• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.723-733
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• 2020

Purpose: This study was designed to draw up problems with the safety and health diagnosis system, and designed a hierarchical survey model of the clauses prescribed by the Occupational Safety and Health Act. The purpose of the survey is to derive the effectiveness of the safety and health diagnosis system at the construction site by targeting three groups of experts. Method: This paper set up the legal status analysis and overseas related system analysis and AHP hierarchy analysis survey model. In addition, the validity of the analysis results was verified by a survey. Results: The analysis of the survey items showed a high importance on items included in the human utilization and the precision of the pre-plan. In particular, mid-sized construction companies showed the highest importance for their active activities after the safety and health diagnosis. Conclusion: Through this study, we should recognize problems for improving the safety and health system of construction industry and prepare policy and institutional improvement measures for experts.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.866-871
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• 2010

Gait analysis is essential for leg diagnosis and rehabilitation for the patients, the handicapped and the elderly. The use of 3D motion capture device for gait analysis is very common for gait analysis. However, this device has several shortcomings including limited workspace, visibility and high price. Instead, we developed gait estimation system using gyroscopes. This system provides gait information including the number of gaits, stride and walking distance. With four gyroscope (one for each leg's thigh and calf) outputs, the proposed gait modeling estimates the movements of the hip, the knees and the feet. Complete pedestrian localization is implemented with gait information and the heading angle estimated from the rate gyro and the magnetic compass measurements. The developed system is very useful for diagnosis and the rehabilitation of the pedestrian at the hospital. It is also useful for indoor localization of the pedestrians.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.45-50
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• 2007

Acoustic emission technology is applied to diagnosis the internal leak and operating conditions of the major valves at nuclear power plants. The purpose of this study is to verify availability of the acoustic emission as in-situ diagnosis method. In this study, acoustic emission tests are performed when the pressurized high temperature steam flowed through gate valve(1st stage reheater valve) and glove valve(main steam dump valve) on the normal size of 4 and 8". The valve internal leak diagnosis system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, signal level analysis and RMS(root mean square) analysis of acoustic signal emitted from the valve operating condition internal leak.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.20 no.2
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• pp.51-65
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• 2016

Objectives This study was conducted to suggest a standard operating procedure (SOP) in order to improve the reliability of tongue-image analysis system. Methods An affecting-factors list was made, which may affect the diagnostic parameters of the tongue-image analysis system. They were sub-classified into two groups: controllable and uncontrollable. Only the controllable factors, which could affect the results and easily set-up, were included into the SOP draft. Affecting factors control experiment was performed to investigate the effects of controllable factors, whose influence on diagnostic parameters of the tongue-image analysis system is ambiguous: rehearsal for tongue extrusion; alignment of camera axis; and presentation of a guideline. Three subjects volunteered for this experiment. Each of three variables was implemented twice in a random order by two operators on the subjects. Finally, 96 tongue images obtained in the aggregate. The diagnostic parameter set as a primary outcome in this experiment was the percentage of tongue coating. Results All of the control variables were not significant in both operators; however, the presentation of a guideline was relatively more affect than two other variables. Interaction effects among the variables were also insignificant. Therefore, the presentation of a guideline was included in the final SOP and the other variables were not included. Conclusions We suggested the SOP which could be used for both experimenter and subject. Moreover, Each of the SOPs applied to various types of tongue-image analysis system should be developed in order to improve the reliability.

• Electrical & Electronic Materials
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• v.7 no.1
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• pp.42-48
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• 1994

The preventive diagnosis technique for power system is being highlighted as a research area for deterioration of insulation in machinery because of high-voltage power system. We make efforts to develop not only diagnosis of aging state but also detection of defects in the initial stage from preventive diagnosis technique. Especially, partial discharge is actively studied as a non-destructive diagnosis technique and very useful because partial discharge measurement reduces damage than conventional diagnosis technique. The loaded stress during this test is smaller than that of other diagnosis techniques. But the continuous research for various complicated analysis method is required because partial discharge has very small signals and its signals have complex forms. In this paper, the measurement of partial discharge was investigated and studied on many specimens with void. We made samples having artificial voids and measured partial discharge. In order to use as a practical diagnosis technique, we studied ways of measurement, measured illustrations and types of partial discharge which could be used in order to diagnose defects of power machinery.

• Journal of Physiology & Pathology in Korean Medicine
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• v.32 no.6
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• pp.355-360
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• 2018

The purpose of this study is to find out the direction of development of the expert diagnosis system by questioning student satisfaction after practicing Oriental Diagnosis System (ODS). A questionnaire survey was conducted for 73 students who attended Pusan National University Graduate School of Korean Medicine and conducted clinical practice from the second semester of 17 years to the first semester of 18 years. The questionnaire survey consisted of 6 questions about the satisfaction of current expert system and 5 questions about supplementation and addition. As a result of the questionnaire analysis, the question about the necessity of the expert diagnosis system and the benefit of clinical practice using the expert diagnosis system indicated high positive response rate. On the other hand, there is a difference in the results of the dialysis before and after using the expert diagnosis system. So, It seems that the reliability of the results of the expert diagnosis system is still low. The results of the questionnaire indicate that the need for expert diagnosis system is beneficial, but it is necessary to supplement the current system in order to improve clinical utility in the future.

• 연구논문집
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• s.34
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• pp.47-58
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• 2004

Power plants rely on air operated valves for the proper operation of many plant system. Many significant problems arise in vital systems of power plants due to air operated valve failures. This paper deals with the diagnosis technique and data acquisition method of an AOV actuator peformance. We constructed AOV diagnosis system and performed some tests to find out whether an AOV actuator was properly designed.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.