• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.33-41
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• 2018

Most important thing when designing and implementing a supply chain network is to consider various problems which may occur in real world situation. In this paper, we propose a supply chain network considering two problems (environmental factor and transportation types) under real world situation. CO2 emission amount as environmental factor is considered since it is usually generated from production and transportation processes. Normal delivery, direct delivery and direct shipment as transportation types are also considered since many customers ask various transportation types for delivery or shipment of their products under on-line or off-line purchase environment. The proposed supply chain network considering environmental factor and transportation types is represented in a mathematical formulation and implemented using hybrid genetic algorithm (HGA) approach. In numerical experiments, several scales of supply chain networks are presented and implemented using HGA approach. The performance of the HGA approach is compared with those of some conventional approaches under various measures of performance. Finally, it is proved that the performance of the HGA approach is superior to those of the others.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2187-2189
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• 2003

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of chaotic systems. The conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on a direct adaptive control method is proposed to control chaotic systems whose mathematical models are not available. The gradient-descent method is used for training a wavelet neural network controller. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic system.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2827-2829
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• 2005

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2498-2500
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• 2004

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.3 no.2
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• pp.18-25
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• 2004

As using of network Increases rapidly, performance of system has been deteriorating because of the overhead and bottleneck. Nowadays, high speed I/O network standard, that is a sort of InfiniBand, PCI Express and so on, has come out to improve the limites of traditional I/O bus. The InfiniBand provides some protocols to service the applications such as SDP, SRP and IPoIB. In our paper, We explain the architecture of SDP(Socket Direct Protocol) and its features in channel based I/O network. And so, we provide a result of performance evaluation of SDP which is compared with current network protocol. Our experimental results also show that SDP is better than TCP/IP protocol.

• Advances in Computational Design
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• v.8 no.3
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• pp.237-253
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• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.404-413
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• 1997

Direct-drive robots are suitable to position and force control with high accuracy, but it is difficult to design a controller which gives satisfactory perfonnance because of the system's nonlinearity and link-interactions. This paper is concerned with the force control of direct-drive robots. The pro¬posed algorithm consists of feedback controllers and a neural network. Mter the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural network con¬troller plays an important role in the control system. Therefore, the optimum adjustment of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algo¬rithm is demonstrated by the experiment on the force control of a parallelogram link-type direct¬drive robot.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.370-374
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• 1999

The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.