• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.745-752
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• 2002

A* algorithm is widely used in optimal car route search which is a kind of informed search, since the locations of starting and ending points are known a priori. Unidirectional A* algorithm requires considerable search time but guarantees a optimal path, bidirectional A* algorithm does not guarantee a optimal path and takes even longer search time than unidirectional search to guarantee a optimal path. In this paper, a new bidirectional A* algorithm which requites less search time and guarantees a optimal path is proposed. To evaluate the efficiency of the proposed algorithm, several experiments are conducted in real road map and the results show that the algorithm is very effective in terms of finding a optimal path and search time.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.242-247
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• 1996

In this paper, an optimal coning compensation algorithm for strapdown system is proposed by minimizing the coning error. The proposed algorithm is derived as a generalized form in that it contains the class of the existing coning algorithms and allows the design of optimal algorithm for various combinations of gyro samples. It is shown the magnitude of resulting algorithm errors depends mainly on the total number of gyro samples including present and previous gyro samples. Based on the results, the proposed algorithm enables the algorithm designers to develop the effective coning compensation algorithm according to their attitude computation specifications with ease. In addition, the multirate method which can efficiently implement the algorithm is presented.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.4
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• pp.498-504
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• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.891-897
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• 2021

In this paper, we propose a destination optimal route algorithm for providing route finding service for the transportation handicapped by using the multi-criteria decision-making technique and the modified A-STAR optimal route search algorithm. This is a method to set the route to the destination centering on safety by replacing the distance cost of the existing A-STAR optimal route search algorithm with the safety cost calculated through AHP/TOPSIS analysis. To this end, 10 factors such as road damage, curb, and road hole were first classified as poor road factors that hinder road driving, and then pairwise comparison of AHP was analyzed and then defined as the weight of TOPSIS. Afterwards, the degree of driving safety was quantified for a certain road section in Busan through TOPSIS analysis, and the development of an optimal route search algorithm for the transportation handicapped that replaces the distance cost with safety in the finally modified A-STAR optimal route algorithm was completed.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.167-177
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• 2012

In this paper, we raised the performance of heuristic algorithm to assign job to workers in parallel line inspection process without sequence. In previous research, we developed the heuristic algorithm. But the heuristic algorithm can't find optimal solution perfectly. In order to solve this problem, we proposed new method to make initial solution called FN(First Next) method and combined the new FN method and old FE method using previous heuristic algorithm. Experiments of assigning job are performed to evaluate performance of this FE+FN heuristic algorithm. The result shows that the FE+FN heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are optimal solutions, this heuristic algorithm can find the optimal solution perfectly.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.112-121
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• 2001

In this paper, an engine-CVT integrated control algorithm is suggested by considering the powertrain loss, inertia torque and the CVT ratio response lag. The integrated control algorithm consists of (1) the optimal engine power calculation and (2) determining of the optimal throttle valve opening and the optimal CVT ratio. The optimal engine power is obtained by compensating the inertia torque due to the CVT ratio change and the powertrain loss that is calculated iteration procedure. In addition, an algorithm to compensate the effect of the CVT ratio response lag on the drive torque is suggested by the engine speed compensation causing the increased optimal CVT ratio. Simulation results show that the engine-CVT integrated control algorithm developed in this study makes it possible to obtain better engine operation on the optimal operating line, which results in the improved fuel economy while satisfying the driver's demand.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.423-430
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• 2010

Particle swarm optimization (PSO) algorithm is designed to find a single global optimal point. However, the PSO needs to be modified in order to find multiple optimal points of a multimodal function. These modifications usually divide a swarm of particles into multiple subswarms; in turn, these subswarms try to find their own optimal point, resulting in multiple optimal points. In this work, we present a new PSO algorithm, called coupling PSO to find multiple optimal points of a multimodal function based on coupling particles. In the coupling PSO, each main particle may generate a new particle to form a couple, after which the couple searches its own optimal point using non-stop-moving PSO algorithm. We tested the suggested algorithm and other ones, such as clustering PSO and niche PSO, over three analytic functions. The coupling PSO algorithm was also applied to solve a significant benchmark problem, the TEAM workshop problem 22.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.645-654
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• 2004

The design of a measurement system to perform Harmonic State Estimation(HSE) is a very complex problem. In particular, the number of available harmonic analysis measurement instruments is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents an optimal algorithm of HSE which is based on an optimal placement of measurement points using Immune Algorithm (IAs). This IA-HSE has been applied to power system for the validation of an optimal algorithm of HSE. The study results have indicated an economical and effective method for optimal placement of measurement points using Immune Algorithm (IAs) in the HSE.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.92-94
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• 2003

The design of a measurement system to perform Harmonic State Estimation(HSE) is a very complex problem. In particular, the number of available harmonic instruments (Continuous Harmonic Analysis in Real Time : CHART) is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents an optimal algorithm of HSE which is based on an optimal placement of measurement points using Immune Algorithm (IAs). This HSE has been applied to power system for the validation of an optimal algorithm of HSE. The study results have indicated an economical and effective method for optimal placement of measurement points using IAs in the HSE.