• Korean Journal of Oriental Medicine
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• v.18 no.1
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• pp.59-74
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• 2012

Objective : The aim of this study is to investigate current status of Sasang constitutional medicine researches and to provide source in setting a development strategy and making a policy on the Sasang constitutional medicine. Method : This study analyzed trends in research as represented in the 142 articles of Korea Institute of Oriental Medicine in the field of Sasang constitution medicine from 2007 to 2010. Result : The main themes of studies were Sasang constitution clinical study (68, 47%) and developing instruments for measuring diagnosis (48, 33%), especially pulse diagnosis (25, 17%). Genetic researches and trends studies on Sasang constitutional medicine were respectively 17 (12%) and 8 (5%). As a result, Korea Institute of Oriental Medicine established Sasang clinical information bank, and discovered clinical character of Sasang constitution to develope various diagnosis tools include constitution survey form and pulse diagnosis instrument. Genetic researches also discover constitution related genes such as FTO and MC4R, and explore association between heredity and Sasang constitution. Conclusion : The above mentioned achievements of Korea Institute of Oriental Medicine were limited to diagnosis process, unquestionably studies for reproducibility and authenticity of constitution diagnosis were important mission of Sasang constitutional medicine. However, to develop the advance of Sasang constitutional medicine, not only constitution diagnosis but also constitutional treatment studies should be conducted.

• Journal of Platform Technology
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• v.6 no.1
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• pp.24-30
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• 2018

LMT consisting of 10 landscape elements is one of psychology diagnosis method for inspecting the psychological state of client. In this paper, we make knowledge base accumulating knowledge about LMT landscape elements. By using this knowledge base, we also propose and implement LMT diagnosis assistance system generating LMT inspection report being a result of diagnosis. This proposed system generates diagnosis report based on LMT knowledge base which accumulate knowledge from plenty of reference and research project, that's why we improve the objectivity of diagnosis results. And new knowledge about LMT can be accumulated in knowledge base, so the system proposed in this paper can be extensible continuously. The implementation of system proposed in this paper offers web-based services. To show effectiveness of the system, we diagnose the actual case by using the system, and show the diagnosis result.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.62-69
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• 2009

For the fault diagnosis of power transformer, we develop a diagnosis algorithm based on support vector machine. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, and identification of fault. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, KEPCO based decision rule is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.

• Journal of Acupuncture Research
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• v.28 no.3
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• pp.85-100
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• 2011

Objectives : The purpose of this thesis is to help the preparation of oriental medicine clinical guidelines for drawing up the standards of oriental medicine demonstration and diagnosis classification about the neck pain. Methods : Statistical analysis about Gyeonghangtong(頸項痛), Nakchim(落枕), Sagyeong(斜頸), Hanggang (項强) classified experts' opinions about neck pain patients by Delphi method is conducted by using oriental medicine diagnosis questionnaire. The result was classified by using linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), diagonal quadratic discriminant analysis (DQDA), K-nearest neighbor classification (KNN), classification and regression trees (CART), support vector machines (SVM). Results : The results are summarized as follows. 1. The result analyzed by using LDA has a hit rate of 84.47% in comparison with the original diagnosis. 2. High hit rate was shown when the test for three categories such as Gyeonghangtong and Hanggang category, Sagyeong caterogy and Nakchim caterogy was conducted. 3. The result analyzed by using DLDA has a hit rate of 58.25% in comparison with the original diagnosis. The result analyzed by using DQDA has a accuracy of 57.28% in comparison with the original diagnosis. 4. The result analyzed by using KNN has a hit rate of 69.90% in comparison with the original diagnosis. 5. The result analyzed by using CART has a hit rate of 69.60% in comparison with the original diagnosis. There was a hit rate of 70.87% When the test of selected 8 significant questions based on analysis of variance was performed. 6. The result analyzed by using SVM has a hit rate of 80.58% in comparison with the original diagnosis. Conclusions : Statistical analysis using oriental medicine diagnosis questionnaire on neck pain generally turned out to have a significant result.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.202-208
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• 2006

The goal of this study is to develop knowledge representation method for the construction and evaluation of ontology for diagnosis in oriental medicine. To develop the expert system for decision making on diagnosis and treatment, the systematic and structural knowledge which can be processible in EMR(Electronic Medical Record) must be precedent, and the Computational Process which control the system as well. This study set up an ontology as a trial model to represent the oriental medical knowledge into the machine processible one. Protege 2.1 has been used to build the ontology, and the serialization format of our ontology is the XML document based on OWL. The components of oriental medical diagnosis was arranged with the combination of symptoms which belong to the certain symptom patterns. Then natural language which expresses the oriental medical diagnosis components were converted into the logical sentence, and individual characteristic symptoms into each values of specific properties. In addition to the study, the diagnosis software for oriental medicine was developed and it used the ontology which we developed. Sequently, we tested the software to confirm the appropriateness of ontology. The result of the test shows that diagnostic questions are automatically formulated according to the diagnosis components of this ontology and that as such diagnostic results are induced. Therefore, the ontology system in this study will be efficient to develop the diagnosis program and useful as a tool for doctors to make decision. But, it is not recommendable to apply the system to the clinical environment until the clear diagnosis standards are introduced, and the more reliable diagnosis program can be developed based on the more appropriate ontology mentioned above.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.9
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• pp.414-420
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• 2005

