• 식품보건융합연구
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• 제7권4호
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• pp.1-11
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• 2021

The purpose of this study is to investigate the ramen selection attributes of consumers. This research assigned taste, price, quantity, design, and brand as selection attributes, all of which have already been verified by previous studies as selection attributes when purchasing processed foods. A total of 500 questionnaires were issued, and the survey results were analysed to ensure validity and reliability. A Structural Equation Model was used to test the hypotheses of the study. Based on the analysis, taste, price, quantity, design, and brand had a statistically significant effect on satisfaction. Furthermore, satisfaction had a statistically significant effect on repurchase intention. Among the selection attributes (taste, price, quantity, design, and brand), only price had a statistically significant effect on repurchase intention. However, the influence of the selection attributes on satisfaction varied depending on the consumer's consumption value. In order to analyse the moderating effect of consumption value, the respondent group was divided into a hedonism group and pragmatism group, and analysed. It empirically proved that the hedonistic value-oriented group valued taste, and the practical value-oriented group valued price the most. This study empirically verified the relationship between ramen selection attributes and consumption value, and provided corresponding theoretical and practical implications.

• International Journal of Advanced Culture Technology
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• 제9권4호
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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.

• International Journal of Contents
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• 제18권3호
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• pp.49-57
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• 2022

The purpose of this study was to investigate the satisfaction of personnel selection process according to type of personnel manager and to examine whether the relationship between the type of personnel manager and the satisfaction with the personnel selection process was moderated by the applicant's perception of procedural justice. This study was conducted using a between-group design with 208 students from a four-year university in Korea. One group watched a video in which a human personnel manager selected employees and the other group watched a video in which an AI personnel manager selected employees. Participants were randomly assigned to a condition, responded to a demographic questionnaire, and answered measures of procedural justice and satisfaction with personnel selection after watching the video. As a result, the selection process satisfaction was significantly higher when the human personnel manager conducted the selection process than when the AI personnel manager conducted such process. In addition, when procedural justice was perceived as low, there was a significant difference in satisfaction between human and AI groups. However, when procedural justice was perceived as high, there was no significant difference in satisfaction between the two groups. Based on study results, the significance and limitations of this study and suggestions for future studies are discussed.

• 유통과학연구
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• 제20권3호
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• pp.1-11
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• 2022

Purpose: This study aims at understanding the impacts of three omnichannel attributes (channel transparency, channel uniformity, channel convenience) and four customer perceptions (perceived innovativeness, perceived personalization, perceived risk, perceived credibility) on customer experience and channel selection decision. Research design and methodology: A quantitative online survey with 356 shoppers was executed. The partial least squares linear structural model (PLS-SEM) and Smart PLS were adopted to analyze the collected data and test the proposed hypotheses. Results: The research findings indicate four dominant results: (i) The customers' channel selection is directly determined by customer experience; perceived innovativeness; perceived personalization; perceived risk; and perceived credibility; and (ii) among these, the perceived risk shows negative impact on the customer's experience and customers' channel selection whereas others reveal the positive status; (iii) The customer experience represents the most decisive impact on the channel selection, then perceived personalization, perceived credibility, perceived innovativeness, and perceived risk. (iv) Three proposed channel attributes (transparency, uniformity, convenience) significantly influence the overall customer experience. Conclusions: This research adds to the body of knowledge in omnichannel retailing, customer experience, and customer channel selection. Furthermore, this research provides omnichannel retailers with practical implications for improving customer channel selection.

