The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Christopher C Yang - One of the best experts on this subject based on the ideXlab platform.
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an associate constraint Network approach to extract multi lingual information for crime analysis
Decision Support Systems, 2007Co-Authors: Christopher C YangAbstract:International crime and terrorism have drawn increasing attention in recent years. Retrieving relevant information from criminal records and suspect communications is important in combating international crime and terrorism. However, most of this information is written in languages other than English and is stored in various locations. Information sharing between countries therefore presents the challenge of cross-lingual semantic interoperability. In this work, we propose a new approach - the associate constraint Network - to generate a cross-lingual concept space from a parallel corpus, and benchmark it with a previously developed technique, the Hopfield Network. The associate constraint Network is a constraint programming based algorithm, and the problem of generating the cross-lingual concept space is formulated as a constraint satisfaction problem. Nodes and arcs in an associate constraint Network represent extracted terms from parallel corpora and their associations. Constraints are defined for the nodes in the associate constraint Network, and node consistency and Network satisfaction are also defined. Backmarking is developed to search for a feasible solution. Our experimental results show that the associate constraint Network outperforms the Hopfield Network in precision, recall and efficiency. The cross-lingual concept space that is generated with this method can assist crime analysts to determine the relevance of criminals, crimes, locations and activities in multiple languages, which is information that is not available in traditional thesauri and dictionaries.
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using associate constraint Network with forward evaluation to overcome cross lingual semantic interoperability challenge for crime information extraction
Systems Man and Cybernetics, 2006Co-Authors: Christopher C Yang, Chihping WeiAbstract:Information extraction is important for crime analysis. Due to the popularity of the Web, information related to crime and terrorism is available in multiple languages. As a result, cross-lingual semantic interoperability is essential when we extract information across multiple languages. In our previous work, we have developed several techniques to generate an automatic cross-lingual thesaurus to support cross-lingual information retrieval based on a parallel corpus collected from the Web. The techniques include Hopfield Network and associate constraint Network with backmarking. Although these techniques obtain satisfactory performance, they have weaknesses in efficiency, consistency, precision or recall. In this work, we develop a new searching technique, namely forward evaluation, on the basis of our previously developed associate constraint Network model. We have conducted an experiment and show that the proposed forward evaluation technique outperforms both Hopfield Network and associate constraint Network with backmarking in terms of precision and recall. In addition, its efficiency is better than Hopfield Network but is not as good as associate constraint Network with backmarking.
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cross lingual semantics for crime analysis using associate constraint Network
Intelligence and Security Informatics, 2004Co-Authors: Christopher C YangAbstract:In light of the Bali bombings, East Asia nations rally the international community into a broad-based coalition to support a war against terrorism. The information sharing among different countries provides a challenge for cross-lingual semantic interoperability. In this work, we model the problem as an associate constraint Network and propagation by backmarking is proposed for creating the cross-lingual concept space. The approach deals with structured as well as unstructured data, addresses relevancy of information, offers the user with associative navigation through the information embedded in the database, enables conduction of multiple languages. Evidence is presented to show that a constraint programming approach performs well and has the advantage in terms of tractability, ordering and efficiency over the Hopfield Network. The research output consisted of a thesaurus-like, semantic Network knowledge base relied on statistical correlation analysis of the semantics embedded in the documents of English/Chinese parallel corpus.
Wen-jing Li - One of the best experts on this subject based on the ideXlab platform.
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Hopfield neural Networks for affine invariant matching
IEEE Transactions on Neural Networks, 2001Co-Authors: Wen-jing LiAbstract:The affine transformation, which consists of rotation, translation, scaling, and shearing transformations, can be considered as an approximation to the perspective transformation. Therefore, it is very important to find an effective means for establishing point correspondences under affine transformation in many applications. In this paper, we consider the point correspondence problem as a subgraph matching problem and develop an energy formulation for affine invariant matching by a Hopfield type neural Network. The fourth-order Network is investigated first, then order reduction is done by incorporating the neighborhood information in the data. Thus we can use the second-order Hopfield Network to perform subgraph isomorphism invariant to affine transformation, which can be applied to an affine invariant shape recognition problem. Experimental results show the effectiveness and efficiency of the proposed method.
