• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.60.2-60
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• 2001

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.33-37
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• 2007

Semantic clustering is important to various fields in the modem information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a hierarchical clustering in latent semantic space for finding out invisible information from the data.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.764-767
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• 2004

The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.133-140
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• 2020

This study proposes a method of blending visible and near infrared images to enhance edge details and local contrast. The proposed method consists of radiance map generation and color compensation. The radiance map is produced by a Laplacian pyramid and a soft mixing method based on principal component analysis. The color compensation method uses the ratio between the composed radiance map and the luminance channel of a visible image to preserve the visible image chrominance. The proposed method has better edge details compared to a conventional visible and NIR image blending method.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.04a
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• pp.110-113
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• 2002

Statistical- based, principal component analysis (PCA) was applied to chemical data from two underground storage systems containing LPG to assess the usefulness of such technique at the initial stage (Pyeongtaek) or middle stage (Ulsan) of hydrochemical studies. For the first case, both natural and anthropogenic contamination characterize regional groundwater. Saline water buffered by Namyang lake affects as a natural factor, whereas cement grouting influence as an artificial factor. For the second study area, contaminations due to operation of LPG caverns, such as disinfection activity and cement grouting effect, deteriorate groundwater quality. This study indicates that principal component analysis would be particularly useful for summarizing large data set for the purpose of subsurface characterization, assessing their vulnerability to contamination and protecting recharge zones.

• Journal of Mechanical Science and Technology
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• v.19 no.5
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• pp.1123-1130
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• 2005

In view of the lack of efficient coordination of interdependent task in the collaborative design system, the mechanisms for temporal and resource coordination problems are established based on Petri Nets, respectively. Both of the mechanisms are encapsulated and implemented in the coordination component so as to increase the flexibility and acceptability of the system. We model the CSCW system based on Petri Nets for simulation, analysis and optimization. A case study on the overhead traveling crane is given to demonstrate and validate our theory.

• Journal of the Korean Home Economics Association
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• v.35 no.1
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• pp.47-58
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• 1997

The purpose of this study was to investigate the relationship between children's gender role attitude measured by component model and social competency. Subjects were 232 elementary school children: 113 4th graders and 119 6th graders. The main results were as follows. First there were significant differences in mean scores of gender role attitude with age and sex variable. And among three dimensions of component model(i.e, gender label-component links within-component links between-component links) the difference was most discriminant in gender label-component links. Second there were significant relationship between gender role attitude and social competency. Among three dimensions of gender role attitude the most predictor variable for social competency was gender label-component links. And among four dimensions of social competency the most effective criterion variable for gender role attitude was leadership. And there were significant differences in social competency score with mother's educational level and sex of children as well as gender role attitude. In conclusion children's gender role attitude influence their social competency. More flexible gender role attitude they have more improved in their social competency , especially in boys.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.539-554
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• 2012

Model-driven and component-oriented development is increasingly being used in the development of embedded systems. When combined, both paradigms provide several advantages, such as higher reuse rates, and improved system quality. Performing model-driven and component-oriented development should be accompanied by a component model and a method that prescribes how the component model is used. This article provides an overview on the MARMOT method, which consists of an abstract component model and a methodology for the development of embedded systems. The paper describes a feasibility study that demonstrates MARMOT's capability to alleviate system design, verification, implementation, and reuse. Results indicate that model-driven and component-based development following the MARMOT method outperforms Agile development for embedded systems, leads to maintainable systems, and higher than normal reuse rates.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.4
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• pp.346-352
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• 2005

The chemical characteristics, enzymatic saccharification, and ethanol fermentation of autohydrolyzed lignocellulosic material that was exposed to steam explosion were investigated using bagasse as the sample. The effects of the steam explosion on the change in pH, organic acids production, degrees of polymerization and crystallinity of the cellulose component, and the amount of extractive components in the autohydrolyzated bagasse were examined. The steam explosion decreased the degree of polymerzation up to about 700 but increased the degree of crystallinity and the micelle width of the cellulose component in the bagasse. The steam explosion, at a pressure of 2.55 MPa for 3 mins, was the most effective for the delignification of bagasse. 40 g/L of glucose and 20 g/L of xylose were produced from 100 g/L of the autohydrolyzed bagasse by the enzymatic saccharification using mixed cellulases, acucelase and meicelase. The maximum ethanol concentration, 20 g/L, was obtained from the enzymatic hydrolyzate of 100 g/L of the autohydrolyzed bagasse by the ethanol fermentation using Pichia stipitis CBS 5773; the ethanol yield from sugars was 0.33 g/g sugars.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.13-26
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• 2011

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.