniki Papageorgiou, Vassilis C. Gerogiannis, George Stamoulis
      First page: 235
      Abstract: This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110235
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 236: Stability Analysis of Jacobian-Free
           Newton’s Iterative Method

    • Authors: Abdolreza Amiri, Alicia Cordero, Mohammad Taghi Darvishi, Juan R. Torregrosa
      First page: 236
      Abstract: It is well known that scalar iterative methods with derivatives are highly more stable than their derivative-free partners, understanding the term stability as a measure of the wideness of the set of converging initial estimations. In multivariate case, multidimensional dynamical analysis allows us to afford this task and it is made on different Jacobian-free variants of Newton’s method, whose estimations of the Jacobian matrix have increasing order. The respective basins of attraction and the number of fixed and critical points give us valuable information in this sense.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110236
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 237: What Do a Longest Increasing Subsequence
           and a Longest Decreasing Subsequence Know about Each Other'

    • Authors: Elizabeth J. Itskovich, Vadim E. Levit
      First page: 237
      Abstract: As a kind of converse of the celebrated Erdős–Szekeres theorem, we present a necessary and sufficient condition for a sequence of length n to contain a longest increasing subsequence and a longest decreasing subsequence of given lengths x and y, respectively.
      Citation: Algorithms
      PubDate: 2019-11-07
      DOI: 10.3390/a12110237
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 238: Microscopic Object Recognition and
           Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo
           

    • Authors: Shi-Xian Yan, Peng-Fei Zhao, Xin-Yu Gao, Qiao Zhou, Jin-Hai Li, Jie-Peng Yao, Zhi-Qiang Chai, Yang Yue, Zhong-Yi Wang, Lan Huang
      First page: 238
      Abstract: Microscopic object recognition and analysis is very important in micromanipulation. Micromanipulation has been extensively used in many fields, e.g., micro-assembly operation, microsurgery, agriculture, and biological research. Conducting micro-object recognition in the in-situ measurement of tissue, e.g., in the ion flux measurement by moving an ion-selective microelectrode (ISME), is a complex problem. For living tissues growing at a rate, it remains a challenge to accurately recognize and locate an ISME to protect living tissues and to prevent an ISME from being damaged. Thus, we proposed a robust and fast recognition method based on local binary pattern (LBP) and Haar-like features fusion by training a cascade of classifiers using the gentle AdaBoost algorithm to recognize microscopic objects. Then, we could locate the electrode tip from the background with strong noise by using the Hough transform and edge extraction with an improved contour detection method. Finally, the method could be used to automatically and accurately calculate the relative distance between the two micro-objects in the microscopic image. The results show that the proposed method can achieve good performance in micro-object recognition with a recognition rate up to 99.14% and a tip recognition speed up to 14 frames/s at a resolution of 1360 × 1024. The max error of tip positioning is 6.10 μm, which meets the design requirements of the ISME system. Furthermore, this study provides an effective visual guidance method for micromanipulation, which can facilitate automated micromanipulation research.
      Citation: Algorithms
      PubDate: 2019-11-07
      DOI: 10.3390/a12110238
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 239: A Hybrid Ontology-Based Recommendation
           System in e-Commerce

    • Authors: Márcio Guia, Rodrigo Rocha Silva, Jorge Bernardino
      First page: 239
      Abstract: The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.
      Citation: Algorithms
      PubDate: 2019-11-08
      DOI: 10.3390/a12110239
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 240: Tensor-Based Algorithms for Image
           Classification

    • Authors: Stefan Klus, Patrick Gelß
      First page: 240
      Abstract: Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised learning problems and propose two novel approaches for image classification. One is a kernel-based reformulation of the previously introduced multidimensional approximation of nonlinear dynamics (MANDy), the other an alternating ridge regression in the tensor train format. We apply both methods to the MNIST and fashion MNIST data set and show that the approaches are competitive with state-of-the-art neural network-based classifiers.
      Citation: Algorithms
      PubDate: 2019-11-09
      DOI: 10.3390/a12110240
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 241: Fingerprints Classification through Image
           Analysis and Machine Learning Method

    • Authors: Huong Thu Nguyen, Long The Nguyen
      First page: 241
      Abstract: The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases.
      Citation: Algorithms
      PubDate: 2019-11-11
      DOI: 10.3390/a12110241
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 242: On Neighborhood Structures and Repair
           Techniques for Blocking Job Shop Scheduling Problems

    • Authors: Julia Lange, Frank Werner
      First page: 242
      Abstract: The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.
      Citation: Algorithms
      PubDate: 2019-11-12
      DOI: 10.3390/a12110242
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 243: An Improved Genetic Algorithm with
           Adaptive Variable Neighborhood Search for FJSP

    • Authors: Xiaolin Gu, Ming Huang, Xu Liang
      First page: 243
      Abstract: For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.
      Citation: Algorithms
      PubDate: 2019-11-14
      DOI: 10.3390/a12110243
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 244: A Novel Multi-Objective Five-Elements
           Cycle Optimization Algorithm

    • Authors: Chunling Ye, Zhengyan Mao, Mandan Liu
      First page: 244
      Abstract: Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.
      Citation: Algorithms
      PubDate: 2019-11-14
      DOI: 10.3390/a12110244
      Issue No: Vol. 12, No. 11 (2019)
       
 
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