• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.257-265
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• 2016

In this study, an optimal design for motor-driven track type traveling system applied into high clearance working machineries in orchard is proposed. Tractive performance and hill climbing ability were predicted and evaluated for the optimal motor traveling system by taking into account of soil characteristics in orchard utilizing the high clearance working machineries. Design criteria for tractive performance were based on the traction force calculated from tractive effort subtracted by motion resistance, while hill climbing ability had its design criteria that fulfill the climbing 20% slope ground at a speed of 3km/h. Based on the evaluation results of traction and climbing ability, two DC48V, 4500rpm, 1.6kW AC motors were independently applied to both left and right side of orbits; each motor is designed to transmit power on driving sprocket of track type traveling system via 50:1 reduction gear ratio. The motor-driven track type traveling system developed in the study found to have 396 kgf of tractive force, which is 12.5% higher than climbing resistance at orchard soil having 20% slope ground (352 kgf), demonstrating sufficient tractive performance and hill climbing ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1523-1531
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• 2011

Multi-axle driving vehicle are favored for military use in off road operations because of their high mobility on extreme terrains and obstacles. Especially, Military Vehicle needs an ability to driving on hills of 60% angle slope. This paper presents the improvement of the ability of hill climbing for 6WD/6WS vehicle through the optimal tire force distribution method. From the driver's commands, the desired longitudinal force, the desired lateral force, and the desired yaw moment were obtained for the hill climbing of vehicle using optimal tire force distribution method. These three values were distributed to each wheel as the torque based on optimal tire force distribution method using friction circle and cost function. To verify the performance of the proposed algorithm, the simulation is executed using TruckSim software. Two vehicles, the one the proposed algorithm is implemented and the another the tire's forces are equivalently distributed, are compared. At the hill slop, the ability to driving on hills is improved by using the optimum tire force distribution method.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.111-113
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• 2006

DEAS is an direct searching and optimization method that based on the binary code space. It can be classified as an direct hill climbing searching. However, because of binary code space based searching, the searching in low resolution has random property. As the resolution of code increases during the search, its property of searching changes like that of hill climbing search. This paper propose a method for improving the performance of minimum seeking ability of DEAS with history information. The cost evaluation is increased. However the minimum searching ability of DEAS is improved along the same starting resolution.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.179-186
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• 2015

This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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• pp.47-55
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• 2012

This work deals with dynamic analysis of a monorail system with magnetic caterpillar where magnets are embedded inside each articulated element of the caterpillar, augmenting traction force of main rubber wheels to climb up slope up to 15 degree grade. Considerations are first given to determine stiffness of the primary and secondary suspension springs in order for the natural frequencies of car body and bogie associated with vertical, pitch, roll and yaw motion to be within generally accepted range of 1-2 Hz. Equations for calculating magnetic force needed to climb up given slope are derived, and a magnetic caterpillar system for 1/6 scale monorail is designed based on the derivation. To assess the hill climbing ability and cornering stability, and make sure smooth operation of the side and vertical guiding wheels which is critical for safety, a multibody model that takes into account of every component level design characteristics of car, bogie, and caterpillar is set up. Through hill climbing simulation and comparison with measurement of the limit slope, the validity of the analysis and design of the magnetic caterpillar system are demonstrated. Also by studying the curving behavior, maximum curving speed without rollover, functioning of lateral motion constraint system, the effects of geometry of guiding rails are studied.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.256-262
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• 2021

In order for small electric vehicles to drive on hilly roads in Korea, methods to improve the climbing ability and power performance of vehicles should be taken. In order to improve the power performance of small electric vehicles, the performance of motors mounted on electric vehicles should be improved. However, if the performance of the motor is improved to improve the power performance of the electric vehicle, it is possible to lower the price competitiveness accordingly. In addition, the power consumption of the battery is rapidly increased to drive the high-performance motor, so in order to introduce the small electric vehicle into the domestic market, various problems must be overcome. In order to commercialize small electric vehicles that do not emit harmful exhaust gases to the human body in the hilly domestic terrain, it is effective to introduce a separate continuously variable transmission system that can improve the climbing ability and power transmission ability. In this study, we propose a proprietary model of continuously variable transmissions that can be applied to small electric vehicles. The proposed continuously variable transmission is equipped with a spring in the driving pulley and the driven pulley, and has the advantage of performing a shift that increases torque in a situation where the vehicle needs to increase torque when driving on a hill. In addition, the basic design for commercialization of the proposed continuously variable transmission was carried out, and the prototype manufactured and attached to the body of a small electric vehicle.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1773-1780
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• 1996

KBANN (knowledge-based artificial neural network) combining the symbolic approach and the numerical approach has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects due to the link-ing of hidden nodes to input nodes and the use of beam search. The algorithm which could solve this TopGen's defects, by adding the hidden nodes linked to next layer nodes and using hill-climbing search with backtracking, is designed.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.7-13
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• 2011

The climate change due to the increased consumption with fossil fuel and rise of the oil price have been serious global issues. Automobile industry consumes 30% of the oil every year and causes air pollution and global warming by the exhaust emissions and carbon dioxide ($CO_2$). The demand of two-wheeled vehicle increases every year due to the parking and traffic problem caused by the increased automobiles in the urban area. Approximately 50,000,000 two-wheeled vehicles were produced in 2008. The development and sales of the hybrid two-wheeled vehicle industry become active due to its increased market demands. In this paper, the change of the motor and battery efficiency, driving distance, hill climbing ability with the change of the motor capacity was analyzed. Simulation of the peculiarities in urban driving schedule(World-wide Motorcycle Test Cycle(WMTC), Manhattan driving schedule), constant speed(10 km/h, 35 km/h) of small electronic two-wheeled vehicle was also carried out. Through the simulation result, appropriate capacities of the motor and battery for urban driving was acquired.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2780-2785
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• 1997

Although KBANN(knowledge-based artificial neural network) has been shown to be more effective than other machine learning algorithms, KBANN doesn't have the theory refinement capability because the topology of the network can't be altered dynamically. Although TopGen algorithm was proposed to extend the ability of KABNN in this respect, it also had some defects due to the connection of hidden nodes from all input nodes and the use of beam search. An algorithm, which could solve this TopGen's defects by adding the hidden nodes connected from only related input nodes and using hill-climbing search with backtracking, is proposed.