• Journal of Korea Game Society
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• v.9 no.4
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• pp.81-88
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• 2009

In this paper, I suggested a way for searching a path of the intelligent character in an action game by using a genetic algorithm. This realized the algorithm which enables not only to chose the nearest path but also to search the optimum path by using genetic algorithm. In this case, if the codes of chromosomes are applied as they are, a lot of lethal genes could occur. In order to solve such a problem, I used a splicing method, one of the DNA's behavior characteristics. The intelligent character searched out a optimum pass as well as a shortcut path with one treatment by using the characteristic of a genetic algorithm which generates multiple candidate solutions in the search process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.980-998
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• 2023

In Low Earth Orbit (LEO) satellite networks, satellites operate fast and the inter-satellite link change period is short. In order to sense the spectrum state in LEO satellite networks in real-time, a space-based satellite network intelligent spectrum sensing algorithm based on artificial neural network (ANN) is proposed, while Geosynchronous Earth Orbit (GEO) satellites are introduced to make fast and effective judgments on the spectrum state of LEO satellites by using their stronger arithmetic power. Firstly, the visibility constraints between LEO satellites and GEO satellites are analyzed to derive the inter-satellite link building matrix and complete the inter-satellite link situational awareness. Secondly, an ANN-based energy detection (ANN-ED) algorithm is proposed based on the traditional energy detection algorithm and artificial neural network. The ANN module is used to determine the spectrum state and optimize the traditional energy detection algorithm. GEO satellites are used to fuse the information sensed by LEO satellites and then give the spectrum decision, thereby realizing the inter-satellite spectrum state sensing. Finally, the sensing quality is evaluated by the analysis of sensing delay and sensing energy consumption. The simulation results show that our proposed algorithm has lower complexity, the sensing delay and sensing energy consumption compared with the traditional energy detection method.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.156-161
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• 1997

In this thesis, based on Genetic Algorithm, a new route search algorithm is presented to search an optimal route between the origin and the destination in intelligent route guidance systems in order to minimize the route traveling time. The proposed algorithm is effectively employed to complex road networks which have diverse turn constrains, time-delay constraints due to cross signals, and stochastic traffic volume. The algorithm is also shown to significantly promote search efficiency by changing the population size of path individuals that exist in each generation through the concept of age and lifetime to each path individual. A virtual road-traffic network with various turn constraints and traffic volume is simulated, where the suggested algorithm promptly produces not only an optimal route to minimize the route cost but also the estimated travel time for any pair of the origin and the destination, while effectively avoiding turn constraints and traffic jam.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.589-594
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the losed loop problem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and efficiently. This algorithm is a hybrid type of using both declarative description (constraint represention) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.71-79
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• 1998

This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.

• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.42-53
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• 2000

The major goal of this research is to develop an optimal route search algorithm for an intelligent route guidance system, one sub-area of ITS. ITS stands for intelligent Transportation System. ITS offers a fundamental solution to various issues concerning transportation and it will eventually help comfortable and swift moves of drivers by receiving and transmitting information on humans, roads and automobiles. Genetic algorithm, and fuzzy logic are utilized in order to implement the proposed algorithm. Using genetic algorithm, the proposed algorithm searches shortest routes in terms of travel time in consideration of stochastic traffic volume, diverse turn constraints, etc. Then using fuzzy logic, it selects driver-preference optimal route among the candidate routes searched by GA, taking into account various driver's preferences such as difficulty degree of driving and surrounding scenery of road, etc. In order to evaluate this algorithm, a virtual road-traffic network DB with various road attributes is simulated, where the suggested algorithm promptly produces the best route for a driver with reference to his or her preferences.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.275-284
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• 2006

This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.116-123
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.53-56
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• 2007

Symmerge is a stable minimum storage algorithm for merging that needs $O(mlog\frac{n}{m})$ element comparisons, where m and n are the sizes of the input sequences with m ${\leqq}$ n. According to the lower bound for merging, the algorithm is asymptotically optimal regarding the number of comparisons. The objective of this paper is to consider the relationship between m and n for the spanning case with the recursion level m-1.