• 한국과학교육학회지
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• 제13권2호
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• pp.172-186
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• 1993

This stady was carried out to define Gifted student for science, model for selection, the tools and methods and related theory for the selection of the Gifted students for the science in primary school level. Also the developed tools and materials are applied to student and analysed the results to generalize the methods for the selection of Gifted students for science. The definition of Gifted students for science was carried out by the three-ring conception model by Renzulli(1982) and Lee long-Sung which defined the characteristics as three parts such as above average ability, creativity and tesk comitment. The Gifted students for science upper 2 percent which have three characteristics at the same times, namely overlapping three characteristics. The model for the selection of Gifted students consist of four step; such as screeing, selection,differentiation, judgement. The materials for the selection are input at each stage, analysed the results and standard for the selection are made. In the first stage screening, 202 students are selected from the 5060 of 4th and 5th graders according to their achievment, intellecture ability and observation of students activity. In second selection and third differentiation stage, 65 students are seletted according to their achievement In this study it is approved that the Gifted students in science have to be selection by various test such as achievement, intellectual ability, aptitude in science, inquiry activity, manual skill etc, rather rather then simple test such as achievement and intellecture ability. Also it is important to select upper 2 percent who have general abilites overlapping three characteristics mentioned in definition of Gifted students in science and selections model

• 한국컴퓨터정보학회논문지
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• 제13권1호
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• pp.1-10
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• 2008

이 기종의 네트워크와 시스템 자원으로 구성된 그리드 컴퓨팅 환경에서 대용량 데이터를 빠르고 정확하게 처리하기 위해서는 효과적인 그리드 자원선택이 필수적이다. 이를 위해 본 논문은 의사결정 트리 기법을 이용한 그리드 자원선택 시스템을 제안한다. 이 시스템은 자원 정보를 기록한 데이터 셋을 바탕으로 사용자들이 선택하는 자원들을 처리 할 데이터의 특성과 사용자의 요구사항으로 분석해서 자원선택을 위한 의사결정 트리를 구축한다. 그리드 사용자의 자원 요청 시 의사결정 트리를 탐색하여 사용자 요구 및 작업 특성에 적합한 자원들을 선택하여 작업을 할당함으로써 사용자 만족도를 향상시킴은 물론 전체 그리드 시스템의 성능을 개선한다. 실험결과는 본 논문에서 제안한 의사결정 트리 기반의 그리드 자원선택 시스템이 기존 그리드 자원선택 시스템인 Condor-G 및 Nimrod-G와 비교하여 더 높은 작업 처리율 및 자원 이용률과 더 적은 작업 손실 및 처리시간을 제공함으로써 그리드 자원선택 및 데이터 분산 처리에 효과적이라는 사실을 증명한다

• 한국통신학회논문지
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• 제37권8B호
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• pp.657-667
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• 2012

본 논문에서는 OLSR의 MPR 선택방법을 개선함으로써 애드혹(ad hoc) 네트워크의 처리율(throughput), 지연 시간(delay) 등의 성능을 향상시킬 수 있는 S-MPR 선택 방법을 제안한다. OLSR의 MPR 선택 방법은 각 노드가 독립적으로 MPR을 선택하기 때문에 대부분의 노드가 MPR로 선택되는 문제가 있다. 이러한 문제를 해결하기 위해 기존에 제안되었던 MPR 후보(candidate) 선택 방법은 MPR의 수는 감소시킬 수 있지만 그로 인해 경로의 효율성과 네트워크의 연결성(connectivity)이 저하되는 문제를 갖고 있다. 본 논문에서 제안하는 S-MPR 방법은 이러한 문제를 해결하기 위해 각 노드 입장에서 가장 중요한 노드를 S-MPR로 선택하고 나머지 MPR은 MPR 후보를 이용하여 선택하는 방법을 사용한다. 따라서 제안 방법은 경로 효율성의 저하를 최소화하면서 MPR로 선택되는 노드의 수를 줄임으로써 TC 메시지로 인한 오버헤드를 최소화하고 MPR간의 충돌을 감소시킴으로써 처리율, 지연 시간 성능을 향상시킬 수 있다. 본 논문에서 제안한 S-MPR의 성능을 알아보기 위해 OPNET을 활용하여 시뮬레이션을 수행하고 제안 S-MPR의 성능이 가장 우수함을 보인다.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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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.

