• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.103-107
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• 2009

This paper presents how to minimize the second-order cone programming problem occurring in the 3D reconstruction of multiple views. The $L_{\infty}$-norm minimization is done by a series of the minimization of the maximum infeasibility. Since the problem has many inequality constraints, we have to adopt methods of the interior point algorithm, in which the inequalities are sequentially approximated by log-barrier functions. An initial feasible solution is found easily by the construction of the problem. Actual computing is done by an iterative Newton-style update. When we apply the interior point method to the problem of reconstructing the structure and motion, every Newton update requires to solve a very large system of linear equations. We show that the sparse bundle-adjustment technique can be utilized in the same way during the Newton update, and therefore we obtain a very efficient computation.

• 한국컴퓨터정보학회논문지
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• 제20권7호
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• pp.41-47
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• 2015

The bandwidth minimization problem (BMP) has been classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. This paper suggests polynomial time heuristic algorithm is to find the solution of bandwidth minimization problem. To find the minimum bandwidth ${\phi}^*=_{min}{\phi}(G)$, ${\phi}(G)=_{max}\{{\mid}f(v_i)-f(v_j):v_i,v_j{\in}E\}$ for given graph G=(V,E), m=|V|,n=|E|, the proposed algorithm sets the maximum degree vertex $v_i$ in graph G into global central point (GCP), and labels the median value ${\lceil}m+1/2{\rceil}$ between [1,m] range. The graph G is partitioned into subgroup, the maximum degree vertex in each subgroup is set to local central point (LCP), and we adjust the label of LCP per each subgroup as possible as minimum distance from GCP. The proposed algorithm requires O(mn) time complexity for label to all of vertices. For various twelve graph, the proposed algorithm can be obtains the same result as known optimal solution. For one graph, the proposed algorithm can be improve on known solution.

• 디지털융복합연구
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• 제10권11호
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• pp.371-376
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• 2012

본 논문에서는 텍스트 영상에 대한 데이터 천이 최소화를 위한 새로운 데이터 코딩기법과 회로를 제안한다. 제안한 회로는 기존의 Modified LVDS(Low Voltage Differential Signaling)의 문제점인 입력되는 데이터간의 동기와 출력되는 데이터간의 동기 문제를 수정한 개선된 MLVDS 회로와 Text image에 대한 천이 최소화를 위한 추가적인 직렬 데이터 코딩 기법인 TMUX 알고리즘으로 한 클럭에 2비트의 신호를 동시에 전송하여 동작 주파수를 줄일 수 있으며, 전자파 장애와 전력 소비를 해결할 수 있다. 시뮬레이션 결과를 통해서 텍스트 영상 데이터 천이 최소화 향상과 입출력간의 동기문제를 보완되었음을 확인하였다.

• 대한전자공학회논문지
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• 제22권2호
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• pp.82-89
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• 1985

MCO 문제의 해석을 하기 위한 weight P-norm방법을 연구하여 새로운 non-inferior해를 구하였다. Weight 최소화방법을 MOSFET NAND 게이트에 적용하여 최적 non-inferior해를 구하였다. 또한 본 논문에서 응용한 최소화방법은 목적함수가 non-convex일때도 적용된다. 본 논문의 최소화 방법의 결과를 Lightner의 결과와 비교하여 효율성을 입증하였다.

• 대한산업공학회지
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• 제21권3호
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• pp.409-428
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• 1995

Job shop scheduling with m different machines and n different jobs is a NP-hard problem of combinatorial optimization. The purpose of the paper is to develop the heuristic method using tabu search for job shop scheduling to minimize makespan or mean flowtime. To apply tabu search to job shop scheduling problem, in this paper we propose the several move methods that employ insert moves in order to generate the neighbor solutions, and present the efficient rescheduling procedure that yields active schedule for a changed operation sequence by a move of operations. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. By experiments, we find the appropriate tabu list size and tabu attributes, and analyze the proposed tabu search techniques with respect to the quality of solutions and the efforts of computation. The experimental results show that the proposed tabu search techniques using long-term memory function have the ability to search a good solution, and are more efficient in the mean flowtime minimization problem than in the makespan minimization.

• Journal of Advanced Marine Engineering and Technology
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• 제37권7호
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• pp.743-751
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• 2013

In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.71-73
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• 1995

This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.488-490
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• 1995

This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.537-540
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• 2000

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.

• IEIE Transactions on Smart Processing and Computing
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• 제4권6호
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• pp.413-421
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• 2015

Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) / L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera / mobile cameras.