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MATHEMATICS (723 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: 32)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 4)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
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: 14)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 3)
Advances in Materials Science     Open Access   (Followers: 14)
Advances in Mathematical Physics     Open Access   (Followers: 4)
Advances in Mathematics     Full-text available via subscription   (Followers: 11)
Advances in Nonlinear Analysis     Hybrid Journal  
Advances in Numerical Analysis     Open Access   (Followers: 5)
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: 6)
Advances in Pure Mathematics     Open Access   (Followers: 6)
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: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
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     Full-text available via subscription   (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: 9)
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  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
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: 13)
Annals of Pure and Applied Logic     Open Access   (Followers: 3)
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  
Annuaire du Collège de France     Open Access   (Followers: 5)
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: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 7)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 9)
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: 5)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
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 Journal of Current Engineering & Maths     Open Access  
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  
BoEM - Boletim online de Educação Matemática     Open Access  
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: 12)
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: 1)
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: 4)
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 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  
Complexus     Full-text available via subscription  
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: 8)
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: 8)
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: 9)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 30)
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: 3)
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  
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Combinatorics     Open Access  
Electronic Journal of Differential Equations     Open Access  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 4)

        1 2 3 4 | Last

Journal Cover
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  [205 journals]
  • Algorithms, Vol. 11, Pages 129: The Fast Detection and Identification
           Algorithm of Optical Fiber Intrusion Signals

    • Authors: Zhiyong Sheng, Dandan Qu, Yuan Zhang, Dan Yang
      First page: 129
      Abstract: With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.
      Citation: Algorithms
      PubDate: 2018-08-23
      DOI: 10.3390/a11090129
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 130: Numerical Modeling of Wave Disturbances in
           the Process of Ship Movement Control

    • Authors: Piotr Borkowski
      First page: 130
      Abstract: The article presents a numerical model of sea wave generation as an implementation of the stochastic process with a spectrum of wave angular velocity. Based on the wave spectrum, a forming filter is determined, and its input is fed with white noise. The resulting signal added to the angular speed of a ship represents disturbances acting on the ship’s hull as a result of wave impact. The model was used for simulation tests of the influence of disturbances on the course stabilization system of the ship.
      Citation: Algorithms
      PubDate: 2018-08-31
      DOI: 10.3390/a11090130
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 131: An efficient algorithm to determine
           probabilistic bisimulation

    • Authors: J.F. Groote, H.J. Rivera Verduzco, E.P. de Vink
      First page: 131
      Abstract: We provide an algorithm to efficiently compute bisimulation for probabilistic labeled transition systems, featuring non-deterministic choice as well as discrete probabilistic choice. The algorithm is linear in the number of transitions and logarithmic in the number of states, distinguishing both action states and probabilistic states, and the transitions between them. The algorithm improves upon the proposed complexity bounds of the best algorithm addressing the same purpose so far by Baier, Engelen and Majster-Cederbaum (Journal of Computer and System Sciences 60:187–231, 2000). In addition, experimentally, on various benchmarks, our algorithm performs rather well; even on relatively small transition systems, a performance gain of a factor 10,000 can be achieved.
      Citation: Algorithms
      PubDate: 2018-09-03
      DOI: 10.3390/a11090131
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 132: Study of Precipitation Forecast Based on
           Deep Belief Networks

    • Authors: Jinglin Du, Yayun Liu, Zhijun Liu
      First page: 132
      Abstract: Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation information for climate prediction and early warning decisions to avoid serious meteorological disasters. For the precipitation prediction problem in the era of climate big data, we propose a new method based on deep learning. In this paper, we will apply deep belief networks in weather precipitation forecasting. Deep belief networks transform the feature representation of data in the original space into a new feature space, with semantic features to improve the predictive performance. The experimental results show, compared with other forecasting methods, the feasibility of deep belief networks in the field of weather forecasting.
      Citation: Algorithms
      PubDate: 2018-09-04
      DOI: 10.3390/a11090132
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 133: A Modified Sufficient Descent
           Polak–Ribiére–Polyak Type Conjugate Gradient Method for Unconstrained
           Optimization Problems

    • Authors: Xiuyun Zheng, Jiarong Shi
      First page: 133
      Abstract: In this paper, a modification to the Polak–Ribiére–Polyak (PRP) nonlinear conjugate gradient method is presented. The proposed method always generates a sufficient descent direction independent of the accuracy of the line search and the convexity of the objective function. Under appropriate conditions, the modified method is proved to possess global convergence under the Wolfe or Armijo-type line search. Moreover, the proposed methodology is adopted in the Hestenes–Stiefel (HS) and Liu–Storey (LS) methods. Extensive preliminary numerical experiments are used to illustrate the efficiency of the proposed method.
      Citation: Algorithms
      PubDate: 2018-09-06
      DOI: 10.3390/a11090133
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 134: Multi-Level Elasticity for Wide-Area Data
           Streaming Systems: A Reinforcement Learning Approach

    • Authors: Gabriele Russo Russo, Matteo Nardelli, Valeria Cardellini, Francesco Lo Presti
      First page: 134
      Abstract: The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically scale their execution on multiple computing resources to process the incoming data flow in parallel. Being that data sources and consumers are usually located at the network edges, nowadays the presence of geo-distributed computing resources represents an attractive environment for DSP. However, controlling the applications and the processing infrastructure in such wide-area environments represents a significant challenge. In this paper, we present a hierarchical solution for the autonomous control of elastic DSP applications and infrastructures. It consists of a two-layered hierarchical solution, where centralized components coordinate subordinated distributed managers, which, in turn, locally control the elastic adaptation of the application components and deployment regions. Exploiting this framework, we design several self-adaptation policies, including reinforcement learning based solutions. We show the benefits of the presented self-adaptation policies with respect to static provisioning solutions, and discuss the strengths of reinforcement learning based approaches, which learn from experience how to optimize the application performance and resource allocation.
      Citation: Algorithms
      PubDate: 2018-09-07
      DOI: 10.3390/a11090134
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 135: Multiple Attribute Decision-Making Method
           Using Linguistic Cubic Hesitant Variables

    • Authors: Jun Ye, Wenhua Cui
      First page: 135
      Abstract: Linguistic decision making (DM) is an important research topic in DM theory and methods since using linguistic terms for the assessment of the objective world is very fitting for human thinking and expressing habits. However, there is both uncertainty and hesitancy in linguistic arguments in human thinking and judgments of an evaluated object. Nonetheless, the hybrid information regarding both uncertain linguistic arguments and hesitant linguistic arguments cannot be expressed through the various existing linguistic concepts. To reasonably express it, this study presents a linguistic cubic hesitant variable (LCHV) based on the concepts of a linguistic cubic variable and a hesitant fuzzy set, its operational relations, and its linguistic score function for ranking LCHVs. Then, the objective extension method based on the least common multiple number/cardinality for LCHVs and the weighted aggregation operators of LCHVs are proposed to reasonably aggregate LCHV information because existing aggregation operators cannot aggregate LCHVs in which the number of their hesitant components may imply difference. Next, a multi-attribute decision-making (MADM) approach is proposed based on the weighted arithmetic averaging (WAA) and weighted geometric averaging (WGA) operators of LCHVs. Lastly, an illustrative example is provided to indicate the applicability of the proposed approaches.
      Citation: Algorithms
      PubDate: 2018-09-07
      DOI: 10.3390/a11090135
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 136: Mixed Order Fractional Observers for
           Minimal Realizations of Linear Time-Invariant Systems

    • Authors: Manuel A. Duarte-Mermoud, Javier A. Gallegos, Norelys Aguila-Camacho, Rafael Castro-Linares
      First page: 136
      Abstract: Adaptive and non-adaptive minimal realization (MR) fractional order observers (FOO) for linear time-invariant systems (LTIS) of a possibly different derivation order (mixed order observers, MOO) are studied in this paper. Conditions on the convergence and robustness are provided using a general framework which allows observing systems defined with any type of fractional order derivative (FOD). A qualitative discussion is presented to show that the derivation orders of the observer structure and for the parameter adjustment are relevant degrees of freedom for performance optimization. A control problem is developed to illustrate the application of the proposed observers.
      Citation: Algorithms
      PubDate: 2018-09-09
      DOI: 10.3390/a11090136
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 137: Learning Heterogeneous Knowledge Base
           Embeddings for Explainable Recommendation

    • Authors: Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang
      First page: 137
      Abstract: Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.
      Citation: Algorithms
      PubDate: 2018-09-13
      DOI: 10.3390/a11090137
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 138: Are Markets Truly Efficient'

    • Authors: Sanjiv Das, Karthik Mokashi, Robbie Culkin
      First page: 138
      Abstract: We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.
      Citation: Algorithms
      PubDate: 2018-09-13
      DOI: 10.3390/a11090138
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 139: An Auto-Adjustable Semi-Supervised
           Self-Training Algorithm

    • Authors: Ioannis E. Livieris, Andreas Kanavos, Vassilis Tampakas, Panagiotis Pintelas
      First page: 139
      Abstract: Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.
      Citation: Algorithms
      PubDate: 2018-09-14
      DOI: 10.3390/a11090139
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 140: Complexity of Hamiltonian Cycle

    • Authors: Asahi Takaoka
      First page: 140
      Abstract: The Hamiltonian cycle reconfiguration problem asks, given two Hamiltonian cycles C 0 and C t of a graph G, whether there is a sequence of Hamiltonian cycles C 0 , C 1 , … , C t such that C i can be obtained from C i − 1 by a switch for each i with 1 ≤ i ≤ t , where a switch is the replacement of a pair of edges u v and w z on a Hamiltonian cycle with the edges u w and v z of G, given that u w and v z did not appear on the cycle. We show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete, settling an open question posed by Ito et al. (2011) and van den Heuvel (2013). More precisely, we show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete for chordal bipartite graphs, strongly chordal split graphs, and bipartite graphs with maximum degree 6. Bipartite permutation graphs form a proper subclass of chordal bipartite graphs, and unit interval graphs form a proper subclass of strongly chordal graphs. On the positive side, we show that, for any two Hamiltonian cycles of a bipartite permutation graph and a unit interval graph, there is a sequence of switches transforming one cycle to the other, and such a sequence can be obtained in linear time.
      Citation: Algorithms
      PubDate: 2018-09-17
      DOI: 10.3390/a11090140
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 141: Generalized Paxos Made Byzantine (and Less

