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  Subjects -> MATHEMATICS (Total: 1014 journals)
    - APPLIED MATHEMATICS (82 journals)
    - GEOMETRY AND TOPOLOGY (21 journals)
    - MATHEMATICS (751 journals)
    - MATHEMATICS (GENERAL) (41 journals)
    - NUMERICAL ANALYSIS (22 journals)
    - PROBABILITIES AND MATH STATISTICS (97 journals)

MATHEMATICS (751 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
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: 15)
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: 35)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 13)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 6)
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: 11)
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: 9)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Decision Sciences     Open Access   (Followers: 3)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 17)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 6)
Advances in Materials Science     Open Access   (Followers: 15)
Advances in Mathematical Physics     Open Access   (Followers: 5)
Advances in Mathematics     Full-text available via subscription   (Followers: 11)
Advances in Nonlinear Analysis     Open Access  
Advances in Numerical Analysis     Open Access   (Followers: 7)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 7)
Advances in Pure Mathematics     Open Access   (Followers: 7)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 14)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Open Access   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 6)
American Journal of Operations Research     Open Access   (Followers: 5)
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   (Followers: 1)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5)
Analysis Mathematica     Full-text available via subscription  
Analysis. International mathematical journal of analysis and its applications     Hybrid Journal   (Followers: 2)
Annales Mathematicae Silesianae     Open Access   (Followers: 1)
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: 12)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription   (Followers: 1)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
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  
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: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 2)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 7)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 8)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 2)
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: 7)
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: 2)
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 21)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 6)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 4)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
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: 1)
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 2)
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
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: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 20)
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: 1)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 19)
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)
ChemSusChem     Hybrid Journal   (Followers: 7)
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  
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
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: 2)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Compositio Mathematica     Full-text available via subscription   (Followers: 1)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 3)
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: 8)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Concrete Operators     Open Access   (Followers: 5)
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: 14)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 31)
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)
Differentsial'nye Uravneniya     Open Access  
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Diskretnaya Matematika     Full-text available via subscription  
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Eco Matemático     Open Access  
Econometrics     Open Access   (Followers: 2)
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Educação Matemática Debate     Open Access  
EduMatSains     Open Access  
Electronic Journal of Combinatorics     Open Access  

        1 2 3 4 | Last

Similar Journals
Journal Cover
Algorithms
Journal Prestige (SJR): 0.217
Citation Impact (citeScore): 1
Number of Followers: 11  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1999-4893
Published by MDPI Homepage  [215 journals]
  • Algorithms, Vol. 12, Pages 88: Review on Electrical Impedance Tomography:
           Artificial Intelligence Methods and its Applications

    • Authors: Talha Ali Khan, Sai Ho Ling
      First page: 88
      Abstract: Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.
      Citation: Algorithms
      PubDate: 2019-04-26
      DOI: 10.3390/a12050088
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 89: An Algorithm for Producing Fuzzy Negations
           via Conical Sections

    • Authors: Souliotis, Papadopoulos
      First page: 89
      Abstract: In this paper we introduced a new class of strong negations, which were generated via conical sections. This paper focuses on the fact that simple mathematical and computational processes generate new strong fuzzy negations, through purely geometrical concepts such as the ellipse and the hyperbola. Well-known negations like the classical negation, Sugeno negation, etc., were produced via the suggested conical sections. The strong negations were a structural element in the production of fuzzy implications. Thus, we have a machine for producing fuzzy implications, which can be useful in many areas, as in artificial intelligence, neural networks, etc. Strong Fuzzy Negations refers to the discrepancy between the degree of difficulty of the effort and the significance of its results. Innovative results may, therefore, derive for use in literature in the specific field of mathematics. These data are, moreover, generated in an effortless, concise, as well as self-evident manner.
      Citation: Algorithms
      PubDate: 2019-04-27
      DOI: 10.3390/a12050089
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 90: Multi-Metaheuristic Competitive Model for
           Optimization of Fuzzy Controllers

    • Authors: Marylu L. Lagunes, Oscar Castillo, Fevrier Valdez, Jose Soria
      First page: 90
      Abstract: This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used.
      Citation: Algorithms
      PubDate: 2019-04-28
      DOI: 10.3390/a12050090
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 91: FASTSET: A Fast Data Structure for the
           Representation of Sets of Integers

    • Authors: Giuseppe Lancia, Marcello Dalpasso
      First page: 91
      Abstract: We describe a simple data structure for storing subsets of { 0 , … , N - 1 } , with N a given integer, which has optimal time performance for all the main set operations, whereas previous data structures are non-optimal for at least one such operation. We report on the comparison of a Java implementation of our structure with other structures of the standard Java Collections.
      Citation: Algorithms
      PubDate: 2019-05-01
      DOI: 10.3390/a12050091
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 92: Optical Flow Estimation with Occlusion
           Detection

    • Authors: Song Wang, Zengfu Wang
      First page: 92
      Abstract: The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.
      Citation: Algorithms
      PubDate: 2019-05-01
      DOI: 10.3390/a12050092
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 93: Power Control and Channel Allocation
           Algorithm for Energy Harvesting D2D Communications

    • Authors: Na Su, Qi Zhu
      First page: 93
      Abstract: This paper assumes that multiple device-to-device (D2D) users can reuse the same uplink channel and base station (BS) supplies power to D2D transmitters by means of wireless energy transmission; the optimization problem aims at maximizing the total capacity of D2D users, and proposes a power control and channel allocation algorithm for the energy harvesting D2D communications underlaying the cellular network. This algorithm firstly uses a heuristic dynamic clustering method to cluster D2D users and those in the same cluster can share the same channel. Then, D2D users in the same cluster are modeled as a non-cooperative game, the expressions of D2D users’ transmission power and energy harvesting time are derived by using the Karush–Kuhn–Tucker (KKT) condition, and the optimal transmission power and energy harvesting time are allocated to D2D users by the joint iteration optimization method. Finally, we use the Kuhn–Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. Simulation results show that the proposed algorithm can effectively improve the system performance.
      Citation: Algorithms
      PubDate: 2019-05-03
      DOI: 10.3390/a12050093
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 94: A Cyclical Non-Linear Inertia-Weighted
           Teaching–Learning-Based Optimization Algorithm

    • Authors: Zongsheng Wu, Ru Xue
      First page: 94
      Abstract: After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.
      Citation: Algorithms
      PubDate: 2019-05-03
      DOI: 10.3390/a12050094
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 95: A Source Domain Extension Method for
           Inductive Transfer Learning Based on Flipping Output

    • Authors: Yasutake Koishi, Shuichi Ishida, Tatsuo Tabaru, Hiroyuki Miyamoto
      First page: 95
      Abstract: Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant potential to enable the application of machine learning to a wide range of real-world problems. However, since the technique is user-dependent, with data prepared as a source domain which in turn becomes a knowledge source for transfer learning, it often involves the adoption of inappropriate data. In such cases, the accuracy may be reduced due to “negative transfer.” Thus, in this paper, we propose a novel transfer learning method that utilizes the flipping output technique to provide multiple labels in the source domain. The accuracy of the proposed method is statistically demonstrated to be significantly better than that of the conventional transfer learning method, and its effect size is as high as 0.9, showing high performance.
      Citation: Algorithms
      PubDate: 2019-05-07
      DOI: 10.3390/a12050095
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 96: Triplet Loss Network for Unsupervised
           Domain Adaptation

    • Authors: Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan, Asad Masood Khattak
      First page: 96
      Abstract: Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.
      Citation: Algorithms
      PubDate: 2019-05-08
      DOI: 10.3390/a12050096
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 97: A New Method of Applying Data Engine
           Technology to Realize Neural Network Control

    • Authors: Song Zheng, Chao Bi, Yilin Song
      First page: 97
      Abstract: This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.
      Citation: Algorithms
      PubDate: 2019-05-09
      DOI: 10.3390/a12050097
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 98: Free Surface Flow Simulation by a Viscous
           Numerical Cylindrical Tank

    • Authors: Xingyue Ren, Fangjie Xiong, Ke Qu, Norimi Mizutani
      First page: 98
      Abstract: In order to numerically investigate the free surface flow evolution in a cylindrical tank, a regular structured grid system in the cylindrical coordinates is usually applied to solve control equations based on the incompressible two-phase flow model. Since the grid spacing in the azimuthal direction is proportionate to the radial distance in a regular structured grid system, very small grid spacing would be obtained in the azimuthal direction and it would require a very small computational time step to satisfy the stability restriction. Moreover, serious mass disequilibrium problems may happen through the convection of the free surface with the Volume of Fluid (VOF) method. Therefore in the present paper, the zonal embedded grid technique was implemented to overcome those problems by gradually adjusting the mesh resolution in different grid blocks. Over the embedded grid system, a finite volume algorithm was developed to solve the Navier–Stokes equations in the three-dimensional cylindrical coordinates. A high-resolution scheme was applied to resolve the free surface between the air and water phases based on the VOF method. Computation of liquid convection under a given velocity field shows that the VOF method implemented with a zonal embedded grid is more advanced in keeping mass continuity than that with regular structured grid system. Furthermore, the proposed model was also applied to simulate the sharp transient evolution of circular dam breaking flow. The simulation results were validated against the commercial software Fluent, which shows a good agreement, and the proposed model does not yield any free surface oscillation.
      Citation: Algorithms
      PubDate: 2019-05-09
      DOI: 10.3390/a12050098
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 99: Evolutionary Machine Learning for
           Multi-Objective Class Solutions in Medical Deformable Image Registration

    • Authors: Kleopatra Pirpinia, Peter A. N. Bosman, Jan-Jakob Sonke, Marcel van Herk, Tanja Alderliesten
      First page: 99
      Abstract: Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.
      Citation: Algorithms
      PubDate: 2019-05-09
      DOI: 10.3390/a12050099
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 100: A Variable Block Insertion Heuristic for
           Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

    • Authors: Kizilay, Tasgetiren, Pan, Gao
      First page: 100
      Abstract: In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993.
      Citation: Algorithms
      PubDate: 2019-05-09
      DOI: 10.3390/a12050100
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 101: An Adaptive Derivative Estimator for
           Fault-Detection Using a Dynamic System with a Suboptimal Parameter

