• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.680-688
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• 2010

In this paper, we present a dynamic programming(DP)-based stereo matching method using image segmentation algorithm. DP has been a classical and popular optimization method for various computer vision problems including stereo matching. However, the performance of conventional DP has not been satisfactory when it is applied to the stereo matching since the vertical correlation between scanned lines has not been properly considered. In the proposed algorithm, accurate edge information is first obtained from segmented image information then we considers the discontinuity of disparity and occlusions region based on the obtained edge information. The experimental results applied to the Middlebury stereo images demonstrate that the proposed algorithm has better performances in stereo matching than the previous DP based algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.418-422
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• 2005

In this paper, a novel algorithm capable of detecting generic obstacles on a flat surface is proposed. The algorithm fundamentally exploits a distortion phenomena taken place in remapping process of original stereo images to a virtual top-view. Based on the distortion phenomena, we construct stereo polar histograms of edge maps, detect peaks on them, and search for matched peaks on both histograms using a Dynamic Programming (DP). Eventually, the searched corresponding peaks lead to estimate obstacles' positions. The advantages of the proposed algorithm are that it is not largely affected by an intensity difference between a pair of stereo images and does not depend on the typical stereo matching methodologies. Furthermore, the algorithm identifies the obstacles' positions quite robustly.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.71-84
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• 2009

This paper considers a remanufacturing and purchasing planning problem, in which either used products(or wastes) are remanufactured or remanufactured products(or final products) are purchased to satisfy dynamic demands of remanufactured products over a discrete and finite time horizon. Also, as remanufactured products are purchased more than or equal to a special quantity Q, a discount price policy is applied. The problem assumes that the related cost(remanufacturing and inventory holding costs of used products, and the purchasing and inventory holding costs of remanufactured products) functions are concave and backlogging is not allowed. The objective of this paper is to determine the optimal remanufacturing and purchasing policy that minimizes the total cost to satisfy dynamic demands of remanufactured products. This paper characterizes the properties of the optimal policy and then, based on these properties, presents a dynamic programming algorithm to find the optimal policy. Also, a network-based procedure is proposed for the case of a large quantity of low cost used products. A numerical example is then presented to demonstrate the procedure of the proposed algorithm.

• Journal of Navigation and Port Research
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• v.30 no.9
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• pp.773-777
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• 2006

Significant increase of container flows in the marine terminals requires more efficient port equipments such as logistic and transfer systems. This paper presents collision avoidance and routing approach based on dynamic programming (DP) algorithm for a linear motor based shuttle car which is considered as a new transfer system in the port terminals. Most of routing problems are focused on automatic guided vehicle (AGV) systems, but its solutions are hardly utilized for LM based shuttle cars since both are mechanically different. Our proposed DP is implemented for real-time searching of an optimal path for each shuttle car in the Agile port terminal located at California in USA.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.98.3-98
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• 2002

Vision-based systems for finding road lanes have to operate robustly under a wide variety of environ-mental conditions including large amount of scene clutters. This paper presents a method for finding the lane boundaries by combining a local line extraction method and dynamic programming as a search tool. The line extractor obtains an initial position estimation of road lane boundaries from the noisy edge fragments. Dynamic programming then improves the initial approximation to an accurate configuration of lane boundaries. Input image frame is divided into a few sub-regions along the vertical direction. The local line extractor then performs to extract candidate lines of road lanes in the...

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.121-127
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• 2004

This paper presents a new vertex selection scheme for shape approximation. In the proposed method, final vertex points are determined by "two-step procedure". In the first step, initial vertices are simply selected on the contour, which constitute a subset of the original contour, using conventional methods such as an iterated refinement method (IRM) or a progressive vertex selection (PVS) method In the second step, a vertex adjustment Process is incorporated to generate final vertices which are no more confined to the contour and optimal in the view of the given distortion measure. For the optimality of the final vertices, the dynamic programming (DP)-based solution for the adjustment of vertices is proposed. There are two main contributions of this work First, we show that DP can be successfully applied to vertex adjustment. Second, by using DP, the global optimality in the vertex selection can be achieved without iterative processes. Experimental results are presented to show the superiority of our method over the traditional methods.

• Korean Management Science Review
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• v.29 no.1
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• pp.1-14
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• 2012

The goal of the multi-reservoir operation planning is to provide an optimal release plan that maximize the reservoir storage and hydropower generation while minimizing the spillages. However, the reservoir operation is difficult due to the uncertainty associated with inflows. In order to consider the uncertain inflows in the reservoir operating problem, we present a Stochastic Dynamic Programming (SDP) model based on the markov decision process (MDP). The objective of the model is to maximize the expected value of the system performance that is the weighted sum of all expected objective values. With the SDP model, multi-reservoir operating rule can be derived, and it also generates the steady state probabilities of reservoir storage and inflow as output. We applied the model to the Geum-river basin in Korea and could generate a multi-reservoir monthly operating plan that can consider the uncertainty of inflow.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.21-29
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• 2007

In this paper, we present a method of detecting the boundary of the intima-media complex for automated measurement based on dynamic programming from carotid artery B-mode ultrasound images and then show the experimental results. We apply the dynamic programming for determining the optimal locations that a cost function is minimized. The cost function includes cost terms which are representing image features such as intensity, intensity gradient and geometrical continuity of the vessel interfaces. Moreover, we improve the boundary continuity by applying the B-spline to smooth the rough boundary due to noise such as speckle, dropout and weak edges. The proposed method has obtained more accurate reproducible results than conventional edge-detection by considering multiple image features and ensures efficient automated measurement by solving the problems of the inter- and intra-observer variability and its inefficiency due to manual measurement.