• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.581-601
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• 1990

A variety of method to diagnose the fault of rotational mechine is suggested, the latest data. This paper uses Bispectrum which is sort of high order spectrum, diagnose the ball bearing of the rotational machine element. Apprehending the physical meaning of Bispectrum, computer simulation is performed. The result from computer simulation and the signal of the faulted ball bearing is studied from all it's aspects. It is found that the Bispectrum is more effective than the conventional Power Spectrum.

• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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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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• v.15 no.1
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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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.

• The Journal of Korean Medical History
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• v.25 no.2
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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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• v.3 no.1
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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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• v.17 no.2
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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.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.