• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.71-73
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• 2000

Considering that there is the lack of standards for storing genome-related on-line documents, the techniques in Natural Language Processing (NLP) is likely to become more and more important. It is necessary to extract useful information from the raw text and to store it in a computer-readable database format. Recent advances in NLP technologies raise new challenges and opportunities for tackling genome-related on-line text for information extraction task, For example, we can obtain many useful information related to genetic network or metabolic pathways simply by analyzing verbs such as 'activate'or 'inhibit'in Medline abstracts in a fully automatic way, Thus, combining NLP techniques with genome informatics extends beyond the traditional realms of either technology to a variety of emerging applications.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.11b
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• pp.126-140
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• 2005

The tomographic gamma scanner (TGS) method, a further of extension of segmented gamma scanner (SGS), is most accurate and precise for assaying heterogeneous drummed nuclear radioactive waste; it is widely used in nuclear power plants and radioactive waste storages and disposal sites. The transmission and emission images are reconstructed by image reconstruction techniques. In the paper, the principle of TGS is introduced; image reconstruction techniques are discussed as well; finally, it is demonstrated that TGS method performance.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.7
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• pp.352-360
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• 2003

This paper deals with a simple method in order to analyze and compare the harmonic characteristics in the multi-level inverter. Generally, the magnitude of harmonic components becomes different according to the multi-carrier Pulse Width Modulation(PWM) techniques, the modulation index($M_i$) and the switching frequency The previous papers analyzed the harmonic characteristics from the viewpoint of the space voltage vector. Hence, the calculation of the harmonic vector becomes more difficult and complex in 4-level or over 5-level. However, the proposed method has reduced an amount of calculation and simplified the process of it, using the relationship between the reference voltage and the output phase voltage to the load neutral point. It is applied to the 5-level cascade inverter and the harmonic characteristics for each multi-carrier PWM technique are compared through the simulation.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.1297-1302
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• 2000

A literature review has been carried out to investigate why bridges have collapsed without warning. The reasons behind the collapses have been categorized into short and long term risks. It is thought that permanent monitoring systems which assess structural adequacy are more appropriate to long term risks. From the knowledge of the Korean bridge stock, its current problems and its likely future problems, it was considered that generally the most useful application for a permanent monitoring system is to monitor where chloride-induced corrosion either of the reinforcement or prestressing tendons is possible. A number of permanent monitoring systems currently in use on existing bridges which include some aspect of corrosion detection have been reviewed. The reasons as to why they are being used, what is being measured, what techniques are being used, and if they are deemed successful has been investigated. Based on these findings, and experimental programme has been constructed to investigate the accuracy, reliability and usefulness of various suitable techniques which could be included in a permanent monitoring system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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• pp.39-52
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• 2001

Usability is playing a more important, role in interactive products or systems (e. g,, information technology Products, computer software) development, and it has become a primary factor in determining the acceptability and consequent success of the products. This study investigates some different techniques and methods to evaluate the usability of the interactive products or system . Various evaluation methods have been tried ranging from formal to informal and empirical techniques. The classification of the usability evaluation methods(UEMs) and comparisons between these evaluation methods are presented. Some issues raised by the UEMs are also discussed. In addition, problems of selecting appropriate usability evaluation methods are discussed.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• The Journal of Information Systems
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• v.30 no.4
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• pp.247-275
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• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.33-46
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• 1996

Inthis paper, we investigate the compatible coding technique, which receives much interest ever since the introduction of HDTV. First, attempts have been made to analyze the theoretical transform coding gains for various hierarchical decomposition techniques, namely subband, pyramid and DCT-based decomposition techniques. It is shown that the spatical domain techniques proide higher transform coding gains than the DCT-based coding technique. Secondly, we compare the performance of these spatial domain techniques, in terms of the PSNR versus various rate allocations to each layer. Based on these analyses, it is believed that the pyramid decomposition is more appropriate for the compatible coding. Also in this paper, we propose a hybrid prediction pyramid coding technique, by combining the spatio-temporal prediction in MPEG-2[3] and the adaptive MC(Motion Compensation)[1]. In the proposed coding technigue, we also employ an adaptive DCT coefficient scanning technique to exploit the direction information of the 2nd-layer signal. Through computer simulations, the proposed hybrid prediction with adaptive scanning technuque shows the PSNR improvement, by about 0.46-1.78dB at low 1st-layer rate(about 0.1bpp) over the adaptive MC[1], and by about 0.33-0.63dB at high 1st-layer rate (about 0.32-0.43bpp) over the spatio-temporal prediction[3].