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Predictive call admission control for multimedia streaming in CDMA cellular environments

Canadian Journal of electrical and computer engineering 2005年第2期30卷 115-123页

作者： Elmallah, Ehab S. Mandal, Mrinal Department of Computing Science University of Alberta Edmonton Alta. T6G 2E8 Multimedia Computing and Communications Laboratory Department of Electrical and Computer Engineering University of Alberta Edmonton Alta. T6G 2V4

Third-generation (3G) wireless networks based on code-division multiple access (CDMA) have been engineered to provide mobile users with voice as well as advanced packet data services. Support of Internet-based multimedia streaming services is considered a critical factor for future deployment of such networks. Provisioning streaming services in a dependable way, however, requires supporting data transmission at relatively high data rates while maintaining session quality during both intracell and intercell user movements. In this paper, an adaptive bandwidth allocation and admission control scheme is proposed for managing network resources for a streaming service. Streaming requests are assumed to be served by an adaptive server capable of choosing suitable video streaming parameters (e.g., video resolution, frame rate, and encoding parameters) in response to possible requests from the wireless network to vary the currently allocated bandwidth. The devised admission control scheme exploits a priori knowledge of user mobility patterns to minimize the risk of running into an overload condition after acceptance of a new multimedia streaming connection. The obtained simulation results show that, compared to a nonpredictive admission control scheme, the proposed scheme achieves a lower forced-termination probability and higher throughput, while consuming less base-station transmission energy.

关键词： Multimedia systems

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A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with statcom

A comparison of PSO and backpropagation for training RBF neu...

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2005 IEEE Swarm Intelligence Symposium, SIS 2005

作者： Mohaghegi, Salman Del Valle, Yamille Venayagamoorthy, Ganesh K. Harley, Ronald G. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States University of KwaZulu Natal South Africa

ISBN: (纸本)0780389166

Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a Radial Basis Function Neural Network (RBFN) is compared with that of Particle Swarm Optimization, for neural network based identification of a small power system with a Static Compensator. The comparison of the two methods is based on the convergence speed and robustness of each method. © 2005 IEEE.

关键词： Radial basis function networks

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An adaptive mamdani fuzzy logic based controller for a static compensator in a multimachine power system

An adaptive mamdani fuzzy logic based controller for a stati...

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13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05

作者： Mohagheghi, S. Venayagamoorthy, G.K. Harley, R.G. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla MO 65409-0249 United States University of KwaZulu-Natal South Africa

ISBN: (纸本)1599750287

An adaptive Mamdani based fuzzy logic controller has been designed for controlling a Static Compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional PI controller during dynamic and transient disturbances. © 2005 ISAP.

关键词： Programmable logic controllers

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real-time dual heuristic program-ming-based neurocontroller for a turbogenerator in a multimachine power system

Real-time dual heuristic program-ming-based neurocontroller ...

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作者： Venayagamoorthy, Ganesh K. Harle, Ronald G. Wunsch, Donald C. Real-Time Power and Intelligent Systems Laboratory University of Missouri Rolla MO 65409 United States Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409 United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States

Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances. © 2005 CRL Publishing Ltd.

关键词： Heuristic programming

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A dynamic recurrent neural network for wide area identification of a multimachine power system with a FACTS device

A dynamic recurrent neural network for wide area identificat...

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13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05

作者： Mohagheghi, S. Venayagamoorthy, G.K. Harley, R.G. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States University of KwaZulu-Natal South Africa

ISBN: (纸本)1599750287

Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for Wide Area Control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy. © 2005 ISAP.

关键词： Electric power systems

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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Optimal control parameters for a UPFC in a multimachine using PSO

Optimal control parameters for a UPFC in a multimachine usin...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： G.K. Venayagamoorthy Real-Time Power and Intelligent System Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the proposed PSO based approach for the design of optimal conventional controllers for a UPFC in a multimachine power system

关键词： Optimal control Power system simulation Power system transients Load flow Power system control Power systems Load flow control Damping Particle swarm optimization Control systems

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Comparison of non-uniform optimal quantizer designs for speech coding with adaptive critics and particle swarm

Comparison of non-uniform optimal quantizer designs for spee...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： W. Zha G.K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

This paper presents the design of a companding non-uniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality (PESQ) standard.

关键词： Speech coding Particle swarm optimization Quantization Neural networks Bit rate Speech analysis Signal to noise ratio Telecommunication standards Humans Signal resolution

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A neural network based optimal wide area control scheme for a power system

A neural network based optimal wide area control scheme for ...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： G.K. Venayagamoorthy S. Ray Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time digital simulator (RTDS/spl reg/). The performance of the WACS as a power system stability agent is studied using the Kundur's two area power system example. The WACS provides better damping of power system oscillations under small and large disturbances even with the inclusion of local power system stabilizers.

关键词： Neural networks Optimal control Control systems Power system control Power systems Power system simulation Power system stability Automatic voltage control Automatic control Power industry

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Neural networks based non-uniform scalar quantizer design with particle swarm optimization

Neural networks based non-uniform scalar quantizer design wi...

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IEEE Swarm Intelligence Symposium, SIS

作者： W. Zha G.K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.

关键词： Neural networks Particle swarm optimization Quantization Bit rate Signal to noise ratio Humans Ear Nonlinear distortion Speech processing Oral communication

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