• Nuclear Engineering and Technology
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• v.38 no.2
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• pp.155-162
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• 2006

It is necessary to monitor periodically the operability of safety-related motor-operated valves (MOVs) in nuclear power plants. However, acquiring diagnostic signals for MOVs is very difficult, and doing so requires an excessive amount of time, effort, and expenditure. This paper introduces an accurate and economical method to evaluate the performance of MOVs remotely. The technique to be utilized includes electrical measurements and signal processing to estimate the motor torque and the stem thrust, which have been cited as the two most effective parameters in diagnosing MOVs by the US Nuclear Regulatory Commission. The motor torque is calculated by using electrical signals, which can be measured in the motor control center (MCC). Some advantages of using the motor torque signature over other signatures are examined. The stem thrust is calculated considering the characteristics of the MOV and the estimated motor torque. The basic principle of estimating stem thrust is explained. The developed method is implemented in diagnostic equipment, namely, the Motor Operated Valve Intelligent Diagnostic System (MOVIDS), which is used to obtain the accuracy of and to validate the applicability of the developed method in nuclear power plants. Finally, the accuracy of the developed method is presented and some examples applied to field data are discussed.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.110-123
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• 1994

The development of automatic production systems have required inteligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inferencing about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. As an essential component of mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration, overloading, misalignment) and environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.224-228
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• 1997

Computer vision system become more important as the researches on inspection systems, intelligent robots , diagnostic medical systems is performed actively. In this paper, 3D measuring system is developed by using stereo vision. The relation between left image and right image is obtained by using 8 point algorithm, and fundamental matrix, epipole and 3D reconstruction algorithm are used to measure 3D dimensions. 3D measuring system was developed by Visual Basic, in which 3D coordinates would be obtained by simple mouse clicks. This software would be applied to construction area, home interior system, rapid measuring system.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.945-961
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• 2019

Tongue diagnosis is one of the most important diagnostic methods in Traditional Chinese Medicine (TCM). Tongue image segmentation aims to extract the image object (i.e., tongue body), which plays a key role in the process of manufacturing an automated tongue diagnosis system. It is still challenging, because there exists the personal diversity in tongue appearances such as size, shape, and color. This paper proposes an innovative segmentation method that uses image thresholding, gray projection and active contour model (ACM). Specifically, an initial object region is first extracted by performing image thresholding in HSI (i.e., Hue Saturation Intensity) color space, and subsequent morphological operations. Then, a gray projection technique is used to determine the upper bound of the tongue body root for refining the initial object region. Finally, the contour of the refined object region is smoothed by ACM. Experimental results on a dataset composed of 100 color tongue images showed that the proposed method obtained more accurate segmentation results than other available state-of-the-art methods.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.93-104
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• 1997

The major economic health problem of dairy cattle is mastitis which can affect 10 to 50% of cow-quarters. This health problem is difficult for many dairy farmers and health advisors to understand, diagnose and control. Without special laboratory testing, most mastitis is overlooked. Estimates of annual mastitis cast per cow vary from $50 to $200. For the nearly 9 million cows in the United States, annual loss to the dairy industry amounts to over one billion. A knowledge-based decision aid has been developed to evaluate mastitis data retrieved electronically from two of nine U. S. regional dairy records processing centers. Heuristic rules to diagnose herd mastitis problems were collected and incorporated into the system from various domain experts. This system information. It allows users to select mastitis control schemes with various degrees of aggressiveness and teaches commonly accepted mastitis control practices.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.3
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• pp.95-99
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• 2003

In this paper, the power system automation based on CAN communication protocol is introduced. Along with digitalization of electrical device, the various on-line services such as remote control, remote monitoring, remote parameter setting, fault data recording and remote diagnostic have been realized and become available. Therefore, it is necessary for those electrical devices to have real-time and reliable communication protocols. Author proposes DNPC(Distributed Network Protocol with CAN) which is proper to the power system SCADA (Supervisory Control And Data Acquisition) and DCS (Distributed Control System). The physical and datalink layer of DNPC protocol consists of the CAN2.0B which has the real-time characteristics and powerful error control scheme. As the transport and application layer, DNP3.0 is adopted because of its flexibility and compatible feature. Using the DNPC protocol, the power system automation is realized.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2006-2007
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• 2007

On-line condition monitoring is a useful tool for maintaining and extending the longevity of power transformers. An intelligent diagnostic system is desirable for operational safety and reliability. Bringing these concepts together results in a powerful support tool for engineers, reducing the volume of data to deal with, and making the data more meaningful. This paper describes how a multi-agent system for diagnosing the cause of transformer partial discharge activity was coupled with a method of UHF partial discharge monitoring, creating an on-line condition monitoring system. The challenges presented by the on-site environment are discussed, along with the implications for the complete system.

• Proceedings of the KIEE Conference
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• 2008.04b
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• pp.96-99
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• 2008

On-line condition monitoring is a useful tool for maintaining and extending the longevity of power transformers. An intelligent diagnostic system is desirable for operational safety and reliability. Bringing these concepts together results in a powerful support tool for engineers, reducing the volume of data to deal with, and making the data more meaningful. This paper describes how a multi-agent system for diagnosing the cause of transformer partial discharge activity was coupled with a method of UHF partial discharge monitoring, creating an on-line condition monitoring system. The challenges presented by the on-site environment are discussed, along with the implications for the complete system.