Theories and applications of artificial neural classifiers are developed, increasing the demand for efficient implementation schemes. In this thesis, reprogrammable dataflow neural classifiers are proposed as an alter...
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Theories and applications of artificial neural classifiers are developed, increasing the demand for efficient implementation schemes. In this thesis, reprogrammable dataflow neural classifiers are proposed as an alternative to traditional implementations. In general, these classifiers are based on functional languages, neural-dataflow transformations, dataflow algorithmic transformations and dataflow multiprocessors. An experimental approach is used to investigate the performance of a large-scale, fine-grained dataflow classifier architecture. In this study, the functional descriptions of high-level data dependency of a supervised learning algorithm are transformed into a machine-executable low-level dataflow graph. The tagged token dataflow algorithmic transformation is applied to exploit the parallelism. Dataflow neural classifiers are used to implement the learning algorithm. No attempt is made to optimize the granularity of the high-level language programming blocks to balance the computation and communication. The proposed classifier architecture is more versatile than other existing architectures. Performance results show the effectiveness of dataflow neural classifiers.
The solar collector is the heart of any solar energy collection system designed for operation in the low to medium temperature ranges. So, an efficient design of solar collector system, giving optimum performance is r...
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The solar collector is the heart of any solar energy collection system designed for operation in the low to medium temperature ranges. So, an efficient design of solar collector system, giving optimum performance is required. Though system performance is optimized by many different techniques, however, intelligent system design is an useful technique to optimize the efficiency of such systems. One of the intelligence techniques is Artificial Neural Network (ANN), and it is used in modeling, simulation and control of the system. ANN tool is faster and more accurate to solve complex and nonlinear problems as compared to other conventional techniques. ANN technique is applied in the field of Science, Engineering, Medicine, Defense, Business and Manufacturing etc. The main task of ANN tool is training of structure, which is done by collected experimental data of solar energy systems and in this method separate programming is not required as in other conventional methods. The aim of this study is to review the applications of ANN to predict the performance of solar energy collector and to identify the research gap for future work. Published research works presented in this paper, show that the ANN technique is very appropriate tool to predict the performance of solar collector systems
Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the ba...
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Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the basis of traditional PSO algorithm. We proposed a cooperative particle swarm optimization model with cooperative multi-population. Applications are given on creative conceptual architectural design. (c) 2006 Elsevier Ltd. All rights reserved.
Group Technology (GT) is a useful way of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation (CF) is a key step in GT. It is used ...
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Group Technology (GT) is a useful way of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation (CF) is a key step in GT. It is used in designing good cellular manufacturing systems using the similarities between parts in relation to the machines in their manufacture. It can identify part families and machine groups. Recently, neural networks (NNs) have been widely applied in GT due to their robust and adaptive nature. NNs are very suitable in CF with a wide variety of real applications. Although Dagli and Huggahalli adopted the ART1 network with an application in machine-part CF, there are still several drawbacks to this approach. To address these concerns, we propose a modified ART1 neural learning algorithm. In our modified ART1, the vigilance parameter can be simply estimated by the data so that it is more efficient and reliable than Dagli and Huggahalli's method for selecting a vigilance value. We then apply the proposed algorithm to machine-part CF in GT. Several examples are presented to illustrate its efficiency. In comparison with Dagli and Huggahalli's method based on the performance measure proposed by Chandrasekaran and Rajagopalan, our modified ART1 neural learning algorithm provides better results. Overall, the proposed algorithm is vigilance parameter-free and very efficient to use in CF with a wide variety of machine/part matrices. (C) 2007 Elsevier B.V. All rights reserved.
作者：
Varma, AshishKumar, A. TharunYamini, B.
Department of Networking and Communncations SRM Nagar Chengalpattu District Tamil Nadu Kattankulathur603203 India
Because of the integration of physical processes, computer resources and communication capabilities, cyber physical systems (CPS) have evolved greatly in a variety of dynamic applications. But, these systems are serio...
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With the large use of wireless sensor devices, the interest in positioning and tracking by means of wireless sensor networks is expected to grow further. Particularly, accurate localization of a moving target is a fun...
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With the large use of wireless sensor devices, the interest in positioning and tracking by means of wireless sensor networks is expected to grow further. Particularly, accurate localization of a moving target is a fundamental requirement in several Machine to Machine monitoring applications. Tracking using Received Signal Strength Indicator (RSSI) has been frequently adopted thanks to the availability and the low cost of this parameter. In this paper, we propose an innovative target tracking algorithm which combines learning regression tree approach and filtering methods using RSSI metric. Regression Tree algorithm is investigated in order to estimate the position using the RSSI. This method is combined to filtering approaches yielding to more refined results. The suggested approach is evaluated through simulations and experiments. We also compare our method to existing algorithms available in the literature. The numerical and experimental results show the relevance and the efficiency of our method. (C) 2017 Elsevier B.V. All rights reserved.
