• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.13-24
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• 2005

The sensor network is a key component of the ubiquitous computing system which is expected to be widely utilized in logistics control, environment/disaster control, medical/health-care services, digital home and other applications. Nodes in the sensor network are small-sized and exposed to adverse environments. They are demanded to perform their missions with very limited power supply only. Also the sensor network is composed of much more nodes than the wireless ad hoc networks are. In case that some nodes consume up their power capacity, the network topology should change, and rerouting/retransmission is necessitated. Communication protocols studied for conventional wireless networks or ad hoc networks are not suited for the sensor network resultantly. Schemes should be devised to control the efficient usage of node power in the sensor network. This paper proposes a medium access protocol to enhance the efficiency of energy consumption in the sensor network node. Its performance is analyzed by simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.461-466
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• 2012

Any future heterogeneous overlay network system must be able to support ubiquitous access across multiple wireless networks. To coordinate these diverse network environments, one challenging task is a call admission decision among different types of network. In this paper, we propose a new call admission control scheme to provide quality of service (QoS) while ensuring system efficiency. Based on the interplay between network structure and dynamics, we estimate the network's QoS level and adjust the service price adaptively with the aim of maximizing the network performance. A simulation shows that the proposed scheme can approximate an optimized solution while ensuring a well-balanced network performance in widely different network environments.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.917-928
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• 2006

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot's kinematics.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2419-2422
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• 2000

A distributed real-time system that is being used now is usually divided into three level : higher level, middle level, and lower level. The higher level network is usually called an information network, the middle level is called a control network, and the lower level is called a field network or a divice network. This dissertation suggests and implements a middle level network which is called PICNET-NP (Plant Implementation and Control Network for Nuclear Power Plant). PICNET-NP is based partly on IEEE 802.4 token-passing bus access methed and partly on IEEE 802.3 physical layer. For this purpose a new interface, a physical layer service translater, is introduced. A control network using this method is implemented and applied to a distributed real-time system.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.11a
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• pp.143-148
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• 1999

A distributed real-time system that is being wed now is usually divided into three level : higher level, middle level, and lower level. The higher level network is usually called an information network, the middle level is called a control network, and the lower level is called a field network or a divice network. This dissertation suggests and implements a middle level network which is called PICNET-NP (Plant Implementation and Control Network for Nuclear Power Plant). PICNET-NP is based partly on IEEE 802.4 token-passing bus access method and partly on IEEE 802.3 physical layer. For this purpose a new interface, a physical layer service translator, is introduced. A control network using this method is implemented and applied to a distributed real-time system.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.202-210
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• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.63-67
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• 2004

We know that Neural Network is in use in many control fields. In time of using as controller, Neural Network controller is needed to learning by Input-output pattern. But in many times of control field. we can not get Input-output pattern of Neural Network controller. As a method solving this problem, in this paper, we try New control method that output node of Neural Network bringing control object. Such a New control method application, we can solve the data taking problem to Neural Network controller Input-output. The effectiveness of proposed control algorithm is verified by simulation results of DC servo motor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.2096-2105
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• 1998

As there can be coincident bursts which may result in congetsion in a node of ATM network, reactive flow control schemes are required to guarantee user's Quality of Service. But, the high speed characteristics of ATM networks make it difficult to control source transmission rate in reacting to congestions in intermediate nodes. Therefore, flow control in Customer Premise Network may be more efficient than end-to-end flow control. In this paper, we propose a management model for flow ontrol in CPN and new Network Access Flow/Congestsion control scheme to utilize efficiently Virtual Path Connection.

• 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.