• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.2
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• pp.41-48
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• 2000

Recently, the requirement of automation in the every fields of the industrial places has been increased remarkably. The shooting range systems in the Past have been designed on the basis of static circumstance. However, Various transformation of the shooting circumstances is required, since the target for real shooting situation not static. In this study we have established for targets Per one shooting line to realize the dynamic shooting circumstance, distributed shooting order into sequence and random ones. and accommodated the shooting time variably. And, we have done an efficient communication possible, using CAN(Controller Area Network) applied to a lot of the car and the building automation. The CAN can support real-time control efficiently by connecting multiple ECU(Electric Control Unit), since it provide the high data transfer rate and the stability of communication.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6310-6316
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• 2014

The increase in the Internet and smart device users requires high-level network security. Network security consists of Web Firewall, Network Firewall, IPS, DDoS system, UTM (Unified Treat Management), VPN, NAC (Network Access Control), Wireless security, Mobile security, and Virtualization. Most network security solutions running on IPv4, and IPv6 network services are not sufficiently ready. Therefore, in this paper, this study designed and implemented important functions of Network Access Control (NAC), which include IPv6 host detection, isolation, blocking and domain assignment for the IPv6 network. In particular, domain assignment function makes 128 bits IPv6 address management easy. This system was implemented on a KISA IPv6 test-bed using well known devices. Finally, the test result showed that all IPv6 based wired and wireless devices were well-controlled (detection, blocking, isolation and domain assignment).

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.375-378
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• 2011

Measurement of network traffic have shown that the self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting the long-range correlation structure in a self-similar traffic for the congestion control. We have advanced the framework of the multiple time scale congestion control and showed its effectiveness at enhancing performance for the rate-based feedback control.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.103-108
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• 2005

Loading/Unloading task in the real industry is performed by crane, but most of the loading/unloading task with the weight of 5kg∼30kg is done by human workers and this kind of work causes industrial disaster of workers. Therefore it is necessary to develop low cost loading/unloading manipulator system to prevent this kind of industrial accidents. This paper is concerned with the design and fabrication of 2 axis pneumatic manipulators using on/off solenoid valves and accurate position control without respect to the external load and low damping in the pneumatic rotary actuator. To overcome the change of external load, switching of control parameter using LVQNN (Learning Vector Quantization Neural Network) is newly applied, which estimates the external loads in the pneumatic cylinder. As an underlying controller, a state feedback controller using position, velocity and acceleration is applied to the switching control system. The effectiveness of the proposed control algorithms are demonstrated through experiments of pneumatic cylinder with various loads.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1380-1397
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• 2018

The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.66
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• pp.19-26
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• 2001

I propose the control chart pattern to provide a more comprehensive scheme for detecting process X and R shifts using individual observations in start-up process. It is important to automate the identification of special disturbances to facilitate real-time manufacturing. This papers formulates X-R charts for interpretation by artificial neural networks. In this papers, which uses the backpropagation algorithm, two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. Simulation results sow that the performance of the proposed control chart using the neural network(NNCC) is quite promising. Using these NN charts, guidelines are given for detecting and classifying process X and R shifts.

• Structural Engineering and Mechanics
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• v.26 no.4
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.170.4-170
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• 2001

Safety and confidence of the communications network is the main purpose for the unmanned control systems, in terms of appling a satellite communications network to the water treatment and supply plant communication system. Since the unmanned control systems were applied in the industrial site, the lack of confidence on the communications network has been presented continuously as a main problem for the unmanned and automation systems. Therefore, satellite communications network was presented as one of the methods to solve this problem, supporting the wire used telecommunication. In addition to the safety of a communications network, there is the retrenchment of expenditure. This dual communications network has ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. 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. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1178-1183
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• 2008

The variable structure control(VSC) with sliding mode is an important and interesting topic in modern control of nonlinear systems. However, the discontinuous control law in VSC leads to undesirable chattering in practice. As a method solving this problem, in this paper, we propose a scheme of the VSC with neural network sliding surface. A neural network sliding surface with boundary layer is employed to solve discontinuous control law. The proposed controller can eliminate the chattering problem of the conventional VSC. The effectiveness of the proposed control scheme is verified by simulation results.