    • Authors: Miguel Pires, Srivatsan Ravi, Rodrigo Rodrigues
      First page: 141
      Abstract: One of the most recent members of the Paxos family of protocols is Generalized Paxos. This variant of Paxos has the characteristic that it departs from the original specification of consensus, allowing for a weaker safety condition where different processes can have a different views on a sequence being agreed upon. However, much like the original Paxos counterpart, Generalized Paxos does not have a simple implementation. Furthermore, with the recent practical adoption of Byzantine fault tolerant protocols in the context of blockchain protocols, it is timely and important to understand how Generalized Paxos can be implemented in the Byzantine model. In this paper, we make two main contributions. First, we attempt to provide a simpler description of Generalized Paxos, based on a simpler specification and the pseudocode for a solution that can be readily implemented. Second, we extend the protocol to the Byzantine fault model, and provide the respective correctness proof.
      Citation: Algorithms
      PubDate: 2018-09-17
      DOI: 10.3390/a11090141
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 142: Estimating the Volume of the Solution
           Space of SMT(LIA) Constraints by a Flat Histogram Method

    • Authors: Wei Gao, Hengyi Lv, Qiang Zhang, Dunbo Cai
      First page: 142
      Abstract: The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of this paper is to count the number of solutions (volume) of a SMT(LIA) formula, which has many important applications and is computationally hard. To solve the counting problem, an approximate method that employs a recent Markov Chain Monte Carlo (MCMC) sampling strategy called “flat histogram” is proposed. Furthermore, two refinement strategies are proposed for the sampling process and result in two algorithms, MCMC-Flat1/2 and MCMC-Flat1/t, respectively. In MCMC-Flat1/t, a pseudo sampling strategy is introduced to evaluate the flatness of histograms. Experimental results show that our MCMC-Flat1/t method can achieve good accuracy on both structured and random instances, and our MCMC-Flat1/2 is scalable for instances of convex bodies with up to 7 variables.
      Citation: Algorithms
      PubDate: 2018-09-18
      DOI: 10.3390/a11090142
      Issue No: Vol. 11, No. 9 (2018)
  • Algorithms, Vol. 11, Pages 109: Long Length Document Classification by
           Local Convolutional Feature Aggregation

    • Authors: Liu Liu, Kaile Liu, Zhenghai Cong, Jiali Zhao, Yefei Ji, Jun He
      First page: 109
      Abstract: The exponential increase in online reviews and recommendations makes document classification and sentiment analysis a hot topic in academic and industrial research. Traditional deep learning based document classification methods require the use of full textual information to extract features. In this paper, in order to tackle long document, we proposed three methods that use local convolutional feature aggregation to implement document classification. The first proposed method randomly draws blocks of continuous words in the full document. Each block is then fed into the convolution neural network to extract features and then are concatenated together to output the classification probability through a classifier. The second model improves the first by capturing the contextual order information of the sampled blocks with a recurrent neural network. The third model is inspired by the recurrent attention model (RAM), in which a reinforcement learning module is introduced to act as a controller for selecting the next block position based on the recurrent state. Experiments on our collected four-class arXiv paper dataset show that the three proposed models all perform well, and the RAM model achieves the best test accuracy with the least information.
      Citation: Algorithms
      PubDate: 2018-07-24
      DOI: 10.3390/a11080109
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 110: Image De-Quantization Using Plate Bending

    • Authors: David Völgyes, Anne Catrine Trægde Martinsen, Arne Stray-Pedersen, Dag Waaler, Marius Pedersen
      First page: 110
      Abstract: Discretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aimed at approximating the high bit depth of the original signal. Our method models a region with a bended plate model, which leads to the biharmonic equation. This method addresses several new aspects: the reconstruction of non-continuous regions when foreground objects split the area into separate regions; the incorporation of confidence about pixel levels, making the model tunable; and the method gives a physics-inspired way to handle local maximal/minimal regions. The solution of the biharmonic equation yields a smooth high-order signal approximation and handles the local maxima/minima problems.
      Citation: Algorithms
      PubDate: 2018-07-24
      DOI: 10.3390/a11080110
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 111: A Weighted Histogram-Based Tone Mapping
           Algorithm for CT Images

    • Authors: David Völgyes, Anne Catrine Trægde Martinsen, Arne Stray-Pedersen, Dag Waaler, Marius Pedersen
      First page: 111
      Abstract: Computed Tomography (CT) images have a high dynamic range, which makes visualization challenging. Histogram equalization methods either use spatially invariant weights or limited kernel size due to the complexity of pairwise contribution calculation. We present a weighted histogram equalization-based tone mapping algorithm which utilizes Fast Fourier Transform for distance-dependent contribution calculation and distance-based weights. The weights follow power-law without distance-based cut-off. The resulting images have good local contrast without noticeable artefacts. The results are compared to eight popular tone mapping operators.
      Citation: Algorithms
      PubDate: 2018-07-25
      DOI: 10.3390/a11080111
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 112: A Novel Parallel Auto-Encoder Framework
           for Multi-Scale Data in Civil Structural Health Monitoring

    • Authors: Ruhua Wang, Ling Li, Jun Li
      First page: 112
      Abstract: In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.
      Citation: Algorithms
      PubDate: 2018-07-27
      DOI: 10.3390/a11080112
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 113: Improved Parameter Identification Method
           for Envelope Current Signals Based on Windowed Interpolation FFT and DE

    • Authors: Xiangfeng Su, Huaiqing Zhang, Lin Chen, Ling Qin, Lili Yu
      First page: 113
      Abstract: Envelope current signals are increasingly emerging in power systems, and their parameter identification is particularly necessary for accurate measurement of electrical energy. In order to analyze the envelope current signal, the harmonic parameters, as well as the envelope parameters, need to be calculated. The interpolation fast Fourier transform (FFT) is a widely used approach which can estimate the signal frequency with high precision, but it cannot calculate the envelope parameters of the signal. Therefore, this paper proposes an improved method based on windowed interpolation FFT (WIFFT) and differential evolution (DE). The amplitude and phase parameters obtained through WIFFT and the envelope parameters estimated by the envelope analysis are optimized using the DE algorithm, which makes full use of the performance advantage of DE. The simulation results show that the proposed method can improve the accuracy of the harmonic parameters and the envelope parameter significantly. In addition, it has good anti-noise ability and high precision.
      Citation: Algorithms
      PubDate: 2018-07-27
      DOI: 10.3390/a11080113
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 114: Revisiting Chameleon Sequences in the
           Protein Data Bank

    • Authors: Mihaly Mezei
      First page: 114
      Abstract: The steady growth of the Protein Data Bank (PDB) suggests the periodic repetition of searches for sequences that form different secondary structures in different protein structures; these are called chameleon sequences. This paper presents a fast (nlog(n)) algorithm for such searches and presents the results on all protein structures in the PDB. The longest such sequence found consists of 20 residues.
      Citation: Algorithms
      PubDate: 2018-07-28
      DOI: 10.3390/a11080114
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 115: Color-Based Image Retrieval Using
           Proximity Space Theory

    • Authors: Jing Wang, Lidong Wang, Xiaodong Liu, Yan Ren, Ye Yuan
      First page: 115
      Abstract: The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.
      Citation: Algorithms
      PubDate: 2018-07-28
      DOI: 10.3390/a11080115
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 116: A Robust and Energy-Efficient Weighted
           Clustering Algorithm on Mobile Ad Hoc Sensor Networks †

    • Authors: Huamei Qi, Fengqi Liu, Tailong Xiao, Jiang Su
      First page: 116
      Abstract: In an Ad hoc sensor network, nodes have characteristics of limited battery energy, self-organization and low mobility. Due to the mobility and heterogeneity of the energy consumption in the hierarchical network, the cluster head and topology are changed dynamically. Therefore, topology control and energy consumption are growing to be critical in enhancing the stability and prolonging the lifetime of the network. In order to improve the survivability of Ad hoc network effectively, this paper proposes a new algorithm named the robust, energy-efficient weighted clustering algorithm (RE2WCA). For the homogeneous of the energy consumption; the proposed clustering algorithm takes the residual energy and group mobility into consideration by restricting minimum iteration times. In addition, a distributed fault detection algorithm and cluster head backup mechanism are presented to achieve the periodic and real-time topology maintenance to enhance the robustness of the network. The network is analyzed and the simulations are performed to compare the performance of this new clustering algorithm with the similar algorithms in terms of cluster characteristics, lifetime, throughput and energy consumption of the network. The result shows that the proposed algorithm provides better performance than others.
      Citation: Algorithms
      PubDate: 2018-08-01
      DOI: 10.3390/a11080116
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 117: Research of the Vibration Source Tracking
           in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by
           Image Processing Method

    • Authors: Yanzhu Hu, Song Wang, Xinbo Ai
      First page: 117
      Abstract: This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture feature extraction, shape feature extraction and intrinsic feature extraction. Subsequently, the signal of each frame image was recognized to track the vibration source. To verify the effect of the proposed method, several experiments were carried out to compare it with popular and traditional approaches. The results show that: Hard drive space is greatly conserved by saving the distributed vibration signals as images; the proposed particle filter is a desirable way to screen the vibration signals for monitoring; the integrated feature extraction outperforms the individual extraction methods for texture features, shape features and intrinsic features; the proposed method has a better effect than other popular integrated feature extraction methods; and, the signal source tracking method has little impact on the positioning accuracy of the vibration source. The research findings provide important insights into the source tracking of Φ-OTDR signals.
      Citation: Algorithms
      PubDate: 2018-08-02
      DOI: 10.3390/a11080117
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 118: Sliding Suffix Tree

