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AI Communications is a journal on Artificial Intelligence (AI) which has a close relationship to ECCAI (the European Coordinating Committee for Artificial Intelligence). It covers the whole AI community: scientific institutions as well as commercial and industrial companies.
AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news. The Editorial and Advisory Board is appointed by the Editor-in-Chief.
Article Type: Research Article
Abstract: With the advancement of technology and expansion of broadcasting around the globe has further boost up biometric surveillance systems. Pattern recognition is the key track in this area. Convolution neural network (CNN) as one of the most prevalent deep learning algorithm has gain high reputation in image features extraction. In this paper, we propose few new twists of unsupervised learning i.e. convolution sparse filter learning (CSFL) to obtain rich and discriminative features of an image. The features extracted by CSFL algorithm are used to initialize the first CNN layer, and then these features are further used in feed forward manner …by the CNN to learn high level features for classification. The linear regression classifier (softmax classifier) is used to serve as the output layer of CNN for providing the probability of an image class. We present and examine five different architectures of CNN and error function mean square error (MSE). The experimental results on a public dataset showcase the merit of the proposed method. Show more
Keywords: Convolution neural network, classification, unsupervised learning, feature extraction
Citation: AI Communications, vol. 30, no. 5, pp. 311-324, 2017
Article Type: Research Article
Abstract: Constraint programming is a family of techniques for solving combinatorial problems, where the problem is modelled as a set of decision variables (typically with finite domains) and a set of constraints that express relations among the decision variables. One key concept in constraint programming is propagation : reasoning on a constraint or set of constraints to derive new facts, typically to remove values from the domains of decision variables. Specialized propagation algorithms (propagators) exist for many classes of constraints. The concept of support is pervasive in the design of propagators. Traditionally, when a domain value ceases to have …support, it may be removed because it takes part in no solutions. Arc-consistency algorithms such as AC2001 [in: Proceedings 17th International Joint Conference on Artificial Intelligence (IJCAI 2001) , 2001 , pp. 309–315] make use of support in the form of a single domain value. GAC algorithms such as GAC-Schema [in: Proceedings 15th International Joint Conference on Artificial Intelligence (IJCAI 97) , 1997 , pp. 398–404] use a tuple of values to support each literal. We generalize these notions of support in two ways. First, we allow a set of tuples to act as support. Second, the supported object is generalized from a set of literals (GAC-Schema) to an entire constraint or any part of it. We design a methodology for developing correct propagators using generalized support. A constraint is expressed as a family of support properties, which may be proven correct against the formal semantics of the constraint. We show how to derive correct propagators from the constructive proofs of the support properties. The framework is carefully designed to allow efficient algorithms to be produced. Derived algorithms may make use of dynamic literal triggers or watched literals [in: Proc. 12th International Conference on the Principles and Practice of Constraint Programming (CP 2006) , 2006 , pp. 182–197] for efficiency. Finally, three case studies of deriving efficient algorithms are given. Show more
Keywords: Constraint satisfaction problem, constraint programming, formal methods
Citation: AI Communications, vol. 30, no. 5, pp. 325-346, 2017
Article Type: Research Article
Abstract: PID controllers are one of the most popular types of controllers found in the industry; they require determining three real values to minimize the error over time and to deal with specific process requirements. Finding such values has been subjected to extensive research, and many popular algorithms and methods exist to accomplish this. One of these methods is Simulated Annealing. In this paper, we study the use of the re-annealing characteristic of Adaptive Simulated Annealing (ASA) for PID tuning in 20 benchmark systems. This adaptive version gives special treatment to each parameter of the search space. We compare the results …of ASA with a simple SA algorithm. An extra comparison, with a Particle Swarm Optimization algorithm, was made to provide some information on how ASA behaves compared against another optimization based method. The results show that using an adaptive algorithm effectively improves the performance of the tested systems. Show more
Keywords: Adaptive Simulated Annealing, meta-heuristics, optimization, automatic control, PID controller
Citation: AI Communications, vol. 30, no. 5, pp. 347-362, 2017
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