• 동의생리병리학회지
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• 제23권4호
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1354-1357
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• 1993

This paper proposes that the best reasoning(i.e. rule evaluation) method which should be used in a fuzzy system significantly depends on the reasoning environment. It is shown that allowing for dynamic switching of reasoning methods leads to better performance, even when only two different reasoning methods are considered. This paper discusses DSFS (Dynamic Switching Fuzzy System) which dynamically switches and finds the best reasoning method (from among 80 different possible reasoning methods) to use depending on the reasoning situation. To overcome the reasoning speed and memory problem of DSFS due to its computational requirements, the DSFS Switching Reasoning Table method is proposed and its higher performance as compared to a conventional fuzzy system is shown. Finally, efforts to obtain general relationships between the characteristics of different reasoning methods and the actual control surface/environment is discussed.

• 인지과학
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• 제16권3호
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• pp.213-224
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• 2005

유연성과 광범위성을 갖는 대규모 인지 시스템의 구축을 위해서는 전문적인 지식 뿐 아니라 상식수준의 지식에 기초한 추론을 제공하는 필수 불가결하다. 본 논문에서는 정성적 추론을 이용하여 일상에서의 다양한 자연 현상에 대한 상식 추론을 제공하는 인지시스템에 대하여 소개하고자 한다. 기존의 정성적 추론은 다양한 일상의 개념과는 동떨어진 추상적인 개념에 기초하여 추론을 전개하여, 상식 추론에 이용되기에는 어려움이 있었다 본 연구에서는 기존의 정성적 추론을 범용의 대용량 Cyc 지식베이스에 접목하여, 스케치와 일상의 개념에 기초한 상식 추론을 제공하고자 하였다. 본 시스템은 구현되어 여러 예제에 적용되어 그 실효성을 입증할 수 있었다.

• 한국안전학회지
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• 제18권1호
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• pp.50-55
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• 2003

This paper presents an intelligent computer system, which can easily diagnose electrical fire causes, without the help of human experts of electrical fires diagnosis. For this system, a database is built with facts and rules driven from real electrical fires, and an intellectual database system which even a beginner can diagnose fire causes has been developed, named as an Electrical Fire Causes Diagnosis System : EFCDS. The database system has adopted, as an inference engine, a mixed reasoning approach which is constituted with the rule-based reasoning and the case-based reasoning. The system for a reasoning model was implemented using Delphi 3, one of program development tools, and Paradox is used as a database building tool. To verify effectiveness and performance of this newly developed diagnosis system, several simulated fire examples were tested and the causes of fire examples were detected effectively by this system. Additional researches will be needed to decide the minimal significant level of the solution and the weighting level of important factors.

• 지능정보연구
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• 제6권1호
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• International Journal of Internet, Broadcasting and Communication
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• 제11권3호
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• pp.49-58
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• 2019

Efficient evidential reasoning is an important issue in the development of advanced knowledge based systems. Efficiency is closely related to the design of problems solving methods adopted in the system. The explicit modeling of problem-solving structures is suggested for efficient and effective reasoning. It is pointed out that the problem-solving method framework is often too coarse-grained and too abstract to specify the detailed design and implementation of a reasoning system. Therefore, as a key step in developing a new reasoning scheme based on properties of the problem, the problem-solving method framework is expanded by introducing finer grained problem-solving primitives and defining an overall control structure in terms of these primitives. Once the individual components of the control structure are defined in terms of problem solving primitives, the overall control algorithm for the reasoning system can be represented in terms of a finite state diagram.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.241-245
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• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• International Journal of Advanced Culture Technology
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• 제7권3호
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• pp.158-165
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• 2019

Knowledge based system includes tools for constructing, testing, validating and refining the system along with user interfaces. An important issue in the design of a complete knowledge based system is the ability to produce explanations. Explanations are not just a series of rules involved in reasoning track. More detailed and explicit form of explanations is required not only for reliable reasoning but also for maintainability of the knowledge based system. This requires the explanation mechanisms to extend from knowledge oriented analysis to task oriented explanations. The explicit modeling of problem solving structures is suggested for explanation generation as well as for efficient and effective reasoning. Unlike other explanation scheme such as feedback explanation, the detailed, smaller and explicit representation of problem solving constructs can provide the system with capability of quality explanation. As a key step to development for explanation scheme, the problem solving methods are broken down into a finer grained problem solving primitives. The system records all the steps with problem solving primitives and knowledge involved in the reasoning. These are used to validate the conclusion of the consultation through explanations. The system provides user interfaces and uses specific templates for generating explanation text.

• 한국안전학회지
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• 제14권1호
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• pp.167-176
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• 1999

Construction accidents occur reiteratively in similar fashions. There have been several attempts to develop a safety program for preventing construction accidents on sites. It will be very effective to use previous accident cases for establishing proper safety plan and managing safety process. This research develops a case-based construction safety management system which enables construction managers or safety managers to prevent potential accidents during the construction process. The case-oriented approach is performed through the representation of previous accident cases in accordant with the similarity to the conditions of current site. It uses a case-based reasoning which is one of the reasoning methods of an expert system. A prototype system for the reasoning model was implemented using one of the case based system development tools. The system was applied to a real construction site to verify its capability and validity. It was founded that the causes of accidents were successfully removed, so the proposed model proved to be reasonable. Additional research is needed to resolve the technical problem how to adapt the countermeasures for accident prevention provided by the reasoning model.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.531-535
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• 1996

Computer aided conceptual solution of engineering problems can be effectively implemented by qualitative reasoning based on a physical model. Qualitative reasoning needs modeling paradigm which provides intellignet control of modeling assumptions and robust inferences without quantitative information about the system. We developed reasoning method using new algebra of qualitative mathematics. The method is applied to a conceptual design scheme of anadaptive control system of cutting process. The method identifies differences between proportional and proportional-integral control scheme of cutting process. It is shown that unfeasible investment could be prevented in the early conceptual stage by the qualitative reasoning procedures proposed in this paper.