    • Authors: Andrej Brodnik, Matevž Jekovec
      First page: 118
      Abstract: We consider a sliding window W over a stream of characters from some alphabet of constant size. We want to look up a pattern in the current sliding window content and obtain all positions of the matches. We present an indexed version of the sliding window, based on a suffix tree. The data structure of size Θ( W ) has optimal time queries Θ(m+occ) and amortized constant time updates, where m is the length of the query string and occ is its number of occurrences.
      Citation: Algorithms
      PubDate: 2018-08-03
      DOI: 10.3390/a11080118
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 119: An Opportunistic Network Routing Algorithm
           Based on Cosine Similarity of Data Packets between Nodes

    • Authors: Yucheng Lin, Zhigang Chen, Jia Wu, Leilei Wang
      First page: 119
      Abstract: The mobility of nodes leads to dynamic changes in topology structure, which makes the traditional routing algorithms of a wireless network difficult to apply to the opportunistic network. In view of the problems existing in the process of information forwarding, this paper proposed a routing algorithm based on the cosine similarity of data packets between nodes (cosSim). The cosine distance, an algorithm for calculating the similarity between text data, is used to calculate the cosine similarity of data packets between nodes. The data packet set of nodes are expressed in the form of vectors, thereby facilitating the calculation of the similarity between the nodes. Through the definition of the upper and lower thresholds, the similarity between the nodes is filtered according to certain rules, and finally obtains a plurality of relatively reliable transmission paths. Simulation experiments show that compared with the traditional opportunistic network routing algorithm, such as the Spray and Wait (S&W) algorithm and Epidemic algorithm, the cosSim algorithm has a better transmission effect, which can not only improve the delivery ratio, but also reduce the network transmission delay and decline the routing overhead.
      Citation: Algorithms
      PubDate: 2018-08-06
      DOI: 10.3390/a11080119
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 120: Probabilistic Interval-Valued Hesitant
           Fuzzy Information Aggregation Operators and Their Application to
           Multi-Attribute Decision Making

    • Authors: Wenying Wu, Ying Li, Zhiwei Ni, Feifei Jin, Xuhui Zhu
      First page: 120
      Abstract: Based on the probabilistic interval-valued hesitant fuzzy information aggregation operators, this paper investigates a novel multi-attribute group decision making (MAGDM) model to address the serious loss of information in a hesitant fuzzy information environment. Firstly, the definition of probabilistic interval-valued hesitant fuzzy set will be introduced, and then, using Archimedean norm, some new probabilistic interval-valued hesitant fuzzy operations are defined. Secondly, based on these operations, the generalized probabilistic interval-valued hesitant fuzzy ordered weighted averaging (GPIVHFOWA) operator, and the generalized probabilistic interval-valued hesitant fuzzy ordered weighted geometric (GPIVHFOWG) operator are proposed, and their desirable properties are discussed. We further study their common forms and analyze the relationship among these proposed operators. Finally, a new probabilistic interval-valued hesitant fuzzy MAGDM model is constructed, and the feasibility and effectiveness of the proposed model are verified by using an example of supplier selection.
      Citation: Algorithms
      PubDate: 2018-08-06
      DOI: 10.3390/a11080120
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 121: Stacked-GRU Based Power System Transient
           Stability Assessment Method

    • Authors: Feilai Pan, Jun Li, Bendong Tan, Ciling Zeng, Xinfan Jiang, Li Liu, Jun Yang
      First page: 121
      Abstract: With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control.
      Citation: Algorithms
      PubDate: 2018-08-09
      DOI: 10.3390/a11080121
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 122: Efficient Model-Based Object Pose
           Estimation Based on Multi-Template Tracking and PnP Algorithms

    • Authors: Chi-Yi Tsai, Kuang-Jui Hsu, Humaira Nisar
      First page: 122
      Abstract: Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.
      Citation: Algorithms
      PubDate: 2018-08-12
      DOI: 10.3390/a11080122
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 123: Time Series Forecasting Using a Two-Level
           Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data
           for Tunnel Fans

    • Authors: Yamur K. Al-Douri, Hussan Hamodi, Jan Lundberg
      First page: 123
      Abstract: The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.
      Citation: Algorithms
      PubDate: 2018-08-13
      DOI: 10.3390/a11080123
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 124: A Simhash-Based Integrative Features
           Extraction Algorithm for Malware Detection

    • Authors: Yihong Li, Fangzheng Liu, Zhenyu Du, Dubing Zhang
      First page: 124
      Abstract: In the malware detection process, obfuscated malicious codes cannot be efficiently and accurately detected solely in the dynamic or static feature space. Aiming at this problem, an integrative feature extraction algorithm based on simhash was proposed, which combines the static information e.g., API (Application Programming Interface) calls and dynamic information (such as file, registry and network behaviors) of malicious samples to form integrative features. The experiment extracts the integrative features of some static information and dynamic information, and then compares the classification, time and obfuscated-detection performance of the static, dynamic and integrated features, respectively, by using several common machine learning algorithms. The results show that the integrative features have better time performance than the static features, and better classification performance than the dynamic features, and almost the same obfuscated-detection performance as the dynamic features. This algorithm can provide some support for feature extraction of malware detection.
      Citation: Algorithms
      PubDate: 2018-08-14
      DOI: 10.3390/a11080124
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 125: An Effective Data Transmission Algorithm
           Based on Social Relationships in Opportunistic Mobile Social Networks

    • Authors: Yeqing Yan, Zhigang Chen, Jia Wu, Leilei Wang
      First page: 125
      Abstract: With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.
      Citation: Algorithms
      PubDate: 2018-08-14
      DOI: 10.3390/a11080125
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 126: Robust Visual Tracking via Patch
           Descriptor and Structural Local Sparse Representation

    • Authors: Zhiguo Song, Jifeng Sun, Jialin Yu, Shengqing Liu
      First page: 126
      Abstract: Appearance models play an important role in visual tracking. Effective modeling of the appearance of tracked objects is still a challenging problem because of object appearance changes caused by factors, such as partial occlusion, illumination variation and deformation, etc. In this paper, we propose a tracking method based on the patch descriptor and the structural local sparse representation. In our method, the object is firstly divided into multiple non-overlapped patches, and the patch sparse coefficients are obtained by structural local sparse representation. Secondly, each patch is further decomposed into several sub-patches. The patch descriptors are defined as the proportion of sub-patches, of which the reconstruction error is less than the given threshold. Finally, the appearance of an object is modeled by the patch descriptors and the patch sparse coefficients. Furthermore, in order to adapt to appearance changes of an object and alleviate the model drift, an outlier-aware template update scheme is introduced. Experimental results on a large benchmark dataset demonstrate the effectiveness of the proposed method.
      Citation: Algorithms
      PubDate: 2018-08-15
      DOI: 10.3390/a11080126
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 127: Application of Angle Related Cost Function
           Optimization for Dynamic Path Planning Algorithm

    • Authors: Mingbin Zeng, Xu Yang, Mengxing Wang, Bangjiang Xu
      First page: 127
      Abstract: In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.
      Citation: Algorithms
      PubDate: 2018-08-15
      DOI: 10.3390/a11080127
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 128: DenseZDD: A Compact and Fast Index for
           Families of Sets

    • Authors: Shuhei Denzumi, Jun Kawahara, Koji Tsuda, Hiroki Arimura, Shin-ichi Minato, Kunihiko Sadakane
      First page: 128
      Abstract: In this article, we propose a succinct data structure of zero-suppressed binary decision diagrams (ZDDs). A ZDD represents sets of combinations efficiently and we can perform various set operations on the ZDD without explicitly extracting combinations. Thanks to these features, ZDDs have been applied to web information retrieval, information integration, and data mining. However, to support rich manipulation of sets of combinations and update ZDDs in the future, ZDDs need too much space, which means that there is still room to be compressed. The paper introduces a new succinct data structure, called DenseZDD, for further compressing a ZDD when we do not need to conduct set operations on the ZDD but want to examine whether a given set is included in the family represented by the ZDD, and count the number of elements in the family. We also propose a hybrid method, which combines DenseZDDs with ordinary ZDDs. By numerical experiments, we show that the sizes of our data structures are three times smaller than those of ordinary ZDDs, and membership operations and random sampling on DenseZDDs are about ten times and three times faster than those on ordinary ZDDs for some datasets, respectively.
      Citation: Algorithms
      PubDate: 2018-08-17
      DOI: 10.3390/a11080128
      Issue No: Vol. 11, No. 8 (2018)
  • Algorithms, Vol. 11, Pages 89: Degradation Trend Prediction for Rotating
           Machinery Using Long-Range Dependence and Particle Filter Approach

    • Authors: Qing Li, Steven Y. Liang
      First page: 89
      Abstract: Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc.
      Citation: Algorithms
      PubDate: 2018-06-26
      DOI: 10.3390/a11070089
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 90: A Novel Method for Control Performance
           Assessment with Fractional Order Signal Processing and Its Application to
           Semiconductor Manufacturing

    • Authors: Kai Liu, YangQuan Chen, Paweł D. Domański, Xi Zhang
      First page: 90
      Abstract: The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties of Hurst exponent estimations and proposes a novel method for feature extraction of the nonlinear multi-input multi-output (MIMO) systems. At first, coupled data from real industry are analyzed by multifractal detrended fluctuation analysis (MFDFA) and the resultant multifractal spectrum is obtained. Secondly, the crossover points with spline fit in the scale-law curve are located and then employed to segment the entire scale-law curve into several different scaling regions, in which a single Hurst exponent can be estimated. Thirdly, to further ascertain the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data. At last, non-Gaussian statistical properties, multifractal properties and Hurst exponents of the process control variables are derived and compared with different sets of tuning parameters. The results have shown that CPA of the MIMO system can be better employed with the help of fractional order signal processing (FOSP).
      Citation: Algorithms
      PubDate: 2018-06-26
      DOI: 10.3390/a11070090
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 91: emgr—The Empirical Gramian Framework