    • Authors: Manuel Schimmack, Paolo Mercorelli
      First page: 101
      Abstract: This paper deals with an approximation of a first derivative of a signal using a dynamic system of the first order. After formulating the problem, a proposition and a theorem are proven for a possible approximation structure, which consists of a dynamic system. In particular, a proposition based on a Lyapunov approach is proven to show the convergence of the approximation. The proven theorem is a constructive one and shows directly the suboptimality condition in the presence of noise. Based on these two results, an adaptive algorithm is conceived to calculate the derivative of a signal with convergence in infinite time. Results are compared with an approximation of the derivative using an adaptive Kalman filter (KF).
      Citation: Algorithms
      PubDate: 2019-05-10
      DOI: 10.3390/a12050101
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 102: A Theoretical Framework to Determine RHP
           Zero Dynamics in Sequential Interacting Sub-Systems

    • Authors: Anca Maxim, Riccardo Ferracuti, Clara M. Ionescu
      First page: 102
      Abstract: A theoretical framework for determining the dynamics of interacting sub-systems is proposed in this paper. Specifically, a systematic analysis is performed that results in an indication about whether an MP or an NMP dynamics occurs in the analyzed process during operation. The analysis stems from the physical process description and the degree of coupling between sub-systems. The presented methodology is generalized for n sub-systems with sequential interaction (i.e., in which the coupling is unidirectional and occurs between consecutive sub-systems), and the outcome is useful investigation tool prior to the controller design phase. Given the generality of the approach, the theoretical framework is valid for any dynamic process with interacting sub-systems in the context of LTI systems.
      Citation: Algorithms
      PubDate: 2019-05-10
      DOI: 10.3390/a12050102
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 103: Improved Neural Networks Based on Mutual
           Information via Information Geometry

    • Authors: Meng Wang, Chuang-Bai Xiao, Zhen-Hu Ning, Jing Yu, Ya-Hao Zhang, Jin Pang
      First page: 103
      Abstract: This paper presents a new algorithm based on the theory of mutual information and information geometry. This algorithm places emphasis on adaptive mutual information estimation and maximum likelihood estimation. With the theory of information geometry, we adjust the mutual information along the geodesic line. Finally, we evaluate our proposal using empirical datasets that are dedicated for classification and regression. The results show that our algorithm contributes to a significant improvement over existing methods.
      Citation: Algorithms
      PubDate: 2019-05-13
      DOI: 10.3390/a12050103
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 104: Balanced Parallel Exploration of
           Orthogonal Regions

    • Authors: Wyatt Clements, Costas Busch, Limeng Pu, Daniel Smith, Hsiao-Chun Wu
      First page: 104
      Abstract: We consider the use of multiple mobile agents to explore an unknown area. The area is orthogonal, such that all perimeter lines run both vertically and horizontally. The area may consist of unknown rectangular holes which are non-traversable internally. For the sake of analysis, we assume that the area is discretized into N points allowing the agents to move from one point to an adjacent one. Mobile agents communicate through face-to-face communication when in adjacent points. The objective of exploration is to develop an online algorithm that will explore the entire area while reducing the total work of all k agents, where the work is measured as the number of points traversed. We propose splitting the exploration into two alternating tasks, perimeter and room exploration. The agents all begin with the perimeter scan and when a room is found they transition to room scan after which they continue with perimeter scan until the next room is found and so on. Given the total traversable points N, our algorithm completes in total O ( N ) work with each agent performing O ( N / k ) work, namely the work is balanced. If the rooms are hole-free the exploration time is also asymptotically optimal, O ( N / k ) . To our knowledge, this is the first agent coordination algorithm that considers simultaneously work balancing and small exploration time.
      Citation: Algorithms
      PubDate: 2019-05-15
      DOI: 10.3390/a12050104
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 105: Achievement of Automatic Copper Wire
           Elongation System

    • Authors: Hsiung-Cheng Lin, Chung-Hao Cheng
      First page: 105
      Abstract: Copper wire is a major conduction material that carries a variety of signals in industry. Presently, automatic wire elongating machines to produce very thin wiresare available for manufacturing. However, the original wires for the elongating process to thin sizes need heating, drawing and then threadingthrough the die molds by the manpower before the machine starts to work. This procedure repeatsuntil the wire threads through all various die molds. To replace the manpower, this paper aims to develop an automatic wire die molds threading system for the wire elongation process. Three pneumatic grippers are designed in the proposed system. The first gripper is used to clamp the wire. The second gripper fixed in the rotating mechanism is to draw the heated wire. The third gripper is used to move the wire for threading through the dies mold. The force designed for drawing the wire can be adjusted via the gear ratio. The experimental results confirm that the proposed system can accomplish the wiredies mold threading processin term of robustness, rapidness and accuracy.
      Citation: Algorithms
      PubDate: 2019-05-15
      DOI: 10.3390/a12050105
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 106: An Introduction of NoSQL Databases Based
           on Their Categories and Application Industries

    • Authors: Chen, Lee
      First page: 106
      Abstract: The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.
      Citation: Algorithms
      PubDate: 2019-05-16
      DOI: 10.3390/a12050106
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 107: Pruning Optimization over Threshold-Based
           Historical Continuous Query

    • Authors: Qin, Ma, Liu
      First page: 107
      Abstract: With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods.
      Citation: Algorithms
      PubDate: 2019-05-19
      DOI: 10.3390/a12050107
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 108: Real-Time Arm Gesture Recognition Using 3D
           Skeleton Joint Data

    • Authors: Georgios Paraskevopoulos, Evaggelos Spyrou, Dimitrios Sgouropoulos, Theodoros Giannakopoulos, Phivos Mylonas
      First page: 108
      Abstract: In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works.
      Citation: Algorithms
      PubDate: 2019-05-20
      DOI: 10.3390/a12050108
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 109: An Adaptive Procedure for the Global
           Minimization of a Class of Polynomial Functions

    • Authors: Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani
      First page: 109
      Abstract: The paper deals with the problem of global minimization of a polynomial function expressed through the Frobenius norm of two-dimensional or three-dimensional matrices. An adaptive procedure is proposed which applies a Multistart algorithm according to a heuristic approach. The basic step of the procedure consists of splitting the runs of different initial points in segments of fixed length and to interlace the processing order of the various segments, discarding those which appear less promising. A priority queue is suggested to implement this strategy. Various parameters contribute to the handling of the queue, whose length shrinks during the computation, allowing a considerable saving of the computational time with respect to classical procedures. To verify the validity of the approach, a large experimentation has been performed on both nonnegatively constrained and unconstrained problems.
      Citation: Algorithms
      PubDate: 2019-05-23
      DOI: 10.3390/a12050109
      Issue No: Vol. 12, No. 5 (2019)
       
  • Algorithms, Vol. 12, Pages 67: A Two-Phase Approach for Single Container
           Loading with Weakly Heterogeneous Boxes

    • Authors: Rommel Dias Saraiva, Napoleão Nepomuceno, Plácido Rogério Pinheiro
      First page: 67
      Abstract: We propose in this paper a two-phase approach that decomposes the process of solving the three-dimensional single Container Loading Problem (CLP) into subsequent tasks: (i) the generation of blocks of boxes and (ii) the loading of blocks into the container. The first phase is deterministic, and it is performed by means of constructive algorithms from the literature. The second phase is non-deterministic, and it is performed with the use of Generate-and-Solve (GS), a problem-independent hybrid optimization framework based on problem instance reduction that combines a metaheuristic with an exact solver. Computational experiments performed on benchmark instances indicate that our approach presents competitive results compared to those found by state-of-the-art algorithms, particularly for problem instances consisting of a few types of boxes. In fact, we present new best solutions for classical instances from groups BR1 and BR2.
      Citation: Algorithms
      PubDate: 2019-03-30
      DOI: 10.3390/a12040067
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 68: A Cross-Layer Optimization QoS Scheme in
           Wireless Multimedia Sensor Networks

    • Authors: Shu Fan
      First page: 68
      Abstract: There are two main challenges in wireless multimedia sensors networks: energy constraints and providing DiffServ. In this paper, a joint flow control, routing, scheduling, and power control scheme based on a Lyapunov optimization framework is proposed to increase network lifetime and scheduling fairness. For an adaptive distribution of transmission opportunities, a differentiated queueing services (DQS) scheme is adopted for maintaining data queues. In the Lyapunov function, different types of queues are normalized for a unified dimension. To prolong network lifetime, control coefficients are designed according to the characteristics of the wireless sensor networks. The power control problem is proved to be a convex optimization problem and two optimal algorithms are discussed. Simulation results show that, compared with existing schemes, the proposed scheme can achieve a better trade-off between QoS performances and network lifetime. The simulation results also show that the scheme utilizing the distributed media access control scheme in scheduling performs best in the transmission of real-time services.
      Citation: Algorithms
      PubDate: 2019-03-30
      DOI: 10.3390/a12040068
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 69: Special Issue on Algorithms in
           Computational Finance

    • Authors: V L Raju Chinthalapati, Edward Tsang
      First page: 69
      Abstract: Algorithms play an important part in finance [...]
      Citation: Algorithms
      PubDate: 2019-03-31
      DOI: 10.3390/a12040069
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 70: Task Assignment of the Improved Contract
           Net Protocol under a Multi-Agent System

    • Authors: Jiarui Zhang, Gang Wang, Yafei Song
      First page: 70
      Abstract: Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L max = 3 , the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.
      Citation: Algorithms
      PubDate: 2019-04-01
      DOI: 10.3390/a12040070
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 71: Parameter Combination Framework for the
           Differential Evolution Algorithm

    • Authors: Jinghua Zhang, Ze Dong
      First page: 71
      Abstract: The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.
      Citation: Algorithms
      PubDate: 2019-04-02
      DOI: 10.3390/a12040071
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 72: An Improved ABC Algorithm and Its
           Application in Bearing Fault Diagnosis with EEMD

    • Authors: Weijia Chen, Yancai Xiao
      First page: 72
      Abstract: The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.
      Citation: Algorithms
      PubDate: 2019-04-04
      DOI: 10.3390/a12040072
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 73: Permuted Pattern Matching Algorithms on
           Multi-Track Strings

    • Authors: Diptarama Hendrian, Yohei Ueki, Kazuyuki Narisawa, Ryo Yoshinaka, Ayumi Shinohara
      First page: 73
      Abstract: A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log σ ) as the matching time, where n, m, k, σ , and denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice.
      Citation: Algorithms
      PubDate: 2019-04-08
      DOI: 10.3390/a12040073
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 74: Bamboo Garden Trimming Problem: Priority
           Schedulings

