• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.81-82
• /
• 2020

Non-convex 최적화 문제의 복잡도를 완화하도록 해주는 오목함수 결정규칙을 제안한다. 전송용량을 나타내는 유틸리티 함수는 신호와 간섭의 함수이며, non-convex이다. 유틸리티 함수를 간섭관점에서 분석한다. '상대간섭'과 '간섭주요화'를 정의한다. 상대간섭은 D2D 수신단에서의 간섭레벨을 나타낸다. 간섭주요화는 간섭을 주요간섭으로 간략화한다. 간섭주요화를 기반으로 하는 오목함수 결정규칙을 제안한다. 실험결과를 통하여 유틸리티 함수는 상대간섭 0.1 이하에서는 오목함수임을 확인하였다.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.9
• /
• pp.149-157
• /
• 2004

This paper presents a new method for hi-objective optimization. Ordinary weighted sum method is easy to implement, but it has two significant drawbacks: (1) the solution distribution by the weighted sum method is not uniform, and (2) the method cannot determine any solutions that reside in non-convex regions of a Pareto front. The proposed adaptive weighted sum method does not solve a multiobjective optimization in a predetermined way, but it focuses on the regions that need more refinement by imposing additional inequality constraints. It is demonstrated that the adaptive weighted sum method produces uniformly distributed solutions and finds solutions on non-convex regions. Two numerical examples and a simple structural problem are presented to verify the performance of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.729-731
• /
• 2004

This paper deals with the design of a low order $H_{\infty}$ controller by using an iterative linear matrix inequality (LMI) method. The low order $H_{\infty}$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the effectiveness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.11
• /
• pp.747-752
• /
• 2004

This paper presents an iterative linear matrix inequality (LMI) approach to the design of a static output feedback (SOF) stabilization controller. A linear penalty function is incorporated into the objective function for the non-convex rank constraint so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. Hence, the overall procedure results in solving a series of semidefinite programs (SDPs). With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Extensive numerical experiments are Deformed to illustrate the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.12
• /
• pp.655-660
• /
• 2004

This paper deals with the design of a fixed-structure $H_\infty$ power system stabilizer (PSS) by using an iterative linear matrix inequality (LMI) method. The fixed-structure $H_\infty$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the practical applicability of the proposed algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.30 no.6
• /
• pp.479-486
• /
• 2019

The fully digital active array radar uses a wide beam for effective mission performance within a limited time. This paper presents a convex optimization algorithm that adjusts only the phase of an array element. First, the algorithm applies a semidefinite relaxation technique to relax the constraint and convert it to a convex set. Then, the constraint is set so that the amplitude is fixed to some extent and the phase is variable. Finally, the optimization is performed to minimize the sum of the eigenvalues obtained through eigenvalue decomposition. Compared to the application results of the existing genetic algorithm, the proposed algorithm is more effective in wide beam design for a fully digital active array radar.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.6
• /
• pp.257-266
• /
• 2015

There is no polynomial-time algorithm that can be obtain the optimal solution for economic load dispatch problem with non-convex fuel cost functions. Therefore, electrical field uses quadratic fuel cost function unavoidably. This paper proposes a valve-point optimization (VPO) algorithm for economic load dispatch problem with non-convex fuel cost functions. This algorithm sets the initial values to maximum powers $P_i{\leftarrow}P_i^{max}$ for each generator. It then reduces the generation power of generator i with an average power cost of $_{max}\bar{c}_i$ to a valve point power $P_{ik}$. The proposed algorithm has been found to perform better than the extant heuristic methods when applied to 13 and 40-generator benchmark data. This paper consequently proves that the optimal solution to economic load dispatch problem with non-convex fuel cost functions converges to the valve-point power of each generator.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.06a
• /
• pp.20-21
• /
• 2018

행렬의 rank 최소화 기법은 영상 잡음 제거, 행렬 완성(completion), low rank 행렬 복원 등 다양한 영상처리 분야에서 효과적으로 이용되어 왔다. 특히 nuclear norm 을 이용한 low rank 최소화 기법은 convex optimization 을 통하여 대상 행렬의 특이값(singular value)을 thresholding 함으로써 간단하게 low rank 행렬을 얻을 수 있다. 하지만, nuclear norm 을 이용한 low rank 최소화 방법은 행렬의 rank 값을 정확하게 근사하지 못하기 때문에 잡음 제거가 효과적으로 이루어지지 못한다. 본 논문에서는 영상의 잡음을 제거 하기 위해 다중 잡음 제거 영상을 이용하여 유사도가 높은 유사 패치 행렬을 구성하고, 유사 패치 행렬의 rank 를 non-convex function 을 이용하여 최소화시키는 방법을 통해 잡음을 제거하는 방법을 제안한다.

• Journal of Korea Multimedia Society
• /
• v.9 no.12
• /
• pp.1636-1648
• /
• 2006

In this paper, we propose a new optimization approach to the rate control problem in a wired-cum-wireless network. A primal-dual interior-point(PDIP) algorithm is used to find the solution of the rate optimization problem. We show a comparison between the dual-based(DB) algorithm and PDIP algorithm for solving the rate control problem in the wired-cum-wireless network. The PDIP algorithm performs much better than the DB algorithm. The PDIP can be considered as an attractive method to solve the rate control problem in network. We also present a numerical example and simulation to illustrate our conclusions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.3
• /
• pp.984-996
• /
• 2014

In this paper, optimal sensing time allocation for adaptive multiband spectrum sensing-transmission procedure is investigated. The sensing procedure consists of an exploration phase and a detection phase. We first formulate an optimization problem to maximize the throughput by designing not only the overall sensing time, but also the sensing time for every stage in the exploration and detection phases, while keeping the miss detection probability for each channel under a pre-defined threshold. Then, we transform the initial non-convex optimization problem into a convex bilevel optimization problem to make it mathematically tractable. Simulation results show that the optimized sensing time setting in this paper can provide a significant performance gain over the previous studies.