• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.365-374
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• 1998

Control Chart is a powerful tool to detect that process is in control or out of control. CIM can have real effect when CIM involve automated quality control. A neural network approach is used for unnatural pattern detecting of control chart. The previous moving window method uses all unnatural pattern that is detected as moving time window. Therefore, It trains a large number of unnatural pattern and takes training time long. In this paper, the proposed method tests a small number of training unnatural pattern which modifies test data without repeating time. We shows that the proposed method has differences In training time and identification rate on the previous moving windows method. As results, we reduced training time and obtain the same identification rate.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.111-116
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• 1998

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simula tion.a tion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.333-340
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• 1999

An ideal traffic control system should consider simultaneously both route guidance of vehicles and signal policies at intersection of a traffic network. It is known that an iterative procedure gives an optimal route to each vehicle in the network. This paper presents an iterative procedure to find an optimal signal plan for the network. We define the optimal solution as a signal equilibrium. From the definition of signal equilibrium, we prove that the fixed point solution of the iterative procedure is a signal equilibrium, when optimal signal algorithms are implemented at each intersection of the network. A combined model of route guidance and signal planning is also suggested by relating the route guidance procedure and the signal planning procedure into a single loop iterative procedure.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.51-52
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• 2012

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5110-5116
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• 2011

In this paper, multiplexing automation system will be proposed for the automobile electromotive mirror using CAN(Controller Area Network) protocol which has been known that it has a high reliability on the signal in the various network protocols. To do this, a master controller and two (input/output) slave controllers (H/W) are being made and application layer (S/W) is being programmed for effective going and communicating between subsystems. The possibility of the effectiveness of application and control ability will be shown when the system has minimum electrical lines by testing the experimental systems which was made up of the automobile electromotive mirror.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1935-1941
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• 2013

We can use NAT(Network Address Translation) technology to solve the lack of IP address. The problem of NAT traversal is happened when the filtering characteristics of NAT remove the packet that has no binding in the address translation table of NAT. There were many kinds of way to solve these problems by using additional device. Lately, network market is changed into integrating wired and wireless network by the IMS(IP Multimedia Subsystem). The IMS integrates to control network of wired and wireless network, has emerged to control convergence network effectively. Lately, the additional devices like IBCF(Interconnection Border Control Function) and IBGF(Interconnection Border Gateway Function) are used to solve the NAT traversal problem in the IMS. In this paper, we propose the solution of NAT traversal using P-CSCF without any additional equipment in the IMS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.830-836
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• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1597-1600
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• 2010

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.85-89
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• 2004

Robust control for feedback control system is needed according to the highest precision of industrial automation. However, when a neural network feedback control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, hybrid control method of neural network controller and PID controller is presented. A neural network controller is operated as a main controller, a PID controller is a assistant controller which operates only when some undesirable phenomena occur, e.q., when the error hit the boundary of constraint set. The robust control function of neural network-PID hybrid controller is demonstrated by speed control of Motor.