    • Authors: Christian Himpe
      First page: 91
      Abstract: System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.
      Citation: Algorithms
      PubDate: 2018-06-26
      DOI: 10.3390/a11070091
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 92: Predictive Current Control of Boost
           Three-Level and T-Type Inverters Cascaded in Wind Power Generation Systems

    • Authors: Guoliang Yang, Haitao Yi, Chunhua Chai, Bingxu Huang, Yuna Zhang, Zhe Chen
      First page: 92
      Abstract: A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the conventional boost converter, the boost-TL converter has a low inductor current ripple, which reduces the torque ripple of the generator, increases the converter’s capacity, and minimizes switching losses. The boost-TL converter can boost the DC output from the rectifier at low speeds. The principles of the boost-TL converter and the T-type three-level inverter are separately introduced. Based on the cascaded structure of the proposed BTL converter and three-level inverter, a model predictive current control (MPCC) method is adopted, and the optimization of the MPCC is presented. The prediction model is derived, and the simulation and experimental research are carried out. The results show that the algorithm based on the proposed cascaded structure is feasible and superior.
      Citation: Algorithms
      PubDate: 2018-06-27
      DOI: 10.3390/a11070092
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 93: Layered Graphs: Applications and Algorithms

    • Authors: Bhadrachalam Chitturi, Srijith Balachander, Sandeep Satheesh, Krithic Puthiyoppil
      First page: 93
      Abstract: The computation of distances between strings has applications in molecular biology, music theory and pattern recognition. One such measure, called short reversal distance, has applications in evolutionary distance computation. It has been shown that this problem can be reduced to the computation of a maximum independent set on the corresponding graph that is constructed from the given input strings. The constructed graphs primarily fall into a class that we call layered graphs. In a layered graph, each layer refers to a subgraph containing, at most, some k vertices. The inter-layer edges are restricted to the vertices in adjacent layers. We study the MIS, MVC, MDS, MCV and MCD problems on layered graphs where MIS computes the maximum independent set; MVC computes the minimum vertex cover; MDS computes the minimum dominating set; MCV computes the minimum connected vertex cover; and MCD computes the minimum connected dominating set. The MIS, MVC and MDS are computed in polynomial time if k=Θ(log V ). MCV and MCD are computed polynomial time if k=O((log V )α), where α<1. If k=Θ((log V )1+ϵ), for ϵ>0, then MIS, MVC and MDS are computed in quasi-polynomial time. If k=Θ(log V ), then MCV and MCD are computed in quasi-polynomial time.
      Citation: Algorithms
      PubDate: 2018-06-28
      DOI: 10.3390/a11070093
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 94: Tensor Completion Based on Triple Tubal
           Nuclear Norm

    • Authors: Dongxu Wei, Andong Wang, Xiaoqin Feng, Boyu Wang, Bo Wang
      First page: 94
      Abstract: Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms.
      Citation: Algorithms
      PubDate: 2018-06-28
      DOI: 10.3390/a11070094
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 95: Experimental Validation of a Novel
           Auto-Tuning Method for a Fractional Order PI Controller on an UR10 Robot

    • Authors: Cristina I. Muresan, Cosmin Copot, Isabela Birs, Robin De Keyser, Steve Vanlanduit, Clara M. Ionescu
      First page: 95
      Abstract: Classical fractional order controller tuning techniques usually consider the frequency domain specifications (phase margin, gain crossover frequency, iso-damping) and are based on knowledge of a process model, as well as solving a system of nonlinear equations to determine the controller parameters. In this paper, a novel auto-tuning method is used to tune a fractional order PI controller. The advantages of the proposed auto-tuning method are two-fold: There is no need for a process model, neither to solve the system of nonlinear equations. The tuning is based on defining a forbidden region in the Nyquist plane using the phase margin requirement and determining the parameters of the fractional order controller such that the loop frequency response remains out of the forbidden region. Additionally, the final controller parameters are those that minimize the difference between the slope of the loop frequency response and the slope of the forbidden region border, to ensure the iso-damping property. To validate the proposed method, a case study has been used consisting of a pick and place movement of an UR10 robot. The experimental results, considering two different robot configurations, demonstrate that the designed fractional order PI controller is indeed robust.
      Citation: Algorithms
      PubDate: 2018-06-30
      DOI: 10.3390/a11070095
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 96: Solving Multi-Document Summarization as an
           Orienteering Problem

    • Authors: Asma Al-Saleh, Mohamed El Bachir Menai
      First page: 96
      Abstract: With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.
      Citation: Algorithms
      PubDate: 2018-06-30
      DOI: 10.3390/a11070096
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 97: A Regional Topic Model Using Hybrid
           Stochastic Variational Gibbs Sampling for Real-Time Video Mining

    • Authors: Lin Tang, Lin Liu, Jianhou Gan
      First page: 97
      Abstract: The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection.
      Citation: Algorithms
      PubDate: 2018-07-01
      DOI: 10.3390/a11070097
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 98: Width, Depth, and Space: Tradeoffs between
           Branching and Dynamic Programming

    • Authors: Li-Hsuan Chen, Felix Reidl, Peter Rossmanith, Fernando Sánchez Villaamil
      First page: 98
      Abstract: Treedepth is a well-established width measure which has recently seen a resurgence of interest. Since graphs of bounded treedepth are more restricted than graphs of bounded tree- or pathwidth, we are interested in the algorithmic utility of this additional structure. On the negative side, we show with a novel approach that the space consumption of any (single-pass) dynamic programming algorithm on treedepth decompositions of depth d cannot be bounded by (2−ϵ)d·logO(1)n for Vertex Cover, (3−ϵ)d·logO(1)n for 3-Coloring and (3−ϵ)d·logO(1)n for Dominating Set for any ϵ>0. This formalizes the common intuition that dynamic programming algorithms on graph decompositions necessarily consume a lot of space and complements known results of the time-complexity of problems restricted to low-treewidth classes. We then show that treedepth lends itself to the design of branching algorithms. Specifically, we design two novel algorithms for Dominating Set on graphs of treedepth d: A pure branching algorithm that runs in time dO(d2)·n and uses space O(d3logd+dlogn) and a hybrid of branching and dynamic programming that achieves a running time of O(3dlogd·n) while using O(2ddlogd+dlogn) space.
      Citation: Algorithms
      PubDate: 2018-07-01
      DOI: 10.3390/a11070098
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 99: A Low Complexity Reactive Tabu Search Based
           Constellation Constraints in Signal Detection

    • Authors: Jiao Feng, Xiaofei Zhang, Peng Li, Dongshun Hu
      First page: 99
      Abstract: For Massive multiple-input multiple output (MIMO) systems, many algorithms have been proposed for detecting spatially multiplexed signals, such as reactive tabu search (RTS), minimum mean square error (MMSE), etc. As a heuristic neighborhood search algorithm, RTS is particularly suitable for signal detection in systems with large number of antennas. In this paper, we propose a strategy to reduce the neighborhood searching space of the traditional RTS algorithms. For this, we introduce a constellation constraints (CC) structure to determine whether including a candidate vector into the RTS searching neighborhood. By setting a pre-defined threshold on the symbol constellation, the Euclidean distance between the estimated signal and its nearest constellation points are calculated, and the threshold and distance are compared to separate the reliable estimated signal from unreliable ones. With this structure, the proposed CC-RTS algorithm may ignore a significant number of unnecessary candidates in the RTS neighborhood searching space and greatly reduce the computational complexity of the traditional RTS algorithm. Simulation results show that the BER performance of the proposed CC-RTS algorithm is very close to that of the traditional RTS algorithm, and with about 50% complexity reduction with the same signal-to-noise (SNR) ratio.
      Citation: Algorithms
      PubDate: 2018-07-03
      DOI: 10.3390/a11070099
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 100: A Self-Adaptive Evolutionary Algorithm for
           the Berth Scheduling Problem: Towards Efficient Parameter Control

    • Authors: Maxim A. Dulebenets, Masoud Kavoosi, Olumide Abioye, Junayed Pasha
      First page: 100
      Abstract: Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time.
      Citation: Algorithms
      PubDate: 2018-07-03
      DOI: 10.3390/a11070100
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 101: Robust Fuzzy Adaptive Sliding Mode
           Stabilization for Fractional-Order Chaos

    • Authors: Bachir Bourouba, Samir Ladaci
      First page: 101
      Abstract: In this paper, a new adaptive fuzzy sliding mode control (AFSMC) design strategy is proposed for the control of a special class of three-dimensional fractional order chaotic systems with uncertainties and external disturbance. The design methodology is developed in two stages: first, an adaptive sliding mode control law is proposed for the class of fractional order chaotic systems without uncertainties, and then a fuzzy logic system is used to estimate the control compensation effort to be added in the case of uncertainties on the system’s model. Based on the Lyapunov theory, the stability analysis of both control laws is provided with elimination of the chattering action in the control signal. The developed control scheme is simple to implement and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the involved signals are uniformly bounded. In the present work, simulation studies on fractional-order Chen chaotic systems are carried out to show the efficiency of the proposed fractional adaptive controllers.
      Citation: Algorithms
      PubDate: 2018-07-07
      DOI: 10.3390/a11070101
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 102: Advanced Artificial Neural Networks

    • Authors: Tin-Chih Toly Chen, Cheng-Li Liu, Hong-Dar Lin
      First page: 102
      Abstract: Artificial neural networks (ANNs) have been extensively applied to a wide range of disciplines, such as system identification and control, decision making, pattern recognition, medical diagnosis, finance, data mining, visualization, and others. With advances in computing and networking technologies, more complicated forms of ANNs are expected to emerge, requiring the design of advanced learning algorithms. This Special Issue is intended to provide technical details of the construction and training of advanced ANNs.
      Citation: Algorithms
      PubDate: 2018-07-10
      DOI: 10.3390/a11070102
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 103: Solutions to the Sub-Optimality and
           Stability Issues of Recursive Pole and Zero Distribution Algorithms for
           the Approximation of Fractional Order Models