    • Authors: Mattia D’Emidio, Gabriele Di Stefano, Alfredo Navarra
      First page: 74
      Abstract: The paper deals with the Bamboo Garden Trimming (BGT) problem introduced in [Gąsieniec et al., SOFSEM’17]. The problem is difficult to solved due to its close relationship to Pinwheel scheduling. The garden with n bamboos is an analogue of a system of n machines that have to be attended (e.g., serviced) with different frequencies. During each day, bamboo b i grows an extra height h i , for i = 1 , ⋯ , n and, on the conclusion of the day, at most one bamboo has its entire height cut.The goal is to design a perpetual schedule of cuts to keep the height of the tallest ever bamboo as low as possible. The contribution in this paper is twofold, and is both theoretical and experimental. In particular, the focus is on understanding what has been called priority schedulings, i.e., cutting strategies where priority is given to bamboos whose current height is above a threshold greater than or equal to H = ∑ i = 1 n h i . Value H represents the total daily growth of the system and it is known that one cannot keep bamboos in the garden below this threshold indefinitely. As the first result, it is proved that, for any distribution of integer growth rates h 1 , ⋯ , h n and any priority scheduling, the system stabilises in a fixed cycle of cuts. Then, the focus is on the so-called ReduceMax strategy, a greedy priority scheduling that each day cuts the tallest bamboo, regardless of the growth rates distribution. ReduceMax is known to provide a O ( log n ) -approximation, with respect to the lower bound H. One of the main results achieved is that, if ReduceMax stabilises in a round-robin type cycle, then it guarantees 2-approximation. Furthermore, preliminary results are provided relating the structure of the input instance, in terms of growth rates, and the behavior of ReduceMax when applied to such inputs. Finally, a conjecture that ReduceMax is 2-approximating for the BGT problem is claimed, hence an extended experimental evaluation was conducted to support the conjecture and to compare ReduceMax with other relevant scheduling algorithms. The obtained results show that ReduceMax : (i) provides 2-approximation in all considered inputs; and (ii) always outperforms other considered strategies, even those for which better worst case approximation guarantees have been proven.
      Citation: Algorithms
      PubDate: 2019-04-13
      DOI: 10.3390/a12040074
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 75: Pulmonary Fissure Detection in 3D CT Images
           Using a Multiple Section Model

    • Authors: Runing Xiao, Jinzhi Zhou
      First page: 75
      Abstract: As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median F1-score of 0.8916, which was much better than conventional methods.
      Citation: Algorithms
      PubDate: 2019-04-15
      DOI: 10.3390/a12040075
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 76: Programming Agents by Their Social
           Relationships: A Commitment-Based Approach

    • Authors: Matteo Baldoni, Cristina Baroglio, Roberto Micalizio, Stefano Tedeschi
      First page: 76
      Abstract: Multiagent systems can be seen as an approach to software engineering for the design and development of complex, distributed software. Generally speaking, multiagent systems provide two main abstractions for modularizing the software: the agents and the environment where agents operate. In this paper, we argue that also the social relationships among the agents should be expressed explicitly and become first-class objects both at design- and at development-time. In particular, we propose to represent social relationships as commitments that are reified as resources in the agents’ environment and can be directly manipulated by the agents via standard operations. We demonstrate that this view induces an agent programming schema that is substantially independent of the actual agent platform, provided that commitments are available as explained. The paper exemplifies the schema on two agent platforms, JADE and JaCaMo, where commitments are made available via the 2COMM library.
      Citation: Algorithms
      PubDate: 2019-04-16
      DOI: 10.3390/a12040076
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 77: Embedding Equality Constraints of
           Optimization Problems into a Quantum Annealer

    • Authors: Tomas Vyskocil, Hristo Djidjev
      First page: 77
      Abstract: Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a minimum-energy state of the quantum system. While many NP-hard problems can be easily formulated as binary quadratic optimization problems, such formulations almost always contain one or more constraints, which are not allowed in a QUBO. Embedding such constraints as quadratic penalties is the standard approach for addressing this issue, but it has drawbacks such as the introduction of large coefficients and using too many additional qubits. In this paper, we propose an alternative approach for implementing constraints based on a combinatorial design and solving mixed-integer linear programming (MILP) problems in order to find better embeddings of constraints of the type ∑ x i = k for binary variables x i. Our approach is scalable to any number of variables and uses a linear number of ancillary variables for a fixed k.
      Citation: Algorithms
      PubDate: 2019-04-17
      DOI: 10.3390/a12040077
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 78: Applications of Non-Uniquely Decodable
           Codes to Privacy-Preserving High-Entropy Data Representation

    • Authors: Muhammed Oğuzhan Külekci, Yasin Öztürk
      First page: 78
      Abstract: Non-uniquely-decodable (non-UD) codes can be defined as the codes that cannot be uniquely decoded without additional disambiguation information. These are mainly the class of non–prefix–free codes, where a code-word can be a prefix of other(s), and thus, the code-word boundary information is essential for correct decoding. Due to their inherent unique decodability problem, such non-UD codes have not received much attention except a few studies, in which using compressed data structures to represent the disambiguation information efficiently had been previously proposed. It had been shown before that the compression ratio can get quite close to Huffman/Arithmetic codes with an additional capability of providing direct access in compressed data, which is a missing feature in the regular Huffman codes. In this study we investigate non-UD codes in another dimension addressing the privacy of the high-entropy data. We particularly focus on such massive volumes, where typical examples are encoded video or similar multimedia files. Representation of such a volume with non–UD coding creates two elements as the disambiguation information and the payload, where decoding the original data from these elements becomes hard when one of them is missing. We make use of this observation for privacy concerns. and study the space consumption as well as the hardness of that decoding. We conclude that non-uniquely-decodable codes can be an alternative to selective encryption schemes that aim to secure only part of the data when data is huge. We provide a freely available software implementation of the proposed scheme as well.
      Citation: Algorithms
      PubDate: 2019-04-17
      DOI: 10.3390/a12040078
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 79: Speech Act Theory as an Evaluation Tool for
           Human–Agent Communication

    • Authors: Nader Hanna, Deborah Richards
      First page: 79
      Abstract: Effective communication in task-oriented situations requires high-level interactions. For human–agent collaboration, tasks need to be coordinated in a way that ensures mutual understanding. Speech Act Theory (SAT) aims to understand how utterances can be used to achieve actions. SAT consists of three components: locutionary act, illocutionary act, and perlocutionary act. This paper evaluates the agent’s verbal communication while collaborating with humans. SAT was used to anatomize the structure of the agent’s speech acts (locutionary acts), the agent’s intention behind the speech acts (illocutionary acts), and the effects on the human’s mental state (perlocutionary acts). Moreover, this paper studies the impact of human perceptions of the agent’s speech acts on the perception of collaborative performance with the agent.
      Citation: Algorithms
      PubDate: 2019-04-17
      DOI: 10.3390/a12040079
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 80: An Improved Squirrel Search Algorithm for
           Global Function Optimization

    • Authors: Yanjiao Wang, Tianlin Du
      First page: 80
      Abstract: An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.
      Citation: Algorithms
      PubDate: 2019-04-17
      DOI: 10.3390/a12040080
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 81: Direct Superbubble Detection

    • Authors: Fabian Gärtner, Peter F. Stadler
      First page: 81
      Abstract: Superbubbles are a class of induced subgraphs in digraphs that play an essential role in assembly algorithms for high-throughput sequencing data. They are connected with the remainder of the host digraph by a single entrance and a single exit vertex. Linear-time algorithms for the enumeration superbubbles recently have become available. Current approaches require the decomposition of the input digraph into strongly-connected components, which are then analyzed separately. In principle, a single depth-first search could be used, provided one can guarantee that the root of the depth-first search (DFS)-tree is not itself located in the interior or the exit point of a superbubble. Here, we describe a linear-time algorithm to determine suitable roots for a DFS-forest that is guaranteed to identify the superbubbles in a digraph correctly. In addition to the advantages of a more straightforward implementation, we observe a nearly three-fold gain in performance on real-world datasets. We present a reference implementation of the new algorithm that accepts many commonly-used input formats for digraphs. It is available as open source from github.
      Citation: Algorithms
      PubDate: 2019-04-17
      DOI: 10.3390/a12040081
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 82: Image Error Concealment Based on Deep
           Neural Network

    • Authors: Zhiqiang Zhang, Rong Huang, Fang Han, Zhijie Wang
      First page: 82
      Abstract: In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.
      Citation: Algorithms
      PubDate: 2019-04-19
      DOI: 10.3390/a12040082
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 83: The Optimization Algorithm of the Forced
           Current Cathodic Protection Base on Simulated Annealing

    • Authors: Song, Wang, Qin, Wang, Liu
      First page: 83
      Abstract: The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is proposed that is an optimization algorithm for the auxiliary anode system of a grounding grid based on improved simulated annealing. The mathematical model of the auxiliary anode system is inferred from the mathematical model of cathodic protection. On that basis, the parameters of the finite element model are optimized with the improved simulated annealing algorithm, thereby the auxiliary anode system of a grounding grid with optimized parameters is structured. Then the algorithm is proven as valid through experiments. The precision of the optimized parameters is improved by about 1.55% with respect to the Variable Metric Method and the Genetic Algorithm, so it can provide a basis for parameter design in the auxiliary anode system of a grounding grid.
      Citation: Algorithms
      PubDate: 2019-04-21
      DOI: 10.3390/a12040083
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 84: Horizontal Bending Angle Optimization
           Method for Scraper Conveyor Based on Improved Bat Algorithm

