• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.800-806
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• 1999

This paper proposes IIKBAG(Intelligent Interlocking Knowledge Base Generator) which can build automatically the interlocking knowledge base utilized as the real-time interlocking strategy of the electronic interlocking system in order to enhance it's reliability and expansion. The IIKBAG consists of the inference engine and the knowledge base. The former has an auto-learning function which searches all the train routes for the given station model based on heuristic search technique while dynamically searching the model, and then generates automatically the interlocking patterns obtained from the interlocking relations of signal facilities on the routes. The latter is designed as the structure which the real-time expert system embedded on IS(Interlocking System) can use directly in order to enhances the reliability and accuracy. The IIKBAG is implemented in C computer language for the purpose of the build and interface of the station structure database. And, a typical station model is simulated to prove the validity of the proposed IIKBAG.

• 대한전기학회논문지:전력기술부문A
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• 제48권11호
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• pp.1457-1463
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• 1999

This paper develops an real-time expert system for the electronic interlocking system. it obtains the higher safety by determining the railway interlocking strategy in order to prevent trains from colliding, and derailing in the viewpoint of veteran expert, considering the situation of station in real-time. The expert system determines the real-time interlocking strategy by confirming the interlocking relationships among signal facilities based on the interlocking knowledge base from input information such as signal, points, and it is implemented as the rule-based system in order to represented accurately and effectively the interlocking relationships. Especially in case of emergency the function which determines the rational route coordinating with IIKBAG on the workstation is designed in order to minimize the spreading effect. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the build and interface of the station structure database. And, the validity of the built expert system is proved by simulating the diversity cases which may occur in the real system for the typical station model.