• 한국작물학회지
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• 제3권
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• pp.89-98
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• 1965

대두증산의 중요성에 미추어 대두의 다수계통 선발을 위하여 전년에 공시한 22품종을 재료로 각 형질의 유전력, 각 형질상호간의 유전상관, 그리고 선발지수 등을 재검토하기 위하여 선발시험을 한 바 대체로 전보 전년의 결과와 같다. 1. 유전력 : 유전력을 추정하기 위하여 파종기별로 각 형질의 평균치의 변동을 보면 제1표와 같이 파종기가 지연됨에 따라 형질의 평균치는 감소하고 (Table 1), 형질중에는 환경에 따라 크게 영향을 받는 것도 있으며(Table 2), 각 형질별 유전력을 추정한 바 그 결과는 제3표와 같다. 즉 개화일수ㆍ결실일수ㆍ생육일수 등 생유기간에 관계되는 형질은 그의 유전력이 높고 형태적 특성에 관계되는 형질중에서도 경장 100입중은 높으나 수량에 관여하는 형질 등은 낮았다. 그리고 파종기에 따라서도 변동을 하며 대체로 파종기가 늦어짐에 따라 감소하는 경향이 보인다(Table 3). 2. 유전상관 : 각 형질 상호간의 유전상관ㆍ표현형상관을 알기 위하여 각 형질의 표현형분산과 각 형질 상호간의 표현형공분산ㆍ유전공분산. 그리고 환경공분산을 산출한 바 그 결과는 제4,5표와 같고(Table 4,5), 그들의 유전상관과 표현형상관은 제6표와 같다(Table 6). 대체로 유전사관의 정도는 표현형상관보다높으나 파종기에 따라서도 변동하고 있다. 일주입중 즉 수랴오가 타형질과의 상관을 보면 대체로 전보와 같이 100입중이외의 제형질은 정의 상관을 보이며, 파종기에 따라서도 그 값이 변동을 하나 전보에서는 일주입중과 분지수 사이에 고도의 상관관계가 보였으나, 본실험에 있어서는 상관의 경향이 보였을 따름이다. 그리고 본실험에 있어서는 일주협수와 일주입수간에서 유전상관이 1이상의 이상치를 보였다. 3. 선발지수 : 선발이 최종 대상형질을 일주입중(형질 Y)으로 하여 여기에 어려 형질을 조합한 선발지수(Selection index A)와 최종 대상형질을 수량과 높은 유전상관이 있는 일주협수(형질 Y')로 하여 여기에 여러 형질을 조합한 선발지수(Selection index B)를 Robinson et al.의 방법에 의하여 작성하여 본 바 제7표와 같다(Table 7). 전보의 결과와 같이 대두의 선발에 있어서는 최종 대상형질을 일주입중 대신에 일주협수로써 선발하여도 대차가 없고, 선발대상형질을 4개형질이상인 경우에는 그 효과가 조사와 산출에 요하는 노력에 비하여 크게 기대될 정도가 못된다. 이와 같은 점에서 다수확을 목표로 하는 선발은 탈곡조제 전에 일주협수ㆍ분지수ㆍ경직경 등에 대하여 조사를 하고 선발지수를 산출하여 Selection score가 큰 계통부터 선발하여 가면 될 것으로 믿는다. 그리고 선발을 위한 대상형질로는 생육일수(X$_1$)ㆍ분지수(X$_2$)ㆍ경직경(X$_3$), 그리고 일주협수(X$_4$)의 4개형질 혹은 분지수(X$_2$)ㆍ경직경(X$_3$), 그리고 일주협수(X$_4$)의 3개형질로 할 것이 요망되고, 이들 형질의 측정치로 선발지수를 작성하여 그 지수에서 산출된 Selection score가 큰 계통부터 선발해 나가는 것이 선발지수의 이용상 실용적이고 효과적인 방법이라 하겠다.