• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.23-29
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• 2012

The control of stator flux, torque angle, excitation torque, reluctance torque and total torque of the direct torque control (DTC) for a permanent magnet synchronous motor (PMSM) are studied in this paper. Simplified expressions to represent the changes of these variables due to the application of a voltage vector are given. Finally, a voltage vector selection area and the implementation of a voltage vector selection strategy are proposed.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2815-2817
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• 2002

Classification method of cancers using cDNA microarrays data was developed using genetic algorithms and neural networks. For gene selection, 2308 genes were ranked using genetic algorithms, and selected by frequency number of selection from 1000 of genetic iterative runs. To calculate fitness values, artificial neural networks are used as classifier. The small, round blue cell tumors (SRBCTs) which is difficult to distinguish via pathological single test was used as test diseases for classification, and the test results showed the 96% of exact classification capability for 25 test samples.

• ETRI Journal
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• v.35 no.2
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• pp.336-339
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• 2013

To enhance the symbol error rate (SER) performance of the two-way relay channels with physical layer network coding, this letter proposes a relay selection scheme, in which the relay with the maximal minimum distance between different points in its constellation among all relays is selected to assist two-way transmissions. We give the closed-form expression of minimum distance for binary phase-shift keying and quadrature phase-shift keying. Additionally, we design a low-complexity method for higher-order modulations based on look-up tables. Simulation results show that the proposed scheme improves the SER performance for two-way relay networks.

• Nutrition Research and Practice
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• v.8 no.5
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• pp.595-601
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• 2014

BACKGROUND/OBJECTIVES: Educational interventions targeted food selection perception, knowledge, attitude, and behavior. Education regarding irradiated food was intended to change food selection behavior specific to it. SUBJECTS AND METHODS: There were 43 elementary students (35.0%), 45 middle school students (36.6%), and 35 high school students (28.5%). The first step was research design. Educational targets were selected and informed consent was obtained in step two. An initial survey was conducted as step three. Step four was a 45 minute-long theoretical educational intervention. Step five concluded with a survey and experiment on food selection behavior. RESULTS: As a result of conducting a 45 minute-long education on the principles, actual state of usage, and pros and cons of irradiated food for elementary, middle, and high-school students in Korea, perception, knowledge, attitude, and behavior regarding the irradiated food was significantly higher after the education than before the education (P < 0.000). CONCLUSIONS: The behavior of irradiated food selection shows high correlation with all variables of perception, knowledge, and attitude, and it is necessary to provide information of each level of change in perception, knowledge, and attitude in order to derive proper behavior change, which is the ultimate goal of the education.

• ETRI Journal
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• v.41 no.5
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• pp.637-647
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• 2019

Separating highly correlated users can reduce the loss caused by spatial correlation (SC) in multiuser multiple-input multiple-output (MU-MIMO) systems. However, few accurate analyses of the loss caused by SC have been conducted. In this study, we define signal-to-interference-plus-noise ratio (SINR) loss to characterize it in multiuser multiple-input single-output (MU-MISO) systems, and use coefficient of correlation (CoC) to describe the SC between users. A formula is deduced to show the accurate relation between SINR loss and CoC. Based on this relation, we propose a user selection method that utilizes CoC to minimize the average SINR loss of users in massive MU-MISO systems. Simulation results verify the correctness of the relation and show that the proposed user selection method is very effective at reducing the loss caused by SC in massive MU-MISO systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.97-116
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• 2021

