• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.73-79
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• 1994

This paper propose Adaptive Maintenance as a new type of maintenance for machine failures which are unpredictable. A purpose of adpative maintenance is to decrease inconsistency. In order to pick up some of problems the traditional maintenance policy, We discussed Time Based Maintenance(TBM) and Condition Based Maintenance(CBM) with Bath-Tub Curve. By using Machine Condition Diagnosis Technique (CDT), Monitored condition maintenance deals with the dynamic decision making for diagnosis procedures at maintenance and caution level. Adaptive Maintenance is a powerful tool for Total Production Maintenance(TPM).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.607-608
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• 2012

• Journal of Korean Medicine Rehabilitation
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• v.23 no.3
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• pp.149-157
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• 2013

Objectives This study was designed to: (1) investigate the clinical feature of tongue diagnosis, (2) make an observation of significant changes in tongue diagnosis according to the patient's physical condition and laboratory result and (3) identify clinical efficacy of tongue diagnosis. Methods 300 patients' tongue diagnosis results were analyzed and the patients were divided to each group according to the physical condition and laboratory result. Then, chi-square test was performed to assess statistical significance between tongue diagnosis results of each group. Results As a result of analyzing the spread of tongue diagnosis according to the patient's physical condition and laboratory result, 18 groups had statistical significance related to specific tongue color and tongue coating. Conclusions Even if there would be possible misinterpretations in one-to-one match between the tongue diagnosis and certain diseases, we identified that tongue diagnosis results were changed somewhat related to patient's physical condition with some tendency and tongue diagnosis could be used for meaningful clinical diagnostic tool.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.9
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• pp.75-81
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• 1999

In order to remain competitive, a manufacturing company needs to maintain the optimal condition of its manufacturing system. Machine tools as an important element of a manufacturing system consist of complex mechanical as well as electronic components. Therefore, diagnosing the troubles of machine tools is a tricky process which requires a lot of experience and knowledge. Since providing machine tool users with necessary services at the right time is very difficult and expensive, a remote diagnosis system is to be regarded as a good alternative, with which users can diagnose and fix the machine troubles. This paper presents a framework for a remote machine tools diagnosis system by combining the world wide web technology and backward reasoning expert system.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.868-871
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• 1995

In order to remain competitive, a manufacturing company needs to maintain the optimal condition of its manufacturing system. Machine tools as an important element of a manufacturing system comprises complex mechanical as well as electronic components. Therefore, diagnosing the troubles of machine tools is a tricky process which requires a lot of experience and knowledge. Since providing machine tool users with necessary services at the right time is very difficult and expensive, a remote diagnosis system is to be regarded as a good alternative,with which users can diagnose and fix the machine troubles. This paper presents a framework for a remote machine tool diagnosis system using the world wide web technology and backward reasoning expert system.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.10
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• pp.135-140
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• 1997

The detection of cutting tool states in machining is important for the automation. The information of cutting tool states in metal cutting process is uncertain. Hence a industry needs the system which can detect the cutting tool states in real time and control the feed motion. Cutting signal features must be sifted before the classification. In this paper the Fisher's linear discriminant function was applied to the pattern recognition of the cutting tool states successfully. Cutting conditions and cutting force para- meters have shown to be sensitive to tool states, so these cutting conditions and cutting force paramenters can be used as features for tool state detection.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.2
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• pp.38-45
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• 1995

The diagnosis and monitoring system of abnormal cutting condition is necessary to realize precision machining proces and factory automation, which are final goal of metal cutting in order to develop this system, theimage processing technique has been investigated in machining process. In theis paper, the measurement system of tool wear using computer vision is designed to detect the wear pattern by non-contact and direct method and get the realiable wear information about cutting tool. We measured the area of the side and front part of the diamond core dril which is used in 40kHz ultrasonic vibration machine.