    • Authors: Ting Liu, Chao Tan, Zhongbin Wang, Jing Xu, Yiqiao Man, Tuo Wang
      First page: 84
      Abstract: The horizontal bending angle of scraper conveyor has a great influence on the running resistance, the current consumption, coal winning efficiency of the working surface, etc. Approximately 1–3° is usually the range of horizontal bending angle, but does not indicate the optimum bending angle of the coal mining face. To find the optimal horizontal bending angle, an optimization method is proposed. A mathematical calculation model of the running resistance of the scraper is established based on the direction of the shearer operation. Then, a method of adjusting the step size of the search by inertia weight and expanding fly distance range obeying the t-distribution is proposed based on the basic bat algorithm (BA). Finally, an industrial application was conducted in 21220 Changcun fully mechanized coal mining face, Henan Province. The results show that the current consumption by the scraper conveyor was reduced by 31% when the horizontal bending angle of the S-bending area was 0.66°. Meanwhile, the theoretical current has good consistency with the experimental data, and the average absolute error was 3%.
      Citation: Algorithms
      PubDate: 2019-04-22
      DOI: 10.3390/a12040084
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 85: Forecasting Economy-Related Data Utilizing
           Weight-Constrained Recurrent Neural Networks

    • Authors: Ioannis E. Livieris
      First page: 85
      Abstract: During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.
      Citation: Algorithms
      PubDate: 2019-04-22
      DOI: 10.3390/a12040085
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 86: Kalman-Filter-Based Tension Control Design
           for Industrial Roll-to-Roll System

    • Authors: Hyeongjin Hwang, Jehwon Lee, Sangjune Eum, Kanghyun Nam
      First page: 86
      Abstract: This paper presents a robust and precise tension control method for a roll-to-roll (R2R) system. In R2R processing, robust and precise tension control is very important because improper web tension control leads to deterioration in the quality of web material. However, tension control is not easy because the R2R system has a model variation in which the inertia of the web in roll form is changed and external disturbances caused by web slip and crumpled web. Therefore, a disturbance observer (DOB) was proposed to achieve robustness against model variations and external disturbances. DOB is a robust control method widely used in various fields because of its simple structure and excellent performance. Moreover, the web passes through various process steps to achieve the finished product in the R2R process. Particularly, it is important to track the tension when magnitude of the tension varies during process. Feedforward (FF) controller was applied to minimize the tracking error in the transient section where tension changes. Moreover, the signal processing of a sensor using the Kalman filter (KF) in the R2R system greatly improved control performance. Finally, the effectiveness of the proposed control scheme is discussed using experimental results.
      Citation: Algorithms
      PubDate: 2019-04-24
      DOI: 10.3390/a12040086
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 87: Oriented Coloring on Recursively Defined
           Digraphs

    • Authors: Frank Gurski, Dominique Komander, Carolin Rehs
      First page: 87
      Abstract: Coloring is one of the most famous problems in graph theory. The coloring problem on undirected graphs has been well studied, whereas there are very few results for coloring problems on directed graphs. An oriented k-coloring of an oriented graph G = ( V , A ) is a partition of the vertex set V into k independent sets such that all the arcs linking two of these subsets have the same direction. The oriented chromatic number of an oriented graph G is the smallest k such that G allows an oriented k-coloring. Deciding whether an acyclic digraph allows an oriented 4-coloring is NP-hard. It follows that finding the chromatic number of an oriented graph is an NP-hard problem, too. This motivates to consider the problem on oriented co-graphs. After giving several characterizations for this graph class, we show a linear time algorithm which computes an optimal oriented coloring for an oriented co-graph. We further prove how the oriented chromatic number can be computed for the disjoint union and order composition from the oriented chromatic number of the involved oriented co-graphs. It turns out that within oriented co-graphs the oriented chromatic number is equal to the length of a longest oriented path plus one. We also show that the graph isomorphism problem on oriented co-graphs can be solved in linear time.
      Citation: Algorithms
      PubDate: 2019-04-25
      DOI: 10.3390/a12040087
      Issue No: Vol. 12, No. 4 (2019)
       
  • Algorithms, Vol. 12, Pages 51: Optimized Deep Convolutional Neural
           Networks for Identification of Macular Diseases from Optical Coherence
           Tomography Images

    • Authors: Qingge Ji, Jie Huang, Wenjie He, Yankui Sun
      First page: 51
      Abstract: Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.
      Citation: Algorithms
      PubDate: 2019-02-26
      DOI: 10.3390/a12030051
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 52: Space-Efficient Fully Dynamic DFS in
           Undirected Graphs †

    • Authors: Kengo Nakamura, Kunihiko Sadakane
      First page: 52
      Abstract: Depth-first search (DFS) is a well-known graph traversal algorithm and can be performed in O ( n + m ) time for a graph with n vertices and m edges. We consider the dynamic DFS problem, that is, to maintain a DFS tree of an undirected graph G under the condition that edges and vertices are gradually inserted into or deleted from G. We present an algorithm for this problem, which takes worst-case O ( m n · polylog ( n ) ) time per update and requires only ( 3 m + o ( m ) ) log n bits of space. This algorithm reduces the space usage of dynamic DFS algorithm to only 1.5 times as much space as that of the adjacency list of the graph. We also show applications of our dynamic DFS algorithm to dynamic connectivity, biconnectivity, and 2-edge-connectivity problems under vertex insertions and deletions.
      Citation: Algorithms
      PubDate: 2019-02-27
      DOI: 10.3390/a12030052
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 53: Tree Compatibility, Incomplete Directed
           Perfect Phylogeny, and Dynamic Graph Connectivity: An Experimental Study

    • Authors: David Fernández-Baca, Lei Liu
      First page: 53
      Abstract: We study two problems in computational phylogenetics. The first is tree compatibility. The input is a collection of phylogenetic trees over different partially-overlapping sets of species. The goal is to find a single phylogenetic tree that displays all the evolutionary relationships implied by . The second problem is incomplete directed perfect phylogeny (IDPP). The input is a data matrix describing a collection of species by a set of characters, where some of the information is missing. The question is whether there exists a way to fill in the missing information so that the resulting matrix can be explained by a phylogenetic tree satisfying certain conditions. We explain the connection between tree compatibility and IDPP and show that a recent tree compatibility algorithm is effectively a generalization of an earlier IDPP algorithm. Both algorithms rely heavily on maintaining the connected components of a graph under a sequence of edge and vertex deletions, for which they use the dynamic connectivity data structure of Holm et al., known as HDT. We present a computational study of algorithms for tree compatibility and IDPP. We show experimentally that substituting HDT by a much simpler data structure—essentially, a single-level version of HDT—improves the performance of both of these algorithm in practice. We give partial empirical and theoretical justifications for this observation.
      Citation: Algorithms
      PubDate: 2019-02-28
      DOI: 10.3390/a12030053
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 54: Parameter Tuning of PI Control for Speed
           Regulation of a PMSM Using Bio-Inspired Algorithms

    • Authors: Juan Luis Templos-Santos, Omar Aguilar-Mejia, Edgar Peralta-Sanchez, Raul Sosa-Cortez
      First page: 54
      Abstract: This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.
      Citation: Algorithms
      PubDate: 2019-03-04
      DOI: 10.3390/a12030054
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 55: Depth Optimization Analysis of Articulated
           Steering Hinge Position Based on Genetic Algorithm

    • Authors: Bing-wei Cao, Xin-hui Liu, Wei Chen, Yong Zhang, Ai-min Li
      First page: 55
      Abstract: Articulated steering is affected by the position of the articulated points of the steering cylinder. When the two steering cylinders turn, there is a stroke difference and arm of force difference. The existence of the above differences causes the pressure fluctuation of the steering system. Firstly, the mathematical model of the steering mechanism is established through theoretical analysis. Then, the coordinates of the hinge points of the steering cylinder are optimized using genetic algorithm (GA) with the stroke difference function and cylinder pressure function as the objective functions. The curves of the stroke difference and the arm of force difference of the steering cylinder are obtained by mathematical modeling, and the correctness of the GA is verified. According to the optimization results, the wheel loader prototype was reconstructed, and the reconstruction verified by corresponding sensors. The experimental curves show that the steering system has no obvious pressure fluctuation. Finally, the arm of force difference and stroke difference curves were analyzed, and it was concluded that the arm of force difference was the main cause of pressure fluctuation. The objective function was improved, and the arm of force function and cylinder pressure function were taken as the objective functions to continue the optimization by GA. The arm of force difference and stroke difference after optimization were reduced, which provides a constructive reference for the design of articulated steering systems in the future.
      Citation: Algorithms
      PubDate: 2019-03-05
      DOI: 10.3390/a12030055
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 56: Matrix Adaptation Evolution Strategy with
           Multi-Objective Optimization for Multimodal Optimization

    • Authors: Wei Li
      First page: 56
      Abstract: The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs). In this paper, an improved algorithm based on the MA-ES, which is called the matrix adaptation evolution strategy with multi-objective optimization algorithm, is proposed to solve multimodal optimization problems (MA-ESN-MO). Taking advantage of the multi-objective optimization in maintaining population diversity, MA-ESN-MO transforms an MMOP into a bi-objective optimization problem. The archive is employed to save better solutions for improving the convergence of the algorithm. Moreover, the peaks found by the algorithm can be maintained until the end of the run. Multiple subpopulations are used to explore and exploit in parallel to find multiple optimal solutions for the given problem. Experimental results on CEC2013 test problems show that the covariance matrix adaptation with Niching and the multi-objective optimization algorithm (CMA-NMO), CMA Niching with the Mahalanobis Metric and the multi-objective optimization algorithm (CMA-NMM-MO), and matrix adaptation evolution strategy Niching with the multi-objective optimization algorithm (MA-ESN-MO) have overall better performance compared with the covariance matrix adaptation evolution strategy (CMA-ES), matrix adaptation evolution strategy (MA-ES), CMA Niching (CMA-N), CMA-ES Niching with Mahalanobis Metric (CMA-NMM), and MA-ES-Niching (MA-ESN).
      Citation: Algorithms
      PubDate: 2019-03-05
      DOI: 10.3390/a12030056
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 57: Parameter Estimation, Robust Controller
           Design and Performance Analysis for an Electric Power Steering System

    • Authors: Van Giao Nguyen, Xuexun Guo, Chengcai Zhang, Xuan Khoa Tran
      First page: 57
      Abstract: This paper presents a parameter estimation, robust controller design and performance analysis for an electric power steering (EPS) system. The parametrical analysis includes the EPS parameters and disturbances, such as the assist motor parameters, sensor-measurement noise, and random road factors, allowing the EPS stability to be extensively investigated. Based on the loop-shaping technique, the system controller is designed to increase the EPS stability and performance. The loop-shaping procedure is proposed to minimize the influence of system disturbances on the system outputs. The simplified refined instrumental variable (SRIV) algorithm, least squares state variable filter (LSSVF) algorithm and instrumental variable state variable filter (IVSVF) algorithm are applied to reduce the model mismatching between the theoretical EPS models and the real EPS model, as the EPS parameters can be accurately identified based on the experimental EPS data. The performance of the proposed method is thus compared to that of the proportional-integral-derivative (PID) test bench results for the EPS system. The experimental results demonstrated that the proposed loop-shaping controller provides good tracking performance while ensuring the stability of the EPS system.
      Citation: Algorithms
      PubDate: 2019-03-05
      DOI: 10.3390/a12030057
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 58: A Selectable Sloppy Heap

