• 한국경영과학회지
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• 제34권2호
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• pp.77-90
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• 2009

Total profit level Increases if a company increase the cost for achieving R&D related goals of equipment productivity enhancement, production cost saving, or for achieving equipment scale target, sales volume goal. But how much money should be invested to achieve a certain level of profit? We formulated the model to set the optimal goal levels to minimize the investment cost under the constraint that certain level of total profit should be guaranteed. This model derived from a case of P steel company. We found that this should be considered in relation with the production sales planning (known as optimal product mix problem) to guarantee the profit. We suggested a nonlinear programming model, 3 valiant form of the p+roduct mix problem. We can find the optimal Investment level for the R&D related goals or sales volume goal, equipment scale target for the P steel company using the model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.293-298
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• 2016

Document summarization is an important task in various areas where the goal is to select a few the most descriptive sentences from a given document as a succinct summary. Even without training data of human labeled summaries, there has been several interesting existing work in the literature that yields reasonable performance. In this paper, within the same unsupervised learning setup, we propose a more principled learning framework for the document summarization task. Specifically we formulate an optimization problem that expresses the requirements of both faithful preservation of the document contents and the summary length constraint. We circumvent the difficult integer programming originating from binary sentence selection via continuous relaxation and the low entropy penalization. We also suggest an efficient convex-concave optimization solver algorithm that guarantees to improve the original objective at every iteration. For several document datasets, we demonstrate that the proposed learning algorithm significantly outperforms the existing approaches.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.881-885
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• 2005

Scheduling repetitive projects under limitations on the amounts of available resources (labor and equipment) has been an active subject because of its practical relevance. Traditionally, the limitation is specified as a deterministic (fixed) number, such as 1000 labor-hours. The limitation, however, is often exposed to uncertainty and variability, especially when the project is lengthy. This paper presents a stochastic optimization model to treat the situations where the limitations of resources are expressed as probability functions in lieu of deterministic numbers. The proposed model transfers each deterministic resource constraint into a corresponding stochastic one and then solves the problem by the use of a chance-constrained programming technique. The solution is validated by comparison with simulation results to show that it can satisfy the resource constraints with a probability beyond the desired confidence level.

• 제어로봇시스템학회논문지
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• 제5권3호
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• pp.249-256
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• 1999

The conventional robot manipulability is decomposed into linear manipulability and angular manipulability so that they may be analysed and visualized in easy way even in the case of 3 dimensional task space with 6 variables. After the Jacobian matrix is decomposed into linear part and angular part, constraint on joint velocities is transformed into linear task velocity and angular task velocity through the decomposed Jacobian matrices. Under the assumption of redundant robot manipulators, several optimization problems which utilize the redundancy are formulated to be solved by linear programming technique or sequential quadratic programming technique. After deriving the solutions of the optimization problems, we give graphical interpretations for the solutions.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제22권1호
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• pp.1-13
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• 2018

Boosting, one of the most successful algorithms for supervised learning, searches the most accurate weighted sum of weak classifiers. The search corresponds to a convex programming with non-negativity and affine constraint. In this article, we propose a novel Conjugate Gradient algorithm with the Modified Polak-Ribiera-Polyak conjugate direction. The convergence of the algorithm is proved and we report its successful applications to boosting.

• ETRI Journal
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• 제38권5호
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• pp.934-940
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• 2016

In this paper, secure multicasting with the help of cooperative decode-and-forward relays is considered for the case in which a source securely sends a common message to multiple destinations in the presence of a single eavesdropper. We show that the secrecy rate maximization problem in the secure multicasting scenario under an overall power constraint can be solved using semidefinite programing with semidefinite relaxation and a bisection technique. Further, a suboptimal approach using zero-forcing beamforming and linear programming based power allocation is also proposed. Numerical results illustrate the secrecy rates achieved by the proposed schemes under secure multicasting scenarios.

• 한국정보처리학회논문지
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• 제6권7호
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• pp.1962-1969
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• 1999

활성 윤곽선은 내부 에너지와 외부에너지에 의해 조절되는 형태 변형이 가능한 에너지 최소화 곡선이다. 내부 에너지는 곡선을 부드럽게 유지하기 위한 제약 조건이고, 외부 지는 곡선을 영상 특징 쪽으로 이끈다. 활성 윤곽선이 제어 점에 의한 B-스플라인 표현은 많은 장점을 갖는다. Mentet[3] 등은 유한 차분 법에 의한 활성 윤곽선이 B-스플라인 표현을 제안하였다. 본 논문에서는 활성 윤곽선을 구간별 3차 B-스플라인으로 표현하고, 이 모델의 에너지를 최소로 하는 제어 점을 찾기 위한 방법으로 동적 프로그래밍을 사용한 방법을 제안한다. 제안된 방법은 유한 차분 법에 의한 B-스플라인 방법에 비해 간단하고 효과적이다.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.217-229
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• 2007

In this paper, Genetic Algorithm (GA) is used to find the Maximum Weight Independent Set (MWIS) of a graph. First, MWIS problem is formulated as a 0-1 integer programming optimization problem with linear objective function and a single quadratic constraint. Then GA is implemented with the help of this formulation. Since GA is a heuristic search method, exact solution is not reached in every run. Though the suboptimal solution obtained is very near to the exact one. Computational result comprising an average performance is also presented here.

• Journal of applied mathematics & informatics
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• 제10권1_2호
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• pp.83-99
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• 2002

Optimality conditions are derived for a nonlinear program in which a support function appears in the objective as well as in each constraint function. Wolfe and Mond-Weir type duals to this program are presented and various duality results are established under suitable convexity and generalized convexity assumptions. Special cases that often occur in the literature are those in which a support function is the square root of a positive semi- definite quadratic form or an Lp norm. It is pointed out that these special cases can easily be generated from our results.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.991-997
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• 2002

The concept of feasible & invariant region plays an important role to derive closed loop stability and achie adequate performance of constrained receding horizon predictive control. In this paper, we define a complex polyhedral feasible & invariant set for all stabilizable input-constrained linear systems by using a complex transform and propose a one-norm based receding horizon control scheme using these invariant sets. In order to get a larger stabilizable set, a convex hull of invariant sets which are defined for different state feedback gains is used as a target invariant set of the constrained receding horizon control. The proposed constrained receding horizon control scheme is formulated so that it can be solved via linear programming.