• Title/Summary/Keyword: Selection Analysis

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Analysis of Evaluation Indicators for the Development of Evaluation Models of Foreign Academic Journals (대학도서관의 외국학술지 평가모형 개발을 위한 평가지표 분석)

  • 김신영;이창수
    • Journal of the Korean Society for information Management
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    • v.21 no.2
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    • pp.45-67
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    • 2004
  • The purposes of this study are to compare and analyze the evaluation indicators for selection of journal suggested by scholars and organizations and to prepare theoretical background for the ideal model to meet opposing paradigms of collection management in academic libraries. A web survey method was employed to investigate applications of various selection criteria (27 for printed and 37 for electronic academic Journal) from the top 40 academic libraries in Korea. In addition, data were analysed statistically using factor analysis, t-test, Analysis of Variance(ANOVA), and Spearman's Rank Oder Correlation. The mean ranking for 9 evaluation indicators for printed were as follows: subscribing volumes per departments, degree of use, selection authority, electronic/print bundle, ISI impact factor, Internationality and reputation, costs for subscription, ILL & DDS, space considerations for printed materials. But, 11 evaluation indicators for electronic were as follows : costs for subscription, accessibility, electronic/print bundle, consortia, selection authority, access expandability, subscribing volumes per departments, scholarly features of the university, ISI impact factor, ILL & DDS, internationality and reputations.

Design of Lazy Classifier based on Fuzzy k-Nearest Neighbors and Reconstruction Error (퍼지 k-Nearest Neighbors 와 Reconstruction Error 기반 Lazy Classifier 설계)

  • Roh, Seok-Beom;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.101-108
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    • 2010
  • In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

A study about the application of GSIS on Airport site selection (공항입지선정(空港立地選定)에 있어서 GSIS의 활용(活用)에 관(關)한 연구(硏究))

  • Jeong, Seung-Hyeon;Lim, Seoung-Hyeon;Kim, Tea-Geun;Cho, Gi-Sung
    • Journal of Korean Society for Geospatial Information Science
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    • v.5 no.1 s.9
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    • pp.27-40
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    • 1997
  • Recently, with the demand for airservice increasing and localization accelerating, airport construction is booming. However, in the case of an unsuitable airport site selection, it might cause a reduction in airport service and a decrease in airport demand. Thus, it is necessary to construct a representative airport and choose a suitable site selection method for economical and efficient airports in order to make the utmost use of airport functions. In this study, GSIS was used to select the airport site and applied to case study areas. GSIS could present a new method for efficient and scientific analysis in airport site selection including various factors over an extensive area. The use of both, the paired comparison method and the delphi method, could improve the objectivity of analysis results in the process considering the relative weight grade of data and priority order of analysis factors, used in airport site selection.

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An Efficient Channel Selection and Power Allocation Scheme for TVWS based on Interference Analysis in Smart Metering Infrastructure

  • Huynh, Chuyen Khoa;Lee, Won Cheol
    • Journal of Communications and Networks
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    • v.18 no.1
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    • pp.50-64
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    • 2016
  • Nowadays, smart meter (SM) technology is widely effectively used. In addition, power allocation (PA) and channel selection (CS) are considered problems with many proposed approaches. In this paper, we will suggest a specific scenario for an SM configuration system and show how to solve the optimization problem for transmission between SMs and the data concentrator unit (DCU), the center that collects the data from several SMs, via simulation. An efficient CS with PA scheme is proposed in the TV white space system, which uses the TV band spectrum. On the basic of the optimal configuration requirements, SMs can have a transmission schedule and channel selection to obtain the optimal efficiency of using spectrum resources when transmitting data to the DCU. The optimal goals discussed in this paper are the maximum capacity or maximum channel efficiency and the maximum allowable power of the SMs used to satisfy the quality of service without harm to another wireless system. In addition, minimization of the interference to the digital television system and other SMs is also important and needs to be considered when the solving coexistence scenario. Further, we propose a process that performs an interference analysis scheme by using the spectrum engineering advanced Monte Carlo analysis tool (SEAMCAT), which is an integrated software tool based on a Monte-Carlo simulation method. Briefly, the process is as follows: The optimization process implemented by genetic evolution optimization engines, i.e., a genetic algorithm, will calculate the best configuration for the SM system on the basis of the interference limitation for each SM by SEAMCAT in a specific configuration, which reaches the solution with the best defined optimal goal satisfaction.

Selection Criteria and Swimsuit Purchase Satisfaction of Female Consumers According to Swimming Experiences and Physical Self-concepts (20~30대 여성의 수영경력과 신체적 자아개념에 따른 수영복 선택기준과 구매만족도)

  • Jeong, Noh Ra;Hwang, Choon Sup
    • Journal of the Korean Society of Clothing and Textiles
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    • v.37 no.8
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    • pp.1015-1028
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    • 2013
  • This study analyzed the relationship among swimming experiences, swimsuit selection criteria, swimsuit purchase satisfaction level, and the physical self-concept of female consumers. This study was based on a descriptive survey method using a questionnaire. The survey was conducted from June 15 through July 20, 2012, and the sample consisted of 330 female consumers in their 20s and 30s residing in the Seoul and Gyeonggi area. Factor analysis and Cronbach's ${\alpha}$ coefficients, ANOVA, Duncan's Test, and multiple regression analysis were employed for the data analysis. The results revealed that individual self-concepts on health, sports competence, and fitness were influenced by swimming experiences. There was a tendency for those with a longer period of swimming experience to have a higher level of brand consideration as a swimsuit selection criterion; in addition, they showed a higher satisfaction level with swimsuits. Individual physical self-concept influenced the consideration given to each swimsuit selection criterion as well as swimsuit purchase satisfaction level. The findings of the study reflect the possibility of utilizing swimming experiences as a criterion for swimsuit market segmentation. Attention to the quality of swimsuits as well as to the physical self-concept of consumers are required for marketing activities.