On the design of multichannel filters, especially in color image restoration, it is not easy to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. In...
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On the design of multichannel filters, especially in color image restoration, it is not easy to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. In this paper, we propose a new class of multichannel filters called adaptive fuzzy hybrid multichannel (AFHM) filters to achieve these three objectives simultaneously. Our novel approach is mainly based on human concept (heuristic rules) and provides a significant framework to take the merits of filtering behavior of three filters: a vector median (VM) filter, a vector directional (VD) filter, and an identity filter. On the design of an AFHM filter, our approach is a powerful and flexible scheme to achieve these three objectives because human concept can be efficiently expressed by fuzzy implicative rules for improving the filtering performance. The AFHM filters are able to effectively inherit the merits of filtering behaviors of these three filters in color image restoration applications. This is the first paper to include human concept to design multichannel filters. Moreover, a faster convergence property of the learning algorithm is investigated to reduce the time complexity of the AFHM filters. Extensive simulation results illustrate that AFHM filters not only achieve these three objectives but also possess the robust and adaptive capabilities, and demonstrate that the performance of AFHM filters outperforms that of other proposed filtering techniques. (C) 1999 Elsevier Science B.V. All rights reserved.
Data fusion is widely used in biometric, multi-media signal and image processing, and wireless sensor networks. Optimal fusion techniques are developed to perform fusion under noisy environments. However, the statisti...
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Data fusion is widely used in biometric, multi-media signal and image processing, and wireless sensor networks. Optimal fusion techniques are developed to perform fusion under noisy environments. However, the statistical analysis carried out to evaluate the relative merits of these optimal methods is very less. The aim of this paper is to fill this gap by evaluating four statistical optimal data fusion methods namely, the linearly constrained least squares (LCLS) fusion method, the covariance intersection (Cl) fusion method, the linearly constrained least absolute deviation (CLAD) fusion method, and the constrained least square (CLS) fusion method. The CLS fusion method presented here is an improved version of the CLAD fusion method. We further analyze the performances of these four methods in terms of optimality, unbiased estimation, robustness, and complexity. Simulations are used to validate the performance of these fusion algorithms. (C) 2014 Elsevier Inc. All rights reserved.
The deployment of heterogeneous networks (HetNets) can significantly boost the network capacity. However, the large number of small cell base stations (SBSs) deployed in HetNets can result in an increased total energy...
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The deployment of heterogeneous networks (HetNets) can significantly boost the network capacity. However, the large number of small cell base stations (SBSs) deployed in HetNets can result in an increased total energy consumption. One of the promising techniques to reduce the energy consumption of networks is base station (BS) ON/OFF switching (sleeping) approaches. Due to device lifetime and energy waste by unnecessary switchings, the number of switchings is considered as an important problem. In this paper, we formulate the ON/OFF switching problem as a satisfaction game, where BSs seek to meet certain performance constraints in order to avoid the frequent BS switchings. Furthermore, BSs can choose their transmission power levels according to the network conditions in a distributed manner. The proposed satisfaction game involves a multi-step process. In the first step, we aim at satisfying the players with the high satisfaction threshold in a predefined time interval. To measure a BS's satisfaction, a utility function is used that includes BS's load and power consumption, in which the load of each BS is coupled with the load of other BSs. Since all players cannot be simultaneously satisfied, unsatisfied players decide to reduce their thresholds, and form a game with the redefined thresholds. To solve the game, a regret-based satisfaction algorithm and a satisfaction equilibrium search algorithm are applied. Simulation results show that the proposed schemes can achieve significant reductions in the number of switchings compared with the benchmark methods.
Minsky and Papert (Perceptons: An Introduction to Computational Geometry, MIT Press: Cambridge, AA, 1969) show that a two-layer perceptron with monotonic activation function cannot solve the Xor problem. We present a ...
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Minsky and Papert (Perceptons: An Introduction to Computational Geometry, MIT Press: Cambridge, AA, 1969) show that a two-layer perceptron with monotonic activation function cannot solve the Xor problem. We present a two-layer perceptron with non-monotonic activation function which can separate non-linearly separated sets of data. This kind of perceptron is then applied to the Xor problem, parity problem and a pattern recognition problem. learning strategy is detailed, and performance is evaluated.
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