A method for partial discharge diagnosis based on UHF narrow band type for GIS has been developed and calibrated. In generally, PD cannot be directly measured under on-line condition, but we can indirectly measure the electromagnetic wave made by PD using the high-frequency antenna. Compared with VHF band, electromagnetic waves of UHF band have a low influence for external noise in high-voltage substation. Therefore, we can detect the real abnormality with several pC in GIS using UHF narrow-band type method. For the case of no internal VHF sensor for GIS of the domestic substation, it has applied to use the external UHF sensor attached in spacer in GIS of existing substation. In this paper, we firstly described the technique of partial discharge measurement using frequency analysis and phase analysis in UHF band. Secondly, we presented the results of sensitivity test, the relationship of dBm-pC and diagnosis result of the cause of PD source by phase analysis. And then, we report the diagnosis result of partial discharge on the real GIS in domestic substation. These results make above method applicable for measurement of quantity and cause of PD for real operation GIS in high-voltage substation.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.12
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• pp.1447-1457
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• 1999

본 논문에서는 일반적인 네트워크에서 적응력 있는(adaptive) 분산형 시스템 레벨 결함 진단을 위한 분할 기법을 제안한다. 적응력 있는 분산형 시스템 레벨 결함 진단 기법에서는 시스템의 형상이 변경될 때마다 시험 할당 알고리즘이 수행되므로 적응력 없는 결함 진단 기법에 비하여 결함 감지를 위한 시험의 갯수를 줄일 수 있다. 기존의 시험 할당 알고리즘들은 전체 시스템을 대상으로 하는 비분할(non-partitioning) 방식을 이용하였는데, 이 기법은 불필요한 과다한 메시지를 생성한다. 본 논문에서는 전체 시스템을 이중 연결 요소(biconnected component) 단위로 분할한 후, 시험 할당은 각 이중 연결 요소 내에서 수행한다. 이중 연결 요소의 관절점(articulation point)의 특성을 이용하여 각 시험 할당에 필요한 노드의 수를 줄임으로서, 비분할 기법들에 비해 초기 시험 할당에 필요한 메시지의 수를 감소시켰다. 또한 결함이 발생한 경우나 복구가 완료된 경우의 시험 재 할당은 직접 영향을 받는 이중 연결 요소내로 국지화(localize) 시켰다. 본 논문의 시스템 레벨 결함 진단 기법의 정확성을 증명하였으며, 기존 비분할 방식의 시스템 레벨 결함 진단 기법과의 성능 분석을 수행하였다.Abstract We propose an adaptive distributed system-level diagnosis using partitioning method in arbitrary network topologies. In an adaptive distributed system-level diagnosis, testing assignment algorithm is performed whenever the system configuration is changed to reduce the number of tests in the system. Existing testing assignment algorithms adopt a non-partitioning approach covering the whole system, so they incur unnecessary extra message traffic and time. In our method, the whole system is partitioned into biconnected components, and testing assignment is performed within each biconnected component. By exploiting the property of an articulation point of a biconnected component, initial testing assignment of our method performs better than non-partitioning approach by reducing the number of nodes involved in testing assignment. It also localizes the testing reassignment caused by system reconfiguration within the related biconnected components. We show that our system-level diagnosis method is correct and analyze the performance of our method compared with the previous non-partitioning ones.

• Journal of IKEEE
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• v.24 no.3
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• pp.883-889
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• 2020

In this paper, we propose a method to use PV(Park's Vector) pattern for inductive motor stator fault diagnosis using CNN(Convolution Neural Network). The conventional CNN based fault diagnosis method was performed by imaging three-phase currents, but this method was troublesome to perform normalization by artificially setting the starting point and phase of current. However, when using PV pattern, the problem of normalization could be solved because the 3-phase current shows a certain circular pattern. In addition, the proposed method is proved to be superior in the accuracy of CNN by 18.18[%] compared to the previous current data image due to the autonomic normalization.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.763-765
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• 1999

Due to the improvement of PC Performance and Windows-based OS, PC-based control system is gradually taking over industrial control in the industry site. In this paper, we suggest the method to configure PC-based fault diagnosis system of I/O cards and the method to generate test signals to find out the short conditions of adjacent channels of cables which connect I/O cards with terminal blocks.