• Bulletin of the Korean Mathematical Society
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• v.46 no.2
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• pp.245-253
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• 2009

A new hard problem called the vector decomposition problem (VDP) was recently proposed by Yoshida et al., and it was asserted that the VDP is at least as hard as the computational Diffie-Hellman problem (CDHP) under certain conditions. Kwon and Lee showed that the VDP can be solved in polynomial time in the length of the input for a certain basis even if it satisfies Yoshida's conditions. Extending our previous result, we provide the general condition of the weak instance for the VDP in this paper. However, when the VDP is practically used in cryptographic protocols, a basis of the vector space ${\nu}$ is randomly chosen and publicly known assuming that the VDP with respect to the given basis is hard for a random vector. Thus we suggest the type of strong bases on which the VDP can serve as an intractable problem in cryptographic protocols, and prove that the VDP with respect to such bases is difficult for any random vector in ${\nu}$.

• Korea-Australia Rheology Journal
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• v.21 no.2
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• pp.143-146
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• 2009

Breakthrough of high Weisenberg number problem is related with keeping the positive definiteness of the conformation tensor in numerical procedures. In this paper, we suggest a simple method to preserve the positive definiteness by use of vector decomposition of the conformation tensor which does not require eigenvalue problem. We also derive the constitutive equation of tensor-logarithmic transform in simpler way than that of Fattal and Kupferman and discuss the comparison between the vector decomposition and tensor-logarithmic transformation.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.1
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• pp.9-13
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• 1985

This paper develops a decomposition method for stochastic programming with a block diagonal structure. Here we assume that the right-hand side random vector of each subproblem is differente each other. We first, transform this problem into a master problem, and subproblems in a similar way to Dantizig-Wolfe's Decomposition Princeple, and then solve this master problem by solving subproblems. When we solve a subproblem, we first transform this subproblem to a Deterministic Equivalent Programming (DEF). The form of DEF depends on the type of the random vector of the subproblem. We found the subproblem with finite discrete random vector can be transformed into alinear programming, that with continuous random vector into a convex quadratic programming, and that with random vector of unknown distribution and known mean and variance into a convex nonlinear programming, but the master problem is always a linear programming.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.27-33
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• 2007

Recently, a new hard problem on a two dimensional vector space called vector decomposition problem (VDP) was proposed by M. Yoshida et al. and proved that it is at least as hard as the computational Diffe-Hellman problem (CDHP) on a one dimensional subspace under certain conditions. However, in this paper we present the VDP relative to a specific basis can be solved in polynomial time although the conditions proposed by M. Yoshida on the vector space are satisfied. We also suggest strong instances based on a certain type basis which make the VDP difficult for any random vector relative to the basis. Therefore, we need to choose the basis carefully so that the VDP can serve as the underlying intractable problem in the cryptographic protocols.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.3-13
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• 1996

In orde to reduce huge memory requirements of multidimensional CMAC problems, building a CMAC system by problem decomposition is investigated. Decomposition is based on resolving a displacement vector in cartesian coordinates into unit vectors that define a few lower-dimensional CMACs in the CMAC system. A CMAC system for an an in verse kinematics problem for a planar manipulator was simulated and the performance of the system was evaluated in terms of training and output quality.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.123-123
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• 2003

More elaborated methods allowing the usage of binary classifiers for the resolution of multi-class classification problems are briefly presented. This way of using FSVC to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K FSVC solving a one-per-class decomposition of the general problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1441-1456
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• 2015

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing,. The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

• Smart Media Journal
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• v.8 no.2
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• pp.74-79
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• 2019

Traditional compound noun decomposition algorithms often face challenges of decomposing compound nouns into separated nouns when unregistered unit noun is included. It is very difficult for those traditional approach to handle such issues because it is impossible to register all existing unit nouns into the dictionary such as proper nouns, coined words, and foreign words in advance. In this paper, in order to solve this problem, compound noun decomposition problem is defined as tag sequence labeling problem and compound noun decomposition method to use syllable unit embedding and deep learning technique is proposed. To recognize unregistered unit nouns without constructing unit noun dictionary, compound nouns are decomposed into unit nouns by using LSTM and linear-chain CRF expressing each syllable that constitutes a compound noun in the continuous vector space.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.585-592
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• 2003

Among various sensitivity evaluation techniques, semi-analytical method(SAM) is quite popular since this method is more advantageous than analytical method(AM) and global finite difference method(FDM). However, SAM reveals severe inaccuracy problem when relatively large rigid body motions are identified fur individual elements. Such errors result from the numerical differentiation of the pseudo load vector calculated by the finite difference scheme. In the present study, an iterative method combined with mode decomposition technique is proposed to compute reliable semi-analytical design sensitivities. The improvement of design sensitivities corresponding to the rigid body mode is evaluated by exact differentiation of the rigid body modes and the error of SAM caused by numerical difference scheme is alleviated by using a Von Neumann series approximation considering the higher order terms for the sensitivity derivatives.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.15-24
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• 2009

Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.