Earticle

Performance of the RF coil, the antenna of the magnetic resonance imaging (MRI) system, is determined by the intensity of the applied B1 field on the target area. In the case of the human head, the size and shape of the head varies. Hence, in this study, a position-adjustable multi-channel RF coil for 3 Tesla is presented. The proposed RF coil has teeth structure that each coil element of the coil can be adjusted to be closer and be similar with the head shape. We check the B1 field and MR image with a human phantom designed to represent the electrical propoerties of the head to analyze the performance of the proposed RF coil. The result shows that the proposed RF coil will enhance the applied B1 field for better performance.

The importance of ultra-high field Magnetic resonance imaging (MRI) has been increased rapidly due to advantages of high resolution and signal-to-noise ratio. In this study, the specific absorption rate (SAR) was analyzed in accordance with the conditions of insertion deep brain stimulation (DBS) system and the electromagnetic field controlled by multi-channel coil. Our research demonstrated the method of field control using convex optimization (CVX) from the magnetic field data can be considered as a good strategy to drive the individual parameters of the RF coil to alleviate inhomogeneity of magnetic field and meet SAR standard, and it is much safer for DBS patients.

Decaying with the increasing of signal propagation distance, Received Signal Strength (RSS) is used in the wireless localization due to its low cost and easily implementation. When the transmit power is unavailable, two convex optimization algorithms including semi definite programming (SDP), second order cone and semi definite programming (SOC/SDP) are designed to estimate the source locations by relaxing the non-convex problem as convex optimization. The corresponding Cramér-Rao lower bound (CRLB) of the problem is derived. The simulations demonstrate that the SOC/SDP algorithm provides the similar accuracy performance compared with the SDP algorithm. However the computational complexity of SOC/SDP is lower than that of the SDP due to the less variables and equality constraints. When perfect knowledge of the path loss exponent is available, the simulations also show that the accuracy performance of the proposed convex optimization algorithms degrades as the path loss exponent increases.

In this paper, a hierarchical nonlocal method for image segmentation is proposed. The method is mainly consisting of two stages. In the first stage, a nonlocal segmentation model based on nonlocal differential operators is used to find a smooth solution. Once the solution is obtained, the segmentation is done by thresholding the solution into different phases in the second stage. The K-means method is used to determine the thresholds. One can obtain any K-phase segmentation ( K ³ 2 ) by choosing ( K - 1 ) thresholds. There is no need to specify the number of segments before finding the solution. Due to the convexity of our proposed model the split-Bregman algorithm is adopted to efficiently solve the minimization problem. When the images are severe intensity inhomogeneity, a nonlocal variation Retinex algorithm is used to preprocessing the given images. The true underlying reflectances of the given images are extracting. Then, these reflectances are used as inputs to the proposed nonlocal model. Experimental results show that our method performs better than many two-phase or multiphase segmentation methods. With the preprocessing of the nonlocal variation Retinex algorithm, our method produce better results and is more efficient than many famous local region-based active contour model (ACM) methods for images with intensity inhomogeneity.

In this paper, an interactive color natural image segmentation method is proposed. The method extends the Gaussian Mixture Model (GMM) by taking into account user markers as useful spatial prior. In this way, a distribution combining with color and spatial location is obtained. The distribution is incorporated in a Bayesian MAP approach. The approach is formalized as an iterative energy minimization problem. A direct global minimization technique based on variational method is employed to get binary solution. After each iteration, the largest connected region from foreground is used to update foreground GMMs and achieve more superior performance than original GrabCut method. Extensive experiments are performed on public benchmark datasets such as GrabCut benchmark, Berkeley segmentation database and Graz benchmark. The results show that the proposed method can extract the object region from the complex background accurately.

비 볼록 발전비용함수에 대한 최적화 문제는 다항시간으로 해를 구하는 알고리즘이 알려져 있지 않아 전기 분야에서는 부득이 2차 함수만을 사용하고 있다. 본 논문은 비 볼록 발전비용함수의 경제급전 최적화 문제에 대한 밸브지점 최적화 알고리즘을 제안하였다. 제안된 알고리즘은 초기 치로 최대 발전량 Pi ←Pi max로 설정하고, 평균 발전단가가 max Ci 인 발전기 i의 발전량을 밸브지점 Pik로 감소시키는 방법을 적용하였다. 제안된 알고리즘을 13과 40-발전기 데이터에 적용한 결과 기존의 휴리스틱 알고리즘보다 좋은 성능을 보였다. 따라서 비 볼록 발전비용함수의 경제급전 문제 최적 해는 각 발전기의 밸브지점 발전량으로 수렴함을 보였다.

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 Pi ←Pi max for each generator. It then reduces the generation power of generator with an average power cost of max Ci to a valve point power Pik. 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.

In this paper, we consider a convex optimization problem with a convex integrable objective function and a geometric constraint set. We characterize the solution set of the problem when we know its one solution.

