• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2698-2706
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• 2010

Most tiled display systems supported a public workspace model where multiple users share the contents and work together on a large public screen. In this research, we developed ICE Display Framework, designed for supporting easy construction of tiled display applications allowing mixed-focus collaboration. Mixed-focus collaboration is a model that allows a number of users to work together as a group or individually on the large workspace. ICE Framework allows users to add personal contents on the tiled display without interrupting other users as well as to put shared works. In this paper, we compare ICE framework with previous research and explain the detail implementation. Then, we introduce the applications built with this framework and discuss the evaluation and analysis of the performance of the new framework.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.693-698
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• 2003

Every baseball game generates various records and on the basis of those records, win/lose prediction about the next game is carried out. Researches on win/lose predictions of professional baseball games have been carried out, but there are not so good results yet. Win/lose prediction is very difficult because the choice of features on win/lose predictions among many records is difficult and because the complexity of a learning model is increased due to overlapping factors among the data used in prediction. In this paper, learning features were chosen by opinions of baseball experts and a heuristic function was formed using the chosen features. We propose a hybrid model by creating a new value which can affect predictions by combining multiple features, and thus reducing a dimension of input value which will be used for backpropagation learning algorithm. As the experimental results show, the complexity of backpropagation was reduced and the accuracy of win/lose predictions on professional baseball games was improved.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.617-622
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• 1997

격납건물내 소격실에서의 수소혼합 정도를 파악하고 격실내 균일한 혼합을 좌우하는 인자의 영향을 분석하기 위하여 소규모 혼합실험을 수행했다. 본 연구에서는 해석적으로 수립된 3차원 혼합 모델의 검증을 위하여 3차원 모사가 가능하도록 실험 장치를 구성하였다. 격납용기 내에서 수소 생성의 주원인이 되는 노심으로부터의 수소거동을 분석하기 위한 기초 실험(실험 A)과 안전주입 탱크 격실에서의 수소거동을 분석하기 위해 원형 혼합 chamber를 구상했다. 기초실험 A에서는 혼합 chamber내 축 방향으로 대칭적인 오리피스형 장애물을 설치하고 실험했고 안전주입 탱크 격실을 모사한 실험 B는 영광 3&4호기를 바탕으로 축소시켜 안전주입탱크 격실내 존재하는 두충과 안전 주입 탱크 사이의 틀을 통한 혼합체의 거동을 분석했다. 실험결과 오리피스형 장애물을 설치한 기초실험에서는 원형 띠모양의 장애물이 혼합체의 거동에 큰 영향을 주지 않는 것이 관측됐지만 안전주입탱크격실 실험에서는 격실내 장애물로 존재하는 두충이 혼합체의 거동에 큰 영향을 주는 것이 관측됐다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.997-1006
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• 2004

The rapid expansion of the Internet has provided users with a diverse range of services. Most Internet users create many different IDs and passwords to subscribe to various Internet services. Thus, the SSO system has been proposed to supplement vulnerable security that may arise from inefficient management system where administrators and users manage a number of ms. The SSO system can provide heightened efficiency and security to users and administrators. Recently commercialized SSO systems integrate a single agent with the broker authentication model. However, this hybrid authentication system cannot resolve problems such as those involving user pre-registration and anonymous users. It likewise cannot provide non-repudiation service between joining objects. Consequently, the hybrid system causes considerable security vulnerability. Since it cannot provide security service for the agent itself, the user's private information and SSO system may have significant security vulnerability. This paper proposed an authentication model that integrates a broker authentication model, out of various authentication models of the SSO system, with a multi-agent system. The proposed method adopts a secure multi-agent system that supplements the security vulnerability of an agent applied to the existing hybrid authentication system. The method proposes an SSO authentication model that satisfies various security requirements not provided by existing broker authentication models and hybrid authentication systems.

• Journal of the Korea Society of Computer and Information
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• v.6 no.1
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• pp.45-50
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• 2001

Congestion may take place in the ATM network because of high-speed cell transmission features, and cell delay and loss also can be caused by unexpected traffic variation. Thus. traffic control mechanisms are needed. One of them to decrease congestion is the cell shaping. This paper proposes a hybrid type cell shaper composed of a Leaky Bucket with token pool, EWMA with time window, and a spacing control buffer. The simulator BONeS with the ON/OFF traffic source model evaluates the performance of the proposed cell shaping method. Simulation results show that the cell shaping concerning the respective source traffics is adapted to and then controlled on the mean bit rate.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.425-432
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• 2009

In this study, the ability of neural network in modeling and predicting of the unsteady aerodynamic force coefficients of 2D airfoil with the data obtained from Euler CFD code has been confirmed. Neural network models are constructed based on supervised training process using Levenberg-Marquardt algorithm, combining this into genetic algorithm, hybrid genetic algorithm and the efficiency of the two cases are analyzed and compared. It is shown that hybrid-genetic algorithm is more efficient for neural network of complex system and the predicted properties of the unsteady aerodynamic force coefficients of 2D airfoil by the neural network models are confirmed to be similar to that of the numerical results and verified as suitable representing reduced models.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.739-747
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• 2018

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.88-93
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• 1997

본 논문에서는 품사 태깅을 위해 여러 통계 모델을 실험을 통하여 비교하였으며 이를 토대로 통계적 모델을 구성하였다. 형태소 패턴 사전을 이용하여 미등록어의 위치와 개수에 관계없는 일반적인 방법의 미등록어 처리 방법을 개발하고 통계모델이 가지는 단점을 보완할 수 있는 오류 수정 규칙을 함께 이용하여 혼합형 품사 태깅 시스템인 $POSTAG^{i}$를 개발하였다. 미등록어를 추정하는 형태소 패턴 사전은 한국어 음절 정보와 용언의 불규칙 정보를 이용하여 구성하고 다어절어 사전을 이용하여 여러 어절에 걸쳐 나타나는 연어를 효과적으로 처리하면서 전체적인 태깅 정확도를 개선할 수 있다. 또 오류 수정 규칙은 Brill이 제안한 학습을 통하여 자동으로 얻어진다. 오류 수정 규칙의 자동 추출시에 몇 가지의 휴리스틱을 사용하여 보다 우수하고 일반적인 규clr을 추출할 수 있게 하였다. 10만의 형태소 품사 말뭉치로 학습하고 학습에 참여하지 않은 2만 5천여 형태소로 실험하여 97.28%의 정확도를 보였다.

• Proceedings of the Optical Society of Korea Conference
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• 1988.06a
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• pp.82-85
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• 1988

임계 연계용액에서의 DC Kerr 효과를 측정하고 임계온도 근처에서 Kerr상수가 발산하는 등의 임계 현상이 나타남을 관측하고 임계지수를 측정한다. 이로부터 현상학적 모델인 방울모델(droplet model) 의 타당성을 조사한다.