• Title, Summary, Keyword: Minimization Problem

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Weighted Parameter Analysis of L1 Minimization for Occlusion Problem in Visual Tracking (영상 추적의 Occlusion 문제 해결을 위한 L1 Minimization의 Weighted Parameter 분석)

  • Wibowo, Suryo Adhi;Jang, Eunseok;Lee, Hansoo;Kim, Sungshin
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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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).

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An Algorithm for the Concave Minimization Problem under 0-1 Knapsack Constraint (0-1 배낭 제약식을 갖는 오목 함수 최소화 문제의 해법)

  • Oh, S.H.;Chung, S.J.
    • 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.

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Design of suboptimal robust kalman filter using LMI approach (LMI기법을 이용한 준최적 강인 칼만 필터의 설계)

  • 진승희;윤태성;박진배
    • 제어로봇시스템학회:학술대회논문집
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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.

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An Application of Generic Algorithms to the Distribution System Loss Minimization Re-cofiguration Problem (배전손실 최소화 문제에 있어서 유전알고리즘의 수속특성에 관한 연구)

  • Choi, Dai-Seub;Lee, Sang-Il;Oh, Geum-Kon;Kim, Chang-Suk;Choi, Chang-Joo
    • Proceedings of the KIEE Conference
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    • pp.6-9
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    • 2001
  • 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 approach 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.

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An Application of Generic Algorithms to the Distribution System Loss Minimization Re -cofiguration Problem (배전손실 최소화 문제에 있어서 유전알고리즘의 수속특성에 관한 연구)

  • Choi, Dai-Seub;Jung, Soo-Yong
    • Proceedings of the KIEE Conference
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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.

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A Study on the area minimization using general floorplan (종합평면을 사용한 면적 최적화에 관한 연구)

  • 이용희;정상범이천희
    • Proceedings of the IEEK Conference
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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.

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A BOUNDARY CONTROL PROBLEM FOR VORTICITY MINIMIZATION IN TIME-DEPENDENT 2D NAVIER-STOKES EQUATIONS

  • KIM, HONGCHUL
    • Korean Journal of Mathematics
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    • v.23 no.2
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    • pp.293-312
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    • 2015
  • We deal with a boundary control problem for the vorticity minimization, in which the ow is governed by the time-dependent two dimensional incompressible Navier-Stokes equations. We derive a mathematical formulation and a process for an appropriate control along the portion of the boundary to minimize the vorticity motion due to the ow in the fluid domain. After showing the existence of an optimal solution, we derive the optimality system for which optimal solutions may be determined. The differentiability of the state solution in regard to the control parameter shall be conjunct with the necessary conditions for the optimal solutions.

A MEMORY EFFICIENT INCREMENTAL GRADIENT METHOD FOR REGULARIZED MINIMIZATION

  • Yun, Sangwoon
    • 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.

The solution of single-variable minimization using neural network

  • Son, Jun-Hyug;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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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.

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A Damping Distribution Method for Inverse Kinematics Problem Near Singular Configurations (특이점 근방에서 역 기구학 해를 구하기 위한 자동 감쇄 분배 방법)

  • 성영휘
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.6
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    • pp.780-785
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    • 1998
  • In this paper, it is shown that the conventional methods for dealing with the singularity problem of a manipulator can be generalized as a local minimization problem with differently weighted objective functions. A new damping method proposed in this article automatically determines the damping amounts for singular values, which are inversely proportional to the magnitude of the singular values. Furthermore, this can be done without explicitly computing the singular values. The proposed method can be applied to all the manipulators with revolute joints.

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