• Journal of the Korean Society of Costume
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• v.13
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• pp.123-134
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• 1989

The purpose of this study was to identify the predictable variables of criteria on clothing selection. Relationships among criteria on clothing selection, psychological variable, lifestyle variable, and demographic variable were tested by Pearsons' correlation coefficients and One-way ANOVA. The predictors of criteria on clothing selection were identified by Regression. The consumers were classified into several benefit-segments by criteria on clothing selection, and then, the character of each segment were identified by Multiple Discriminant Analysis. Data was obtained from 593 women living in Pusan by self-administered questionnaires. The results of the study were as follows; 1. Relationship between criteria on clothing selection and relative variables. 1) The important variables to criteria on clothing selection were "down-to-earth-sophisticated", "traditional-morden", "conventional-different", "conscientious-expendient", need for exhibitionism, need for sex, fashion / appearance. 2) The important factor of clothing selection criteria was comfort and it has significant difference among ages. 3) The higher of social-economic status have the more appearance-oriented selection. 2. Predictors of criteria on clothing selection. There were several important predictors of criteria on clothing selection like lifestyle, need, and self-image. Especially, fashion / appearance in lifestyle variable was very important. 3. Segmentation by the criteria on clothing selection. There are four groups Classified by the criteria on clothing selection, that is practical-oriented group, appearance-oriented group, practical and appearance-oriented group, and indifference group. The significant discriminative variables were Fashion / appearance factor, need for exhibitionism, and need for sex. The result of this study can be used for a enterprise to analysis the consumer and to build the strategy of advertisement clothing.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.19-25
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• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• ETRI Journal
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• v.44 no.1
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• pp.117-124
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• 2022

In this paper, the performance of precoding-aided differential spatial modulation (PDSM) systems with optimal transmit antenna subset (TAS) selection is examined analytically. The average bit error rate (ABER) performance of the optimal TAS selection-based PDSM systems using a zero-forcing (ZF) precoder is evaluated using theoretical upper bound and Monte Carlo simulations. Simulation results validate the analysis and demonstrate a performance penalty < 2.6 dB compared with precoding-aided spatial modulation (PSM) with optimal TAS selection. The performance analysis reveals a transmit diversity gain of (N_T-N_R+1) for the ZF-based PDSM (ZF-PDSM) systems that employ TAS selection with N_T transmit antennas, N_S selected transmit antennas, and N_R receive antennas. It is also shown that reducing the number of activated transmit antennas via optimal TAS selection in the ZF-PDSM systems degrades ABER performance. In addition, the impacts of channel estimation errors on the performance of the ZF-PDSM system with TAS selection are evaluated, and the performance of this system is compared with that of ZF-based PSM with TAS selection.

• ETRI Journal
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• v.38 no.4
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• pp.606-611
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• 2016

This paper presents antenna selection schemes for recently proposed quadrature spatial modulation (QSM) systems. The antenna selection strategy is based on Euclidean distance optimized antenna selection (EDAS). The symbol error rate (SER) performance of these schemes is compared with that of the corresponding algorithm associated with spatial modulation (SM) systems. It is shown through simulations that QSM systems using EDAS offer significant improvement in terms of SER performance over SM systems with EDAS. Their SER performance gains are seen to be about 2 dB-4 dB in $E_s/N_0$ values.

• Journal of Korean Library and Information Science Society
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• v.30 no.4
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• pp.1-26
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• 1999

This study purposes to identify how the 'selection' of library materials shold be changed in the electronic environment to improve library services fundamentally. The study scrutinized the essence of selection, the relationship between selection and collection management, and the results of various studies to define the selection. The study implies: 1) the selection must be changed to the function providing users with assessment information of library materials, which helps the users access to entire scholarly information. 2) the assessment information could be input from different libraries and used by all users connected to the library network.

• Honam Mathematical Journal
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• v.35 no.4
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• pp.757-764
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• 2013

In this paper, we give sufficient conditions for a map with nonconvex values to have a continuous selection and the selection extension property in LC-metric spaces under the one-point extension property. And we apply it to weakly lower semicontinuous maps and generalize previous results. We also get a continuous selection theorem for almost lower semicontinuous maps with closed sub-admissible values in $\mathbb{R}$-trees.