• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.372-378
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• 2003

Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.89-92
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• 2003

It is often that a large-scale linear programming(LP) problem may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. Presolving or preprocessing is a series of operations which removes the underlying redundancy or detects infeasibility in the given LP problem. It is essential for the speedup of an LP system solving large-scale problems to implement presolving techniques. For the recovery of an optimal solution for the original problem from an optimal solution for the presolved problem, a special procedure, so called postsolving, must be applied. In this paper, we present how a postsolving procedure is constructed and implemented in LPABO, a interior-point based LP system. Briefly, all presolving processes are logged in a data structure in LPABO, and after the end of the solution method an optimal solution for the original problem is obtained by tracing the logs. In each stage of the postsolving procedure, the optimality of intermediate solutions is maintained. We tested our postsolving procedure on Netlib, Gondzio and Kennington LP data sets, and concluded that the computational burden of the procedure is relatively negligible compared with the total solving time.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.731-732
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• 2006

Limitations on the Linear Discriminant Analysis (LDA) for face recognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for small number of samples. The Principal Component Analysis (PCA) followed by the LDA mapping may be an alternative that can overcome this limitation. We also show that processing time is reduced by wavelet transform.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.923-925
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• 2012

대용량 고차원의 생물학 데이터가 매우 빠른 속도로 생산되는 현재, 단순히 고전적인 알고리즘들로는 풀 수 없는 문제들을 맞이하게 되었다. 이러한 문제들의 경우 시스템 생물학의 관점으로 다양한 생물 데이터의 융합을 통하여 접근할 경우 효율적으로 Computational Infeasibility(계산 불가능)를 해결함은 물론 그 해석 및 새로운 정보 획득에 매우 유리하다. 인간 DNA의 고차원 SNP 정보들의 군집화 및 질병 발현 패턴 분석은 그 조합의 수가 입력 데이터의 차원수에 따라 지수적(Exponentially)으로 증가하지만 PPI(단백질 상호작용) 네트워크 정보에 결합하여 필요한 중요부위를 선택적으로 이용할 경우 효율적으로 필요 SNP들의 선택 및 이로 인한 공간 축소가 가능하다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.1
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• pp.29-37
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• 2014

In this paper we show that a dynamic ID authentication scheme using smart cards proposed by Tsai et al. is not secure against DoS attack and insider attack. Further we claim that their scheme may raise a security problem when a user changes his/her password. Then we come up with a security-enhanced version only with small additional computational cost. Our scheme is based on the security of cryptographic hash function and the infeasibility assumption of discrete logarithm problem. In addition, we provide details of security and computational cost analysis.

• Transactions of Materials Processing
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• v.27 no.1
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• pp.28-36
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• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.