• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.39-45
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• 2022

For national industrial development in the Fourth Industrial Revolution, it is necessary to provide researchers with appropriate job information. This can be achieved by interconnecting the National Science and Technology Standard Classification System used for management of research activity with the Korean Employment Classification of Occupations used for job information management. In the present study, an automatic linkage model of classification systems is introduced based on a pre-trained language model for interconnecting science and technology information with job information. We propose for the first time an automatic model for linkage of classification systems. Our model effectively maps similar classes between the National Science & Technology Standard Classification System and Korean Employment Classification of Occupations. Moreover, the model increases interconnection performance by considering hierarchical features of classification systems. Experimental results show that precision and recall of the proposed model are about 0.82 and 0.84, respectively.

• 정보교육학회논문지
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• 제16권3호
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• pp.299-307
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• 2012

일반적인 영재아들에 대한 교육방법론과 교육과정들은 많이 존재하지만 정보영재아들을 위한 교육과정들은 거의 존재하고 있지 않다. 본 연구에서는 정보 영재아들을 위한 교육과정을 제안한다. 본 교육과정의 특징은 정보 분야뿐만 아니라 과학, 수학분야를 같이 공부하는 통합되고 모듈화된 교육과정이다. 초등학교에서는 정보를 정규 교육과정으로 운영하지 않기 때문에 이 모델은 타당하다. 또한 정보 과학 분야의 영재성을 발견하기 위해서 여러 영역을 교육할 필요가 있다. 교육과정은 모둘간의 관계도를 최소화하고 모듈내에서의 깊이를 심도있게 구성한다. 본 연구에서 제안한 모델은 3년간 60명의 학생들을 대상으로 실험하였다. 실험결과 본 연구에서 제안한 모듈형 교육과정 모델은 효과가 있다는 것을 알았다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권2호
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• pp.664-682
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• 2012

To improve the accuracy and enhance the applicability of existing models, this paper proposes a generalized Markov chain model for IEEE 802.11 Distributed Coordination Function (DCF) under the widely adopted assumption of ideal transmission channel. The IEEE 802.11 DCF is modeled by a two dimensional Markov chain, which takes into account unsaturated traffic, backoff freezing, retry limits, the difference between maximum retransmission count and maximum backoff exponent, and limited buffer size based on the M/G/1/K queuing model. We show that existing models can be treated as special cases of the proposed generalized model. Furthermore, simulation results validate the accuracy of the proposed model.

• 한국문헌정보학회지
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• 제26권
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• pp.167-185
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• 1994

The purpose of this study is to develop the optimal user/librarian interface in information searching. In order to achive the purpose, the 150 unskilled students as subjects have participated in the study. According to the change of the subjects' psychological information states by the access points within the library system, the subjects have been classified into the five types of model: the initial information state, the accepted identification information state, the bibliographic information state, the stack information state, and the location information state. Librarian's searching support is done for 10 minutes at the each access points. To develop the optimal user/librarian interface, the expected values of the models are calculated. The resultants are as follows: 1) The expected value of the initial information states model is 18.94: 2) The expected value of the accepted identification information model is 27.06: 3) The expected value of the bibliographic information state model is 27.06: 4) The expected value of the stack information state model is 22.38: 5) The expected value of the location information state model is 22.38. Those expected values are compared with each other. The model with the lowest expected value is chosen as the optimal user/librarian interface model. In the result, the user's initial information state model of the optimal user/librarian interface in information searching is developed. In order to search the information with the most effect, user must be interfaced with the librarian at his/her own initial information state.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.95-102
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• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• International Journal of Contents
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• 제6권3호
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• pp.10-14
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• 2010

Researchers in Korea and elsewhere have carried out a wide variety of important research activities in their respective fields, producing valuable research results. For such diverse research results to be shared and exchanged among researchers working in the same discipline and research subject there needs to be a community environment based on free utilization of information. Against this backdrop, this study seeks to classify and reprocess the reference/factual content owned by the KISTI (Korea Institute of Science and Technology Information), a state-run distributor of information on science and technology, by the different research subjects. It also seeks to develop and provide a community model based on the concepts of open archiving and open access for the researchers specialized in the related fields of research. This community model is developed focusing on the research results from the field of bioscience, where the most extensive studies are currently being conducted. To develop the community model, this study: (a) surveys the current status of the content owned by KISTI; (b) analyzes the patterns and characteristics of biological scientific content among the KISTI-owned content; and (c) designs a web platform where researchers can freely upload/download research results.

• 한국경영과학회지
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• 제10권1호
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• pp.79-83
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• 1985

This purpose of this study is to suggest a tentative model for project selection of information system. In constructing a mathematical model, quantification of decision criteria is tried to lessen difficulties of measuring benefits of information system project. Suggested model enables us to select projects in the context of portfolio and information system policy.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
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• pp.947-950
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• 2007

최근들어 대부분의 공공기관에서 ITA(Information Technology Architecture : 정보기술아키텍처)와 BSC(Balance ScoreCard : 균형성과표) 시스템을 도입하고 있다. ITA에서 제시되는 참조모델(Reference Model)은 ITA의 일관성, 재사용성, 상호운용성 등을 확보하기 위한 도구인데 그 중 하나인 성과참조모델(PRM : Performance Reference Model)에 있어서 BSC와의 연계가 필요한 이유와 그 방법에 관해 논하고자 한다.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.218-227
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• 2018

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.