• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.78-101
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• 2017

High-quality industrial control is critical to ensuring production quality, reducing production costs, improving management levels and stabilizing equipment and long-term operations. WiFi-based Long Distance (WiLD) networks have been used as remote industrial control networks. Real-time performance is essential to industrial control. However, the original mechanism of WiLD networks does not minimize end-to-end delay and restricts improvement of real-time performance. In this paper, we propose two algorithms to obtain the transmitting/receiving phase cycle length for each node such that real time constraints can be satisfied and phase switching overhead can be minimized. The first algorithm is based on the branch and bound method, which identifies an optimal solution. The second is a fast heuristic algorithm. The experimental results show that the execution time of the algorithm based on branch and bound is less than that of the heuristic algorithm when the network is complex and that the performance of the heuristic algorithm is close to the optimal solution.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.94.5-94
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• 2002

The current study of complex dynamical networks is pervading all kinds of sciences today, ranging from physical to biological, even to social sciences. its impact on modern engineering and technology is prominent and will be far-reaching. Typical complex dynamical networks include the World Wide Web, the Internet, various wireless communication networks, meta-bolic networks, biological neural networks, social connection networks, scientific cooperation and citation networks, and so on. Research on fundamental properties and dynamical features of such complex networks have become overwhelm ing. This talk will provide a brief overview of some basic concepts about com plex dynamical netwo...

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.39-43
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• 2007

In high speed communication networks, the determination of a transmission rate is critical for the stability of a closed-loop network system with the congestion control scheme. In ATM networks, the available bit rate (ABR) service is based on a feedback mechanism, i.e., the network status is transferred to the ABR source by a resource management (RM) cell. RM cells contain the traffic information of the downstream nodes for the traffic rate control. However, the traffic status of the downstream nodes can not be directly transferred to the source node in the IP based networks. In this paper, a new rate-based congestion control scheme using an exponential weighted moving average algorithm is proposed to build an efficient feedback control law for congestion avoidance in high speed communication networks. The proposed congestion control scheme assures the stability of switch buffers and higher link utilization of the network. Moreover, we note that the proposed congestion scheme can flexibly work along with the increasing number of input sources in the network, which results in an improved scalability.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.453-456
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.8
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• pp.1105-1110
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• 2011

This paper presents an improved direct torque control based on artificial neural networks technique. The major problem that is usually associated with DTC drive is the high torque(speed) ripple. To overcome this problem a torque hysteresis band with variable amplitude is proposed based on artificial neural networks. The artificial neural networks proposed controller is shown to be able to reducing the torque(speed) ripple and dependency on motor parameter and to improve performance DTC especially at high speed and reversal running.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.304-308
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• 2007

This paper deals with ANN(Artificial Neural Networks) and its application to automatic ship berthing. Due to the characteristic of ship's manoeuvre comparing with other moving objects on land, it has been known that the automatic control for ship's berthing cannot cope with various berthing situations such as various port shape and approaching directions. for these reasons. the study on automatic berthing using ANN usually have been carried out based on one port shape and predetermined approaching direction. In this paper, new algorithm with ANN controller was suggested to cope with these problems. Under newly suggested algorithm, the controller can select appropriate weights on the link of neural networks according to various situations. so the ship can maintain stable berthing operation even in different situations. Numerical simulations are carried out with this control system to find its improvement.

• 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.