• Journal of information and communication convergence engineering
• /
• 제12권3호
• /
• pp.140-144
• /
• 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.

• 대한임베디드공학회논문지
• /
• 제8권2호
• /
• pp.111-119
• /
• 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.

• International Journal of Control, Automation, and Systems
• /
• 제5권2호
• /
• pp.218-222
• /
• 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.

• 국제학술발표논문집
• /
• The 1th International Conference on Construction Engineering and Project Management
• /
• pp.802-807
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2548-2551
• /
• 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.

• 대한산업공학회지
• /
• 제19권2호
• /
• pp.3-13
• /
• 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.

• 경영정보학연구
• /
• 제5권2호
• /
• pp.203-217
• /
• 2003

제약식 프로그래밍과 최적화 솔버는 공통된 문제를 풀기 위한 해법으로서 서로 다른 영역에서 발전되어왔다. 특히 제약식 확산법과 선형 계획법은 두 영역의 주된 기법으로서 조합 최적화 문제를 푸는데 함께 사용될 수 있는 통합가능한 보완 기법들이다. 지금까지 이를 통합하기 위한 시도는 주로 한 기법을 다른 기법의 모형 틀안에 포함시키는 것이었다. 본 논문은 둘의 통합을 통한 잇점들은 충분히 사용하기 위해서는 모형 역시 통합될 필요가 있음과 그 모형 통합의 틀을 보이고 그 틀 안에서 어떻게 두 기법의 솔버의 수준으로 통합되어 새로운 혼합 솔버를 구축할 수 있는지를 보인다.

• 대한산업공학회지
• /
• 제27권1호
• /
• pp.25-29
• /
• 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.

• 한국경영과학회지
• /
• 제26권3호
• /
• pp.95-104
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.530-535
• /
• 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.