Semantic-based Genetic Algorithm for Feature Selection (의미 기반 유전 알고리즘을 사용한 특징 선택)

  • Kim, Jung-Ho;In, Joo-Ho;Chae, Soo-Hoan
    • Journal of Internet Computing and Services
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    • v.13 no.4
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    • pp.1-10
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    • 2012
  • In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved through experiments using internet news classification with low features.

Study on location selection of integrated depot of warehouse stores utilizing AHP method (AHP법을 활용한 창고형 매장의 통합 Depot 위치선정에 관한 연구)

  • Park, Byoung-Jun;Nam, Tae-Hyun;Yeo, Gi-Tae
    • Journal of Digital Convergence
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    • v.17 no.2
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    • pp.135-144
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    • 2019
  • The importance of logistics of warehouse stores has increased as their prices are cheaper and more convenient than those of large supermarkets. However, few studies on integrated depot location selection of warehouse stores have been conducted. In this regard, this study aims to derive factors for integrated depot location selection and calculate weights and select the location priority of target candidates by introducing an analytic hierarchy process (AHP). The analysis results exhibited that the most important selection factor was the cost reduction in transportation and delivery (0.198) followed by distance reduction in transportation and delivery (0.168), and time reduction in transportation. This study quantified the reduction in cost and increase in efficiency if depots were integrated and operated thereby presenting more realistic foundational data to hands-on workers. For the future study, the analysis model will be needed to be advanced through additional investigation on the factors in the analysis.

A Study on Reconstruction and Remodeling's Selection Factors of Old Apartment Houses Using PROMETHEE-AHP (PROMETHEE-AHP 기법을 이용한 노후 공동주택의 재건축과 리모델링 사업선택요소 선정에 관한 연구)

  • Yoo, Seung-Min;Kwon, Oh-kyung;Choi, Yoonki
    • Korean Journal of Construction Engineering and Management
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    • v.20 no.3
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    • pp.77-85
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    • 2019
  • Reconstruction and remodeling have been introduced as a part of renewal (redevelopment) projects for old apartments built after the mid-1970's and government policies for revitalizing each project has been changed continually. However, the frequent changes of the policies have caused conflicts among business entities in selecting business methods. The conflicts from their early stages have made serious problems in the entire business process. Therefore, this study deduced factors of business selection by applying comparison analysis between the two business projects on how certain factors have an influence on selecting reconstruction and remodeling business projects. Based on the analysis, four categories and 26 factors were finally selected. After then, the priority of each selection factor was deduced through the AHP method and PROMETHEE method used for analysis of relative importance and impact values regarding to the business selection.

Exploring the Performance of Multi-Label Feature Selection for Effective Decision-Making: Focusing on Sentiment Analysis (효과적인 의사결정을 위한 다중레이블 기반 속성선택 방법에 관한 연구: 감성 분석을 중심으로)

  • Jong Yoon Won;Kun Chang Lee
    • Information Systems Review
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    • v.25 no.1
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    • pp.47-73
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    • 2023
  • Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.

Analysis of the axle load of an agricultural tractor during plow tillage and harrowing

  • Hong, Soon-Jung;Park, Seung-Je;Kim, Wan-Soo;Kim, Yong-Joo;Park, Seong-un
    • Korean Journal of Agricultural Science
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    • v.43 no.4
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    • pp.665-669
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    • 2016
  • Analysis of the load on the tractor during field operations is critical for the optimal design of the tractor. The purpose of this study was to do a load analysis of an agricultural tractor during plowing and harrowing. First, a load measurement system was developed and installed in a 71 kW agricultural tractor. Strain-gauges with a telemetry system were installed in the shaft located between the axles and the wheels, and used to measure the torque of the four driving axles. Second, field experiments were conducted for two types of field operations (plowing, harrowing), each at two gear levels (M2, M3). Third, load analysis was conducted according to field operation and gear level. At M2 gear selection for plowing, the maximum, minimum, and average (S. D.) torque values were 13,141 Nm; 4,381 Nm; and 6,971 Nm (${\pm}397.8Nm$, respectively). For harrowing, at M2 gear selection, torque values were, 14,504 Nm; 1,963 Nm; and 6,774 Nm (${\pm}459.4Nm$, respectively). At M3 gear selection for plowing, the maximum, minimum, and average (S. D.) torque values were,17,098 Nm; 6,275 Nm; and 8,509 Nm (${\pm}462.4Nm$, respectively). For harrowing at M3 gear selection, maximum, minimum, and average (S. D.) torque values were, 20,266 Nm; 2,745 Nm; and 9,968 Nm (${\pm}493.2$). The working speed of the tractor increased by approximately 143% when shifted from M2 (7.2 km/h) to M3 (10.3 km/h); while during plow tillage and harrowing, the load of the tractor increased approximately 1.2 times and 1.5 times, respectively.