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Hopfield neural Networks for affine invariant matching
IEEE Transactions on Neural Networks, 2001Co-Authors: Wen-jing LiAbstract:The affine transformation, which consists of rotation, translation, scaling, and shearing transformations, can be considered as an approximation to the perspective transformation. Therefore, it is very important to find an effective means for establishing point correspondences under affine transformation in many applications. In this paper, we consider the point correspondence problem as a subgraph matching problem and develop an energy formulation for affine invariant matching by a Hopfield type neural Network. The fourth-order Network is investigated first, then order reduction is done by incorporating the neighborhood information in the data. Thus we can use the second-order Hopfield Network to perform subgraph isomorphism invariant to affine transformation, which can be applied to an affine invariant shape recognition problem. Experimental results show the effectiveness and efficiency of the proposed method.
Pauchoo Chung - One of the best experts on this subject based on the ideXlab platform.
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two layer competitive based Hopfield neural Network for medical image edge detection
Optical Engineering, 2000Co-Authors: Chuanyu Chang, Pauchoo ChungAbstract:In medical applications, the detection and outlining of boundaries of organs and tumors in computed tomography (CT) and magnetic resonance imaging (MRI) images are prerequisite. A two-layer Hopfield neural Network called the competitive Hopfield edge-finding neural Network (CHEFNN) is presented for finding the edges of CT and MRI images. Different from conventional 2-D Hopfield neural Networks, the CHEFNN extends the one-layer 2-D Hopfield Network at the original image plane a two-layer 3-D Hopfield Network with edge detection to be implemented on its third dimension. With the extended 3-D architecture, the Network is capable of incorporating a pixel's contextual information into a pixel-labeling procedure. As a result, the effect of tiny details or noises will be effectively removed by the CHEFNN and the drawback of disconnected fractions can be overcome. Furthermore, by making use of the competitive learning rule to update the neuron states, the problem of satisfying strong constraints can be alleviated and results in a fast convergence. Our experimental results show that the CHEFNN can obtain more appropriate, more continued edge points than the Laplacian- based, Marr-Hildreth, Canny, and wavelet-based methods.
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polygonal approximation using a competitive Hopfield neural Network
Pattern Recognition, 1994Co-Authors: Pauchoo Chung, Chingtsorng Tsai, Eliang ChenAbstract:Abstract Polygonal approximation plays an important role in pattern recognition and computer vision. In this paper, a parallel method using a Competitive Hopfield Neural Network (CHNN) is proposed for polygonal approximation. Based on the CHNN, the polygonal approximation is regarded as a minimization of a criterion function which is defined as the arc-to-chord deviation between the curve and the polygon. The CHNN differs from the original Hopfield Network in that a competitive winner-take-all mechanism is imposed. The winner-take-all mechanism adeptly precludes the necessity of determining the values for the weighting factors in the energy function in maintaining a feasible result. The proposed method is compared to several existing methods by the approximation error norms L2 and L∞ with the result that promising approximation polygons are obtained.
Weerakorn Ongsakul - One of the best experts on this subject based on the ideXlab platform.
