• Title/Summary/Keyword: Linear constraint

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Optimum Array Processing with Variable Linear Constraint

  • Chang, Byong Kun
    • Journal of information and communication convergence engineering
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    • v.12 no.3
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    • pp.140-144
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    • 2014
  • A general linearly constrained adaptive array is examined in the weight vector space to illustrate the array performance with respect to the gain factor. A narrowband linear adaptive array is implemented in a coherent signal environment. It is shown that the gain factor in the general linearly constrained adaptive array has an effect on the linear constraint gain of the conventional linearly constrained adaptive array. It is observed that a variation of the gain factor of the general linearly constrained adaptive array results in a variation of the distance between the constraint plane and the origin in the translated weight vector space. Simulation results are shown to demonstrate the effect of the gain factor on the nulling performance.

Photometric Stereo Calibration using Constraint on Light Source Directions (광원 방향에 대한 제한조건을 이용한 Photometric Stereo Calibration)

  • Jeong, Seong-Cheol;Nguyen, Viet Hung;Won, Sang-Chul
    • IEMEK Journal of Embedded Systems and Applications
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    • v.8 no.2
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    • pp.111-119
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    • 2013
  • This paper proposes one method on photometric stereo calibration using the constraint on light source directions in which the light sources have the unknown tilt and slant angles but the slant angles are the same. First, the constraint is analyzed based on the equation of linear ambiguity which leads to the conclusion that another constraint should be added to solve the calibration completely. Later, the combination of constraint on light source directions and the constraint that there exists at least six surface patches having known albedos is exploited to resolve the linear ambiguity up to an accurate and close-form solution. The effective performance of the proposed method is demonstrated through experiment results.

Perfect Tracking Control for Linear Systems with State Constraint

  • Baang, Dane;Choi, Jin-Young;Shim, Hyung-Bo
    • International Journal of Control, Automation, and Systems
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    • v.5 no.2
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    • pp.218-222
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    • 2007
  • This paper presents a new Perfect Tracking Control (PTC) scheme for linear systems with state constraint. The proposed controller increases the number of the steps on-line for perfect tracking to satisfy the given ellipsoid-type state constraint. The unavoidable step delay that we impose is minimized by solving LMI feasibility problems and the possible feedback information loss is avoided. The proposed schemes are easy to develop, theoretically simple and clear, and include the conventional PTC as its special case.

FLEXIBLE OPTIMIZATION MODEL FOR LINEAR SCHEDULING PROBLEMS

  • Shu-Shun Liu;Chang-Jung Wang
    • International conference on construction engineering and project management
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    • 2005.10a
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    • pp.802-807
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    • 2005
  • For linear projects, it has long been known that resource utilization is important in improving work efficiency. However, most existing scheduling techniques cannot satisfy the need for solving such issues. This paper presents an optimization model for solving linear scheduling problems involving resource assignment tasks. The proposed model adopts constraint programming (CP) as the searching algorithm for model formulation, and the proposed model is designed to optimize project total cost. Additionally, the concept of outsourcing resources is introduced here to improve project performance.

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Robust FIR filter for Linear Discrete-time System

  • Quan, Zhong-Hua;Han, Soo-Hee;Kwon, Wook-Hyun
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.2548-2551
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    • 2005
  • In this paper, a robust receding horizon finite impulse response(FIR) filter is proposed for a class of linear discrete time systems with uncertainty satisfying an integral quadratic constraint. The robust state estimation problem involves constructing the set of all possible states at the current time consistent with given system input, output measurements and the integral quadratic constraint.

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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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On Implementing a Hybrid Solver from Constraint Programming and Optimization (제약식프로그래밍과 최적화를 이용한 하이브리드 솔버의 구현)

  • Kim, Hak-Jin
    • Information Systems Review
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    • v.5 no.2
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    • pp.203-217
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    • 2003
  • Constraint Programming and Optimization have developed in different fields to solve common problems in real world. In particular, constraint propagation and linear Programming are their own fundamental and complementary techniques with the potential for integration to benefit each other. This intersection has evoked the efforts to combine both for a solution method to combinatorial optimization problems. Attempts to combine them have mainly focused on incorporating either technique into the framework of the other with traditional models left intact. This paper argues that integrating both techniques into an old modeling fame loses advantages from another and the integration should be molded in a new framework to be able to exploit advantages from both. The paper propose a declarative modeling framework in which the structure of the constraints indicates how constraint programming and optimization solvers can interact to solve problems.

On a Two Dimensional Linear Programming Knapsack Problem with the Extended GUB Constrain (확장된 일반상한제약을 갖는 이차원 선형계획 배낭문제 연구)

  • Won, Joong-Yeon
    • Journal of Korean Institute of Industrial Engineers
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    • v.27 no.1
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    • pp.25-29
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    • 2001
  • We present a two dimensional linear programming knapsack problem with the extended GUB constraint. The presented problem is an extension of the cardinality constrained linear programming knapsack problem. We identify some new properties of the problem and derive a solution algorithm based on the parametric analysis for the knapsack right-hand-side. The solution algorithm has a worst case time complexity of order O($n^2logn$). A numerical example is given.

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The Maximin Linear Programming Knapsack Problem With Extended GUB Constraints (확장된 일반상한제약을 갖는 최대최소 선형계획 배낭문제)

  • 원중연
    • Journal of the Korean Operations Research and Management Science Society
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    • v.26 no.3
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    • pp.95-104
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    • 2001
  • In this paper, we consider a maximin version of the linear programming knapsack problem with extended generalized upper bound (GUB) constraints. We solve the problem efficiently by exploiting its special structure without transforming it into a standard linear programming problem. We present an O(n$^3$) algorithm for deriving the optimal solution where n is the total number of problem variables. We illustrate a numerical example.

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State set estimation based MPC for LPV systems with input constraint

  • Jeong, Seung-Cheol;Kim, Sung-Hyun;Park, Poo-Gyeon
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.530-535
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    • 2004
  • This paper considers a state set estimation (SSE) based model predictive control (MPC) for linear parameter- varying (LPV) systems with input constraint. We estimate, at each time instant, a feasible set of all states which are consistent with system model, measurements and a priori information, rather than the state itself. By combining a state-feedback MPC and an SSE, we design an SSE-based MPC algorithm that stabilizes the closed-loop system. The proposed algorithm is solved by semi-de�nite program involving linear matrix inequalities. A numerical example is included to illustrate the performance of the proposed algorithm.

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