• 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.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.226-236
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• 2021

The purpose of this study is to analyze the type of latent profile for general high school students' subject selection criteria and to identify the characteristics of the latent class. The survey data of 1072 general high school students (male; 648, female; 424) in G city, Jeollabuk-do and the scale composed of 8 sub-factors: 'SAT orientation', 'academic achievement', 'ability orientation', 'pursuit of interest', 'teacher orientation', 'career development', 'others' recommendation', and 'subject availability' were used for latent profile analysis and cross-analysis between potential layers. As a result of the analysis, high school students' perceptions of subject selection were classified into four latent profiles. The four groups were named 'High Perception Type', 'Low Perception Type', 'Self-Directed Type', and 'Stability-Oriented Type' according to their types. It was found that there was a difference between the latent classes in the importance and performance level of the subject selection criteria. These results can help identify the subject selection tendencies of latent groups in the operation of the 2015 revised curriculum and the 2025 high school credit system that emphasizes the student-centered course selection curriculum and they can also provide customized course selection guidance considering individual differences.

• Progress in Superconductivity and Cryogenics
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• v.23 no.3
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• pp.26-31
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• 2021

Advanced classification of Cs contaminated soil by using a magnetic force-assisted selection pipe was investigated. A selection pipe is a device that sort particles depending on their particle size, based on the relationship between buoyancy, drag, and gravity force acting on the particles. Radioactive cesium is concentrated in small-particle size soil components with a large specific surface area. Hence, the volume of the Cs contaminated soil can be reduced by recycling the large-particle size soil components with low radioactive concentration. One of the problems of the selection pipe was that the radioactive concentration of the stayed soil in the selection pipe exceeds 8000 Bq/kg, which is the standard value of recycling of Cs contaminated soil, due to low classification accuracy. In this study, magnetic fields were applied to the lab-scale selection pipe from upper side to improve the classification accuracy and to reduce the radioactive concentration of the stayed soil.

• 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.

• The International Journal of Costume Culture
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• v.2 no.1
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• pp.1-9
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• 1999

This study aims to figure out the levels of self-concept, to reveal the aspects of clothing selection behavior, and to clarify the relation of self-concept to clothing selection behavior in high school girls. Questionnaire was used to collect data. The subjects were made up of 298 second-grade high school girls from four parts in Taegu. The findings of this study are as follows : 1. The level of physical self, personal self, family self, and social self of high school girls are shown as above average. 2. In general trend of their clothing selection behavior, the scores related to practicality, economy, exhibitionism except fashionability are shown as high. 3. It seems that high school girls have a tendency to firstly weigh exhibitionism, and then economy, practicality, fashionability are followed one after another. 4. Physical self is shown as significantly different in fashionability, exhibitionism among clothing selection behavior.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.119-131
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• 2022

The purpose of this study is to analyze the difference in the degree of self-determination between the criteria that general high school students consider important when selecting subjects (hereinafter referred to as 'importance') and the criteria that are actually applied when selecting subjects (hereinafter referred to as 'implementation'), based on the existing motivation type discrimination scale and subject selection criteria scale. As a result of analysis based on the data of a total of 786 high school students, the degree of self-determination was found to be different for all 34 questions and 8 factors in importance and implementation. In general, the questions and factors showed a simple structure with the motivation types and showed the lowest correlation with the motivations at both ends of the self-determination continuum. Among the factors that students consider important when selecting subjects and the factors that are actually applied, the 'SAT' factor showed the highest positive correlation with identification control. In addition, it was found that autonomous subject selection was more preferred than subject selection based on extrinsic motivation. These results are not only meaningful as the first study to analyze the degree of self-determination in the subject selection of high school students, but also can be used as useful data for customized subject selection guidance according to the degree of self-determination. The implications of this study and suggestions for follow-up studies were discussed.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.221-222
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• 2008

We propose a LLR (log-likelihood ratio) selection combining technique that reduces much of complexity. This technique chooses the most reliable branch based on the magnitude of the LLR of each branch. We show that the proposed selection combining achieves significant power gains over conventional selection combining and nearly matches the performance provided by MRC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• New & Renewable Energy
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• v.19 no.2
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• pp.40-58
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• 2023

Several offshore wind power conflicts occur due to the problems in which the site selection process led by private operators is improperly managed. To review the institutional improvement measures that solve this problem, domestic and foreign institutions and operational cases were comparatively analyzed, focusing on key actors from the multi-level governance perspective. First, the status of the site selection process in the Republic of Korea, major issues in stakeholder conflicts, and discussions on the planned site system-related laws (draft) were reviewed. Next, the site selection process and relevant cases in Germany, the Netherlands, and Japan were analyzed. In all these countries, site selection is done by the central government. In Germany and the Netherlands, maritime-related ministries establish overall offshore wind power site plans and conduct strategic environmental assessments for these plans. Futhermore, in the process of determining each individual site, extensive site investigation including environmental assessments are conducted. This aspect needs to be supplemented in the discussion on the direction of institutional improvement in the Republic of Korea.

• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.