• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.407-414
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• 2002

Being recently increased interests of our healthcare, a host of symptoms-diagnostic sites has been introduced on the World Wide Web. But conventional healthcare sites provide users with only a very restricted functions. In this paper, we propose the use of Artificial Neural Networks (ANNs) as a flexible symptoms-diagnostic tool that enables learning effects of ANNs (not expert's knowledge) to be incorporated into the diagnostic process. We develop a novel algorithm for predicting patient\`s disease that satisfy user (or expert)-specified symptoms on WWW. Our algorithm provides two important benefits : 1) enables users (patients) to be taken early diagnostic, and 2) enables experts to perform confidently diagnostic by referencing the predicted diseases-list with its respective possibility.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.217-224
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• 1999

Trouble shooting of modern machine tools equipped with sophisticated electronic as well as mechanical parts is so difficult that it is usually depends upon the experience and accumulated knowledge of the diagnosing persons. On the other hand, tool users are scattered in wide area, which makes it expensive for a machine tool maker to run a vast service network. An unmanned diagnosis system to which users can have access at all times could be an efficient alternative. For this purpose, a rule-based expert system for diagnosing machine tools is developed. This paper describes the structure of diagnostic knowledge, the rule firing mechanism, the diagnosis flow, and user query process. An example shows the feasibility of problem solving on site without help of a service expert from machine tool maker.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.453-456
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• 1992

Diagnostic prototype expert system was developed by analyzing the measured acoustical data of automobile. For the utilities of this system, 1/3 octave filter(band-pass filter) and A/D converter were used for data acquisition and then information was analyzed using signal processing technique and pattern recognition by Hamming network algorithm. In order to raise the reliability of the diagnostic results, fuzzy inference technique was applied and, the results were displayed as graphical method to help the novice in diagnostic field. The validation of this diagnostic system was checked through experiments and it showed and acceptable performance for diagnostic process.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.1-7
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• 1997

The inference process for medical expert system is mostly formed by diagnostic knowledge on the if-then rule base. Oriental medicine diagnostic knowledge, however, may involve uncertain knowledge caused by ambiguous concept. In this paper, we analyze an oriental medicine diagnostic process by a rule-based inference system, and propose a method for representing and processing uncertain oriental medicine diagnostic knowledge using CLP( R ) which is a kind of constraint satisfaction program.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.461-467
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• 2007

The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.

• Proceedings of the KIEE Conference
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• 2002.07c
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• pp.1694-1697
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• 2002

KEPCO is planing to adopt a preventive diagnostic system to obtain the reliability of transformer and GIS in 765kV substation. KEPRI has developed the preventive diagnostic system for 765kV substation since 1997. We used various sensors and fault detecting devices such as a dissolved gas analyzer in oil, a ultrasonic detector and LA leakage current detector, etc., and carried out adaptation tests at the both a laboratory and a site. We developed a data acquisition system, a communication control unit and a server system as well. Furthermore, monitoring program and diagnostic expert system were also developed. This paper describes the preventive diagnostic system for 765kV Sin-Ga-Pyong and Sin-Tae-Baek substations which will be operated from 2004.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.94-100
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• 2010

Nowadays, several nontraditional machining(NTM) processes are widely used to machine a complex and accurate shape part of hard materials, such as titanium, ceramics, high strength temperature resistant and refractory materials which are difficult to machine and having high strength, hardness, toughness. Machining of these complex shapes in such materials by traditional machining processes are very difficult. The NTM processes is important in the areas of micro- and nano scale machining, where high accuracy and superior surface characteristics are required, which can only be achieved using these NTM processes. So, for effective selection of different NTM processes, careful decision making for a given NTM application is often necessary. An appropriate NTM process for a given material and shape condition is very difficult for the novice engineers. In this paper, an expert system based on an analytic network process(ANP) is suggested for a best selection of NTM process in a NTM application considering an prior interdependency effect among various factors.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.643-646
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• 2005

The paper discusses the main problems in induction motor diagnosis by motor current and vibration signals, possible faults and effects produced by these faults in the signal spectrums. Decision Tree is introduced as a tool to diagnose the motor status, this expert system is implemented to detect the incipient defects, supervise and predict them, and plan the maintenance of the motor.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.495-498
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• 1999

This paper examines a diagnostic model based on the concept of rule and fuzzy pattern recognition. One example is presented to demonstrate the feasibility of the model in diagnosing crack formations in reinforced concrete structures and the result by the expert system is generally satisfactory

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.4
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• pp.152-157
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• 2002

This paper describes an effect of a single- and a quadruple-lightning impulse currents on electrical characteristics of ZnO blocks, and an expert system to diagnose arrestor deterioration. To deduce the parameters needed for diagnosing arrester deterioration, an accelerate deterioration teat is carried out. In the experiment, leakage current components are measured. Also, wave height distribution of the leakage current according to the progress of arrester deterioration is analyzed. From the experimental results, the wave height distribution of the leakage current showed conspicuous difference even in an immaterial leakage current increase. Therefore, the use of wave height distribution of the leakage current in deterioration diagnostic technique makes more accurate diagnosis than the conventional method by using only a leakage currant value. Finally, the expert system based on the experimental results is developed and the system can diagnose arrestor deterioration by measuring the leakage current and its wade height distribution.