Due to the decrease in the school-age population and government regulations, universities have made great efforts to secure their own competitiveness. In particular, the selection of universities with financial support based on the recent evaluation of the Ministry of Education has become a major concern enough to affect the existence of the university itself. This paper extracts three-year data from 124 major private universities nationwide, and quantitatively analyzes the variables of major universities selected as self-improvement universities, competency reinforcement universities, and universities with limited financial support. As a result of estimating the selection of self-powered universities using the ordered logit model by hierarchically inputting 12 variables, student competitiveness in the metropolitan area (1.318^**), Educational Restitution Rate (4.078^***), University operation expenditure index rate (1.088^***) values were found. Significant positive coefficient values were found in the admission enrollment rate (45.98^***) and the enrollment rate (13.25^***). As a result of analyzing the marginal effects, the increase in the rate of reduction of education costs has always been positive in the selection of self-powered universities, but it was observed that the rate of increase decreases in areas of increase of 150% or more. On the contrary, the probability of becoming a Em-powered university was negative in all sectors, but on the contrary, it was analyzed that marginal effects increased at the same time point. On the other hand, the employment rate of graduates was not able to find direct significance with the result of the selection of Self powered universities. Through this paper, it is expected that each university will analyze the possibility and shortcomings of the selection of Self powered universities in policy making, and in particular, the risk of dropout of selection for the vulnerable field can be predicted using marginal effects. It can be used as major research data for both university evaluators, university officials and students.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.217-231
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• 2019

The purpose of this study is to analyze the factors influencing consumers' selection of web-toon service through AHP (Analytic Hierarchy Process) analysis and to provide the strategy of web-toon service. To accomplish this study, theories, existing research and references related to AHP were sufficiently examined and selected the factors in the selection criteria. Surveys from consumers who used the web-toon service were conducted with selected factors. Through this, the results were analyzed by AHP analysis to find out the weighting values and the differences were examined and analyzed. The highest weighting factor in the first layer that consists of web-toon service was cinematic quality. The cinematic quality was the most important factor in the selection criteria of customers who use the web-toon service regardless of their preferred genre. Furthermore, it was confirmed that the weighting value or ranking changed in the second layer by genre. In this study, the effective basis of strategy were suggested by ranking the quantitative selection factors according to the preferred genre of consumers using web-toon services. In addition, This research provides some practical implications. That is, the web-toon service provider can easily recognize and respond to the customer's requirements, which factors are important when the customer selects a specific genre from the web-toon genre.

• Journal of the Korean Society of Food Culture
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• v.25 no.5
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• pp.516-524
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• 2010

Consumers are highly interested in processed fishery products that are healthy and superior in terms of convenience, nourishment, and taste. However, current domestic research on processed fishery products is marginal. We systematically analyzed consumer consumption patterns and the relationship to pursuit benefits, selection attributes, satisfaction levels, and reasons for purchase. Consumers considered product information the most important selection attribute, whereas convenience scored highest for pursuit benefits. Furthermore, the influences of selection attributes and pursuit benefits on satisfaction level and the reason for purchasing an item were analyzed using demographic properties as control variables. The variables that affected satisfaction level were residential district (region: B= -0.268, p<0.05.), recipe (B=0.098, p<0.05), nutrients (B=0.124, p<0.05), convenience (B=0.283, p<0.001), and economics (B=0.138, p<0.05). The variables affecting the reason for purchasing were nutrients (B=0.173, p<0.001), convenience (B=0.277, p<0.001) and satisfaction level (B=0.163, p<0.001). Pursuit intention had significant effects on purchase intention; however, selection attributes had no significant effect on purchase intention. Therefore, consumer satisfaction had a significant effect on purchase intention. This result showed that if consumers were satisfied, they intended to repurchase. Attempts to increase repurchases by consumer are needed by fulfilling consumer satisfaction. These data can be utilized as a fundamental reference for sales promotions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1521-1524
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• 2003

In this paper, we propose a "Distance Correlation Selection operator (DCS)" as a new selection operator. For Genetic Algorithm (GA), many improvements have been proposed. The MGG (Minimal Generation Gap) model proposed by Satoh et.al. shows good performance. The MGG model has all advantages of conventional models and the ability of avoiding the premature convergence and suppressing the evolutionary stagnation. The proposed method is an extension of selection operator in the original MGG model. Generally, GA has two types of selection operators, one is "selection for reproduction", and the other is "selection for survival"; the former is for crossover and the latter is the individuals which survive to the next generation. The proposed method is an extension of the former. The proposed method utilizes distance information between individuals. From this extension, the proposed method aims to expand a search area and improve ability to search solution. The performance of the proposed method is examined with several standard test functions. The experimental results show good performance better than the original MGG model.