This paper deals with a design method and capacity loss of an efficient multi-level modulation scheme for dimmable visible light communications (VLC) systems that use light-emitting diodes (LEDs) with efficiency droop. To this end, the impact of such an impairment on dimmable VLC is addressed with respect to multi-level modulations based on pulse-amplitude modulation (PAM) via data-rate optimization formulation.

Boundedness for the set of all the $\epsilon$-approximate solutions for convex optimization problems are considered. We give necessary and sufficient conditions for the sets of all the $\epsilon$-approximate solutions of a convex optimization problem involving finitely many convex functions and a convex semidefinite problem involving a linear matrix inequality to be bounded. Furthermore, we give examples illustrating our results for the boundedness.

A sequential optimality condition characterizing the efficient solution without any constraint qualification for an abstract convex vector optimization problem is given in sequential forms using subdifferentials and ${\epsilon}$-subdifferentials. Another sequential condition involving only the subdifferentials, but at nearby points to the efficient solution for constraints, is also derived. Moreover, we present a proposition with a sufficient condition for an efficient solution to be properly efficient, which are a generalization of the well-known Isermann result for a linear vector optimization problem. An example is given to illustrate the significance of our main results. Also, we give an example showing that the proper efficiency may not imply certain closeness assumption.

This paper aims to develop new theoretical foundations for solving convex multi-objective fuzzy optimization problems in a quotient space of fuzzy numbers. The primary objective is to extend the Karush-Kuhn-Tucker optimality conditions, originally designed for single-objective fuzzy optimization, to the multi-objective setting under convexity and differentiability assumptions. The methodology relies on defuzzification using midpoint functions, derived from $\alpha$-cuts and Mare? cores, which transform fuzzy problems into classical equivalents. The proposed framework allows for the definition of Pareto, weak Pareto, and strong Pareto solutions in a fuzzy context. Key results include the necessary and sufficient Karush-Kuhn-Tucker optimality conditions. This approach bridges the gap between fuzzy mathematical theory and practical optimization techniques in uncertain environments.

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.

The previous LQ-servo PI design methods have some serious design problems happened from the frequency matching of the maximum and minimum singular values of loop transfer function at both low and high frequency regions on the Bode plot. To solve these problems, this paper proposes a new design technique based on the inverse-optimal control and convex optimization.

This paper proposes a method to select the overshoot design parameters of the LQ-PID controller by using convex optimization in order to satisfy the design specifications. The tuning parameters of LQ-PID controller are determinated by the relationships between the design parameter to control both the overshoot and the settling time and the weighting factors Q and R in LQR.

This paper gives a simple parameterization of all stable unbiased filters to solve the suboptimal mixed $H_2/H_{\infty}$ filtering problem. Using the central filter, mixed $H_2/H_{\infty}$ filter is designed which minimizes the upper bound for the $H_2$ norm of the transfer matrix from a white noise to the estimation error subject to an $H_{\infty}$ norm constraint on the transfer matrix from an energy-bounded noise to the estimation error. The problem of finding suitable estimator gain can be converted into a convex optimization problem involving linear matrix inequalities.

레이다는 한정된 시간내에 효과적인 임무수행을 위해 광역빔을 이용한다. 본 논문에서는 완전 디지털 능동배열레이다의 광역빔 설계에 적용 가능한 위상조정 convex 최적화 알고리즘을 제안한다. 먼저 SDR(SemiDefinite Relaxation) 개념을 적용하여 제한 조건을 완화시켜 non-convex 집합을 convex 집합으로 전환한다. 그 후 배열소자의 크기를 어느 정도 고정하고 위상만을 조정하도록 제한조건을 적용하고, 고유값 분해를 통해 획득한 고유값의 합을 최소화하도록 최적화 과정을 수행하였다. 기존 유전알고리즘 적용결과와의 비교를 통해 제안된 알고리즘이 소자의 위상값만을 이용한 광역빔 설계에 효과적임을 확인하였고, 완전 디지털 능동배열레이다를 이용하는 차기호위함/구축함에 적용할 수 있을 것으로 기대된다.

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.

This paper deals with an output feedback H control problem for linear time ivariant systems with state delay. The proposed output feedback controller is represented by the lower linear fractional transformation of alinear time invariant system and a delay operator. Sufficient conditions for the existence of the output feedback controller are given in the form of linear matrix inequalities which are less conservative than those for the existence of a rational output feedback controler. We also present a numerical example to demonstrate the efficacy of the proposed method.of the proposed method.

Structural optimization using improved higer-order convex approximation is proposed in this paper. The proposed method is a generalization of the convex approximation method. The order of the approximation function for each constraint is automatically adjusted in the optimization process. And also the order of each design variable is differently adjusted. This self-adjusted capability makes the approximate constraint values conservative enough to maintain the optimum design point of the approximate problem in feasible region. The efficiency of proposed algorithm, compared with conventional algorithm is successfully demonstrated in the Three-bar Truss example.