• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.159-165
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• 2004

본 논문의 목적은 모바일 서비스를 위해 GML 프로파일을 개발하는 것이다. 현재 모바일 서비스를 위하여 다양한 형태의 지도 표현과 공간정보 서비스가 개발되고 있다. 그러나, 각 모바일 서비스마다 표준화되지 않은 독자적인 방법을 통해 공간 데이터를 전송 및 처리하고 있어서 모바일 서비스간의 상호운용성을 확보하기 어려운 상황이다. 이를 해결하기 위하여 GML을 기반으로 모바일 서비스용 GML 프로파일을 개발한다. 모바일 서비스 환경을 위한 실용적인 GML 프로파일을 개발하기 위해서는 GML 명세와 모바일 서비스를 먼저 분석하여야 한다 GML의 구성요소와 모바일 서비스 및 DB분석을 기반으로 MATRIX분석을 수행하여 모바일 서비스를 위해 필요한 GML 구성요소의 부분집합을 정의한다. 이를 기반으로 모바일 서비스용 GML 프로파일을 개발한다.

• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.7-36
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• 2018

This study aimed to analyze the content of Records Data Management (RDM) training programs provided by 51 out of 121 university libraries in North America that implemented RDM services, and to provide implications from the results. For the content analysis, 317 titles of classroom training programs and 42 headings at the highest level from the tables of content of online tutorials were collected and coded based on 12 data literacy competencies identified from previous studies. Among classroom training programs, those regarding data processing and analysis competency were offered the most. The highest number of the libraries provided classroom training programs in relation to data management and organization competency. The third most classroom training programs dealt with data visualization and representation competency. However, each of the remaining 9 competencies was covered by only a few classroom training programs, and this implied that classroom training programs focused on the particular data literacy competencies. There were five university libraries that developed and provided their own online tutorials. The analysis of the headings showed that the competencies of data preservation, ethics and data citation, and data management and organization were mainly covered and the difference existed in the competencies stressed by the classroom training programs. For effective RDM training program, it is necessary to understand and support the education of data literacy competencies that researchers need to draw research results, in addition to competencies that university librarians traditionally have taught and emphasized. It is also needed to develop educational resources that support continuing education for the librarians involved in RDM services.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.76-82
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• 2017

Malware, including ransomware to quickly detect, in this study, to provide an analysis method of malicious code through the image analysis that has been learned in the deep learning of artificial intelligence. First, to analyze the 2,400 malware data, and learning in artificial neural network Convolutional neural network and to image data. Extracts subgraphs to convert the graph of abstracted image, summarizes the set represent malware. The experimentally analyzed the malware is not how similar. Using deep learning of artificial intelligence by classifying malware and It shows the possibility of accurate malware detection.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.803-813
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• 2013

The purchase of items in e-commerce is a little bit different from that of items in off-line. The recommendation of items in off-line is conducted by salespersons' recommendation, However, the item recommendation in e-commerce cannot be recommended by salespersons, and so different types of methods can be recommended in e-commerce. Recommender system is a method which recommends items in e-commerce. Preferences of customers who want to purchase new items can be predicted by the preferences of customers purchasing existing items. In the recommender system, the items with estimated high preferences can be recommended to customers. The algorithm of collaborative filtering is used in recommender system of e-commerce, and the list of recommended items is made by estimated values, and then the list is recommended to customers. The dataset used in this research are 100k dataset and 1 million dataset in Movielens dataset. Similar results in two dataset are deducted for generalization. To suggest a new algorithm, distribution features of estimated values are analyzed by the existing algorithm and transformed algorithm. In addition, respondent'distribution features are analyzed respectively. To improve the collaborative filtering algorithm in neighborhood recommender system, a new algorithm method is suggested on the basis of existing algorithm and transformed algorithm.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.733-744
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• 2012

Dynamic threshold public-key encryption provides dynamic setting of the group of all users, receivers and the threshold value. Over recent years, there are many studies on the construction of scheme, called ID-based dynamic threshold encryption, which combines the ID-based encryption with dynamic threshold encryption. In this paper, we analyze the ID-based dynamic threshold encryption proposed by Xing and Xu in 2011, and show that their scheme has a structural problem. We propose a conversion method from ID-based encryption which uses the bilinear map to ID-based dynamic threshold encryption. Additionally, we prove this converted scheme has CPA security under the full model.

• Korean Journal of Culture and Social Issue
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• v.18 no.1
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• pp.27-51
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• 2012

The current paper examines the effect of collectivism on perceived youth unemployment anxiety as well as mental health and the mediating effects of employment self-efficacy; self-esteem; and the frequency of upward and downward social comparisons. In Study 1, data were gathered from 179 university students in upper-ranking schools and middle-ranking schools in Seoul, Korea. Our results indicated that (a) collectivism was positively correlated to, and also an significant predictor of perceived youth unemployment anxiety and mental health and (b) employment self-efficacy and self-esteem had mediating effects on the relationship between collectivism and perceived youth unemployment anxiety and mental health. In Study 2, data were gathered from 118 students in upper-ranking schools in Seoul, Korea. Our results indicated that (c) upward social comparison had mediating effects on the relationship between collectivism and perceived youth unemployment anxiety and mental health, while downward social comparison did not. The findings are discussed in terms of their general implications for understanding the importance of culture in employment seeking settings.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.33-40
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• 2000

복합명사의 한 구조는 구성 명사간의 수식관계의 집합이라고 본다. 한 복합명사에 대하여 가능한 여러 구조 중에서 올바른 구조를 알아 내는 것이 본 논문의 목표이다. 이를 위하여 우리는 최근에 유행하는 통계 기반 분석 기법을 이용한다. 먼저 우리의 복합 명사 분석 문제에 알맞은 통계 모델을 개발하였다. 이 모델을 이용하면 분석하려는 복합명사의 가능한 분석 구조마다 확률 값을 얻게 된다. 그 다음 가능한 구조들 중에서 가장 확률값이 큰 구조를 복합명사의 구조로 선택한다. 통계 기반 기법에서 항상 문제가 되는 것이 데이터 부족문제이다. 우리는 이를 해결하기 위해 개념적 계층구조의 하나인 워드넷(WordNet)을 이용한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.532-534
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• 2004

본 논문은 분석가들에게 Naive Geography에 기반 한 형상 추출기술과 상식적 공간추론 기술을 제공하는 문제 해결 환경인 NG Analyst의 개발 사례에 대해 다뤘다. 지형과 각각의 객체에 대한 구성 정보는 분산된 지형공간의 지식을 사실적으로 묘사하는 추론집합에 의해 표현되며 사용자가 형상정보를 인지적으로 이해할 수 있도록 3차원으로 표현한다. 여러 그래픽 적인 요소들로 표현된 Naive Geography 정보들은 분석가들에게 실세계의 공간과 객체들을 유사하게 구성하여 제공함으로서 직관적으로 이해하고 상호작용 할 수 있는 문제 해결 환경을 제공한다.