• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.309-316
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• 2004

In this paper, we proposed the TDS(T-shape Diamond Search) based on the directions of above, below, left and right points to estimate the motion vector fast and more correctly in this method, we exploit the facts that most motion vectors are enclosed in a circular region with a radius of 2 fixels around search center(0,0). At first, the 4 points in the above, below, left and right around the search center is calculated to decide the point of the MBD(Minimum Block Distortion). And then w. above point of the MBD is checked to calculate the SAD. If the SAD of the above point is less than the previous MBD, this process is repeated. Otherwise, the right and left points of MBD are calculated to decide The points that have the MBD between right point and left point. Above processes are repeated to the predicted direction for motion estimation. Especially, if the motions of image are concentrated in the crossing directions, the points of other directions are omitted. As a result, we can estimate motion vectors fast. Experiments show that the speedup improvement of the proposed algorithm over Diamond Search algorithm(DS) and HEXgon Based Search(HEXBS) can be up to 38∼50％ while maintaining similar image Quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.105-111
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• 2004

DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.

• Journal of the Korean Society for Railway
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• v.14 no.3
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• pp.276-284
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• 2011

In our study, we present a new LOGIT-based traffic assignment model applicable to intercity railway network. Most traffic assignment models have been developed for public transit assignment in urban area, so that they are known to produce unrealistic results in intercity railway demand analysis. Especially, since the introduction of KTX, more passengers are using a route including KTX service and the schedule becomes more compatible with transfer. Our study presents a new LOGIT-based traffic assignment model considering passenger transfer. To do so, we suggest a new route search algorithm to find K paths with non increasing order in the utility value.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.59-68
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• 2011

The analysis of optimal infiltration path is one of the representative fields in which the GIS technology can be useful for the military purpose. Usually the analysis of the optimal path is done with network data. However, for military purpose, it often needs to be done with raster data. Because raster data needs far more computation than network data, it is difficult to apply the methods usually used in network data, such as Dijkstra algorithm. The genetic algorithm, which has shown great outcomes in optimization problems, was applied. It was used to minimize the detection probability of infiltration route. 2D binary array genes and its crossover and mutation were suggested to solve this problem with raster data. 30 tests were performed for each population size, 500, 1000, 2000, and 3000. With each generation, more adoptable routes survived and made their children routes. Results indicate that as the generations increased, average detection probability decreased and the routes converged to the optimal path. Also, as the population size increases, more optimal routes were found. The suggested genetic algorithm successfully finds the optimal infiltration route, and it shows better performance with larger population.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.425-430
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• 2007

Without the aid of stationary infrastructure, maintaining routing information for all nodes is inefficient in the Mobile Ad hoc Networks(MANET). It is more efficient when every time routing information is necessary that the source node broadcasts a query message to neighbour nodes. The source node using Ad hoc On-Demand distance Vector(AODV), which is one of the routing protocols of MANET, uses the Expanding Ring Search(ERS) algorithm which finds a destination node efficiently. In order to reduce the congestion of the network, ERS algorithm does not broadcast Route REQuest(RREQ) messages in the whole network. When the timer expires, if source node does not receive Route REPly(RREP) messages from the destination node, it gradually increases TTL value and broadcasts RREQ messages. Existing AODV cost a great deal to find a destination node because it uses a fixed NODE_TRAVERSAL_TIME value. Without the message which is added in existing AODV protocols, this paper measures delay time among the neighbours' nodes by making use of HELLO messages. We propose Adaptive ERS(AERS) algorithm that makes NET_TRAVERSAL_TIME optimum which apply to the measured delay time to NODE_TRAVERSAL_TIME. AERS suppresses the unnecessary messages, making NET_TRAVERSAL_TIME optimum in this paper. So we will be able to improve a network performance. We prove the effectiveness of the proposed method through simulation.

• Convergence Security Journal
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• v.11 no.1
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• pp.71-77
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• 2011

NCW(Network Centric- Warfare) encompasses the concept to use computer data processing and network linkage communications techniques, share information and furthermore, enhance the effectiveness of computer-operating systems. As IT(Information & Technology) have become developed in the recent years, the existing warfare system-centered conventional protocol is not use any longer. Instead, network-based NCW is being widely-available, today. Under this changing computer environment, it becomes important to establish algorithm and build the stable communication systems. Tools to identify malign node factors through Wireless Ad-hoc network cause a tremendous error to analyze and use paths of even benign node factors misreported to prove false without testing or indentifying such factors to an adequate level. These things can become an obstacle in the process of creating the optimum network distribution environment. In this regard, this thesis is designed to test and identify paths of benign node factors and then, present techniques to transmit data through the most significant open short path, with the tool of MP-SAR Protocol, security path search provider, in Ad-hoc NCW environment. Such techniques functions to identify and test unnecessary paths of node factors, and thus, such technique users can give an easy access to benign paths of node factors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.871-872
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• 2014

In this paper, I propose the method of cooperative work of swarm robot for escaping maze. The robots can communicate with each other using Zigbee, but the central control system send commands to robots because of low processing power of robots. Robots navigate the blinded maze and send information such as movement to the central control system for building map. The central control system analysis the received data and find path to escape from maze.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.131-137
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• 2010

Today, many museums are changing their forms with ubiquitous environment. Unlike traditional museums providing only static text-based information attached to its corresponding artifacts to visitors, those ubiquitous museums provide not only artifacts' text information, but also many different forms of information such as sound or media through personal digital assistance or cell phones. However, these existing ubiquitous museums still provide each visitor only with artifact-centric information in very simple ways. Also this disadvantageous feature causes high gallery congestion problem resulting from providing a uniform path for every visitor. These limitations may be the biggest barrier to providing more various and useful information about artifacts to visitors through considering each visitor's preference. This paper propose a new optimal viewing path search algorithm to provide comfortable museum viewing for each visitor according to its preference. Also, a new congestion control method is developed to protect visitors from being put in some hot spots on their museum viewing, improving its comfort to a maximum.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.265-270
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• 2010

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like TD-learning and TD(${\lambda}$)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present GDRLS algorithm for finding the shortest path faster in a maze environment. GDRLS is select the candidate states that can guide the shortest path in maze environment, and learn only the candidate states to find the shortest path. Through experiments, we can see that GDRLS can search the shortest path faster than TD-learning and TD(${\lambda}$)-learning in maze environment.