• Journal of Sasang Constitutional Medicine
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• v.21 no.2
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• pp.107-114
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• 2009

1. Objectives : We analyzed the case reports published by the journal of Sasang Constitutional Medicine, to study Sasang Constitution diagnosis and characteristic of clinical practice. 2. Methods : It has been examined 138 case reports of Sasang Constitution society published from 1994 to 2008. We analyzed. patient's number, publication year, distribution of Sasang Constitution, title's characteristics and bases of constitution diagnosis. 3. Results and conclusions : 1) In the analysis of patient's number, 1 case report was almost part of the case reports, and the case reports ware many published after 2001. 2) In the analysis distribution of Sasang Constitution, Soyangin was the most Sasang Constitution, and Tae-umin was the next one and Soeumin, Taeyangin followed them. 3) In the analysis of the journal's title, Sasang Constitution, disease and symptom of Sasang Constitution were more important than prescription recently. 4) In the analysis of the journal's purpose, unique disease and response of treatment were almost part of the case reports. 5) In the analysis of diagnosis bases, subjective bases were more important than objective bases and Sasang Constitutional drug response. therefore we should do researches in objective bases and drug response in the future.

• Proceedings of the KIEE Conference
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• 2005.05b
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• pp.4-6
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• 2005

This paper deals with the technical map of diagnosis for power cable systems. For this purpose, failure status, failure and degradation causes, and newly suggested diagnostic techniques were investigated and analyzed. From the analysis, the technical map of diagnosis for power cable systems was proposed.

• The Mathematical Education
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• v.38 no.2
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• pp.145-157
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• 1999

Ability diagnosis is similar to medical diagnosis from a number of perspectives. However, a medical diagnosis is carried out by a direct observation through medical apparatuses, while an ability diagnosis is made by an indirect observation in the from of testing. In this respect, ability diagnosis is more difficult than medical diagnosis. Confined to middle school 1st year Mathematics, we collected survey data in 1996 from monthly tests. The data consist of student responses to diagnosis results on their abilities and of the effects of catch-up guidances for individual students which are provided based on their ability diagnosis outcomes. We analyzed the data and summarized the result in the paper. One of the main results is that the ability diagnosis as used in the paper has a very positive effect on catch-up study. But it is important to note that the effects vary across the ability groups, the effect appearing weaker in the lower ability group than in the higher ability group. This calls our attention to the need that the ability diagnosis and guidance for the catch-up study be differentiated among ability groups.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.1
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• pp.79-87
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• 1999

The most part of the faculty drop a trouble and damage of machine equipment even if whatever cause they break out take place at local and trifling place and the factor dominating their trouble is due to wear debris occurred in the lubricated machine moving surface. This study has been car-ried out to identify morphology of wear debris on the lubricated machine moving system by means of computer image analysis. Namely the wear debris contained in lubricating oil extracted from movable machine equipment will be filtered through membrane filter(void diameter 0.45${\mu}m$) and will be analyzed with its data information such as 50% volume diameter aspect roundness and reflectivity. Morphological characteristic of wear debris is easily distinguished by four shape parameters it is necessary to divide small class of every 100 wear debris in total wear particles in order to distinguish morphological characteristic of wear debris more easily by computer image analysis. We are sure that operation condition diagnosis of the lubricated machine moving surfaces is possible by computer image analysis.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.103-111
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• 2008

Recently, skin diagnosis has been suggested as a promising tool for discrimination of Sasang Constitution, reported by examining the skin characteristics such as thickness, stiffness, slip, and skin textures like wrinkles and furrows. However, the works had a limitation in that clinical decision on the skin characteristics was made by relying upon oriental medicine doctors' subjective sense of touch. In order to objectify the skin diagnosis and claim its efficacy on the discrimination of the Sasang Constitutions, it is necessary to demonstrate its discrimination capability by providing numerical values in terms of physical quantities obtained from measurements using today's sensors and equipment technologies, which motivated this work as a priliminary step towards objectification of skin diagnosis. The skin characteristics focused in this work is the slip property of the back-hand skin that has been exploited using the dynamic friction measurement system. First, curved geometric effects of the back-hand skin on the measured lateral/vertical force signals were estimated using the artificially designed silicon coated structures, which led to a suggestion on a quality controlled experimental design based upon a empirical analysis model. Second, the experimental design thus suggested has been applied to the measurement of dynamic friction coefficients for two healthy male subjects of Taeumin (TE) and Soyangin (SY), respectively. The result shows that the dynamic friction coefficient is less for the SY subject than for the TE subject around the area of the skin used for diagnosis by the oriental medicine doctor, implying the TE subject's skin is more slippery than the SE subject's that is consistent with the oriental medicine doctor's diagnosis. Hopefully, this work can provide guidelines for obtaining quality data in friction measurement to be collected for discussion on the efficacy of the skin diagnosis and its objectification through statistical analysis.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.2 no.1
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• pp.51-56
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• 2009

This study was tried to analyze the diagnosis of patients with cerebral infarction by sequence modeling that was one of data mining analysis method and find out previous disease or complication of patients with cerebral infarction. Mass data that the diagnosis code of cerebral infarction was 163 in 2000 to 2007 were extracted from A hospital's database and then the data mart was constructed for analysis. Total 2,267 patients illnesses were diagnosed as cerebral infarction and 32,692 cases related diagnosis were extracted. Sequence modeling in Clementine 12.0 program was used to analyze diagnosis of patients with cerebral infarction and 8 meaningful rules were found in this paper. This result could be used as a basic data to make secondary cerebral infarction prevention program and to prevent complication of cerebral infarction.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.17
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• pp.7187-7193
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• 2014

Background: The aim of this meta-analysis was to compare sensitivities and specificities of fine needle aspiration (FNA) and core needle biopsy (CNB) in the diagnosis of thyroid cancer. Materials and Methods: Articles were screened in Medline, the Cochrane Library, EMBASE and Google Scholar, and subsequently included and excluded based on the patient/problem-intervention-comparison-outcome (PICO) principle. Primary outcome was defined in terms of diagnostic values (sensitivity and specificity) of FNA and CNB for thyroid cancer. Secondary outcome was defined as the accuracy of diagnosis. Compiled FNA and CNB results from the final studies selected as appropriate for meta-analysis were compared with cases for which final pathology diagnoses were available. Statistical analyses were performed for FNA and CNB for all of the selected studies together, and for individual studies using the leave-one-out approach. Results: Article selection and screening yielded five studies for meta-analysis, two of which were prospective and the other three retrospective, for a total of 1,264 patients. Pooled diagnostic sensitivities of FNA and CNB methods were 0.68 and 0.83, respectively, with specificities of 0.93 and 0.94. The areas under the summary ROC curves were 0.905 (${\pm}0.030$) for FNA and 0.745 (${\pm}0.095$) for CNB, with no significant difference between the two. No one study had greater influence than any other on the pooled estimates for diagnostic sensitivity and specificity. Conclusions: FNA and CNB do not differ significantly in sensitivity and specificity for diagnosis of thyroid cancer.