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augmented lagrange Hopfield Network for solving economic dispatch problem in competitive environment
PROCEEDINGS OF THE SIXTH GLOBAL CONFERENCE ON POWER CONTROL AND OPTIMIZATION, 2012Co-Authors: Dieu Ngoc Vo, Weerakorn Ongsakul, Khai Phuc NguyenAbstract:This paper proposes an augmented Lagrange Hopfield Network (ALHN) for solving economic dispatch (ED) problem in the competitive environment. The proposed ALHN is a continuous Hopfield Network with its energy function based on augmented Lagrange function for efficiently dealing with constrained optimization problems. The ALHN method can overcome the drawbacks of the conventional Hopfield Network such as local optimum, long computational time, and linear constraints. The proposed method is used for solving the ED problem with two revenue models of revenue based on payment for power delivered and payment for reserve allocated. The proposed ALHN has been tested on two systems of 3 units and 10 units for the two considered revenue models. The obtained results from the proposed methods are compared to those from differential evolution (DE) and particle swarm optimization (PSO) methods. The result comparison has indicated that the proposed method is very efficient for solving the problem. Therefore, the proposed...
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economic dispatch with multiple fuel types by enhanced augmented lagrange Hopfield Network
Applied Energy, 2012Co-Authors: Weerakorn OngsakulAbstract:Abstract This paper proposes an enhanced augmented Lagrange Hopfield Network (EALHN) for solving economic dispatch (ED) with piecewise quadratic cost functions. The EALHN is an augmented Lagrange Hopfield neural Network (ALHN), a continuous Hopfield neural Network with its energy function based on augmented Lagrangian function, enhanced by a heuristic search for determination of fuel type. The proposed EALHN solves the ED problem in two phases. In the first phase, a heuristic search based on the average production cost of generating units is used to determine the most suitable fuel type for each unit so that total maximum power generation from all units is sufficient for supplying to load demand. In the last phase, the ALHN is applied to find optimal solution corresponding to the chosen fuel types. The proposed method is tested on several systems with various load demands and the obtained test results are compared to those from many other methods in the literature. Test results have indicated that the proposed method is efficient and fast for the ED problems with multiple fuel types represented by quadratic cost functions.
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ramp rate constrained unit commitment by improved priority list and augmented lagrange Hopfield Network
Electric Power Systems Research, 2008Co-Authors: Vo Ngoc Dieu, Weerakorn OngsakulAbstract:Abstract This paper proposes an improved priority list (IPL) and augmented Hopfield Lagrange neural Network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed IPL-ALH minimizes the total production cost subject to the power balance, 15 min spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The IPL is a priority list enhanced by a heuristic search algorithm based on the average production cost of units, and the ALH is a continuous Hopfield Network whose energy function is based on augmented Lagrangian relaxation. The IPL is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For hours with insufficient power due to ramp rate or 15 min spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed IPL-ALH is tested on the 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural Network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), constraint logic programming (CLP), fuzzy optimization (FO), matrix real coded genetic algorithm (MRCGA), absolutely stochastic simulated annealing (ASSA), and hybrid parallel repair genetic algorithm (HPRGA). The test results indicate that the IPL-ALH obtain less total costs and faster computational times than some other methods.
Chihping Wei - One of the best experts on this subject based on the ideXlab platform.
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using associate constraint Network with forward evaluation to overcome cross lingual semantic interoperability challenge for crime information extraction
Systems Man and Cybernetics, 2006Co-Authors: Christopher C Yang, Chihping WeiAbstract:Information extraction is important for crime analysis. Due to the popularity of the Web, information related to crime and terrorism is available in multiple languages. As a result, cross-lingual semantic interoperability is essential when we extract information across multiple languages. In our previous work, we have developed several techniques to generate an automatic cross-lingual thesaurus to support cross-lingual information retrieval based on a parallel corpus collected from the Web. The techniques include Hopfield Network and associate constraint Network with backmarking. Although these techniques obtain satisfactory performance, they have weaknesses in efficiency, consistency, precision or recall. In this work, we develop a new searching technique, namely forward evaluation, on the basis of our previously developed associate constraint Network model. We have conducted an experiment and show that the proposed forward evaluation technique outperforms both Hopfield Network and associate constraint Network with backmarking in terms of precision and recall. In addition, its efficiency is better than Hopfield Network but is not as good as associate constraint Network with backmarking.