    • Authors: Jocelyn Sabatier
      First page: 103
      Abstract: This paper analyses algorithms currently found in the literature for the approximation of fractional order models and based on recursive pole and zero distributions. The analysis focuses on the sub-optimality of the approximations obtained and stability issues that may appear after approximation depending on the pole location of the initial fractional order model. Solutions are proposed to reduce this sub-optimality and to avoid stability issues.
      Citation: Algorithms
      PubDate: 2018-07-12
      DOI: 10.3390/a11070103
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 104: The Gradient and the Hessian of the
           Distance between Point and Triangle in 3D

    • Authors: Igor Gribanov, Rocky Taylor, Robert Sarracino
      First page: 104
      Abstract: Computation of the distance between point and triangle in 3D is a common task in numerical analysis. The input values of the algorithm are coordinates of three points of the triangle and one point from which the distance is determined. An existing algorithm is extended to compute the gradient and the Hessian of that distance with respect to coordinates of involved points. Derivation of exact expressions for gradient and Hessian is presented, and numerical accuracy is evaluated for various cases. The algorithm has O(1) time and space complexity. The included open-source code may be used in applications where derivatives of point-triangle distance are required.
      Citation: Algorithms
      PubDate: 2018-07-12
      DOI: 10.3390/a11070104
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 105: Distributed Combinatorial Maps for
           Parallel Mesh Processing

    • Authors: Guillaume Damiand, Aldo Gonzalez-Lorenzo, Florence Zara, Florent Dupont
      First page: 105
      Abstract: We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical definition ensures the global consistency of the meshes at their interfaces. Thus, an n-dmap can be used to represent a mesh, to traverse it, or to modify it by using different mesh processing algorithms. Moreover, an nD mesh with a huge number of elements can be considered, which is not possible with a sequential approach and a regular data structure. We illustrate the interest of our solution by presenting a parallel adaptive subdivision method of a 3D hexahedral mesh, implemented in a distributed version. We report space and time performance results that show the interest of our approach for parallel processing of huge meshes.
      Citation: Algorithms
      PubDate: 2018-07-13
      DOI: 10.3390/a11070105
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 106: Fractional-Order Closed-Loop Model
           Reference Adaptive Control for Anesthesia

    • Authors: Gerardo Navarro-Guerrero, Yu Tang
      First page: 106
      Abstract: The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.
      Citation: Algorithms
      PubDate: 2018-07-14
      DOI: 10.3390/a11070106
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 107: An Ensemble Extreme Learning Machine for
           Data Stream Classification

    • Authors: Rui Yang, Shuliang Xu, Lin Feng
      First page: 107
      Abstract: Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.
      Citation: Algorithms
      PubDate: 2018-07-17
      DOI: 10.3390/a11070107
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 108: Evolving the Controller of Automated
           Steering of a Car in Slippery Road Conditions

    • Authors: Natalia Alekseeva, Ivan Tanev, Katsunori Shimohara
      First page: 108
      Abstract: The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.
      Citation: Algorithms
      PubDate: 2018-07-21
      DOI: 10.3390/a11070108
      Issue No: Vol. 11, No. 7 (2018)
  • Algorithms, Vol. 11, Pages 78: A Modified Artificial Bee Colony Algorithm
           Based on the Self-Learning Mechanism

    • Authors: Bao Pang, Yong Song, Chengjin Zhang, Hongling Wang, Runtao Yang
      First page: 78
      Abstract: Artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-inspired optimization algorithms, but there still exist several insufficiencies due to the inefficient solution search equation (SSE), which does well in exploration but poorly in exploitation. To improve accuracy of the solutions, this paper proposes a modified ABC algorithm based on the self-learning mechanism (SLABC) with five SSEs as the candidate operator pool; among them, one is good at exploration and two of them are good at exploitation; another SSE intends to balance exploration and exploitation; moreover, the last SSE with Lévy flight step-size which can generate smaller step-size with high frequency and bigger step-size occasionally not only can balance exploration and exploitation but also possesses the ability to escape from the local optimum. This paper proposes a simple self-learning mechanism, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments on a set of 9 benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. The experimental results illustrated that the SLABC algorithm achieves significant improvement compared with other competitive algorithms.
      Citation: Algorithms
      PubDate: 2018-05-24
      DOI: 10.3390/a11060078
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 79: A Fire Detection Algorithm Based on
           Tchebichef Moment Invariants and PSO-SVM

    • Authors: Yongming Bian, Meng Yang, Xuying Fan, Yuchao Liu
      First page: 79
      Abstract: Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.
      Citation: Algorithms
      PubDate: 2018-05-25
      DOI: 10.3390/a11060079
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 80: Scheduling a Single Machine with Primary
           and Secondary Objectives

    • Authors: Nodari Vakhania
      First page: 80
      Abstract: We study a scheduling problem in which jobs with release times and due dates are to be processed on a single machine. With the primary objective to minimize the maximum job lateness, the problem is strongly NP-hard. We describe a general algorithmic scheme to minimize the maximum job lateness, with the secondary objective to minimize the maximum job completion time. The problem of finding the Pareto-optimal set of feasible solutions with these two objective criteria is strongly NP-hard. We give the dominance properties and conditions when the Pareto-optimal set can be formed in polynomial time. These properties, together with our general framework, provide the theoretical background, so that the basic framework can be expanded to (exponential-time) implicit enumeration algorithms and polynomial-time approximation algorithms (generating the Pareto sub-optimal frontier with a fair balance between the two objectives). Some available in the literature experimental results confirm the practical efficiency of the proposed framework.
      Citation: Algorithms
      PubDate: 2018-06-05
      DOI: 10.3390/a11060080
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 81: A Randomized Algorithm for Optimal PID

    • Authors: Yossi Peretz
      First page: 81
      Abstract: A randomized algorithm is suggested for the syntheses of optimal PID controllers for MIMO coupled systems, where the optimality is with respect to the H ∞ -norm, the H 2 -norm and the LQR functional, with possible system-performance specifications defined by regional pole-placement. Other notions of optimality (e.g., mixed H 2 / H ∞ design, controller norm or controller sparsity) can be handled similarly with the suggested algorithm. The suggested method is direct and thus can be applied to continuous-time systems as well as to discrete-time systems with the obvious minor changes. The presented algorithm is a randomized algorithm, which has a proof of convergence (in probability) to a global optimum.
      Citation: Algorithms
      PubDate: 2018-06-05
      DOI: 10.3390/a11060081
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 82: Research on Fault Diagnosis of a Marine
           Fuel System Based on the SaDE-ELM Algorithm

    • Authors: Yi Wei, Yaokun Yue
      First page: 82
      Abstract: Since the traditional fault diagnosis method of the marine fuel system has a low accuracy of identification, the algorithm solution can easily fall into local optimum, and they are not fit for the research on the fault diagnosis of a marine fuel system. Hence, a fault diagnosis method for a marine fuel system based on the SaDE-ELM algorithm is proposed. First, the parameters of initializing extreme learning machine are adopted by a differential evolution algorithm. Second, the fault diagnosis of the marine fuel system is realized by the fault diagnosis model corresponding to the state training of marine fuel system. Based on the obtained fault data of a marine fuel system, the proposed method is verified. The experimental results show that this method produces higher recognition accuracy and faster recognition speed that are superior to the traditional BP neural network, SVM support vector machine diagnosis algorithm, and the un-optimized extreme learning machine algorithm. The results have important significance relevant to fault diagnosis for a marine fuel system. The algorithm based on SaDE-ELM is an effective and practical method of fault diagnosis for a marine fuel system.
      Citation: Algorithms
      PubDate: 2018-06-07
      DOI: 10.3390/a11060082
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 83: A Combined Syntactical and Statistical
           Approach for R Peak Detection in Real-Time Long-Term Heart Rate
           Variability Analysis

    • Authors: David Pang, Tomohiko Igasaki
      First page: 83
      Abstract: Long-term heart rate variability (HRV) analysis is useful as a noninvasive technique for autonomic nervous system activity assessment. It provides a method for assessing many physiological and pathological factors that modulate the normal heartbeat. The performance of HRV analysis systems heavily depends on a reliable and accurate detection of the R peak of the QRS complex. Ectopic beats caused by misdetection or arrhythmic events can introduce bias into HRV results, resulting in significant problems in their interpretation. This study presents a novel method for long-term detection of normal R peaks (which represent the normal heartbeat in electrocardiographic signals), intended specifically for HRV analysis. The very low computational complexity of the proposed method, which combines and exploits the advantages of syntactical and statistical approaches, enables real-time applications. The approach was validated using the Massachusetts Institute of Technology–Beth Israel Hospital Normal Sinus Rhythm and the Fantasia database, and has a sensitivity, positive predictivity, detection error rate, and accuracy of 99.998, 99.999, 0.003, and 99.996%, respectively.
      Citation: Algorithms
      PubDate: 2018-06-07
      DOI: 10.3390/a11060083
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 84: Efficient Approximation for Restricted
           Biclique Cover Problems

    • Authors: Alessandro Epasto, Eli Upfal
      First page: 84
      Abstract: Covering the edges of a bipartite graph by a minimum set of bipartite complete graphs (bicliques) is a basic graph theoretic problem, with numerous applications. In particular, it is used to characterize parsimonious models of a set of observations (each biclique corresponds to a factor or feature that relates the observations in the two sets of nodes connected by the biclique). The decision version of the minimum biclique cover problem is NP-Complete, and unless P=NP, the cover size cannot be approximated in general within less than a sub-linear factor of the number of nodes (or edges) in the graph. In this work, we consider two natural restrictions to the problem, motivated by practical applications. In the first case, we restrict the number of bicliques a node can belong to. We show that when this number is at least 5, the problem is still NP-hard. In contrast, we show that when nodes belong to no more than two bicliques, the problem has efficient approximations. The second model we consider corresponds to observing a set of independent samples from an unknown model, governed by a possibly large number of factors. The model is defined by a bipartite graph G=(L,R,E), where each node in L is assigned to an arbitrary subset of up to a constant f factors, while the nodes in R (the independent observations) are assigned to random subsets of the set of k factors where k can grow with size of the graph. We show that this practical version of the biclique cover problem is amenable to efficient approximations.
      Citation: Algorithms
      PubDate: 2018-06-12
      DOI: 10.3390/a11060084
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for
           Fractional Order Linear Distributed Parameter Systems

