• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Journal of the Korean Home Economics Association
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• v.47 no.10
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• pp.1-11
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• 2009

The purpose of this study was to explore differences in style preference, information source, and store selection behaviors of Korean and Chinese wedding dress shoppers. Data obtained from 141 Chinese and 143 Korean females were analyzed through descriptive analysis, t-test, cross-tabulation and factor analysis. Results for Chinese and Korean consumers depended on the marketer-driven information source as well as on the consumer-driven information source. Both groups considered the aesthetics of the wedding dress as the most important criterion for store selection. There were some differences between Chinese and Korean consumers in style preferences and store selection criteria. Chinese consumers liked a wider range of wedding dress styles compared to Korean consumers. Also, Chinese consumers regarded fashionability as more significant whereas Koreans considered economic benefits more importantly when selecting stores for a wedding dress.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.213-221
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• 2019

The study aims to investigate some criteria of supplier selection in the textile and apparel (T&A) sector in Vietnam. Most research on supplier selection criteria for T&A sector was mainly conducted based on the review of literature. Therefore, the purpose of this study is to explore these criteria based on a framework in which an integrated approach of qualitative and quantitative was employed. First, an in-depth interview was used to explore what supplier selection criteria T&A companies were utilized after the literature on supplier selection criteria had been reviewed. Next, a prequestionnaire was built and sent to some practitioners and experts for their revision. Then, a pilot survey of 31 T&A companies with numerous statistical tests was conducted to validate the questionnaire. Finally, an official study of 282 respondents was conducted to determine supplier selection criteria which are best suited for T&A companies through exploratory factor analysis. The findings of the study suggest that there are eight supplier selection criteria including Quality, Cost, Delivery, Service, Capability, Company's image, Relationship, and Sourcing country. Each criterion comprises certain sub-criteria to make the supplier selection criteria set more comprehensive. The findings will be a contribution to the selection process of T&A companies as they can utilize these criteria to select capable suppliers.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.371-381
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• 1993

When the multicollinearity presents in the standard linear regression model, ridge regression might be used to mitigate the effects of collinearity. As the prediction-oriented criterion, the integrated mean sqare error criterion $J_w(k)$ was introduced by Lim, Choi & Park(1980). By noting the equivalent relationship between the $C_k$ criterion and $J_w(k)$ with a special choice of weight function $W(x)$, we propose a more reasonable selection rule of k w.r.t. the $C_k$ criterion than that given in Myers(1986). Next, to find the $\beta(k)$ which behaves reasonably well w.r.t. competing criteria, we adopt the minimax principle in the sense of maximizing the worst relative efficiency of k among competing criteria.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.215-219
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• 2012

This paper applies transmit antenna selection algorithms to spatial-temporal combining-based spatial multiplexing (SM) ultra-wideband (UWB) systems. The employed criterion is based on the largest minimum output signal-to-noise ratio of the multiplexed streams. It is shown via simulations that the bit error rate (BER) performance of the SM UWB systems based on the two-dimensional Rake receiver is significantly improved by antenna diversity through transmit antenna selection on a log-normal multipath fading channel. When the transmit antenna diversity through antenna selection is exploited in the SM UWB systems, the BER performance of the spatial-temporal combining-based zero-forcing (ZF) receiver is also compared with that of the ZF detector followed by the Rake receiver.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.165-168
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• 2015

In this paper, we analyze a SFBC-OFDM(Space Frequency Block Code-Orthogonal Frequency Division Multiplexing) system with antenna selection method using pilot symbols. An antenna selection criterion is based on channel coefficients estimated from pilot symbols. At each frequency, the channel coefficients is arranged and the best is selected, and then data is sent to those antenna with the best coefficients. Also, The coding and diversity gain of the proposed system are analyzed.

• International Journal of Costume and Fashion
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• v.18 no.2
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• pp.45-64
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• 2018

This study assessed scuba divers' wetsuit selection criteria based on the gender, age and scuba diving commitment level along with identifying currently owned and preferred wetsuit types. Lamb and Kallal's Functional, Expressive, and Aesthetic Consumer Needs (FEA) Model was the conceptual framework used for this study. Scuba diving has seen consistent growth, worldwide, it is necessary to investigate with wetsuit needs of this consumer group. A survey of 302 active scuba divers participated in the study. Total participants included 202 male and 100 female scuba divers. Divers stated fit was the most highly rated criteria with don/doff being most problematic. Female and older divers regarded functional performance criterion greatly(p<.05). Highly committed divers regarded the functional quality (p<.01) and aesthetic/expressive features (p<.05) of the wetsuit as important and owned more wetsuits(p<.01). Gender saw differences in required sizes ranges(p<.001) and style preferences(p<.05). Results suggest gender, age, and commitment levels all impact the wetsuit selection criteria of scuba divers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.114-123
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• 2003

The current paper proposes a new vertex selection scheme for polygon-based contour coding. To efficiently characterize the shape of an object, we incorporate the curvature information in addition to the conventional maximum distance criterion in vertex selection process. The proposed method consists of "two-step procedure." At first, contour pixels of high curvature value are selected as key vortices based on the curvature scale space (CSS), thereby dividing an overall contour into several contour-segments. Each segment is considered as an open contour whose end points are two consecutive key vortices and is processed independently. In the second step, vertices for each contour segment are selected using progressive vertex selection (PVS) method in order to obtain minimum number of vertices under the given maximum distance criterion ( $D_{max}$$^{*}$). Furthermore, the obtained vortices are adjusted using the dynamic programming (DP) technique to optimal positions in the error area sense. Experimental results are presented to compare the approximation performances of the proposed and conventional methods.imation performances of the proposed and conventional methods.