    • Authors: Adrian Dumitrescu
      First page: 58
      Abstract: We study the selection problem, namely that of computing the ith order statistic of n given elements. Here we offer a data structure called selectable sloppy heap that handles a dynamic version in which upon request (i) a new element is inserted or (ii) an element of a prescribed quantile group is deleted from the data structure. Each operation is executed in constant time—and is thus independent of n (the number of elements stored in the data structure)—provided that the number of quantile groups is fixed. This is the first result of this kind accommodating both insertion and deletion in constant time. As such, our data structure outperforms the soft heap data structure of Chazelle (which only offers constant amortized complexity for a fixed error rate 0 < ε ≤ 1 / 2 ) in applications such as dynamic percentile maintenance. The design demonstrates how slowing down a certain computation can speed up the data structure. The method described here is likely to have further impact in the field of data structure design in extending asymptotic amortized upper bounds to same formula asymptotic worst-case bounds.
      Citation: Algorithms
      PubDate: 2019-03-06
      DOI: 10.3390/a12030058
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 59: Autonomous Population Regulation Using a
           Multi-Agent System in a Prey–Predator Model That Integrates Cellular
           Automata and the African Buffalo Optimization Metaheuristic

    • Authors: Boris Almonacid, Fabián Aspée, Francisco Yimes
      First page: 59
      Abstract: This research focused on the resolution of a dynamic prey–predator spatial model. This model has six life cycles and simulates a theoretical population of prey and predators. Cellular automata represent a set of prey and predators. The cellular automata move in a discrete space in a 2d lattice that has the shape of a torus. African buffaloes represent the predators, and the grasslands represent the prey in the African savanna. Each buffalo moves in the discrete space using the proper motion equation of the African buffalo optimization metaheuristic. Two types of approaches were made with five experiments each. The first approach was the development of a dynamic prey–predator spatial model using the movement of the African buffalo optimization metaheuristic. The second approach added the characteristic of regulating the population of buffaloes using autonomous multi-agents that interact with the model dynamic prey–predator spatial model. According to the obtained results, it was possible to adjust and maintain a balance of prey and predators during a determined period using multi-agents, therefore preventing predators from destroying an entire population of prey in the coexistence space.
      Citation: Algorithms
      PubDate: 2019-03-06
      DOI: 10.3390/a12030059
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 60: Heterogeneous Distributed Big Data
           Clustering on Sparse Grids

    • Authors: David Pfander, Gregor Daiß, Dirk Pflüger
      First page: 60
      Abstract: Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our computed kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager–worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a ten-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198 s using 128 nodes of Piz Daint. This translates to an overall performance of 352 TFLOPS . On the node-level, we provide results for two GPUs, Nvidia’s Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all computed kernels and devices, demonstrating the performance portability of our approach.
      Citation: Algorithms
      PubDate: 2019-03-07
      DOI: 10.3390/a12030060
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 61: A Novel Coupling Algorithm Based on
           Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving
           Multi-Objective Optimization Problems

    • Authors: Wang, Cui, Li
      First page: 61
      Abstract: In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP.
      Citation: Algorithms
      PubDate: 2019-03-11
      DOI: 10.3390/a12030061
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 62: Multi-View Network Representation Learning
           Algorithm Research

    • Authors: Zhonglin Ye, Haixing Zhao, Ke Zhang, Yu Zhu
      First page: 62
      Abstract: Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several excellent research achievements, including DeepWalk, node2vec and GraRep and so forth. We conduct our experiment on several real-world citation datasets and demonstrate that MVNR outperforms some new approaches using neural matrix factorization. Specifically, we demonstrate the efficiency of MVNR on network classification, visualization and link prediction tasks.
      Citation: Algorithms
      PubDate: 2019-03-12
      DOI: 10.3390/a12030062
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 63: Synchronization Control Algorithm of
           Double-Cylinder Forging Hydraulic Press Based on Fuzzy Neural Network

    • Authors: Xu, Bai, Shao
      First page: 63
      Abstract: In order to solve the poor control accuracy problem of the traditional synchronous control algorithm for a double-cylinder forging hydraulic press, a synchronous control algorithm for double-cylinder forging hydraulic press based on a fuzzy neural network was proposed. According to the flow equation of valve and hydraulic cylinder, the balance equation and force balance equation of forging hydraulic cylinder are established by using the theory of electro-hydraulic servo systems, and the cylinder-controlled transfer function of forging hydraulic cylinder is deduced. By properly simplifying the transfer function, the mathematical model of synchronous control of double cylinder forging hydraulic press is established. According to the implementation process of traditional fuzzy neural networks, the properties of compensation operation are introduced. The traditional fuzzy neural network is optimized, and the optimized neural network is used to realize the synchronous control of the double cylinder forging hydraulic press. The experimental results show that the amplitude curve of the algorithm is very close to the expected amplitude curve, the error amplitude is only 0.3 mm, and the average control time is about 140 s, which fully shows that the algorithm has high accuracy and a good control effect.
      Citation: Algorithms
      PubDate: 2019-03-14
      DOI: 10.3390/a12030063
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 64: A Weighted Voting Ensemble Self-Labeled
           Algorithm for the Detection of Lung Abnormalities from X-Rays

    • Authors: Ioannis E. Livieris, Andreas Kanavos, Vassilis Tampakas, Panagiotis Pintelas
      First page: 64
      Abstract: During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods.
      Citation: Algorithms
      PubDate: 2019-03-16
      DOI: 10.3390/a12030064
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 65: High-Precision Combined Tidal Forecasting
           Model

    • Authors: Jiao Liu, Guoyou Shi, Kaige Zhu
      First page: 65
      Abstract: To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient.
      Citation: Algorithms
      PubDate: 2019-03-26
      DOI: 10.3390/a12030065
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 66: An Approach to the Dynamics and Control of
           Uncertain Robot Manipulators

    • Authors: Xiaohui Yang, Xiaolong Zhang, Shaoping Xu, Yihui Ding, Kun Zhu, Xiaoping Liu
      First page: 66
      Abstract: In this paper, a novel constraint-following control for uncertain robot manipulators that is inspired by analytical dynamics is developed. The motion can be regarded as external constraints of the system. However, it is not easy to obtain explicit equations for dynamic modeling of constrained systems. For a multibody system subject to motion constraints, it is a common practice to introduce Lagrange multipliers, but using these to obtain explicit dynamical equations is a very difficult task. In order to obtain such equations more simply, motion constraints are handled here using the Udwadia-Kalaba equation(UKE). Then, considering real-life robot manipulators are usually uncertain(but bounded), by using continuous controllers compensate for the uncertainties. No linearizations/approximations of the robot manipulators systems are made throughout, and the tracking errors are bounds. A redundant manipulator of the SCARA type as the example to illustrates the methodology. Numerical results are demonstrates the simplicity and ease of implementation of the methodology.
      Citation: Algorithms
      PubDate: 2019-03-26
      DOI: 10.3390/a12030066
      Issue No: Vol. 12, No. 3 (2019)
       
  • Algorithms, Vol. 12, Pages 28: FPGA Implementation of ECT Digital System
           for Imaging Conductive Materials

    • Authors: Wael Deabes
      First page: 28
      Abstract: This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density.
      Citation: Algorithms
      PubDate: 2019-01-22
      DOI: 10.3390/a12020028
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 29: A Distributed Execution Pipeline for
           Clustering Trajectories Based on a Fuzzy Similarity Relation

    • Authors: Soufiane Maguerra, Azedine Boulmakoul, Lamia Karim, Hassan Badir
      First page: 29
      Abstract: The proliferation of indoor and outdoor tracking devices has led to a vast amount of spatial data. Each object can be described by several trajectories that, once analysed, can yield to significant knowledge. In particular, pattern analysis by clustering generic trajectories can give insight into objects sharing the same patterns. Still, sequential clustering approaches fail to handle large volumes of data. Hence, the necessity of distributed systems to be able to infer knowledge in a trivial time interval. In this paper, we detail an efficient, scalable and distributed execution pipeline for clustering raw trajectories. The clustering is achieved via a fuzzy similarity relation obtained by the transitive closure of a proximity relation. Moreover, the pipeline is integrated in Spark, implemented in Scala and leverages the Core and Graphx libraries making use of Resilient Distributed Datasets (RDD) and graph processing. Furthermore, a new simple, but very efficient, partitioning logic has been deployed in Spark and integrated into the execution process. The objective behind this logic is to equally distribute the load among all executors by considering the complexity of the data. In particular, resolving the load balancing issue has reduced the conventional execution time in an important manner. Evaluation and performance of the whole distributed process has been analysed by handling the Geolife project’s GPS trajectory dataset.
      Citation: Algorithms
      PubDate: 2019-01-22
      DOI: 10.3390/a12020029
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 30: An Exploration of a Balanced Up-Downwind
           Scheme for Solving Heston Volatility Model Equations on Variable Grids

    • Authors: Chong Sun, Qin Sheng
      First page: 30
      Abstract: This paper studies an effective finite difference scheme for solving two-dimensional Heston stochastic volatility option-pricing model problems. A dynamically balanced up-downwind strategy for approximating the cross-derivative is implemented and analyzed. Semi-discretized and spatially nonuniform platforms are utilized. The numerical method comprised is simple and straightforward, with reliable first order overall approximations. The spectral norm is used throughout the investigation, and numerical stability is proven. Simulation experiments are given to illustrate our results.
      Citation: Algorithms
      PubDate: 2019-01-22
      DOI: 10.3390/a12020030
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 31: Particle Probability Hypothesis Density
           Filter Based on Pairwise Markov Chains

