• Korean Journal of Acupuncture
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• v.37 no.3
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• pp.203-208
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• 2020

Objectives : Since there are complex associations between diseases/symptoms and acupoints, one-to-one correspondence may not be the proper approach. Pattern identification has been being used as a clinical framework to make treatment decisions by extracting and synthesizing clinical data including patients' signs and symptoms. In this article, we propose two different models explaining the relationships between diseases and acupoints based on the branch treatment [Zhibiaofa] and the root treatment [Zhibenfa]. Methods : We explained the relationships between diseases/symptoms and acupoints from the example data from our previous study on traditional acupuncture point selection patterns for pain control. Diseases include low back pain, migraine, irritable bowel syndrome, osteoarthritis, ankle sprain, carpal tunnel syndrome, and dysmenorrhea, and acupoints included LI4, BL23, BL25, SP6, BL60, TE5, and CV4. Results : The relationships between diseases/symptoms and acupoints can be explained directly based on the branch treatment, and also can be explained indirectly through pattern identification based on the root treatment. Pattern identifications included both meridian-based pattern identification based on the spatial information of diseases and visceral organ-based pattern identification based on the characteristics of diseases. Conclusions : In the East Asian traditional medicine, Korean medicine doctors choose the most appropriate acupoints based either on the diseases/symptoms (i.e., branch treatment) or on the results of pattern identifications (i.e., root treatment). It is necessary to understand the two different approaches to choose specific acupoints for the targeted diseases.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.645-651
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• 2002

we propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVM were substantially reduced.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.464-473
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• 2009

The purpose of this study was to analyze women's selection pattern on kindergarten/child care centers and private tutoring for young children. For this purpose, 1,810 women whose young children's age was between 2 and 6 were selected from KLoWF data. After categorizing the education centers as daycare center, kindergarten, and private institute, it was investigated the women's characteristics related to their decision making for selection of the centers. The results of survey data analyses showed that younger children's mothers preferred to send their children to daycare centers, older children's mothers did to kindergarten. In terms of their dual selection on education centers, the frequency of 6-old-year children attending kindergarten and private institutes was very high. As the children's age was increasing, the frequency of women's multiple selection on daycare center, kindergarten and private institute was also increasing. In addition, regardless of the women's job pattern and existence, they preferred to send children to daycare centers. With these findings, limitations and suggestions for the future studies were also discussed.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.10 no.1
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• pp.54-63
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• 2006

Objectives: In this study a pattern questionnaire for damum patterns was developed by means of literature review and statistical analysis. The individual approach of korean medicine is based on the concept of pattern identification, the objectivity and validity issues of which hold important meaning in the pratice and research. Methods: Review of literatures led to the selection of 22 items describing Dam or Um pattern. A preliminary questinnaire consisted of these items that may be scored with positive score at zero to seven. Results: Damum is all over the body syndromes. That is abnormal body conditions. Damun arouse body pain, breath disorder, digestion disorder, nerves disorder, excretion disorder. Conclusions: Review of literatures led to selection of 29 items describing Damun. A questionnaire is in preparation.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.106-110
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• 2000

The researches classifying the artificial flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including user defined function is developed and the total procedure is made up the digital signal processing, feature extraction, feature selection, classfier design. Specially it is composed with and discussed using the ststistical classfier such as the linear discriminant function classfier, the empirical Bayesian classfier.

• 한국정보디스플레이학회:학술대회논문집
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• 2003.07a
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• pp.730-733
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• 2003

Dual coding technique is one of the popular techniques to reduce the dynamic false contours on PDP. Subfield pattern is a key factor affecting the performance of dual coding technique. In this paper, an optimum subfield selection method based on genetic algorithm is proposed. Two types of string structures are defined to account for all the possible configurations of the dual coding subfield patterns. Genetic operators are proposed for optimization of dual coding subfield pattern. Quantitative measures to describe degrees of dynamic false contours and checkerboard patterns are defined. Experimental results indicate that dual coding subfield pattern that is determined by proposed method reduces dynamic false contours and checkerboard patterns.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Genomics & Informatics
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• v.20 no.4
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• pp.39.1-39.9
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• 2022

Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.