• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.539-547
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• 2012

Increasing the mode transfer volume between public transportation modes has known to be necessary for efficiency improvement of public transportation system operation and it is also found to be important to have relevant transfer point selection with reflection of current travel pattern. This study is in regards to providing a selection guideline for the location of transfer point between public transport modes. This case study has been carried out for Daegu Metropolitan City especially for public transportation users behaviour by analysis of daily usage of transportation card to identify the transfer travel pattern. A cluster analysis was applied to categorize the pattern of transfer stop which induces many users and a discriminant analysis also utilized for grouping the stops by number of transfer trip. This research produces the estimation result of transfer volume for urban railway system no.3 in Daegu City which is currently under construction. In addition, the locations of transfer center has also been proposed.

• ETRI Journal
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• v.36 no.1
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• pp.99-105
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• 2014

This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.205-213
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• 2004

In this paper, we proposed a wavelet phase-only filter modulation synthetic discriminant function joint transform correlator(WPOfSDF-JTC) for scale invariant pattern recognition, and an improved algorithm to reduce the filter synthesis time. Computer simulation showed that the proposed filter has better SNR than CWMF if input image has random noise and the improved synthesis algorithm can reduce the iteration time. We used frequency selective JTC to solve the problem of the optical alignment and eliminate the autocorrelation and crosscorrelation between each input image.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.589-595
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• 2010

In discriminant analysis, we consider an intraclass correlation pattern by principal component analysis. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider two procedures, i.e., the test and proportion procedures, for selecting the principal components in classifica-tion. We compare the regular classification method and the proposed two procedures. We consider two methods for estimating error rate, i.e., the leave-one-out method and the bootstrap method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.1-7
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• 2014

This paper presents a novel method for simultaneously and automatically choosing the nonlinear structures of regressors or discriminant functions, as well as the number of terms to include in a rule-based regression model or pattern classifier. Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it; fuzzy sets are used to model the terms. Candidate interconnections (causal combinations) of either a term or its complement are formed, where the connecting word is AND which is modeled using the minimum operation. The data establishes which of the candidate causal combinations survive. A novel theoretical result leads to an exponential speedup in establishing this.

• The Research Journal of the Costume Culture
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• v.5 no.2
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• pp.207-216
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• 1997

The purpose of this study is to find out more rational way to manage clothing. The study is based on a survey of daily clothing practices. 112 female college students who are majoring clothing, textiles, and/or home economics have responded to the questionnaires. With the samples, frequency, factor analysis, discriminant analysis, ANOVA, and Scheffe test are pursued respectively. Main results of the survey analysis could be summarized as follows : 1. Female college students are found to possess 70 units on average. They possess more of casual clothes such as polo·T shirts, casual pants, and casual shirts (from the highest frequency in order). They possess less of formal clothes such as one-piece and two-pieces (from the lowest frequency in oder). 2. It is found that 12 per cent of the possessed clothings are not used at all during the year. The unused rate is higher for the formal suits while it is lower for the casuals. 3. The possession pattern is affected by clothintg life style factors : brand and economic factors for the casuals ; fashion and individuality factors for the formals. 4. The possession pattern is also affected by the purchasing behavior factors, purchasing price among others.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Korean Journal of Food Science and Technology
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• v.49 no.4
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• pp.349-354
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• 2017

The geographical origins of raw ginseng (RG) were discriminated using an electronic tongue. Taste screening, DFA (discriminant function analysis), and CDA (canonical discriminant analysis) were used to statistically analyze the data. The taste profile patterns of umami, bitterness, and sweetness of the Korean RG was different from those of the Chinese RG. The Korean RG was stronger than the Chinese RG regarding the taste of umami. DFA discriminated the geographical origins of 154 samples, with a few overlapping samples, between the Korean and Chinese RG. CDA showed that the accuracy of origin discrimination for the Korean and Chinese RGs were 87.01 and 94.81%, respectively. The final accuracy of origin discrimination was 90.91%. The distance between the centroids of each group was 2.7463. Thus, the electronic tongue analysis can be used to efficiently differentiate the geographical origins of RG.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.4
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• pp.531-539
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• 1996

The research for the classification of the artificial defects in welding parts is performed using the pattern recognition technology of ultrasonic signal. The signal pattern recognition package including the user defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection. The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian classifier are compared and discussed. The pattern recognition technique is applied to the classification of artificial defects such as notchs and a hole. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the artificial defects.