• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.319-320
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• 2005

This paper describes the principles and structures of an expert system for arrester diagnosis. The expert system analyzes and decides the arrester condition by total leakage current, its harmonic component, and temperature because the deterioration of arresters appeared in an increase of leakage current and surface temperature of it. Additionally, influence of system voltage harmonics and ambient temperatures on leakage current changes were considered in the design. The expert system is consisted of a data acquisition module and a computer for monitoring. The acquired analog data are digitalized and transmitted to the computer by an optical link which is free from interference.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.143-145
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• 1996

This paper presents the hybrid type based expert system for fault diagnosis in transformers. The proposed system uses the novel fault diagnostic technique based on dissolved gas analysis(DGA) in oil-immersed transformers. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set. Also, the uncertainty of the fault diagnostic rules are handled by using fuzzy measures. Finally, kohnen's feature map performs fault classification in transformers. To verify the effectiveness of the proposed diagnosis technique, the hybrid type based expert system for fault diagnosis has been tested by using KEPCO's transformer gas records.

• Korean Management Science Review
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• v.12 no.1
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• pp.19-34
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• 1995

The objective of this paper is to present a framework for an expert system for fault diagnosis in an FMS (Flexible Manufacturing Systyem). First, a system is analyzed structurally and functionally, giving the relationships between the system's components. These relationships, represented by strata, are are then stored in a deep knowledge base (DKB). Next, the specific knowledge, represented by echelons, about the symptoms and their probable causes for each component is stored in a shallow knowledge base (SKB) in the form of rule. When the fault diagnosis process begins, it starts to search the DKB and then the SKB, which is called hybrid reasoning in artificial intelligence.

• Journal of the Korean Society of Safety
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• v.7 no.1
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• pp.31-38
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• 1992

This paper presents a study on application of expert system technique for the diagnosis of fire-causes in plants. A need is recognized for new methods to diagnose exactly the causes of fires without the help of the human experts. To cope with the difficulty, the expert system techiuque is applied to this area. The expert system suggested in this paper is developed to infer the causes of fires(or, ignition source ) by using the information drawn from the circumstances in fire. For the convenience of inference, ignition sources we classified into eight types ; elecoic spark, adiabatic compression, welding spark, material of high temperature, impact and friction, spontaneous ignition, naked fire, and static electricity. The knowledge base is composed of the rule base and dynamic database, which contain the rules and facts obtained by the expenence in this area, respectively. Both depth-first search and backward chaining schemes are used in reasoning process. This expert system is written in an artificial intelligence language "PROLOG", and its availability is demonstrated through the case study.

• Journal of Bio-Environment Control
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• v.12 no.1
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• pp.30-37
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• 2003

An expert system was developed for the stress diagnosis of cucumber and tomato using FoxPro. The principle points in building the system were integration with Korean, effective processing of mass information, and easy access for non-experts such as farmers. The method of inferencing was forward chaining based on pattern matching. Knowledge base was expressed with IF∼THEN rules and was expressed in the form of tree. Also, the expert system was designed so that additions and modifications of all information could easily be performed on windows. The results tested by farmers with the developed system showed that the expert system was reliable for the practical use. It was expected the expert system could be directly applied to the stress diagnosis of other vegetable plants by modifying only data bases.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.19-25
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• 2001

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.863-871
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• 1999

While Logic Based Expert System (LBES) has a merit of rapid and complete inference, it also has a defect of huge knowledge base. Hierarchical LBES (HLBES) replaces the assertion time inference of LBES with the multi-level logic minimization procedure, and it guarantees smaller knowledge base comparing with LBES. This paper has two contributions. The one is proposing so-called fact-minimization procedure which reduces not only the number of facts or measured events but also the size of knowledge base dramatically. The other contribution is application of HLBES and the proposed fact-minimization to the fault diagnosis of power system. The application is successfully performed in the example with the transmission system which takes 72 goals and 352 facts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.665-670
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• 1999

This paper introduces a fuzzy expert systems for fault diagnosis, where the causal relationships between faults and protective devices are defined as fuzzy relations. The uncertainties existing in the fault diagnosis are figured out using the possibility theory and the possibility measure is associated with the fuzzy relation to evaluate the possibilities of faults. Besides, the knowledge base in the expert system is described and explained. In this way, multiple-fault can be handled easily and simultaneously together with single faults.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.69-70
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• 2006

The arrester's condition is related directly to protected equipment's safety. The most important is, therefore, arrester aging diagnosis. In this study, arrester aging diagnosis expert system is implemented to use JESS shell-engine and the leakage current detection technique.