    • Authors: Jiangyi Liu, Chunping Wang, Wei Wang, Zheng Li
      First page: 31
      Abstract: Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.
      Citation: Algorithms
      PubDate: 2019-01-31
      DOI: 10.3390/a12020031
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 32: Fog-Computing-Based Heartbeat Detection and
           Arrhythmia Classification Using Machine Learning

    • Authors: Alessandro Scirè, Fabrizio Tropeano, Aris Anagnostopoulos, Ioannis Chatzigiannakis
      First page: 32
      Abstract: Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.
      Citation: Algorithms
      PubDate: 2019-02-02
      DOI: 10.3390/a12020032
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 33: Optimized Sonar Broadband Focused
           Beamforming Algorithm

    • Authors: Bi, Feng, Zhang
      First page: 33
      Abstract: Biases of initial direction estimation and focusing frequency selection affect the final focusing effect and may even cause algorithm failure in determining the focusing matrix in the coherent signal–subspace method. An optimized sonar broadband focused beamforming algorithm is proposed to address these defects. Initially, the robust Capon beamforming algorithm was used to correct the focusing matrix, and the broadband signals were then focused on the optimal focusing frequency by the corrected focusing matrix such that the wideband beamforming was transformed into a narrowband problem. Finally, the focused narrowband signals were beamformed by the second-order cone programming algorithm. Computer simulation results and water pool experiments verified that the proposed algorithm provides a good performance.
      Citation: Algorithms
      PubDate: 2019-02-05
      DOI: 10.3390/a12020033
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 34: From the Quantum Approximate Optimization
           Algorithm to a Quantum Alternating Operator Ansatz

    • Authors: Stuart Hadfield, Zhihui Wang, Bryan O'Gorman, Eleanor G. Rieffel, Davide Venturelli, Rupak Biswas
      First page: 34
      Abstract: The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.
      Citation: Algorithms
      PubDate: 2019-02-12
      DOI: 10.3390/a12020034
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 35: Research on Quantitative Investment
           Strategies Based on Deep Learning

    • Authors: Fang, Chen, Xue
      First page: 35
      Abstract: This paper takes 50 ETF options in the options market with high transaction complexity as the research goal. The Random Forest (RF) model, the Long Short-Term Memory network (LSTM) model, and the Support Vector Regression (SVR) model are used to predict 50 ETF price. Firstly, the original quantitative investment strategy is taken as the research object, and the 15 min trading frequency, which is more in line with the actual trading situation, is used, and then the Delta hedging concept of the options is introduced to control the risk of the quantitative investment strategy, to achieve the 15 min hedging strategy. Secondly, the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment marked with 50 ETF are the seven key factors affecting the price of 50 ETF. Then, two different types of LSTM-SVR models, LSTM-SVR I and LSTM-SVR II, are used to predict the final transaction price of the 50 ETF in the next time segment. In LSTM-SVR I model, the output of LSTM and seven key factors are combined as the input of SVR model. In LSTM-SVR II model, the hidden state vectors of LSTM and seven key factors are combined as the inputs of the SVR model. The results of the two LSTM-SVR models are compared with each other, and the better one is applied to the trading strategy. Finally, the benefit of the deep learning-based quantitative investment strategy, the resilience, and the maximum drawdown are used as indicators to judge the pros and cons of the research results. The accuracy and deviations of the LSTM-SVR prediction models are compared with those of the LSTM model and those of the RF model. The experimental results show that the quantitative investment strategy based on deep learning has higher returns than the traditional quantitative investment strategy, the yield curve is more stable, and the anti-fall performance is better.
      Citation: Algorithms
      PubDate: 2019-02-12
      DOI: 10.3390/a12020035
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 36: Conjugate Gradient Hard Thresholding
           Pursuit Algorithm for Sparse Signal Recovery

    • Authors: Yanfeng Zhang, Yunbao Huang, Haiyan Li, Pu Li, Xi’an Fan
      First page: 36
      Abstract: We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.
      Citation: Algorithms
      PubDate: 2019-02-13
      DOI: 10.3390/a12020036
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 37: Stream Data Load Prediction for Resource
           Scaling Using Online Support Vector Regression

    • Authors: Zhigang Hu, Hui Kang, Meiguang Zheng
      First page: 37
      Abstract: A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.
      Citation: Algorithms
      PubDate: 2019-02-14
      DOI: 10.3390/a12020037
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 38: A Two-Level Rolling Optimization Model for
           Real-time Adaptive Signal Control

    • Authors: Zhihong Yao, Yibing Wang, Wei Xiao, Bin Zhao, Bo Peng
      First page: 38
      Abstract: Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.
      Citation: Algorithms
      PubDate: 2019-02-15
      DOI: 10.3390/a12020038
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 39: A Hybrid Adaptive Large Neighborhood
           Heuristic for a Real-Life Dial-a-Ride Problem

    • Authors: Slim Belhaiza
      First page: 39
      Abstract: The transportation of elderly and impaired people is commonly solved as a Dial-A-Ride Problem (DARP). The DARP aims to design pick-up and delivery vehicle routing schedules. Its main objective is to accommodate as many users as possible with a minimum operation cost. It adds realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper tackles the DARP with time windows (DARPTW) from a new and innovative angle as it combines hybridization techniques with an adaptive large neighborhood search heuristic algorithm. The main objective is to improve the overall real-life performance of vehicle routing operations. Real-life data are refined and fed to a hybrid adaptive large neighborhood search (Hybrid-ALNS) algorithm which provides a near-optimal routing solution. The computational results on real-life instances, in the Canadian city of Vancouver and its region, and DARPTW benchmark instances show the potential improvements achieved by the proposed heuristic and its adaptability.
      Citation: Algorithms
      PubDate: 2019-02-16
      DOI: 10.3390/a12020039
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 40: An INS-UWB Based Collision Avoidance System
           for AGV

    • Authors: Shunkai Sun, Jianping Hu, Jie Li, Ruidong Liu, Meng Shu, Yang Yang
      First page: 40
      Abstract: As a highly automated carrying vehicle, an automated guided vehicle (AGV) has been widely applied in various industrial areas. The collision avoidance of AGV is always a problem in factories. Current solutions such as inertial and laser guiding have low flexibility and high environmental requirements. An INS (inertial navigation system)-UWB (ultra-wide band) based AGV collision avoidance system is introduced to improve the safety and flexibility of AGV in factories. An electronic map of the factory is established and the UWB anchor nodes are deployed in order to realize an accurate positioning. The extended Kalman filter (EKF) scheme that combines UWB with INS data is used to improve the localization accuracy. The current location of AGV and its motion state data are used to predict its next position, decrease the effect of control delay of AGV and avoid collisions among AGVs. Finally, experiments are given to show that the EKF scheme can get accurate position estimation and the collisions among AGVs can be detected and avoided in time.
      Citation: Algorithms
      PubDate: 2019-02-18
      DOI: 10.3390/a12020040
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 41: Computation of Compact Distributions of
           Discrete Elements

    • Authors: Chen, Yang, Yang
      First page: 41
      Abstract: In our daily lives, many plane patterns can actually be regarded as a compact distribution of a number of elements with certain shapes, like the classic pattern mosaic. In order to synthesize this kind of pattern, the basic problem is, with given graphics elements with certain shapes, to distribute a large number of these elements within a plane region in a possibly random and compact way. It is not easy to achieve this because it not only involves complicated adjacency calculations, but also is closely related to the shape of the elements. This paper attempts to propose an approach that can effectively and quickly synthesize compact distributions of elements of a variety of shapes. The primary idea is that with the seed points and distribution region given as premise, the generation of the Centroidal Voronoi Tesselation (CVT) of this region by iterative relaxation and the CVT will partition the distribution area into small regions of Voronoi, with each region representing the space of an element, to achieve a compact distribution of all the elements. In the generation process of Voronoi diagram, we adopt various distance metrics to control the shape of the generated Voronoi regions, and finally achieve the compact element distributions of different shapes. Additionally, approaches are introduced to control the sizes and directions of the Voronoi regions to generate element distributions with size and direction variations during the Voronoi diagram generation process to enrich the effect of compact element distributions. Moreover, to increase the synthesis efficiency, the time-consuming Voronoi diagram generation process was converted into a graphical rendering process, thus increasing the speed of the synthesis process. This paper is an exploration of elements compact distribution and also carries application value in the fields like mosaic pattern synthesis.
      Citation: Algorithms
      PubDate: 2019-02-18
      DOI: 10.3390/a12020041
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 42: Design Optimization of a VX Gasket
           Structure for a Subsea Connector Based on the Kriging Surrogate
           Model-NSGA-II Algorithm Considering the Load Randomness

    • Authors: Zeng, Ren, Yu, Huang
      First page: 42
      Abstract: The VX gasket is an important part of the wellhead connector for a subsea Christmas tree. Optimization of the gasket’s structure can improve the connector’s sealing performance. In this paper, we develop an optimization approach for the VX gasket structure, taking into consideration working load randomness, based on the Kriging surrogate model-NSGA-II algorithm. To guarantee the simulation accuracy, a random finite element (R-FE) model of the connector’s sealing structure was constructed to calculate the gasket’s sealing performance under random working load conditions. The working load’s randomness was simulated using the Gaussian distribution function. To improve the calculation efficiency of the sealing performance for individuals within the initial populations, Kriging surrogate models were constructed. These models accelerated the optimization speed, where the training sample was obtained using an experimental method design and the constructed R-FE model. The effectiveness of the presented approach was verified in the context of a subsea Christmas tree wellhead connector, which matched the 20'' casing head. The results indicated that the proposed method is effective for VX gasket structure optimization in subsea connectors, and that efficiency was significantly enhanced compared to the traditional FE method.
      Citation: Algorithms
      PubDate: 2019-02-18
      DOI: 10.3390/a12020042
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 43: An Improved Genetic Algorithm for Emergency
           Decision Making under Resource Constraints Based on Prospect Theory

