• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.565-572
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• 2004

This paper proposes an expert system which can enhance the accuracy of real-time bus reconfiguration strategy by adopting local minimum tree search method and minimize the spreading effect of the fault by considering totally the operating condition when a main transformer fault occurs in the automated substation. The local minimum tree search method to expand the best-first search method. This method has an advantage which can improve the performance of solution within the limits of the real-time condition. The inference strategy proposed expert system consists of two stages. The first stage determines the switching candidate set by searching possible switching candidates starting from the main transformer or busbar related to the event. And, second stage determines the rational real-time bus reconfiguration strategy based on heuristic rules for the obtained switching candidate set. Also, this paper studies the generalized distribution substation modelling using graph theory and a substation database is designed based on the study result. The inference engine of the expert system and the substation database is implemented in MFC function of Visual C++. Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and local minimum tree search solution based on diversity event simulations for typical distribution substation.

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.177-185
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• 2005

This paper proposes an expert system that can enhance the accuracy of real-time bus reconfiguration strategy by adopting the local minimum tree search method and that can minimize the spreading effect of the fault by considering the operating condition when a main transformer fault occurs in an automated substation. The local minimum tree search method is used to expand the best-first search method. This method has the advantage that it can improve the solution performance within the limits of the real-time condition. The inference strategy proposed expert system consists of two stages. The first stage determines the switching candidate set by searching possible switching candidates starting from the main transformer or busbar related to the event. The second stage determines the rational real-time bus reconfiguration strategy based on heuristic rules from the obtained switching candidate set. Also, this paper proposes generalized distribution substation modeling using graph theory, and a substation database based on the study results is designed.

• Korean Management Science Review
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• v.23 no.1
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.17-17
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• 2004

자율 이동로봇은 현재 각광을 받고 있는 서비스로봇의 연구와 더불어 활발히 연구되고 있다. 그 중 경로계획 부분에 대한 연구는 Roadmap Method, Cell Decomposition, Potential Field Method로 크게 구분하여 연구되고 있다. 그러나 경로계획 기법에 있어서 기존의 정형화된 방법 이외에 다른 방법들이 제시 되지 않고 있다. 기존 경로계측의 문제점들은 다음과 같다. 국부최소(local minimum)를 회피하지 못하거나, 많은 계산량으로 인해 넓은 범위에 적용시킬 수 없다는 문제점, 오프라인으로 경로의 최적성에만 치중하여 실시간으로 적용하기가 쉽지 않으며, 돌발적인 상황에 대처하기 어렵다는 문제점 등을 가지고 있다.(중략)

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.781-788
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• 1999

In this paper, an expert system is proposed, which can be effectively applied to the large-scale systems with the diversity time constraints, the objectives and the unfixed system structure. The inference scheme of the expert system have the integrated structure composed of the intuitive inference module and logical inference module in order to support effectively the operating constraints of system. The intuitive inference module is designed using the pattern matching or pattern recognition method in order to search a same or similar pattern under the fixed system structure. On the other hand, the logical inference module is designed as the structure with the multiple inference mode based on the heuristic search method in order to determine the optimal or near optimal control strategies satisfing the time constraints for system events under the unfixed system structure, and in order to use as knowledge generator. Here, inference mode consists of the best-first, the local-minimum tree, the breadth-iterative, the limited search width/time method. Finally, the application results for large-scale distribution SCADA system proves that the inference scheme of the expert system is very effective for the large-scale system. The expert system is implemented in C language for the dynamic mamory allocation method, database interface, compatability.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.977-985
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• 2009

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.