• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.385-389
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• 2009

Multi-hop wireless mesh networks (WMNs) suffer from significant packet losses due to insufficient available bandwidth and high channel error probability. To conquer packet losses, end-to-end (E2E) error control schemes have been proposed. However, in WMNs, E2E error control schemes are not effective in adapting to the time-varying network condition due to large delay. Thus, in this paper, we propose a network-adaptive error control for video streaming over WMNs that flexibly operates E2E and hop-by-hop (HbH) error control according to network condition. Moreover, to provide lightweight support at intermediate nodes for HbH error control, we use path-partition-based adaptation. To verify the proposed scheme, we implement it and evaluate its transport performance through MPEG-2 video streaming over a real IEEE 802.11a-based WMN testbed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.7 s.124
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• pp.662-667
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• 2007

A new neuro-control scheme for active control of structures is proposed. It utilizes lattice pattern of state vector as training data of probabilistic neural network(PNN). Therefore. it is the so-called lattice probabilistic neural network(LPNN). PNN makes control forces by using all the training patterns. Therefore, it takes much time to obtain a control force in application. This inevitably may delay the control action. However. control force of LPNN is calculated by using only the adjacent information of LPNN input. So, the response of LPNN is greatly faster than PNN. The proposed control algorithm is applied for three story building under California and El Centro earthquakes. Also, control results of the LPNN are compared with those of the conventional PNN. The structural responses have been suppressed effectively by the proposed algorithm.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• 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 Korea Convergence Society
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• v.5 no.4
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• pp.163-169
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• 2014

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.257-261
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• 2005

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a WAVENET control technique to control efficiently its elevation angle and azimuth one. Wavelet neural network(WAVENET) can construct systematically initial neural network as applying wavelet theory to feedforward network. It is proved through computer simulation that WAVENET has more excellent approximation capability than existing neural network. The simulation results using MATLAB are introduced.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.1-9
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• 2022

The recent development of information and communication technology is also affecting audio/video systems used in industry. The audio/video device configuration system changes from analog to digital, and the network-based audio/video system control has the advantage of reducing costs in accordance with system operation. However, audio/video systems released on the market have limitations in that they can only control their own products or can only be performed on specific platforms (Windows, Mac, Linux). This paper is a study on a device (Network Audio Video Integrated Control: NAVICS) that can integrate and control multiple audio / video devices with different functions, and can control digitalized audio / video devices through network and serial communication. As a result of the study, it was confirmed that individual control and integrated control were possible through the protocol provided by each audio/video device by NAVICS, and that even non-experts could easily control the audio/video system. In the future, it is expected that network-based audio/video integrated control technology will become the technical standard for complex audio/video system control.