• KEPCO Journal on Electric Power and Energy
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• v.3 no.2
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• pp.119-125
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• 2017

Regarding the implementation of ROMM project (Rehabilitation, Operation, Maintenance & Management), which is one of overseas development projects, it is very important to diagnose the exact current status of aged thermal power plant. However, when people visit the power plant for the purpose of prediagnosis to implement the ROMM project, most target power plants for diagnosis, in general, are under operation. This can be a big interference factor to diagnose the exact current status of power plants. Therefore, in order to solve such interference factor, based on the 30 years of know-how in the field, the present study has developed a regression curve for a simple life time assessment and the calculation of rehabilitation cost that may be used as a reference relatively for the quantitative diagnosis on the status of a relevant power plant even during the operation of the power plant.

• The Journal of Korean Medicine
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• v.22 no.4
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• pp.164-170
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• 2001

In clinic, it's very difficult to diagnose and treat a Taeyangin patient accurately. Taeyangin are rarer than other constitutions in number and there are few references in documents. Therefore, it will be helpful to understand Taeyangin if we accumulate their clinical cases. To that end, I introduce a case study concerning 3 persons. Among patients who have visited the Oriental Hospital of Dongguk University, these 3 persons were classified as Taeyangin and then their symptoms were reduced by Taeyangin treatment. Through reporting the case studies concerning these 3 persons - a male patient aged 14 complaining of feeling oppressed and dyspnea, a female patient aged 39 complaining of dyspepsia, feeling heavy in the head, and fatigue, and a male patient aged 23 complaining of acid regurgitation and nausea, I want to contribute to diagnosis and treatment in Taeyangin.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.251-256
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• 2005

There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault and is composed to fault detection knowledge base and fault diagnosis knowledge base. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.274-277
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• 2001

This paper describes the simulation study. conducted on the propagation characteristics of AE signal. In the case of gas insulation, such as $SF_6$, the equipment is less affected by the environment condition than air insulation, because the component parts of equipment were placed in the enclosure, which is filled with compressed gas. But. when the breakdown in the electric insulation occurs. it takes much time and economical efforts to repair. Therefore it is very important to diagnose the equipment before the accident. And. in general. UHF and AE signal is the most common transducer to diagnose the state of the power equipment, so, in this investigation, we make a experimental apparatus with aluminum plate and transient analysis with ANSYS to observe the propagation characteristics of AE signal. Through the result of the analysis. we can make a further understanding on the propagation characteristics of AE signal, and get the fundamental skills for the GIS diagnosis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1994.11a
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• pp.134-138
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• 1994

According to request of insulating materials for high-voltage, recently we make effort not only to develop diagnostic skills of aging state but also to discover defects in insulating material in the early. Especially, partial discharge has been actively studied as a non-destructive diagnosis technique and very useful method. Because the method of partial discharge measurement has damages less than other conventional diagnosis technique. In this paper, the characteristics of partial discharge was investigated and studied on many samples with voids. In order to adapt as a practical diagnosis technique, it is studied on the characteristics of partial discharge and insulation breakdown in the high voltage. We suggest that partial discharge measurement can be used in order to diagnose defects in high-voltage insulation materials.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.632-637
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• 1996

An expert system for cucumber disease and nutritional disorders(ESCD) was developed using HCLIPS in which several interface programs were added to CLIPS developed in NASA. The knowledges for constructing knowledge base was obtained from interviews with farmers, agrochemical vendors and employees of agrochemical company. The ESCD is able to diagnose eleven diseases and twelve nutritional disorders which farmers, agrochemical vendors and employees of agrochemical company. The ESCD is able to diagnose eleven diseases and twelve nutritional disorders which could frequently found in fields and greenhouses. Validation of ESCD by twenty -four experts proved a possibility of its practical use for diagnosing cucumber diseases and nutritional disorders in the future.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.8
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• pp.675-683
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• 2004

The motor current signature provides an important source of the information for the faults diagnosis of three-phase induction motor. The theoretical principles behind the generation of unique signal characteristics, which are indicative of failure mechanisms, are Presented. The fault detection techniques that can be used to diagnose mechanical Problems, stator and rotor winding failure mechanisms, and air-gap eccentricity are described. A theoretical analysis is presented which predicts the presence of unique signature patterns in the current that are only characteristics of the fault. The predictions are verified by experimental results from a special fault Producing test rig and on-site tests in a steel company. And this study have made new diagnostic algorithm for the operating induction motors with the test results. These developments are including the use of monitoring and analysis of electric current to diagnose mechanical and electrical problems and gave the precise test results automatically.

• Proceedings of the KIEE Conference
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• 1999.07e
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• pp.2126-2129
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• 1999

We have developed the automatic diagnostic system for power cable insulation using DC voltage decay method in this work. Test condition, test method and diagnose criteria of our diagnostic system are adjustable to a class of power cables such as 3.3, 6.6, 11 and 22kV power cables. We have also developed measurement and diagnostic programs for win95 which is suitable for the management of database such as test condition, diagnose results and cable operation data.