• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1B
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• pp.39-46
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• 2005

As the multimedia application traffic takes more portion in the internet traffic, it is necessary to control the network congestion through the congestion control protocol. In addition, the QoS-enabled networks such as DiffServ become an indispensable technology when running the multimedia applications. However, the previously proposed end-to-end congestion control algorithms take the round trip time to react the network congestion. Thus, as the RTT becomes larger, the reaction against the congestion gets delayed further, while the network congestion gets worse. In addition the performance of end-to-end congestion control algorithm is degraded if the QoS-enabled network runs the congestion control mechanism in the network level without any coordination between them. In this paper, we propose the early rate adaptation protocol for the DiffServ network which effectively linke the congestion control algorithm at the host and the congestion mechanism in the network together. By taking advantage of early congestion notification from the network it is possible to react the network congestion more quickly and effectively.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.524-529
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• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3193-3199
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• 2009

The real-time intelligent home network control system is the system which can control and monitor intelligent home network anytime and anywhere with mobile devices. In this study, to embody the real-time control system for intelligent home network, I designed the sub-module which can control various USN senses with using ZigBee, and organized the GUI environment into the client module to drive by users with mobiles devices.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.887-892
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• 2007

In this paper, we design, implement and apply network physical layer to 100 BaseFx optical cable interface module based on industrial ethernet protocol EtherCAT that has ensure its open standard ethernet compatibility which haying been provided with real time of control in network of intelligent service robot, can process numerous data to sensor and motor control system. Through various tests, we try to propose suitability as internal network of intelligent service robot.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.1
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• pp.39-45
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• 1997

In this paper, a high speed motor drive system using an indirect adaptive neural network controller is proposed. In the variable high speed motor drives, the speed response can be deteriorated by long settling time and high overshoot. To obtain a good dynamical performance, an adaptive feedforward controller consisted of Neural Network Controller(NNC) and Neural Network Emulator(NNE) is applied. The NNE is used to identify the parameters and characteristics of high speed motor. To train the controller, the weights are dynamically adjusted using the back propagation algorithm. Computer simulation and implementation of the proposed system is described.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.3
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• pp.273-281
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• 2001

In this paper, we propose a CMAC(Cerebellar Model Articulation Controller) neural network for controlling a non-linear system. CMAC is a neural network that models the human cerebellum. CMAC uses a table look-up method to resolve the complex non-linear system instead of numerical calculation method. It is very fast learn compared with other neural networks. It does not need a calculation time to generate control signals. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC Motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.432-439
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• 2012

In this paper, instead of the conventional PD controller for balancing control of two wheeled inverted pendulum robots, an improved PD controller using the neural network is proposed and implemented for performance verification. First, a two wheeled inverted pendulum robot system is constructed for experiment. Next proper gains of the conventional PD controller according to users' weights are obtained for balancing the robot by use of the trial and error method. The PD gains based on the trial and error method are generalized through the neural network. Experiment results show that the PD controller based on the neural network has better performance than the conventional PD controller.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.183-191
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• 2013

International network standards for SOLAS ship are composed of instrument network that controls and monitors machine in real time, shipboard control network that controls and monitors system through computer by human and telecommunication network that connects ship and shore. This paper describes development of stack for instrument network protocol complied with NMEA 2000 that is IEC 61162-3 international standards for SOLAS ship and also that of engine room monitoring system using the developed stack. Developed engine room monitoring system is certified by NMEA according to standards that require to pass about 1,600 test procedures.

• Smart Media Journal
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• v.1 no.1
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• pp.58-63
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• 2012

It is a superordinate concept to a network control system which optimally distributes the battery power to overall length parts through the linkage control drive and absorbs/integrates network diagnostics and overlapped functions with overall length control systems. This study is to develop a system that maximize the battery power and motor effectiveness by controlling motor battery controlling module with common MCU Integration linkage controlling system, and to develop S/W and H/W that can be controlled by linked with each controlling module in CAN method through using Autosar's standardized software.