• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.84-91
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• 2007

Recently, the researches based on vision about user attention and action awareness are being pushed actively for human computer interaction. Among them, various applications of tabletop display system are developed more in accordance with touch sensing technique, co-located and collaborative work. Formerly, although supported only one user, support multi-user at present. Therefore, collaborative work and interaction of four elements (human, computer, displayed objects, physical objects) that is ultimate goal of tabletop display are realizable. Generally, tabletop display system designs according to four key aspects. 1)multi-touch interaction using bare hands. 2)implementation of collaborative work, simultaneous user interaction. 3)direct touch interaction. 4)use of physical objects as an interaction tool. In this paper, we describe a critical analysis of the state-of-the-art in advanced multi-touch sensing techniques for tabletop display system according to the four methods: vision based method, non-vision based method, top-down projection system and rear projection system. And we also discuss some problems and practical applications in the research field.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.679-688
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• 2006

Protein-protein interaction data obtained from high-throughput experiments includes high false positives. In this paper, we introduce a new protein-protein interaction reliability verification system. The proposed system integrates various biological features related with protein-protein interactions, and then selects the most relevant and informative features among them using a feature selection method. To assess the reliability of each protein-protein interaction data, the system construct a classifier that can distinguish true interacting protein pairs from noisy protein-protein interaction data based on the selected biological evidences using a classification technique. Since the performance of feature selection methods and classification techniques depends heavily upon characteristics of data, we performed rigorous comparative analysis of various feature selection methods and classification techniques to obtain optimal performance of our system. Experimental results show that the combination of feature selection method and classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Also, we investigated the effects on performances of feature selection methods and classification techniques in the proposed protein interaction verification system.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.128-133
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• 2006

This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

• Rapid Communication in Photoscience
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• v.5 no.1
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• pp.8-10
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• 2016

The potential curves of OH bonds of liquid water are inhomogeneous because of a variety of interactions with other molecules and this leads to a wide distribution of vibrational frequency which hampers our understanding of the structure and dynamics of water molecules. Mixed quantum/classical (QM/CM) calculation methods are powerful theoretical techniques to help us analyze experimental data of various vibrational spectroscopies to study such inhomogeneous systems. In a type of those approaches, the interaction energy between OH bonds and other molecules is approximately represented by the interaction between the charges located at the appropriate interaction sites of water molecules. For this purpose, we re-calculated the values of charges by comparing the approximate interaction energies with quantum chemical interaction energies. We determined a set of charges at the TIP4P charge sites which better represents the quantum mechanical potential curve of OH bonds of liquid water.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.542-547
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• 2010

Recently, thanks to advanced computational power and numerical techniques, it is made possible to analyze the flow around moving bodies using computational fluid dynamics techniques. In those simulations, moving mesh techniques should be able to represent both the body motion and boundary deformation which are frequently encounterd in fluid-structure interaction and/of six degree-of-freedom problems. There are several moving mesh techniques such as the Laplacian operator based, tension spring based and elastic deformation based methods. In the present study, the Laplacian operator based method was utilized and the results were validated. For the validation, the flow around an oscillating two-dimensional cylinder was simulated and analyzed.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• Journal of Ship and Ocean Technology
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• v.9 no.1
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• pp.38-46
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• 2005

Survivability improvement method for naval ship design has been continually developed. In order to design naval ships considering survivability, it is demanded that designers should establish reasonable damage conditions by air explosion. Explosion may induce local damage as well as global collapse to the ship. Therefore possible damage conditions should be realistically estimated in the design stage. In this study the authors used ALE technique, one of the structure-fluid interaction techniques, to simulate air explosion and investigated survival capability of damaged naval ships. Lagrangian-Eulerian coupling algorithm, equation of the state for explosive and air, and simple calculation method for explosive loading were also reviewed. It is shown that air explosion analysis using ALE technique can evaluate structural damage after being attacked. This procedure can be applied to the real structural design quantitatively by calculating surviving time and probability.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.29.2-29
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• 2001

For the design of human-centered systems in which a human and machine such as a robot form a human-in system, human-friendly interaction/interface is essential. Human-friendly interaction is possible when the system is capable of recognizing human biosigns such as5 EMG Signal, hand gesture and facial expressions so the some humanintention and/or emotion can be inferred and is used as a proper feedback signal. In the talk, we report our experiences of applying the Soft computing techniques including Fuzzy, ANN, GA and rho rough set theory for efficiently recognizing various biosigns and for effective inference. More specifically, we first observe characteristics of various forms of biosigns and propose a new way of extracting feature set for such signals. Then we show a standardized procedure of getting an inferred intention or emotion from the signals. Finally, we present examples of application for our model of rehabilitation robot named.

• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.41-56
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• 2020

This study suggests multisensory interaction techniques to help visually impaired people appreciate and understand artworks through non-visual senses such as tactile, auditory, and olfactory senses. A user study was conducted on the basis of a qualitative interview about the experience of appreciating artwork through the multisensory interaction system to visually impaired people so as to verify the development of the interaction techniques. The user test shows that the multisensory interactions in artwork generally not only help them appreciate and understand it but also give them satisfaction through artwork appreciation. However, it also indicates that some multisensory interactions caused the visually impaired people confusion and could not be perceived during the appreciation. On the basis of these outcomes, implications in this study are as follows. This study has contributed to providing specific development guidelines and indicators of non-visual multisensory interactions as a technical alternative to improve accessibility to cultural and artistic activities for the visually impaired. Furthermore, this study is expected to contribute to building a technical background, which can provide comprehensive sensory experiences with not only blind people but also non-blind people such as children and the elderly through universal interaction techniques beyond existing visual-oriented fragmentary experience.