• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.672-674
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• 1997

In this paper, we study the idle speed control of the spark ignition engine. Engine idle speed control is a difficult problem because of troublesome characteristics such as severe process nonlinearities, variable time delays, time-varying dynamics and unobservable internal system states and disturbances. We investigate the intelligent control algorithms such as neural network controller and fuzzy controller for 4-cylinder 4-stroke engine.

• Journal of Power System Engineering
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• v.4 no.4
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• pp.92-98
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• 2000

Modern manufacturing systems should cope with the frequent changes in a product model and disturbances in manufacturing process. The control system of such systems must cover a constant adaptation and high flexibility. Holonic Flexible Manufacturing Cell(HFMC) is introduced to handle these issues more successfully. It is based on the concept of autonomous co-operating agent, called 'Holon', which is a building block of a manufacturing system for transforming, transporting, storing and/or validating information and physical objects. In this paper the basic structure of the HFMC is represented by using Unified Modeling Language and Open architecture cell controller is developed for effective integration components of a manufacturing system. Also a new control model, called MuLOM(Multi-Layered Operation Model), is suggested to represent the control behaviour for a holonic flexible manufacturing cell control system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.9
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• pp.2399-2411
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• 1995

Crane operation for transporting heavy loads inherently causes swinging motion at the loads due to crane's acceleration or deceleration. This motion not only lowers the handling safety but also slows down the handling process. To complement such a problem, Korea Atomic Energy Research Institute(KAERI) has designed several anti-swing controllers using open loop and closed loop approaches. They are namely a pre-programmed feedback controller and a fuzzy controller. These controllers are implemented on a 1-ton crane system at KAERI and their control performances are compared. Test operations show that the new controllers are superior to that of conventional cranes in terms of robustness to the disturbances and adaptation capability to the change of rope length.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.822-830
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• 2000

A conventional advanced control algorithm is proposed in this paper for improved temperature regulation of a TV-glass melting furnace. The TV-Glass melting furnace is a typical MIMO(Multi-Input Multi Output) system which is subject to various thermal disturbances. Because of its complexity, a detailed mathematical model of the furnace is hard to establish. To design a temperature control control system of the furnace, major input-output variables are selected first, and simple FOPDT(First Order Plus Dead Time) models are established based on the physical meaning and experimental process data. Based on the FOPDT models, a multi-loop control system composed of cascade and single loops are designed for effective control of the MIMO system. Practical implementation on the 150 ton/day furnace using the DCS(Distributed Control System) showed that the proposed control technique performs better than manual control.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.58-64
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• 2001

In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Journal of Navigation and Port Research
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• v.30 no.4
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• pp.259-265
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• 2006

This paper presents an improved adaptive neural network autopilot based on our previous study for track-keeping control of ships. The proposed optimal neural network controller can automatically adapt its learning rate and number of iterations. Firstly, the track-keeping control system of ships is described For the track-keeping control task, a way-point based guidance system is applied To improve the track-keeping ability, the off-track distance caused by external disturbances is considered in learning process of neural network controller. The simulations of track-keeping performance are presented under the influence of sea current and wind as well as measurement noise. The toolbox for track-keeping simulation on Mercator chart is also introduced.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.4
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• pp.176-181
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• 2001

As the data rate between chip-to-chip gets high, the skin effect and load of pins deteriorate noise margin. With these, noise disturbances on the bus channel make it difficult for receiver circuits to read the data signal. This paper has proposed a new pre-emphasis driver circuit which achieves wide noise margin by enlarging the signal voltage range during data transition. When data is transferred from a memory chip to a controller, the output boltage of the driver circuit reaches the final values through the intermediate voltage level. The proposed driver supplies more currents applicable to a packet-based memory system, because it needs no additional control signal and realizes very small area. The circuit has been designed in a 0.18 ${\mu}m$ CMOS process, and HSPICE simulation results have shown that the data rate of 1.32 Gbps be achieved. Due to its result, the proposed driver can achieved higher speed than conventional driver by 10%.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.724-729
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• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.89-95
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• 2000

In order to achieve effective prediction of residual stresses, the series experiment were carried out and the residual stresses were measured using the backgpropagation algorithm from the neural network and the sectional method. Using the experimental results, the optimal control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on residual stresses during GMA welding processes. The results obtained from the comparison between the measured and calculated results, showed that the neural network based on backpropagation algorithm can be sued in order to control weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also, improve the quantity control for welded structures. The development of the system is goal in this study.

• YAKHAK HOEJI
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• v.45 no.5
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• pp.522-528
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• 2001

Tyrosinase plays an important role in the process of melanin biosynthesis, and it is a biochemical target enzyme for skin-whitening agents and the remedy for disturbances in pigmentation. We have screened 25 natural plant extracts for inhibitory effects against DOPA oxidase activity of tyrosinase. For the inhibitory effect against DOPA oxidase activity of tyrosinase, the recombinant human tyrosinase and the purified mushroom tyrosinase were used. Each of the dried plants extracted with methanol, and then the extracts were subjected to sequential fractionations with methylene chloride, ethyl acetate, n-butanol and water, respective The methylene chloride fractions of Angelica tenuissima, Nardostachys chinensis, Bombyx mop and Saposhnikovia divaricata, and the n-butanol fraction of Bombyx mori notably inhibited the human tyrosinase activity as well as mushroom tyrosinase activity (more than 90% inhibition). This study suggests that the above extracts have a potential as whitening agents as single ingredients or in combination with other of the extracts.