• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.429-436
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• 2004

This paper proposes an independent component analyses(ICAs) of the fixed-point (FP) algorithm based on Newton and secant method by adding the kurtosis, respectively. The kurtosis is applied to cluster the analyzed components, and the FP algorithm is applied to get the fast analysis and superior performance irrelevant to learning parameters. The proposed ICAs have been applied to the problems for separating the 6-mixed signals of 500 samples and 10-mixed images of $512\times512$ pixels, respectively. The experimental results show that the proposed ICAs have always a fixed analysis sequence. The results can be solved the limit of conventional ICA without a kurtosis which has a variable sequence depending on the running of algorithm. Especially. the proposed ICA can be used for classifying and identifying the signals or the images. The results also show that the secant method has better the separation speed and performance than Newton method. And, the secant method gives relatively larger improvement degree as the problem size increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.336-341
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• 2004

This paper proposes an independent component analysis(ICA) of the fixed-point (FP) algorithm based on secant method and the kurtosis. The FP algorithm based on secant method is applied to improve the analysis speed and performance by simplifying the calculation process of the complex derivative in Newton method, the kurtosis is applied to cluster the components. The proposed ICA has been applied to the problems for separating the 6-mixed signals of 500 samples and 8-mixed images of $512{\times}512$ pixels, respectively. The experimental results show that the proposed ICA has always a fixed analysis sequence. The result can be solved the limit of conventional ICA based on secant method which has a variable sequence depending on the running of algorithm. Especially, the proposed ICA can be used for classifying and identifying the signals or the images.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.381-386
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• 2004

This paper proposes an independent component analysis(ICA) of the fixed-point(FP) algorithm based on Newton method by adding the kurtosis. The kurtosis is applied for clustering the components, and the FP algorithm of Newton method is applied for improving the analysis speed and performance. The proposed ICA has been applied to the problems for separating the 6-mixed signals of 500 samples and 8-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has always a fixed analysis sequence. The result can be solved the limit of conventional ICA which has a variable sequence depending on the running of algorithm. Especially, the proposed ICA can be used to classify and identify the signals or the images.

• The Korean Journal of Elementary Counseling
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• v.11 no.3
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• pp.351-369
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• 2012

The purpose of this study is to examine the psychometric properties of the Korean Children's Rejection Sensitivity Questionnaire (K-CRSQ). In order to investigate the reliability and validity of the K-CRSQ, the data were collected from 286 elementary students. The results are as follow : Internal consistency and test -retest reliability of the K-CRSQ were .83~.89, .72~.79, respectively. To test the convergent and discriminant validity, participants were separated into two samples and administered the K-CRSQ and ten validity indices. The correlation of K-CRSQ and validity indices scores suggested that the convergent and discriminant validity are high. High rejection sensitivity group revealed high score on intentional attribution, depression, anxiety, and aggression. In contrast, low rejection sensitivity group revealed high score on self-esteem, perceived competence, and social support. The results were compared with previous studies and the implication and limitation were discussed.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.891-899
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• 2010

The purpose of this study is to estimate economic value of forest landscape function using conditional logit model, applied by Choice Experiment. For the study, we have chosen attributes and levels of forest landscape. In specific, topographical forest type, forest type, forest density, recreational factor (side trip, accessibility of valley) and WTP were included in attributes. Based on factors, we have made 48 choice sets with Balanced and Orthogonal form using SAS 9.1. The efficiency of questionnaire was 6.02 (D-Error: 0.1) and choice set and socio-economic variable were selected. In order to reduce cognitive load of respondent, 96 choice sets were divided into 4 types in questionnaire so that respondent could respond to 12 choice sets respectively. Population was citizens from 7 metropolitan cities including Seoul, and the interview survey was conducted to find out average annual WTP per household for the total 280 interviewees. As a result, In the Non-ASC model, Mcfadden' ${\rho}$ had 0.21, and Log Likelihood: -2,631. Average annual WTP per household for forest landscape was 266,723 Won(Korean currency).

• Journal of Korean Library and Information Science Society
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• v.42 no.2
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• pp.105-125
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• 2011

This paper grasps about various features of the coauthorship network based on the co-author frequency in the Korean LIS Research Community. This issue includes many topics such as changable aspects of coauthorship network, properties of higher cooperative authors groups. This work is mostly analyzed through a bibliographic analysis of articles which is published from 2000 to 2009(10 years) in Korean Library & Information Science major four journals. The results show three major implications. 1) There is a various structural changes of coauthorship network on the change of the co-author frequency. 2) There are 21 research pairs in the higher cooperative authors groups with the co-author frequency more than five. 3) There seems that any subjective relations between the articles which is produced by 21 research pairs were not clearly presents.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.309-312
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• 2011

Handling fuzzy query in voice search on smartphones is one of the most difficult problems. It is mainly derived from the complexity and the degree of freedom of natural language. To reduce the complexity and the degree of freedom of fuzzy query in voice search on smartphones, attribute-driven approach for fuzzy query is proposed. In addition, a new page ranking algorithm based on the values of attributes for handling fuzzy query is proposed. It provides a smartphone user with location-based personalized page ranking based on user's search intentions. It is a further step toward location-based personalized web search for smartphone users. In this paper, we design a prototype model for handling fuzzy query in voice search on smartphones and show the experimental results of the proposed approach compared to existing smartphones.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.23-44
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• 2021

This research proposed an approach for interlinking bibliographic data used in the existing multicultural institutions in order to support information services of multicultural institutions. It constructed a set of linkage properties that can link the FRBR-based BIBFRAME structure with the existing metadata standards currently adopted in multicultural institutions, including MODS, EAD, and CDWA. As a result, the descriptive elements for the Work level, Instance level, Item level, statement of responsibility, subjects were established in the BIBFRAME syntax. A total of 19 upper level elements, 18 sub-elements, and 12 linkage properties were proposed for the linking of bibliographic data, so that a bibliographic environment was established that can interconnect heterogeneous metadata adopted depending on the types of collections. Through the proposed structure, it is expected that it can be applied to provide sufficient information services more efficiently by linking the dispersed bibliographic data of multicultural institutions in an integrated way.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.261-274
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• 2019

This paper aims to develop an appropriate methodology for establishing an investment strategy for 'demonstration of artificial rainfall technology using UAV' and that include establishment of a technology classification, set of indicators for technology evaluation, suggestion of final key technology as a whole study area. It is designed to complement the latest research trend analysis results and expert committee opinions using quantitative analysis. The key indicators for technology evaluation consisted of three major items (activity, technology, marketability) and 10 detailed indicators. The AHP questionnaire was conducted to analyze the importance of indicators. As a result, it was analyzed that the attribute of the technology itself is most important, and the order of closeness to the implementation of the core function (centrality), feasibility (feasibility). Among the 16 technology groups, top investment priority groups were analyzed as ground seeding, artificial rainfall verification, spreading and diffusion of seeding material, artificial rainfall numerical modeling, and UAV sensor technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.123-130
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• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.