    • Authors: Yong-Hong Lan, Zhe-Min Cui
      First page: 85
      Abstract: This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.
      Citation: Algorithms
      PubDate: 2018-06-14
      DOI: 10.3390/a11060085
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 86: Performance Optimal PI controller Tuning
           Based on Integrating Plus Time Delay Models

    • Authors: Christer Dalen, David Di Ruscio
      First page: 86
      Abstract: A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, and the proportional constant is seen inversely proportional to the relative time delay error. The keystone in the method is the method product parameter, i.e., the product of the PI controller proportional constant, the integral time constant, and the integrator plus time delay model, velocity gain. The method product parameter is found to be constant for various PI controller tuning methods. Optimal suggestions are given for choosing the method product parameter, i.e., optimal such that the integrated absolute error or, more interestingly, the Pareto performance objective (i.e., integrated absolute error for combined step changes in output and input disturbances) is minimised. Variants of the presented tuning method are demonstrated for tuning PI controllers for motivated (possible) higher order process model examples, i.e., the presented method is combined with the model reduction step (process–reaction curve) in Ziegler–Nichols.
      Citation: Algorithms
      PubDate: 2018-06-17
      DOI: 10.3390/a11060086
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 87: Special Issue on Algorithms for Scheduling

    • Authors: Frank Werner, Larysa Burtseva, Yuri N. Sotskov
      First page: 87
      Abstract: This special issue of Algorithms is devoted to the development of scheduling algorithms based on innovative approaches for solving hard scheduling problems either exactly or approximately. Submissions were welcome both for traditional scheduling problems as well as for new practical applications. The main topics include sequencing and scheduling with additional constraints (setup times or costs, precedence constraints, resource constraints, and batch production environment) and production planning and scheduling problems arising in real-world applications.
      Citation: Algorithms
      PubDate: 2018-06-20
      DOI: 10.3390/a11060087
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 88: Efficient Deep Learning-Based Automated
           Pathology Identification in Retinal Optical Coherence Tomography Images

    • Authors: Qingge Ji, Wenjie He, Jie Huang, Yankui Sun
      First page: 88
      Abstract: We present an automatic method based on transfer learning for the identification of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retinal optical coherence tomography (OCT) images. The algorithm aims to improve the classification performance of retinal OCT images and shorten the training time. Firstly, we remove the last several layers from the pre-trained Inception V3 model and regard the remaining part as a fixed feature extractor. Then, the features are used as input of a convolutional neural network (CNN) designed to learn the feature space shifts. The experimental results on two different retinal OCT images datasets demonstrate the effectiveness of the proposed method.
      Citation: Algorithms
      PubDate: 2018-06-20
      DOI: 10.3390/a11060088
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 57: Optimal Control Algorithms and Their
           Analysis for Short-Term Scheduling in Manufacturing Systems

    • Authors: Boris Sokolov, Alexandre Dolgui, Dmitry Ivanov
      First page: 57
      Abstract: Current literature presents optimal control computational algorithms with regard to state, control, and conjunctive variable spaces. This paper first analyses the advantages and limitations of different optimal control computational methods and algorithms which can be used for short-term scheduling. Second, it develops an optimal control computational algorithm that allows for the solution of short-term scheduling in an optimal manner. Moreover, qualitative and quantitative analysis of the manufacturing system scheduling problem is presented. Results highlight computer experiments with a scheduling software prototype as well as potential future research avenues.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050057
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 58: Computing Fault-Containment Times of
           Self-Stabilizing Algorithms Using Lumped Markov Chains

    • Authors: Volker Turau
      First page: 58
      Abstract: The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050058
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 59: Decision-Making Approach Based on
           Neutrosophic Rough Information

    • Authors: Muhammad Akram, Nabeela Ishfaq, Sidra Sayed, Florentin Smarandache
      First page: 59
      Abstract: Rough set theory and neutrosophic set theory are mathematical models to deal with incomplete and vague information. These two theories can be combined into a framework for modeling and processing incomplete information in information systems. Thus, the neutrosophic rough set hybrid model gives more precision, flexibility and compatibility to the system as compared to the classic and fuzzy models. In this research study, we develop neutrosophic rough digraphs based on the neutrosophic rough hybrid model. Moreover, we discuss regular neutrosophic rough digraphs, and we solve decision-making problems by using our proposed hybrid model. Finally, we give a comparison analysis of two hybrid models, namely, neutrosophic rough digraphs and rough neutrosophic digraphs.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050059
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 60: Vessel Traffic Risk Assessment Based on
           Uncertainty Analysis in the Risk Matrix

    • Authors: Molin Sun, Zhongyi Zheng
      First page: 60
      Abstract: Uncertainty analysis is considered to be a necessary step in the process of vessel traffic risk assessment. The purpose of this study is to propose the uncertainty analysis algorithm which can be used to investigate the reliability of the risk assessment result. Probability and possibility distributions are used to quantify the two types of uncertainty identified in the risk assessment process. In addition, the algorithm for appropriate time window selection is chosen by considering the uncertainty of vessel traffic accident occurrence and the variation trend of the vessel traffic risk caused by maritime rules becoming operative. Vessel traffic accident data from the United Kingdom’s marine accident investigation branch are used for the case study. Based on a comparison with the common method of estimating the vessel traffic risk and the algorithm for uncertainty quantification without considering the time window selection, the availability of the proposed algorithms is verified, which can provide guidance for vessel traffic risk management.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050060
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 61: Relaxed Data Types as Consistency
           Conditions †

    • Authors: Edward Talmage, Jennifer L. Welch
      First page: 61
      Abstract: In the quest for higher-performance shared data structures, weakening consistency conditions and relaxing the sequential specifications of data types are two of the primary tools available in the literature today. In this paper, we show that these two approaches are in many cases different ways to specify the same sets of allowed concurrent behaviors of a given shared data object. This equivalence allows us to use whichever description is clearer, simpler, or easier to achieve equivalent guarantees. Specifically, for three common data type relaxations, we define consistency conditions such that the combination of the new consistency condition and an unrelaxed type allows the same behaviors as Linearizability and the relaxed version of the data type. Conversely, for the consistency condition k-Atomicity, we define a new data type relaxation such that the behaviors allowed by the relaxed version of a data type, combined with Linearizability, are the same as those allowed by k-Atomicity and the original type. As an example of the possibilities opened by our new equivalence, we use standard techniques from the literature on consistency conditions to prove that the three data type relaxations we consider are not comparable to one another or to several similar known conditions. Finally, we show a particular class of data types where one of our newly-defined consistency conditions is comparable to, and stronger than, one of the known consistency conditions we consider.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050061
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 62: A Feature-Weighted SVR Method Based on
           Kernel Space Feature

    • Authors: Minghua Xie, Decheng Wang, Lili Xie
      First page: 62
      Abstract: Support Vector Regression (SVR), which converts the original low-dimensional problem to a high-dimensional kernel space linear problem by introducing kernel functions, has been successfully applied in system modeling. Regarding the classical SVR algorithm, the value of the features has been taken into account, while its contribution to the model output is omitted. Therefore, the construction of the kernel space may not be reasonable. In the paper, a Feature-Weighted SVR (FW-SVR) is presented. The range of the feature is matched with its contribution by properly assigning the weight of the input features in data pre-processing. FW-SVR optimizes the distribution of the sample points in the kernel space to make the minimizing of the structural risk more reasonable. Four synthetic datasets and seven real datasets are applied. A superior generalization ability is obtained by the proposed method.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050062
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 63: The Supplier Selection of the Marine Rescue
           Equipment Based on the Analytic Hierarchy Process (AHP)-Limited Diversity
           Factors Method

    • Authors: Minghui Shao, Yan Song, Biao Wu, Yanjie Chang
      First page: 63
      Abstract: Supplier selection is an important decision-making link in bidding activity. When overall scores of several suppliers are similar, it is hard to obtain an accurate ranking of these suppliers. Applying the Diversity Factors Method (Diversity Factors Method, DFM) may lead to over correction of weights, which would degrade the capability of indexes to reflect the importance. A Limited Diversity Factors Method (Limited Diversity Factors Method, LDFM) based on entropy is presented in this paper in order to adjust the weights, in order to relieve the over correction in DFM and to improve the capability of identification of indexes in supplier selection. An example of salvage ship bidding demonstrates the advantages of the LDFM, in which the raking of overall scores of suppliers is more accurate.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050063
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 64: Utility Distribution Strategy of the Task
           Agents in Coalition Skill Games

    • Authors: Ming Lan Fu, Hao Wang, Bao Fu Fang
      First page: 64
      Abstract: This paper focuses on the rational distribution of task utilities in coalition skill games, which is a restricted form of coalition game, where each service agent has a set of skills and each task agent needs a set of skills in order to be completed. These two types of agents are assumed to be self-interested. Given the task selection strategy of service agents, the utility distribution strategies of task agents play an important role in improving their individual revenues and system total revenue. The problem that needs to be resolved is how to design the task selection strategies of the service agents and the utility distribution strategies of the task agents to make the self-interested decisions improve the system whole performance. However, to the best of our knowledge, this problem has been the topic of very few studies and has not been properly addressed. To address this problem, a task allocation algorithm for self-interested agents in a coalition skill game is proposed, it distributes the utilities of tasks to the needed skills according to the powers of the service agents that possess the corresponding skills. The final simulation results verify the effectiveness of the algorithm.
      Citation: Algorithms
      PubDate: 2018-05-05
      DOI: 10.3390/a11050064
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 65: Control Strategy of Speed Servo Systems
           Based on Deep Reinforcement Learning

