• Journal of the Korean Society for Library and Information Science
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• v.26
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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.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.197-204
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• 2009

In the model selection problem, the main objective is to choose the true model from a manageable set of candidate models. An information criterion gauges the validity of a statistical model and judges the balance between goodness-of-fit and parsimony; "how well observed values ran approximate to the true values" and "how much information can be explained by the lower dimensional model" In this study, we introduce some information criteria modified from the Akaike Information Criterion (AIC) and the Bayesian Information Criterion(BIC). The information criteria considered in this study are compared via simulation studies and real application.

• Korean Journal of Computational Design and Engineering
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• v.10 no.2
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• pp.133-142
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• 2005

This paper proposes an object-oriented conceptual information model of mechanical assemblies, named open assembly model (OAM). The proposed assembly model primarily defines hierarchical relationships between parts and subassemblies. Together with the assembly hierarchy. the model also provides a way to represent tolerances, kinematic information, and parametric assembly constraints. Relational information such as mating conditions and degree of freedom between parts and subassemblies is captured via assembly features and relationships thereof. The information model is described using class diagrams of the Unified Modeling Language (UML), and instance diagrams are used to exemplify the proposed information model. The conceptual model presented in this paper is an integrated information model for assembly representation, which could supply necessary information for tolerance analysis and synthesis, kinematic simulation, and assembly simulation. Such a conceptual information model plays an important role for the exchange of information between modeling, analysis and planning systems. Hence, the proposed model could serve as a framework for developing data exchange standards of mechanical assemblies. The proposed model is demonstrated through a case study of a planetary gear assembly.

• The Journal of Information Technology and Database
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• v.3 no.2
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• pp.75-96
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• 1996

This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.951-969
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• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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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.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.853-864
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• 2019

Microblogging services (such as Twitter) are the representative information communication networks during the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for information dissemination in online social networks due to its large number of users. In this study, a microblog information dissemination model is presented. Related concepts are introduced and analyzed based on the dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptible-infective-removal (SIR) information dissemination model. Correlation analysis is conducted on the existing information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method in reducing the rumor dissemination of microblogs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• Journal of KIBIM
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• v.4 no.3
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• pp.1-9
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• 2014

As BIM application continues to increase in civil engineering, in this study, 3D information model for RC(Reinforced Concrete) bridge pylon was developed and verified its effectiveness at the structural-design stage. To define 3D information model of RC A-Type pylon, characteristics of pylon were analyzed and 3D model structure was constructed. The 3D information model, one of the core product of BIM, manages all information generated during all life-cycle of a structure and consequently maximizes the efficiency of utilizing information. Also, this study proposes interface module between input data in structural analysis and 3D model of RC pylon. The module can create the input data for non-linear structural analysis. It is essential to study on method of developing 3D information model and propose a structural analysis model by utilizing 3D model for the effective use of BIM techniques in construction industry. The results of this study can be used as the base data for developing the 3D information model of RC pylon in the structural analysis field.

• Language and Information
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• v.8 no.1
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• pp.101-116
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• 2004

This paper proposes a statistical model for the translation of prepositions in English-Korean machine translation. In the proposed model, statistical information acquired from unlabeled Korean corpora is used to choose the best translation from several possible translations. Such information includes functional word-verb co-occurrence information, functional word-verb distance information, and noun-postposition co-occurrence information. The model was evaluated with 443 sentences, each of which has a prepositional phrase, and we attained 71.3% accuracy.