• 지능정보연구
• /
• 제6권1호
• /
• pp.97-112
• /
• 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.

• 대한산업공학회지
• /
• 제23권1호
• /
• pp.95-113
• /
• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• 셀메드
• /
• 제14권2호
• /
• pp.4.1-4.4
• /
• 2024

The development of clinical reasoning, which is the basis of medical education, is of great importance in medical universities. One of the founders of modern structural clinical reasoning, based on the knowledge of pathological physiology, is the inventor of the stethoscope Rene Laennec (1781-1826). He described the pioneering experience of clinical reasoning in the pages of his treatise A Treatise on the Diseases of the Chest and on Mediate Auscultation, which is of lasting value, since every rookie physicians in his professional development goes through the path of Laennec. Laennec's practice is of great importance for novice physicians since Laennec's treatise contains a diagnostic analysis of the most common clinical cases. Each such analysis demonstrates the algorithm of clinical reasoning. The purpose of this study was to analyze the approaches of clinical reasoning by René Laennec, which made it possible to identify two basic principles. Laennec's diagnostic reasoning involved two principles: pathogenetic analysis of clinical manifestations and a syndrome-based approach to differential diagnosis. These principles help distinguish between diseases with similar symptoms and physical findings are used to demonstrate the practical application of syndrome-based differential diagnosis. These principles can be easily mastered by understanding the pathogenesis of clinical manifestations. Thanks to the pathogenetic basis, the principles of clinical reasoning of Rene Laennec are universal and applicable to the analysis of any signs of the disease: not only physical but also laboratory and instrumental.

• Transactions on Control, Automation and Systems Engineering
• /
• 제1권1호
• /
• pp.54-61
• /
• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• 한국안전학회지
• /
• 제20권3호
• /
• pp.19-26
• /
• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• Transactions on Control, Automation and Systems Engineering
• /
• 제1권1호
• /
• pp.62-68
• /
• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. Authors have already proposed a diagnostic system based on FCM to utilized to identify the true origin of fault by on-line pattern diagnosis. In FCM based fault diagnosis, Temporal Associative Memories (TAM) recall of FCM is utilized to identify the true origin of fault by on-line pattern match where predicted pattern sequences obtained from TAM recall of fault FCM models are compared with actually observed ones. In engineering processes, the propagation delays are induced by the dynamics of processes and may vary with variables involved. However, disregarding such propagation delays in FCM-based fault diagnosis may lead to erroneous diagnostic results. To solve the problem, a concept of FTCM(Fuzzy Time Cognitive Map) is introduced into FCM-based fault diagnosis in this work. Expecially, translation method of FTCM makes it possible to diagnose the fault for some discrete time. Simulation studies through two-tank system is carried out to verify the effectiveness of the proposed diagnostic scheme.

• Journal of the Korean Data and Information Science Society
• /
• 제10권2호
• /
• pp.359-367
• /
• 1999

본 논문에서는 의료용 전문가 시스템에 사용 가능한 의료지식을 수리논리적으로 표현하고 이에 대한 연역 및 진단추론을 행하는 방법을 제시하였다. 문제해결을 위해서는 연역추론을 행하며 원인의 규명을 위해서는 진단추론을 행하지만 일차논리 언어로 표현된 의료지식에서 두 종류의 추론을 병행할 수 있다. 그리고 의료지식에 자주 발생하는 시간에 따라 가변적인 결과의 추론방법도 함께 고찰하였다.

• 컴퓨터교육학회논문지
• /
• 제6권4호
• /
• pp.95-113
• /
• 2003

본 논문에서는 기존의 LCMS에서 사용되는 평가시스템에 퍼지 추론 규칙을 이용한 적응형 퍼지평가시스템(AFES ; Adaptie Fuzzy Evaluation System)을 제안한다. AFES 는 학습자가 하나의 학습코스(learning course)에 들어가기 전에 퍼지진단평가(fuzzy diagnostic evealuation)를 통해 학습자에게 코스수준(course level)을 부여한다. 학습자는 코스수준에 따른 맞춤식 학습경로(learning path)로 학습을 종료한 후, 퍼지최종평가(fuzzy final evaluation)를 통해 최종성적(final grade)을 AFES 으로부터 부여 받는다. AFES의 가장 큰 특징은 최종성적의 점수 부여 규칙에 있는데, 만약 서로 다른 학습자가 동일한 문제 수에 대하여 같은 수의 정답을 냈더라도, AFES 는 125 가지 퍼지 추론 규칙(fuzzy reasoning rule)에 의거하여 탄력적으로 서로 다른 최종성적을 학습자에게 부여한다.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.860-863
• /
• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.