    • Authors: Pengzhan Chen, Zhiqiang He, Chuanxi Chen, Jiahong Xu
      First page: 65
      Abstract: We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional–integral–derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance.
      Citation: Algorithms
      PubDate: 2018-05-05
      DOI: 10.3390/a11050065
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 66: Single Machine Scheduling Problem with
           Interval Processing Times and Total Completion Time Objective

    • Authors: Yuri N. Sotskov, Natalja G. Egorova
      First page: 66
      Abstract: We consider a single machine scheduling problem with uncertain durations of the given jobs. The objective function is minimizing the sum of the job completion times. We apply the stability approach to the considered uncertain scheduling problem using a relative perimeter of the optimality box as a stability measure of the optimal job permutation. We investigated properties of the optimality box and developed algorithms for constructing job permutations that have the largest relative perimeters of the optimality box. Computational results for constructing such permutations showed that they provided the average error less than 0 . 74 % for the solved uncertain problems.
      Citation: Algorithms
      PubDate: 2018-05-07
      DOI: 10.3390/a11050066
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 67: Automated Processing of fNIRS Data—A
           Visual Guide to the Pitfalls and Consequences

    • Authors: Lia M. Hocke, Ibukunoluwa K. Oni, Chris C. Duszynski, Alex V. Corrigan, Blaise deB. Frederick, Jeff F. Dunn
      First page: 67
      Abstract: With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.
      Citation: Algorithms
      PubDate: 2018-05-08
      DOI: 10.3390/a11050067
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 68: Hybrid Flow Shop with Unrelated Machines,
           Setup Time, and Work in Progress Buffers for Bi-Objective Optimization of
           Tortilla Manufacturing

    • Authors: Victor Yaurima-Basaldua, Andrei Tchernykh, Francisco Villalobos-Rodríguez, Ricardo Salomon-Torres
      First page: 68
      Abstract: We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.
      Citation: Algorithms
      PubDate: 2018-05-09
      DOI: 10.3390/a11050068
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 69: A Multi-Stage Algorithm for a Capacitated
           Vehicle Routing Problem with Time Constraints

    • Authors: Lucia Cassettari, Melissa Demartini, Roberto Mosca, Roberto Revetria, Flavio Tonelli
      First page: 69
      Abstract: The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach.
      Citation: Algorithms
      PubDate: 2018-05-10
      DOI: 10.3390/a11050069
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 70: Estimating Functional Connectivity Symmetry
           between Oxy- and Deoxy-Haemoglobin: Implications for fNIRS Connectivity

    • Authors: Samuel Montero-Hernandez, Felipe Orihuela-Espina, Luis Sucar, Paola Pinti, Antonia Hamilton, Paul Burgess, Ilias Tachtsidis
      First page: 70
      Abstract: Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis.
      Citation: Algorithms
      PubDate: 2018-05-11
      DOI: 10.3390/a11050070
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 71: Improving Monarch Butterfly Optimization
           Algorithm with Self-Adaptive Population

    • Authors: Hui Hu, Zhaoquan Cai, Song Hu, Yingxue Cai, Jia Chen, Sibo Huang
      First page: 71
      Abstract: Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.
      Citation: Algorithms
      PubDate: 2018-05-14
      DOI: 10.3390/a11050071
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 72: Gray Wolf Optimization Algorithm for
           Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

    • Authors: Yixuan Ren, Tao Ye, Mengxing Huang, Siling Feng
      First page: 72
      Abstract: In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.
      Citation: Algorithms
      PubDate: 2018-05-15
      DOI: 10.3390/a11050072
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 73: The NIRS Brain AnalyzIR Toolbox

    • Authors: Hendrik Santosa, Xuetong Zhai, Frank Fishburn, Theodore Huppert
      First page: 73
      Abstract: Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.
      Citation: Algorithms
      PubDate: 2018-05-16
      DOI: 10.3390/a11050073
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 74: Using Metaheuristics on the Multi-Depot
           Vehicle Routing Problem with Modified Optimization Criterion

    • Authors: Petr Stodola
      First page: 74
      Abstract: This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm—based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances—has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau’s benchmark instances are used.
      Citation: Algorithms
      PubDate: 2018-05-18
      DOI: 10.3390/a11050074
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 75: A New Oren–Nayar Shape-from-Shading
           Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme

    • Authors: Guohui Wang, Yuanbo Chu
      First page: 75
      Abstract: 3D shape reconstruction from images has been an important topic in the field of robot vision. Shape-From-Shading (SFS) is a classical method for determining the shape of a 3D surface from a one intensity image. The Lambertian reflectance is a fundamental assumption in conventional SFS approaches. Unfortunately, when applied to characterize the reflection attribute of the diffuse reflection, the Lambertian model is tested to be inexact. In this paper, we present a new SFS approach for 3D reconstruction of diffuse surfaces whose reflection attribute is approximated by the Oren–Nayar reflection model. The partial differential Image Irradiance Equation (IIR) is set up with this model under a single distant point light source and an orthographic camera projection whose direction coincides with the light source. Then, the IIR is converted into an eikonal equation by solving a quadratic equation that includes the 3D surface shape. The viscosity solution of the resulting eikonal equation is approximated by using the high-order Godunov-based scheme that is accelerated by means of an alternating sweeping strategy. We conduct the experiments on synthetic and real-world images, and the experimental results illustrate the effectiveness of the presented approach.
      Citation: Algorithms
      PubDate: 2018-05-18
      DOI: 10.3390/a11050075
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 76: PHEFT: Pessimistic Image Processing
           Workflow Scheduling for DSP Clusters

    • Authors: Alexander Yu. Drozdov, Andrei Tchernykh, Sergey V. Novikov, Victor E. Vladislavlev, Raul Rivera-Rodriguez
      First page: 76
      Abstract: We address image processing workflow scheduling problems on a multicore digital signal processor cluster. We present an experimental study of scheduling strategies that include task labeling, prioritization, resource selection, and digital signal processor scheduling. We apply these strategies in the context of executing the Ligo and Montage applications. To provide effective guidance in choosing a good strategy, we present a joint analysis of three conflicting goals based on performance degradation. A case study is given, and experimental results demonstrate that a pessimistic scheduling approach provides the best optimization criteria trade-offs. The Pessimistic Heterogeneous Earliest Finish Time scheduling algorithm performs well in different scenarios with a variety of workloads and cluster configurations.
      Citation: Algorithms
      PubDate: 2018-05-22
      DOI: 10.3390/a11050076
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 77: A Novel Design of Sparse Prototype Filter
           for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks

    • Authors: Wei Xu, Yi Li, Jinghong Miao, Jiaxiang Zhao, Xin Gao
      First page: 77
      Abstract: Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR) cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP) performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks.
      Citation: Algorithms
      PubDate: 2018-05-22
      DOI: 10.3390/a11050077
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 143: An Algorithm for Mapping the Asymmetric
           Multiple Traveling Salesman Problem onto Colored Petri Nets

    • Authors: Furqan Hussain Essani, Sajjad Haider
      First page: 143
      Abstract: The Multiple Traveling Salesman Problem is an extension of the famous Traveling Salesman Problem. Finding an optimal solution to the Multiple Traveling Salesman Problem (mTSP) is a difficult task as it belongs to the class of NP-hard problems. The problem becomes more complicated when the cost matrix is not symmetric. In such cases, finding even a feasible solution to the problem becomes a challenging task. In this paper, an algorithm is presented that uses Colored Petri Nets (CPN)—a mathematical modeling language—to represent the Multiple Traveling Salesman Problem. The proposed algorithm maps any given mTSP onto a CPN. The transformed model in CPN guarantees a feasible solution to the mTSP with asymmetric cost matrix. The model is simulated in CPNTools to measure two optimization objectives: the maximum time a salesman takes in a feasible solution and the collective time taken by all salesmen. The transformed model is also formally verified through reachability analysis to ensure that it is correct and is terminating.
      Citation: Algorithms
      PubDate: 2018-09-25
      DOI: 10.3390/a11100143
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 144: Multi-Branch Deep Residual Network for
           Single Image Super-Resolution

    • Authors: Peng Liu, Ying Hong, Yan Liu
      First page: 144
      Abstract: Recently, algorithms based on the deep neural networks and residual networks have been applied for super-resolution and exhibited excellent performance. In this paper, a multi-branch deep residual network for single image super-resolution (MRSR) is proposed. In the network, we adopt a multi-branch network framework and further optimize the structure of residual network. By using residual blocks and filters reasonably, the model size is greatly expanded while the stable training is also guaranteed. Besides, a perceptual evaluation function, which contains three parts of loss, is proposed. The experiment results show that the evaluation function provides great support for the quality of reconstruction and the competitive performance. The proposed method mainly uses three steps of feature extraction, mapping, and reconstruction to complete the super-resolution reconstruction and shows superior performance than other state-of-the-art super-resolution methods on benchmark datasets.
      Citation: Algorithms
      PubDate: 2018-09-27
      DOI: 10.3390/a11100144
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 145: Reducing the Operational Cost of Cloud
           Data Centers through Renewable Energy

    • Authors: Demetrio Laganà, Carlo Mastroianni, Michela Meo, Daniela Renga
      First page: 145
      Abstract: The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.
      Citation: Algorithms
      PubDate: 2018-09-27
      DOI: 10.3390/a11100145
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 146: Fast Tuning of the PID Controller in An

    • Authors: Abdoalnasir Almabrok, Mihalis Psarakis, Anastasios Dounis
      First page: 146
      Abstract: This article presents a novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. The optimal design and power efficiency of an HVAC system depend on how fast the integrated controller, e.g., PID controller, is adapted in the changes of the environmental conditions. In this paper, to achieve high tuning speed, we rely on a fast convergence evolution algorithm, called Big Bang–Big Crunch (BB–BC). The BB–BC algorithm is implemented, along with the PID controller, in an FPGA device, in order to further accelerate of the optimization process. The FPGA-in-the-loop (FIL) technique is used to connect the FPGA board (i.e., the PID and BB–BC subsystems) with the plant (i.e., MATLAB/Simulink models of HVAC) in order to emulate and evaluate the entire system. The experimental results demonstrate the efficiency of the proposed technique in terms of optimization accuracy and convergence speed compared with other optimization approaches for the tuning of the PID parameters: sw implementation of the BB–BC, genetic algorithm (GA), and particle swarm optimization (PSO).
      Citation: Algorithms
      PubDate: 2018-09-28
      DOI: 10.3390/a11100146
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 147: WSN Nodes Placement Optimization Based on
           a Weighted Centroid Artificial Fish Swarm Algorithm

