• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.635-638
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• 2011

유한요소법은 편미분방정식(Partial Differential Equation)의 수치적 근사 해를 구하기 위한 가장 일반적이고 효율적인 방법으로 다양한 공학 분야에서 널리 사용되어지고 있다. 유한요소법의 해석은 연속적인 범위를 가지는 문제를 여러 개의 요소로 나누어 다항식의 형상함수를 만들게 되며 결과적으로 근사 해를 구하게 된다. 이때 해석의 정확성을 높이기 위하여 형상함수의 차수를 높이고 요소의 개수를 늘리게 되면, 이에 따른 수치 계산량의 급격한 증가로 인해 수치해석의 효율성은 떨어지게 된다. 이를 보완하기 위해 유한요소법에 영역분할기법을 적용하여 병렬해석을 수행하면 해의 정확성과 효율성을 동시에 높인다. 병렬해석을 수행하는데 있어서 클러스터의 구조적 특성은 해석의 효율성에 영향을 미치게 된다. 따라서 본 논문에서는 일반적인 모델에 대하여 병렬해석의 수행을 통하여 클러스터의 구조적 특성이 병렬해석의 효율성에 미치는 영향에 대해 확인한다.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.7-12
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• 2003

Because some systems limit the supported maximal degree, the degree reduction of NURBS is necessary in Parametric curves and surfaces of the different geometric modeling systems. Therefore an approximate degree reduction method of NURBS curves was introduced in this research. Also the existing Eck's B$\'{e}$zier degree reduction method and knot removal algorithm were used to reduce data in the degree reduction process. Finally we found that this method was stable, efficient for implementations, and easy to use algorithms.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2280-2282
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• 2003

신경 진동자는 이미지정보의 해석, 음성인식, 지질 변화 예측 등의 진동하는 시스템에 응용되어진다. 이러한 진동하는 시스템에 기존의 역전파 알고리즘을 이용하는 경우, 복잡 다양한 입력 패턴을 추정하기가 어려우므로 학습단계에서 더 많은 양의 학습 데이터가 필요하게 되며 수렴 속도의 지연과 근사화가 어렵다. 따라서 본 논문에서는 모델에 대한 함수의 근사화가 쉬우며 학습하는 구조를 가지는 신경 진동자에 의한 위상 동기화 특성을 연구하고 이를 이용한 한글 문자 인식시스템을 구현하였다.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.8
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• pp.411-417
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• 2005

We present a method to reconstruct a canal surface from a point cloud (a set of unorganized points). A canal surface is defined as a swept surface of a moving sphere with varying radii. By using the shrinking and moving least-squares methods, we reduce a point cloud to a thin curve-like point set which can be approximated to the spine curve of a canal surface. The distance between a point in the thin point cloud and a corresponding point in the original point set represents the radius of the canal surface.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1309-1317
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• 2013

There are various parameter estimation methods for the generalized exponential distribution under progressive type I interval censoring. Chen and Lio (2010) studied the parameter estimation method by the maximum likelihood estimation method, mid-point approximation method, expectation maximization algorithm and methods of moments. Among those, mid-point approximation method has the smallest mean square error in the generalized exponential distribution under progressive type I interval censoring. However, this method is difficult to derive closed form of solution for the parameter estimation using by maximum likelihood estimation method. In this paper, we propose two type of approximate maximum likelihood estimate to solve that problem. The simulation results show the obtained estimators have good performance in the sense of the mean square error. And proposed method derive closed form of solution for the parameter estimation from the generalized exponential distribution under progressive type I interval censoring.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.149-157
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• 2012

In this thesis, the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. It is shown formally that neither approximation can be addressed effectively for a sliding-window join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequency based model of stream arrival. There exists a sampling problem for this model. More importantly, it is shown that a broad class of applications for which an age-based model of stream arrival is more appropriate, and both approximation scenarios under this new model are addressed. Finally, for the case of multiple joins being executed with an overall memory constraint, an algorithm for memory allocation across the join that optimizes a combined measure of approximation in all scenarios considered is provided.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.59-66
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• 2011

Resource constrained project scheduling problem with multiple resource constraints as well as precedence constraints is well-known as one of the NP-hard problem. Since these problems can't be solved by the deterministic method during reasonable time, the heuristics are generally used for getting a sub-optimal during reasonable time. In this paper, we introduce an efficient genetic algorithm for resource constrained project scheduling problem using crossover which is applying schema theory and real world tournament selection strategy. Experimental results showed that the proposed algorithm is superior to conventional algorithm.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.3-11
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• 1999

The conventional stereophonic acoustic echo cancellers need two adaptive filters to estimate one channel echo signal. Since the two channel signals are strongly correlated, the ESR of the input signals is considerably increased whatever the input signals may be. This causes the slow convergence of the adaptive filter for echo cancellation. To speed up the convergence, the AP algorithm is frequently used for the stereophonic acoustic echo canceller although there isn't a fast version for 2-channel case. The AP algorithm can be approximated with the Gram-Schmidt orthogonalization and a TDL structure. We propose a two channel algorithm for stereophonic acoustic echo canceller with the approximated AP algorithm.

• The KIPS Transactions:PartB
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• v.8B no.3
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• pp.231-236
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• 2001

일반적인 다층 신경망에서 가중치의 갱신 알고리즘으로 사용하는 오류 역전과 방식은 가중치 갱신 결과를 고정된(fixed) 한 개의 값으로 결정한다. 이는 여러 갱신의 가능성을 오직 한 개의 값으로 고정하기 때문에 다양한 가능성들을 모두 수용하지 못하는 면이 있다. 하지만 모든 가능성을 확률적 분포로 표현하는 갱신 알고리즘을 도입하면 이런 문제는 해결된다. 이러한 알고리즘을 사용한 베이지안 신경망 모형(Bayesian Neural Networks Models)은 주어진 입력값(Input)에 대해 블랙 박스(Black-Box)와같은 신경망 구조의 각 층(Layer)을 거친 출력값(Out put)을 계산한다. 이 때 주어진 입력 데이터에 대한 결과의 예측값은 사후분포(posterior distribution)의 기댓값(mean)에 의해 계산할 수 있다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 우도함수(likelihood functions)에 의해 계산한 사후확률의 함수는 매우 복잡한 구조를 가짐으로 기댓값의 적분계산에 대한 어려움이 발생한다. 따라서 수치해석적인 방법보다는 확률적 추정에 의한 근사 방법인 몬테 칼로 시뮬레이션을 이용할 수 있다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 좋은 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘을 적용한 신경망이 기존의 CHAID, CART 그리고 QUEST와 같은 여러 가지 분류 알고리즘에 비해서 우수한 결과를 제공하는 것을 나타내고 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.322-328
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• 2009

This paper proposes new simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes. The proposed SSP algorithm can replace multiplications and divisions with additions and subtractions without extra computations. In addition, the proposed SSP algorithm can simplify both the In[tanh(x)] and tanh-1 [exp(x)] by using two quantization tables which can reduce tremendous computational complexity. Moreover, the simulation results show that the proposed SSP algorithm can improve about $0.3\;{\sim}\;0.8\;dB$ of BER performance compared with the existing modified sum-product algorithms.