• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.101-103
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• 2016

Recently, the target object can be represented as sparse coefficient vector in visual tracking. Due to this reason, exploitation of the compressibility in the transform domain by using L1 minimization is needed. Further, L1 minimization is proposed to handle the occlusion problem in visual tracking, since tracking failures mostly are caused by occlusion. Furthermore, there is a weighted parameter in L1 minimization that influences the result of this minimization. In this paper, this parameter is analyzed for occlusion problem in visual tracking. Several coefficients that derived from median value of the target object, mean value of the arget object, the standard deviation of the target object are, 0, 0.1, and 0.01 are used as weighted parameter of L1 minimization. Based on the experimental results, the value which is equal to 0.1 is suggested as weighted parameter of L1 minimization, due to achieved the best result of success rate and precision performance parameter. Both of these performance parameters are based on one pass evaluation (OPE).

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.379-392
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• 1999

The problem of the state-minimization for the nonde-terministic finite Rabin-Scott's automata is considered. A new algo-rithm for this problem is obtained. The obtained algorithm has the exponential effectiveness like the earlier-known algorithms for this problem. But each of previous algo-rithms amounts to the search of minimum generative system for local reaction of equal automaton of canonical form and unlike them we use in this paper two special function marking states of the given automaton.

• Kyungpook Mathematical Journal
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• v.62 no.1
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• pp.89-106
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• 2022

We are concerned with finding a common solution to an equilibrium problem associated with a bifunction, and a constrained convex minimization problem. We propose an iterative fixed point algorithm and prove that the algorithm generates a sequence strongly convergent to a common solution. The common solution is identified as the unique solution of a certain variational inequality.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.3-13
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• 1993

In this study, we develop a B & B type algorithm for the concave minimization problem with 0-1 knapsack constraint. Our algorithm reformulates the original problem into the singly linearly constrained concave minimization problem by relaxing 0-1 integer constraint in order to get a lower bound. But this relaxed problem is the concave minimization problem known as NP-hard. Thus the linear function that underestimates the concave objective function over the given domain set is introduced. The introduction of this function bears the following important meanings. Firstly, we can efficiently calculate the lower bound of the optimal object value using the conventional convex optimization methods. Secondly, the above linear function like the concave objective function generates the vertices of the relaxed solution set of the subproblem, which is used to update the upper bound. The fact that the linear underestimating function is uniquely determined over a given simplex enables us to fix underestimating function by considering the simplex containing the relaxed solution set. The initial containing simplex that is the intersection of the linear constraint and the nonnegative orthant is sequentially partitioned into the subsimplices which are related to subproblems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1477-1480
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• 1997

This paper is concerned with the design of a suboptimal robust Kalman filter using LMI approach for system models in the state space, which are subjected to parameter uncertainties in both the state and measurement atrices. Under the assumption that augmented system composed of the uncertain system and the state estimation error dynamics should be stable, a Lyapunov inequality is obtained. And from this inequaltiy, the filter design problem can be transformed to the gneric LMI problems i.e., linear objective minimization problem and generalized eigenvalue minimization problem. When applied to uncertain linear system modles, the proposed filter can provide the minimum upper bound of the estimation error variance for all admissible parameter uncertainties.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.580-582
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• 2005

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new a roach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1021-1024
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• 1998

Computer-aided design of VLSI circuits is usually carried out in three synthesis steps; high-level synthesis, logic synthesis and layout synthesis. Each synthesis step is further kroken into a few optimization problems. In this paper we study the area minimization problem in floorplanning(also known as the floorplan sizing problem). We propose the area minimization algorithms for general floorplans.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.693-703
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• 2001

In this paper we consider non-deterministic finite Rabin-Scott’s automata. We use a special structure to descibe all the possible edges of non-determinstic finite automaton defining the given regular language. Such structure can be used for solving various problems of finite automata theory. One of these problems is edge-minimization of non-deterministic automata. As we have not touched this problem before, we obtain here two versions of the algorithm for solving this problem to continue previous series of articles.

• Bulletin of the Korean Mathematical Society
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• v.53 no.2
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• pp.589-600
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• 2016

In this paper, we propose a new incremental gradient method for solving a regularized minimization problem whose objective is the sum of m smooth functions and a (possibly nonsmooth) convex function. This method uses an adaptive stepsize. Recently proposed incremental gradient methods for a regularized minimization problem need O(mn) storage, where n is the number of variables. This is the drawback of them. But, the proposed new incremental gradient method requires only O(n) storage.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2528-2530
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• 2004

Neural network minimization problems are often conditioned and in this contribution way to handle this will be discussed. It is shown that a better conditioned minimization problem can be obtained if the problem is separated with respect to the linear parameters. This will increase the convergence speed of the minimization. One of the most powerful uses of neural networks is in function approximation(curve fitting)[1]. A main characteristic of this solution is that function (f) to be approximated is given not explicitly but implicitly through a set of input-output pairs, named as training set, that can be easily obtained from calibration data of the measurement system. In this context, the usage of Neural Network(NN) techniques for modeling the systems behavior can provide lower interpolation errors when compared with classical methods like polynomial interpolation. This paper solve of single-variable minimization using neural network.