• 대한예방의학회:학술대회논문집
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• 대한예방의학회 1994년도 교수 연수회(역학)
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• pp.154-157
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• 1994

The evaluation of diagnostic tests attempts to obtain one or more statistical parameters which can indicate the intrinsic diagnostic utility of a test. Sensitivity. specificity and predictive value are not appropriate for this use. The likelihood ratio has been proposed as a useful measure when using a test to diagnose one of two disease states (e.g. disease present or absent). In this paper, we generalize the likelihood ratio concept to a situation in which the goal is to diagnose one of several non-overlapping disease states. A formula is derived to determine the post-test probability of a specific disease state. The post-test odds are shown to be related to the pre-test odds of a disease and to the usual likelihood ratios derived from considering the diagnosis between the target diagnosis and each alternate in turn. Hence, likelihood ratios derived from comparing pairs of diseases can be used to determine test utility in a multiple disease diagnostic situation.

• 한국통신학회논문지
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• 제15권7호
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• pp.581-601
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• 1990

회전기기 이상진단을 해석하는 방법으로 최근까지 여러가지 방법이 제시되고 있다. 본 논문에서는 고차 스펙트럼의 일종인 Bispectrum해석법을 이용하여, 회전기기 요소중의 하나인 베어링의 결함을 진단하는 기법으로서 그 타당성을 검증하고 Bispectrum 해석법의 물리적인 의미를 파악하고자 컴퓨터 시뮬레이션을 행하였다. 컴퓨터 시뮬레이션과 실제의 베어링 결함신호에 대한 결과를 고찰하였으며, 종래의 Power Spectrum보다 베어링 이상진단에 유효함을 알 수 있었다.

• 지능정보연구
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• 제3권2호
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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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 추계학술대회 논문집
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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 SF6, 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.

• Journal of Power Electronics
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• 제15권1호
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• pp.288-297
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• 2015

This paper presents a novel approach to diagnose interturn insulation faults in three-phase transformers that operate at different loading conditions. This approach is based on the loci of instantaneous symmetrical components and requires the measurement of three input primary winding currents and voltages to diagnose faults in the transformer. The effect of unbalance supply conditions, load variations, constructional imbalance, and measurement errors when this methodology is used is also investigated. Wing size or length determines the loading on the transformer. Wing travel and area determine the degree of severity of fault. Experimental results are presented for a 400/200 V, 7.5 kVA transformer to validate this method.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1997년도 추계학술대회 논문집
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• pp.302-305
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• 1997

Treeing due to partial discharge(PD) is one of the main causes of breakdown of the insulating materials and reduction of tile insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation. using statically operator such as the center of gravity (G). the gradient of the discharge distribution(C), we have analyzed far tole prediction of life which we can be obtained the time, occurred of many pulse of small discharge amplitude.

• 전기학회논문지
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• 제65권7호
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• pp.1186-1192
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• 2016

This paper proposes techniques to diagnose short-circuit faults of both the diodes and power FET in switching mode power supply (SMPS) by using a simple analog tester. The diodes in full-bridge rectifier, power FET, switching transformer, and some sensors are modelled with resistor. The total resistance value measured at the input terminal of a SMPS is analyzed when the short-circuit faults of diodes in a full bridge rectifier or power FET are occurred. The short-circuit faults of one or two diodes in a full bridge rectifier, power FET, and both the diodes in a full bridge rectifier and power FET can be detected by a range of total resistance, which is measured by the analog tester. Through experiments, the theoretical analysis for total resistance under short-circuit faults can be verified.

• 한국의사학회지
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• 제25권2호
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• pp.105-112
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• 2012

The purpose of this paper is to suggest diagnosis support system for diseases of mind so that users can make effective decisions without professional knowledge by developing efficient knowledge system and utilizing ontology with which questions and logic inference are possible to diagnose diseases of mind. Furthermore, this diagnosis support system could be applied to supplement previous diagnosis method which depends on experiences by activating the diagnose of mind diseases thru ontology and determining state of mind effectively without technical knowledge. As a result of this experiment, diagnosis support system for diseases of mind was found to be accordance with the result of consulting instructions and show additional relevance thru utility extension.

• Journal of Interdisciplinary Genomics
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• 제3권1호
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• pp.1-6
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• 2021

Maturity-onset diabetes of the young (MODY) is characterized by a heterogeneous group of monogenic diabetes. MODY has autosomal dominant inheritance, a primary defect in pancreatic β-cell, and an early onset. Discriminating MODY from type 1 or type 2 diabetes is often challenging at first. To date, 14 different disease causing mutations have been identified in MODY patients worldwide. Targeted DNA sequencing is the gold standard to diagnose MODY and their asymptomatic relatives. Next-generation sequencing may help successfully to diagnose MODY patients and identify new MODY genes. In this review, the current perspectives on diagnosis and treatment of MODY and discrepancy in the disease-causing mutations between the Asian and Caucasian patients with MODY are summarized.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.