• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.264-267
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• 1998

We are dealing with the preliminary diagnosis from the information of headache interview chart. We quantify the qualitative information based on the interview chart by dual scaling. Prototype of fuzzy diagnostic sets and the neural linear regression methods are established with these quantified data, These new methods can be used to classify new patient's tone of diseases with certain degrees of belief and its concerned symptoms. We call these procedures as neural Fuzzy Differential Diagnosis of Headache (NFDDH-1). Also we investigate three measures to medical diagnosis, where relations between symptoms and diseases are described by intutionistic fuzzy set (IFS) data. Two measures are described by nin-max and max-min IFS operators, respectively. Another measure is the similarity degree, i.e., IFS distance between patient's symptoms and prototypes of diseases. We consider some reasonable criteria for three measures in order to determine the label of headache, We will establish hree measures in NFDDH-2 and combine two packages as NFDDH

• The Journal of the Society of Korean Medicine Diagnostics
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• v.10 no.1
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• pp.141-152
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• 2006

Background and Objectives: The diagnosis of flood stasis pattern is one of the most important methods in oriental medicine. The aim of this study is to Develop clinically useful questionaire of Blood stasis(瘀血) and quantitative analysis. Methods: The questionnaire including symptoms and signs for diagnosis of Blood stasis pattern is studied by delphi method and statistical analysis. Result: By the delphi method and statistical analysis, 14 Items of questionnaries are choosen. Conclusion: Further research is necessary for improvement reliabilities and validities of the questionnaires of Blood stasis pattern.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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• pp.53-57
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• 2001

This paper presents the new methods of fault diagnosis through multiple alarm processing of protective relays and circuit breakers in power systems using probabilistic neural networks. In this paper, fault section detection neural network (FSDNN) for fault diagnosis is designed using the alarm information of relays or circuit breakers. In contrast to conventional methods, the proposed FSDNN determines the fault section directly and fast. To show the possibility of the proposed method, it is simulated through simulation panel for Sinyangsan substation system in KEPCO (Korea Electric Power Corporation) and the case studies show the effectiveness of the probabilistic neural network mehtod for the fault diagnosis.

• Journal of Sasang Constitutional Medicine
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• v.27 no.1
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• pp.110-124
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• 2015

Objectives This study was purposed to develop the clinical practice guideline for examination of Sasangin disease and diagnosis for Sasang Constitution. Methods "Donguisusebowon" and many articles were reviewed and examined for developing clinical practice guidelines. And "Guideline for Educating Public Health Korean Medical Doctors on Diagnosis of Sasang Constitution" was basically used to develop clinical guidelines. Results & Conclusions By researching and discussing the examination of Sasangin disease and diagnosis for Sasang Constitution, we make the principle of the clinical practice guideline, including methods using body shape, facial shape, vocal characteristic and personality etc.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.870-881
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• 2020

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (kNN) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.

• Korean Journal of Veterinary Research
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• v.43 no.3
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• pp.471-476
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• 2003

Malignant catarrhal fever (MCF) is a systemic disease of ruminants caused by ovine herpesvirus 2 (OvHV-2). OvHV-2 is a gamma herpesvirus, which induces frequent latent infection and often difficult to detect its antigens and even specific nucleic acids because of its low viral copies in the infected tissues. Histopathology, serology and polymerase chain reaction (PCR) were compared for the diagnosis of MCF using 10 bison infected with OvHV-2. Histopathological diagnosis was performed using the criteria which was based upon the pathognomic lesions. Serological diagnosis was conducted using its serum with competitive ELISA for the detection of antibodies of OvHV-2. Also, the nest PCR was performed with peripheral blood leukocytes for the detection of OvHV-2-specific DNAs. Primers 556 and 775 were used for the primary amplification, and primers 556 and 555 were used for the secondary amplification. As the results, positive cases were 6 by histopahology, 9 by serology and 10 by PCR. As comparing with other diagnostic methods, PCR was found to be more sensitive than histopathology and serology. The recent development of molecular diagnostic assays has provided powerful tools for investigating how viruses survive in nature. Development of PCR specific for viruses has dramatically improved the accuracy of diagnosis of viruses in clinically infected animals. Furthermore, amplification of viral genomic material by nest PCR represents the most sensitive method for the detection of viruses and might be detected successfully even though very low viral DNA copies. So, it could be used as the first choice for the detection of viral DNAs with low copies such as the status of latent infection. However, it has also some limitation of application like as false negative results by PCR inhibitors and false positive results by contamination. The results of this study suggest that the use of molecular biological methods like PCR may increase the accuracy for the diagnosis of infectious diseases. However, in diagnostic laboratory, it is recommended that PCR assay must be conducted with other diagnostic methods for more reliable diagnosis.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.6
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• pp.1471-1474
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• 2005

Sasang constitution medicine is to do different treatment accordining to sasang constitution. Therefore, the constitution diagnosis in the Sasang constitution medicine is very important thing. The Process of Sasang constitution diagnosis Is difficult thing, because of consuming much time, making every effort. It is apt to be subjective tendency. So it need to make objective method. The QSCC II (Questionnaire of Sasang Constitution Classification II ) have several problems- can't do diagnosis of Taeyangin, the accuracy rate of Sasang constitution diagnosis is not high (probably 60%), and so on. So, we need the new methods for the Sasang constitution Diagnosis. We will modify the problems of QSCC II. The First is the problems of the study execution process, not-multicenter study, a low data, the absent of Taeyangin cases. So, we have to do the multicenter study. The Second is the problems of a query and the method of statistics analysis. We will modify the problems of self-report Questionnaire. That is the problems of self-report Questionnaire, the lack of objective estimation( body type, personal appearance, etc), the absent of the estimation on typical or non-typical type constitution. We modified the problems of QSCC II. Therefore we made the new self-report Questionnaire for patients. We modified the problems of self-report Questionnaire. Therefore we made the new Constituion diagnosis Questionnaire for doctors. We develop the Questionnaire of two ways for the Sasang constitution Diagnosis. The one is the new self-report Questionnaire for patients. The other is the new Constitution diagnosis Questionnaire for doctors. We have to melt down the Questionnaire of two ways for the Sasang constitution Diagnosis.

• Journal of Korean Medical classics
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• v.22 no.1
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• pp.71-85
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• 2009

For having good therapeutic value, putting the ideal of diagnosis and treatment based on overall analysis of symptoms and signs[辨證施治] into practice is very important, so the Eastern Medicine's peculiar diagnosis without interference by the western medical diagnosis is very important. The peculiar method of Eastern Medicine's diagnosis is four methods of diagnosis(四診法), a joint term for inspection, auscultation and olfaction, interrogation, pulse feeling and palpation. In the process of interrogation[問診], from analyzing the perspiration phase, doctor can get various physical information. Especially perspiration from particular region can be a clue for founding focus or cause of disease. This thesis divide the phase of perspiration into eight bodily region, perspiration from all body, head, face, back, chest and armpit, stomach, pubic region and lower part, hands and feet, and inquire the possible causes and principles of these perspirations. In conclusion, the regional perspiration can be a clue point out the origin of fever and condition of Gi(氣) flow. Perspiration from head, back, chest and armpit, hands and feet means that certain fever cannot extend to the outside of body, and at the same time, means the Gi(氣) flow of outside is being intercepted. So the perspirations from that region become an object of medical treatment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.