    • Authors: Leiwen Chen, Yingming Wang, Geng Guo
      First page: 43
      Abstract: The study of emergency decision making (EDM) is helpful to reduce the difficulty of decision making and improve the efficiency of decision makers (DMs). The purpose of this paper is to propose an innovative genetic algorithm for emergency decision making under resource constraints. Firstly, this paper analyzes the emergency situation under resource constraints, and then, according to the prospect theory (PT), we further propose an improved value measurement function and an emergency loss levels weighting algorithm. Secondly, we assign weights for all emergency locations using the best–worst method (BWM). Then, an improved genetic algorithm (GA) based on prospect theory (PT) is established to solve the problem of emergency resource allocation between multiple emergency locations under resource constraints. Finally, the analyses of example show that the algorithm can shorten the decision-making time and provide a better decision scheme, which has certain practical significance.
      Citation: Algorithms
      PubDate: 2019-02-18
      DOI: 10.3390/a12020043
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 44: Integrated Speed Planning and Friction
           Coefficient Estimation Algorithm for Intelligent Electric Vehicles

    • Authors: Chentong Bian, Tong Zhu, Guodong Yin, Liwei Xu
      First page: 44
      Abstract: To improve the safety of intelligent electric vehicles and avoid side slipping on curved roads with changing friction coefficients, an integrated speed planning and friction coefficient estimation algorithm is proposed. With this algorithm, the speeds of intelligent electric vehicles can be planned online using estimated road friction coefficients to avoid lane departures. When a decrease in the friction coefficient is detected on a curved road with a large curvature, the algorithm will plan a low and safe speed to avoid side slipping. When a normal friction coefficient is detected, the algorithm will plan a higher speed for normal driving. Simulations using MATLAB and CarSim have been performed to demonstrate the effectiveness of the designed algorithm. The simulation results suggest that the proposed algorithm is applicable to speed planning on curved roads with changing friction coefficients.
      Citation: Algorithms
      PubDate: 2019-02-20
      DOI: 10.3390/a12020044
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 45: A Heuristic Approach for a Real-World
           Electric Vehicle Routing Problem

    • Authors: Mengting Zhao, Yuwei Lu
      First page: 45
      Abstract: To develop a non-polluting and sustainable city, urban administrators encourage logistics companies to use electric vehicles instead of conventional (i.e., fuel-based) vehicles for transportation services. However, electric energy-based limitations pose a new challenge in designing reasonable visiting routes that are essential for the daily operations of companies. Therefore, this paper investigates a real-world electric vehicle routing problem (VRP) raised by a logistics company. The problem combines the features of the capacitated VRP, the VRP with time windows, the heterogeneous fleet VRP, the multi-trip VRP, and the electric VRP with charging stations. To solve such a complicated problem, a heuristic approach based on the adaptive large neighborhood search (ALNS) and integer programming is proposed in this paper. Specifically, a charging station adjustment heuristic and a departure time adjustment heuristic are devised to decrease the total operational cost. Furthermore, the best solution obtained by the ALNS is improved by integer programming. Twenty instances generated from real-world data were used to validate the effectiveness of the proposed algorithm. The results demonstrate that using our algorithm can save 7.52% of operational cost.
      Citation: Algorithms
      PubDate: 2019-02-20
      DOI: 10.3390/a12020045
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 46: The Prediction of Intrinsically Disordered
           Proteins Based on Feature Selection

    • Authors: Hao He, Jiaxiang Zhao, Guiling Sun
      First page: 46
      Abstract: Intrinsically disordered proteins perform a variety of important biological functions, which makes their accurate prediction useful for a wide range of applications. We develop a scheme for predicting intrinsically disordered proteins by employing 35 features including eight structural properties, seven physicochemical properties and 20 pieces of evolutionary information. In particular, the scheme includes a preprocessing procedure which greatly reduces the input features. Using two different windows, the preprocessed data containing not only the properties of the surroundings of the target residue but also the properties related to the specific target residue are fed into a multi-layer perceptron neural network as its inputs. The Adam algorithm for the back propagation together with the dropout algorithm to avoid overfitting are introduced during the training process. The training as well as testing our procedure is performed on the dataset DIS803 from a DisProt database. The simulation results show that the performance of our scheme is competitive in comparison with ESpritz and IsUnstruct.
      Citation: Algorithms
      PubDate: 2019-02-20
      DOI: 10.3390/a12020046
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 47: Real-time Conflict Resolution Algorithm for
           Multi-UAV Based on Model Predict Control

    • Authors: Chen, Nan, Yang
      First page: 47
      Abstract: A real-time conflict resolution algorithm based on model predictive control (MPC) is introduced to address the flight conflict resolution problem in multi-UAV scenarios. Using a low-level controller, the UAV dynamic equations are abstracted into simpler unicycle kinematic equations. The neighboring UAVs exchange their predicted trajectories at each sample time to predict the conflicts. Then, under the predesignated resolution rule and strategy, decentralized coordination and cooperation are performed to resolve the predicted conflicts. The controller structure of the distributed nonlinear model predictive control (DNMPC) is designed to predict potential conflicts and calculate control variables for each UAV. Numerical simulations of multi-UAV coordination are performed to verify the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can resolve the conflicts sufficiently in real time, while causing no further conflicts.
      Citation: Algorithms
      PubDate: 2019-02-22
      DOI: 10.3390/a12020047
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 48: Selective Offloading by Exploiting ARIMA-BP
           for Energy Optimization in Mobile Edge Computing Networks

    • Authors: Ming Zhao, Ke Zhou
      First page: 48
      Abstract: Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.
      Citation: Algorithms
      PubDate: 2019-02-25
      DOI: 10.3390/a12020048
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 49: Secrecy Control of Wireless Networks with
           Finite Encoding Blocklength

    • Authors: Qiuming Liu, Shumin Liu, Chunshui Zeng, Xiaohong Qiu, He Xiao
      First page: 49
      Abstract: We consider wireless multi-hop networks in which each node aims to securely transmit a message. To guarantee the secure transmission, we employ an independent randomization encoding strategy to encode the confidential message. We aim to maximize the network utility. Based on the finite length of a secrecy codewords strategy, we develop an improved control algorithm, subject to network stability and secrecy outage requirements. On the basis of the Lyapunov optimization method, we design an control algorithm, which is decomposed into end-to-end secrecy encoding, flow control and routing scheduling. The simulation results show that the proposed algorithm can achieve a utility result that is arbitrarily close to the optimal value. Finally, the performance of the proposed control policy is validated with various network conditions.
      Citation: Algorithms
      PubDate: 2019-02-25
      DOI: 10.3390/a12020049
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 50: Randomized Parameterized Algorithms for the
           Kidney Exchange Problem

    • Authors: Mugang Lin, Jianxin Wang, Qilong Feng, Bin Fu
      First page: 50
      Abstract: In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically 2 ≤ L ≤ 5), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the parameterized complexity and randomized algorithms for the kidney exchange problem without chains from theory. We construct two different parameterized models of the kidney exchange problem for two cases L = 3 and L ≥ 3 , and propose two randomized parameterized algorithms based on the random partitioning technique and the randomized algebraic technique, respectively.
      Citation: Algorithms
      PubDate: 2019-02-25
      DOI: 10.3390/a12020050
      Issue No: Vol. 12, No. 2 (2019)
       
  • Algorithms, Vol. 12, Pages 12: Edge-Nodes Representation Neural Machine
           for Link Prediction

    • Authors: Guangluan Xu, Xiaoke Wang, Yang Wang, Daoyu Lin, Xian Sun, Kun Fu
      First page: 12
      Abstract: Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics.
      Citation: Algorithms
      PubDate: 2019-01-02
      DOI: 10.3390/a12010012
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 13: Dissimilarity Space Based Multi-Source
           Cross-Project Defect Prediction

    • Authors: Ren, Zhang, Munir, Xia
      First page: 13
      Abstract: Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods.
      Citation: Algorithms
      PubDate: 2019-01-02
      DOI: 10.3390/a12010013
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 14: A Hybrid Proposed Fundus Image Enhancement
           Framework for Diabetic Retinopathy

    • Authors: Imran Qureshi, Jun Ma, Kashif Shaheed
      First page: 14
      Abstract: Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.
      Citation: Algorithms
      PubDate: 2019-01-04
      DOI: 10.3390/a12010014
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 15: Total Optimization of Energy Networks in a
           Smart City by Multi-Population Global-Best Modified Brain Storm
           Optimization with Migration

    • Authors: Sato, Fukuyama, Iizaka, Matsui
      First page: 15
      Abstract: This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.
      Citation: Algorithms
      PubDate: 2019-01-07
      DOI: 10.3390/a12010015
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 16: Learning an Efficient Convolution Neural
           Network for Pansharpening

    • Authors: Yecai Guo, Fei Ye, Hao Gong
      First page: 16
      Abstract: Pansharpening is a domain-specific task of satellite imagery processing, which aims at fusing a multispectral image with a corresponding panchromatic one to enhance the spatial resolution of multispectral image. Most existing traditional methods fuse multispectral and panchromatic images in linear manners, which greatly restrict the fusion accuracy. In this paper, we propose a highly efficient inference network to cope with pansharpening, which breaks the linear limitation of traditional methods. In the network, we adopt a dilated multilevel block coupled with a skip connection to perform local and overall compensation. By using dilated multilevel block, the proposed model can make full use of the extracted features and enlarge the receptive field without introducing extra computational burden. Experiment results reveal that our network tends to induce competitive even superior pansharpening performance compared with deeper models. As our network is shallow and trained with several techniques to prevent overfitting, our model is robust to the inconsistencies across different satellites.
      Citation: Algorithms
      PubDate: 2019-01-08
      DOI: 10.3390/a12010016
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 17: Shadowed Type-2 Fuzzy Systems for Dynamic
           Parameter Adaptation in Harmony Search and Differential Evolution
           Algorithms

    • Authors: Oscar Castillo, Patricia Melin, Fevrier Valdez, Jose Soria, Emanuel Ontiveros-Robles, Cinthia Peraza, Patricia Ochoa
      First page: 17
      Abstract: Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.
      Citation: Algorithms
      PubDate: 2019-01-09
      DOI: 10.3390/a12010017
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 18: A Novel Hybrid Ant Colony Optimization for
           a Multicast Routing Problem

    • Authors: Xiaoxia Zhang, Xin Shen, Ziqiao Yu
      First page: 18
      Abstract: Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality.
      Citation: Algorithms
      PubDate: 2019-01-10
      DOI: 10.3390/a12010018
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 19: Algorithm for Producing Rankings Based on
           Expert Surveys