    • Authors: Hong Yin, Ying Zhang, Xu He
      First page: 147
      Abstract: Aiming at optimal placement of wireless sensor network (WSN) nodes of wind turbine blade for health inspection, a weighted centroid artificial fish swarm algorithm (WC-AFSA) is proposed. A weighted centroid algorithm is applied to construct an initial fish population so to enhance the fish diversity and improve the search precision. Adaptive step based on dynamic parameter is used to jump out local optimal solution and improve the convergence speed. Optimal sensor placement is realized by minimizing the maximum off-diagonal elements of the modal assurance criterion as the objective function. Five typical test functions are applied to verify the effectiveness of the algorithm, and optimal placement of WSNs nodes on wind turbine blade is carried out. The results show that WC-AFSA has better optimization effect than AFSA, which can solve the problem of optimal arrangement of blade WSNs nodes.
      Citation: Algorithms
      PubDate: 2018-09-30
      DOI: 10.3390/a11100147
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 148: Fuzzy Q-Learning Agent for Online Tuning
           of PID Controller for DC Motor Speed Control

    • Authors: Panagiotis Kofinas, Anastasios I. Dounis
      First page: 148
      Abstract: This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller.
      Citation: Algorithms
      PubDate: 2018-09-30
      DOI: 10.3390/a11100148
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 149: Cover Time in Edge-Uniform
           Stochastically-Evolving Graphs

    • Authors: Ioannis Lamprou, Russell Martin, Paul Spirakis
      First page: 149
      Abstract: We define a general model of stochastically-evolving graphs, namely the edge-uniform stochastically-evolving graphs. In this model, each possible edge of an underlying general static graph evolves independently being either alive or dead at each discrete time step of evolution following a (Markovian) stochastic rule. The stochastic rule is identical for each possible edge and may depend on the past k ≥ 0 observations of the edge’s state. We examine two kinds of random walks for a single agent taking place in such a dynamic graph: (i) The Random Walk with a Delay (RWD), where at each step, the agent chooses (uniformly at random) an incident possible edge, i.e., an incident edge in the underlying static graph, and then, it waits till the edge becomes alive to traverse it. (ii) The more natural Random Walk on what is Available (RWA), where the agent only looks at alive incident edges at each time step and traverses one of them uniformly at random. Our study is on bounding the cover time, i.e., the expected time until each node is visited at least once by the agent. For RWD, we provide a first upper bound for the cases k = 0 , 1 by correlating RWD with a simple random walk on a static graph. Moreover, we present a modified electrical network theory capturing the k = 0 case. For RWA, we derive some first bounds for the case k = 0 , by reducing RWA to an RWD-equivalent walk with a modified delay. Further, we also provide a framework that is shown to compute the exact value of the cover time for a general family of stochastically-evolving graphs in exponential time. Finally, we conduct experiments on the cover time of RWA in edge-uniform graphs and compare the experimental findings with our theoretical bounds.
      Citation: Algorithms
      PubDate: 2018-10-02
      DOI: 10.3390/a11100149
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 150: SLoPCloud: An Efficient Solution for
           Locality Problem in Peer-to-Peer Cloud Systems

    • Authors: Mohammed  Gharib, Marzieh Malekimajd, Ali Movaghar
      First page: 150
      Abstract: Peer-to-Peer (P2P) cloud systems are becoming more popular due to the high computational capability, scalability, reliability, and efficient data sharing. However, sending and receiving a massive amount of data causes huge network traffic leading to significant communication delays. In P2P systems, a considerable amount of the mentioned traffic and delay is owing to the mismatch between the physical layer and the overlay layer, which is referred to as locality problem. To achieve higher performance and consequently resilience to failures, each peer has to make connections to geographically closer peers. To the best of our knowledge, locality problem is not considered in any well known P2P cloud system. However, considering this problem could enhance the overall network performance by shortening the response time and decreasing the overall network traffic. In this paper, we propose a novel, efficient, and general solution for locality problem in P2P cloud systems considering the round-trip-time (RTT). Furthermore, we suggest a flexible topology as the overlay graph to address the locality problem more effectively. Comprehensive simulation experiments are conducted to demonstrate the applicability of the proposed algorithm in most of the well-known P2P overlay networks while not introducing any serious overhead.
      Citation: Algorithms
      PubDate: 2018-10-02
      DOI: 10.3390/a11100150
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 151: K-Means Cloning: Adaptive Spherical
           K-Means Clustering

    • Authors: Abdel-Rahman Hedar, Abdel-Monem M. Ibrahim, Alaa E. Abdel-Hakim, Adel A.  Sewisy
      First page: 151
      Abstract: We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster `colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm.
      Citation: Algorithms
      PubDate: 2018-10-06
      DOI: 10.3390/a11100151
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 152: Accelerated Iterative Learning Control of
           Speed Ripple Suppression for a Seeker Servo Motor

    • Authors: Dongqi Ma, Hui Lin
      First page: 152
      Abstract: To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.
      Citation: Algorithms
      PubDate: 2018-10-10
      DOI: 10.3390/a11100152
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 153: Chronotype, Risk and Time Preferences, and
           Financial Behaviour

    • Authors: Di Wang, Frank McGroarty, Eng-Tuck Cheah
      First page: 153
      Abstract: This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results reveal that morningness is negatively associated with delinquent credit card payments. Morningness also indirectly predicts delinquent credit card payments through time preference, but this relationship only exists when individuals’ monthly income is at a low and average level. On the other hand, financial risk preference accounts for the effect of morningness on stock market participation. Consequently, an additional finding is that morningness is positively associated with financial risk preference, which contradicts previous findings in the literature. Finally, based on the empirical evidence, we discuss the plausible mechanisms that may drive these relationships and the implications for theory and practice. The current study contributes to the literature by examining the links between circadian typology and particular financial behaviour of experienced workers.
      Citation: Algorithms
      PubDate: 2018-10-10
      DOI: 10.3390/a11100153
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 154: Two Hesitant Multiplicative
           Decision-Making Algorithms and Their Application to Fog-Haze Factor
           Assessment Problem

    • Authors: Lidan Pei, Feifei Jin
      First page: 154
      Abstract: Hesitant multiplicative preference relation (HMPR) is a useful tool to cope with the problems in which the experts utilize Saaty’s 1–9 scale to express their preference information over paired comparisons of alternatives. It is known that the lack of acceptable consistency easily leads to inconsistent conclusions, therefore consistency improvement processes and deriving the reliable priority weight vector for alternatives are two significant and challenging issues for hesitant multiplicative information decision-making problems. In this paper, some new concepts are first introduced, including HMPR, consistent HMPR and the consistency index of HMPR. Then, based on the logarithmic least squares model and linear optimization model, two novel automatic iterative algorithms are proposed to enhance the consistency of HMPR and generate the priority weights of HMPR, which are proved to be convergent. In the end, the proposed algorithms are applied to the factors affecting selection of fog-haze weather. The comparative analysis shows that the decision-making process in our algorithms would be more straight-forward and efficient.
      Citation: Algorithms
      PubDate: 2018-10-10
      DOI: 10.3390/a11100154
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 155: Real-Time Tumor Motion Tracking in 3D
           Using Planning 4D CT Images during Image-Guided Radiation Therapy

    • Authors: Jang-Hwan Choi, Sooyeul Lee
      First page: 155
      Abstract: In this paper we propose a novel method for tracking the respiratory phase and 3D tumor position in real time during treatment. The method uses planning four-dimensional (4D) computed tomography (CT) obtained through the respiratory phase, and a kV projection taken during treatment. First, digitally rendered radiographs (DRRs) are generated from the 4DCT, and the structural similarity (SSIM) between the DRRs and the kV projection is computed to determine the current respiratory phase and magnitude. The 3D position of the tumor corresponding to the phase and magnitude is estimated using non-rigid registration by utilizing the tumor path segmented in the 4DCT. This method is evaluated using data from six patients with lung cancer and dynamic diaphragm phantom data. The method performs well irrespective of the gantry angle used, i.e., a respiration phase tracking accuracy of 97.2 ± 2.5%, and tumor tracking error in 3D of 0.9 ± 0.4 mm. The phantom study reveals that the DRRs match the actual projections well. The time taken to track the tumor is 400 ± 53 ms. This study demonstrated the feasibility of a technique used to track the respiratory phase and 3D tumor position in real time using kV fluoroscopy acquired from arbitrary angles around the freely breathing patient.
      Citation: Algorithms
      PubDate: 2018-10-11
      DOI: 10.3390/a11100155
      Issue No: Vol. 11, No. 10 (2018)
  • Algorithms, Vol. 11, Pages 156: Online Uniformly Inserting Points on the

    • Authors: Rong Zhou, Chun Chen, Liqun Sun, Francis C. M. Lau, Sheung-Hung Poon, Yong Zhang
      First page: 156
      Abstract: Uniformly inserting points on the sphere has been found useful in many scientific and engineering fields. Different from the offline version where the number of points is known in advance, we consider the online version of this problem. The requests for point insertion arrive one by one and the target is to insert points as uniformly as possible. To measure the uniformity we use gap ratio which is defined as the ratio of the maximal gap to the minimal gap of two arbitrary inserted points. We propose a two-phase online insertion strategy with gap ratio of at most 3.69 . Moreover, the lower bound of the gap ratio is proved to be at least 1.78 .
      Citation: Algorithms
      PubDate: 2018-10-16
      DOI: 10.3390/a11100156
      Issue No: Vol. 11, No. 10 (2018)
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