    • Authors: Indra Overland, Javlon Juraev
      First page: 19
      Abstract: This paper develops an automated algorithm to process input data for segmented string relative rankings (SSRRs). The purpose of the SSRR methodology is to create rankings of countries, companies, or any other units based on surveys of expert opinion. This is done without the use of grading systems, which can distort the results due to varying degrees of strictness among experts. However, the original SSRR approach relies on manual application, which is highly laborious and also carries a risk of human error. This paper seeks to solve this problem by further developing the SSRR approach by employing link analysis, which is based on network theory and is similar to the PageRank algorithm used by the Google search engine. The ranking data are treated as part of a linear, hierarchical network and each unit receives a score according to how many units are positioned below it in the network. This approach makes it possible to efficiently resolve contradictions among experts providing input for a ranking. A hypertext preprocessor (PHP) script for the algorithm is included in the article’s appendix. The proposed methodology is suitable for use across a range of social science disciplines, especially economics, sociology, and political science.
      Citation: Algorithms
      PubDate: 2019-01-10
      DOI: 10.3390/a12010019
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 20: Robust Guaranteed-Cost Preview Repetitive
           Control for Polytopic Uncertain Discrete-Time Systems

    • Authors: Yong-Hong Lan, Jun-Jun Xia, Yue-Xiang Shi
      First page: 20
      Abstract: In this paper, a robust guaranteed-cost preview repetitive controller is proposed for a class of polytopic uncertain discrete-time systems. In order to improve the tracking performance, a repetitive controller, combined with preview compensator, is inserted in the forward channel. By using the L-order forward difference operator, an augmented dynamic system is constructed. Then, the guaranteed-cost preview repetitive control problem is transformed into a guaranteed-cost control problem for the augmented dynamic system. For a given performance index, the sufficient condition of asymptotic stability for the closed-loop system is derived by using a parameter-dependent Lyapunov function method and linear matrix inequality (LMI) techniques. Incorporating the controller obtained into the original system, the guaranteed-cost preview repetitive controller is derived. A numerical example is also included, to show the effectiveness of the proposed method.
      Citation: Algorithms
      PubDate: 2019-01-10
      DOI: 10.3390/a12010020
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 21: Acknowledgement to Reviewers of Algorithms
           in 2018

    • Authors: Algorithms Editorial Office
      First page: 21
      Abstract: Rigorous peer-review is the corner-stone of high-quality academic publishing [...]
      Citation: Algorithms
      PubDate: 2019-01-10
      DOI: 10.3390/a12010021
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 22: Gyro Error Compensation in Optoelectronic
           Platform Based on a Hybrid ARIMA-Elman Model

    • Authors: Xingkui Xu, Chunfeng Wu, Qingyu Hou, Zhigang Fan
      First page: 22
      Abstract: As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.
      Citation: Algorithms
      PubDate: 2019-01-11
      DOI: 10.3390/a12010022
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 23: On Finding and Enumerating Maximal and
           Maximum k-Partite Cliques in k-Partite Graphs

    • Authors: Charles A. Phillips, Kai Wang, Erich J. Baker, Jason A. Bubier, Elissa J. Chesler, Michael A. Langston
      First page: 23
      Abstract: Let k denote an integer greater than 2, let G denote a k-partite graph, and let S denote the set of all maximal k-partite cliques in G. Several open questions concerning the computation of S are resolved. A straightforward and highly-scalable modification to the classic recursive backtracking approach of Bron and Kerbosch is first described and shown to run in O(3n/3) time. A series of novel graph constructions is then used to prove that this bound is best possible in the sense that it matches an asymptotically tight upper limit on S . The task of identifying a vertex-maximum element of S is also considered and, in contrast with the k = 2 case, shown to be NP-hard for every k ≥ 3. A special class of k-partite graphs that arises in the context of functional genomics and other problem domains is studied as well and shown to be more readily solvable via a polynomial-time transformation to bipartite graphs. Applications, limitations, potentials for faster methods, heuristic approaches, and alternate formulations are also addressed.
      Citation: Algorithms
      PubDate: 2019-01-15
      DOI: 10.3390/a12010023
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 24: A Pricing Strategy of E-Commerce
           Advertising Cooperation in the Stackelberg Game Model with Different
           Market Power Structure

    • Authors: Ling Zhu, Jie Lin
      First page: 24
      Abstract: A lot of research work has studied the auction mechanism of uncertain advertising cooperation between the e-commerce platform and advertisers, but little has focused on pricing strategy in stable advertising cooperation under a certain market power structure. To fill this gap, this paper makes a study of the deep interest distribution of two parties in such cooperation. We propose a pricing strategy by building two stackelberg master-slave models when the e-commerce platform and the advertiser are respectively the leader in the cooperation. It is analyzed that the optimization solution of the profits of both parties and the total system are affected by some main decision factors including the income commission proportion, the advertising product price and the cost of advertising effort of both parties’ brand in different dominant models. Then, some numerical studies are used to verify the effectiveness of the models. Finally, we draw a conclusion and make some suggestions to the platforms and the advertisers in the e-commerce advertising cooperation.
      Citation: Algorithms
      PubDate: 2019-01-18
      DOI: 10.3390/a12010024
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 25: Power Allocation Algorithm for an
           Energy-Harvesting Wireless Transmission System Considering Energy Losses

    • Authors: Su Zhao, Gang Huang, Qi Zhu
      First page: 25
      Abstract: For an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average throughput of the system within a certain period of time for both a time-invariant channel and time-varying channel. Convex optimization methods such as the Lagrange multiplier method and the KKT (Karush–Kuhn–Tucker) condition are used to solve the optimization problem; then, an optimal offline power allocation algorithm which has a three-threshold structure is proposed. In the three-threshold algorithm, two thresholds can be achieved by using a linear search method while the third threshold is calculated according to the channel state information and energy losses; then, the offline power allocation is based on the three thresholds and energy arrivals. Furthermore, inspired by the optimal offline algorithm, a low-complexity online algorithm with adaptive thresholds is derived. Finally, the simulation results show that the offline power allocation algorithms proposed in this paper are better than other algorithms, the performance of the online algorithm proposed is close to the offline one, and these algorithms can help improve the average throughput of the system.
      Citation: Algorithms
      PubDate: 2019-01-18
      DOI: 10.3390/a12010025
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 26: Ensemble and Deep Learning for
           Language-Independent Automatic Selection of Parallel Data

    • Authors: Despoina Mouratidis, Katia Lida Kermanidis
      First page: 26
      Abstract: Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a popular research field of machine learning. This kind of information can be quite helpful for machine translation. Our parallel corpora (English-Greek and English-Italian) are based on educational data, which are quite difficult to translate. We apply two state of the art architectures, Random Forest (RF) and Deeplearnig4j (DL4J), to our data (which constitute three translation outputs). To our knowledge, this is the first time that deep learning architectures are applied to the automatic selection of parallel data. We also propose new string-based features that seem to be effective for the classifier, and we investigate whether an attribute selection method could be used for better classification accuracy. Experimental results indicate an increase of up to 4% (compared to our previous work) using RF and rather satisfactory results using DL4J.
      Citation: Algorithms
      PubDate: 2019-01-18
      DOI: 10.3390/a12010026
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 27: Data Analysis, Simulation and Visualization
           for Environmentally Safe Maritime Data

    • Authors: Manolis Maragoudakis
      First page: 27
      Abstract: Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem and safety. This danger is further deteriorated by the absence of regulations on allowed waterways. Marine accidents could cause a major ecological disaster in the area and pose big socio-economic impacts in Greece. Monitoring marine traffic data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO’s and the public in general. Apart from real-time applications, the knowledge discovery from historical data is also significant. Towards this direction, a data analysis and simulation framework for maritime data has been designed and developed. The framework analyzes historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the levels of hazard in certain regions and clusters such data according to their similarity. Upon this unsupervised step, the degree of hazard is estimated and along with other important parameters is fed into a special type of Bayesian network, in order to infer on future situations, thus, simulating future data based on past conditions. Another innovative aspect of this work is the modeling of shipping traffic as a social network, whose analysis could provide useful and informative visualizations. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework.
      Citation: Algorithms
      PubDate: 2019-01-21
      DOI: 10.3390/a12010027
      Issue No: Vol. 12, No. 1 (2019)
       
  • Algorithms, Vol. 12, Pages 10: Diagonally Implicit Runge–Kutta Type
           Method for Directly Solving Special Fourth-Order Ordinary Differential
           Equations with Ill-Posed Problem of a Beam on Elastic Foundation

    • Authors: Nizam Ghawadri, Norazak Senu, Firas Adel Fawzi, Fudziah Ismail, Zarina Bibi Ibrahim
      First page: 10
      Abstract: In this study, fifth-order and sixth-order diagonally implicit Runge–Kutta type (DIRKT) techniques for solving fourth-order ordinary differential equations (ODEs) are derived which are denoted as DIRKT5 and DIRKT6, respectively. The first method has three and the another one has four identical nonzero diagonal elements. A set of test problems are applied to validate the methods and numerical results showed that the proposed methods are more efficient in terms of accuracy and number of function evaluations compared to the existing implicit Runge–Kutta (RK) methods.
      Citation: Algorithms
      PubDate: 2018-12-29
      DOI: 10.3390/a12010010
      Issue No: Vol. 12, No. 1 (2018)
       
  • Algorithms, Vol. 12, Pages 11: Facial Expression Recognition Based on
           Discrete Separable Shearlet Transform and Feature Selection

    • Authors: Yang Lu, Shigang Wang, Wenting Zhao
      First page: 11
      Abstract: In this paper, a novel approach to facial expression recognition based on the discrete separable shearlet transform (DSST) and normalized mutual information feature selection is proposed. The approach can be divided into five steps. First, all test and training images are preprocessed. Second, DSST is applied to the preprocessed facial expression images, and all the transformation coefficients are obtained as the original feature set. Third, an improved normalized mutual information feature selection is proposed to find the optimal feature subset of the original feature set, thus we can retain the key classification information of the original data. Fourth, the feature extraction and selection of the feature space is reduced by employing linear discriminant analysis. Finally, a support vector machine is used to recognize the expressions. In this study, experimental verification was carried out on four open facial expression databases. The results show that this method can not only improve the recognition rate of facial expressions, but also significantly reduce the computational complexity and improve the system efficiency.
      Citation: Algorithms
      PubDate: 2018-12-31
      DOI: 10.3390/a12010011
      Issue No: Vol. 12, No. 1 (2018)
       
 
 
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