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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...

引用

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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Power-aware real-time scheduling using pinwheel model and profiling technique

Power-aware real-time scheduling using pinwheel model and pr...

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IEEE International Conference on Embedded and real-time Computing systems and Applications (RTCSA)

作者： Hsin-hung Lin Chih-Wen Hsueh Real-Time Systems Laboratory Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

Energy consumption is a critical design issue in embedded systems, especially in battery-operated systems. Maintaining high performance while extending the battery life is an interesting challenge for system designers. Dynamic voltage scaling and dynamic frequency scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Because of the high complexity involved, most solutions depend on heuristics for online power-aware real-time scheduling or offline time-consuming scheduling. In this paper, we discuss how we can apply pinwheel model to power-aware real-time scheduling so that task information, including start times, finish times, preemption times, etc, can be efficiently derived using pinwheel model. System predictability is thus increased and under better control on power-awareness. However, job execution time may be only a small portion of its worst case execution time and can only be determined at runtime. We implement a profiling tool to insert codes for collecting runtime information of real-time tasks. Worst case execution time is updated online for scheduler to perform better rescheduling according to actual execution. Simulations have shown that at most 50% energy can be saved by the proposed scheduling algorithm. Moreover, at most additional 33% energy can be saved when the profiling technique is applied.

关键词： Processor scheduling Power system modeling Dynamic voltage scaling Frequency Runtime Energy consumption Embedded system Batteries Energy management Control systems

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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...

引用

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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A neural network based Wide Area Monitor for a power system

A neural network based Wide Area Monitor for a power system

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2005 IEEE Power engineering Society General Meeting

作者： Li, Xiaomeng Venayagamoorthy, Ganesh K. IEEE 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 United States South African Institute of Electrical Engineers IEEE St. Louis Computational Intelligence Department of Task Force on Intelligent Control Systems Secretary of the Intelligent Systems Subcommittee IEEE Power Engineering Society

ISBN: (纸本)078039156X

With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) 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. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an online Wide Area Monitor (WAM) using a neural network called the Wide Area Neuroidentifier (WANI). The WANI is used to predict ahead the speed deviations of generators in the different areas using phasor measurement unit (PMU). Results are presented to show the effectiveness of the wide area monitor for different disturbances. © 2005 IEEE.

关键词： Wide area networks

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Deadline spanning: a graph based approach

Deadline spanning: a graph based approach

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IEEE International Conference on Embedded and real-time Computing systems and Applications (RTCSA)

作者： S.M. Petters School for Computer Science and Engineering University of New South Wales Sydney Australia Embedded Real-Time Operating Systems Program NICTA Sydney Australia

Microkernel based systems tend to depend heavily on IPC. This paper addresses the problem of a system response spanning more than one task in an embedded real-time system. The approach is based on a mix of classical response time analysis equations and a graph based approach to estimate the impact of different parts of the system on the time needed by the system to respond. This approach has the major advantage of being intuitive.

关键词： Delay real time systems Yarn Kernel Operating systems computer science Differential equations Embedded system Australia Council Control systems

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

作者： S. Mohagheghi G.K. Venayagamoorthy R.G. Harley Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

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

关键词： Power system dynamics Recurrent neural networks Power systems Power system modeling Power system simulation Multilayer perceptrons Radial basis function networks Neural networks Multi-layer neural network Degradation

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A novel method for predicting harmonic current injection from non-linear loads using neural networks

A novel method for predicting harmonic current injection fro...

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Annual Conference of Industrial Electronics Society

作者： J. Mazumdar R. Harley F. Lambert G.K. Venayagamoorthy School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MA USA

Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Neural networks Power system harmonics Current measurement Pollution measurement Voltage Instruments Power generation Power system measurements Distortion measurement Power measurement

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Using a Neural Network to Distinguish Between the Contributions to Harmonic Pollution of Non-Linear Loads and the Rest of the Power System

Using a Neural Network to Distinguish Between the Contributi...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

Harmonics are one of the important power quality measurable quantities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. Harmonics may therefore be classified as contributions from the load on the one hand and contributions from the power system or supply harmonics on the other hand. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Power system harmonics Neural networks Pollution measurement Distortion measurement Power measurement Current measurement Voltage Instruments Power system measurements Power quality

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Adaptive critic designs for optimal control of power systems

Adaptive critic designs for optimal control of power systems

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

作者： G.K. Venayagamoorthy R.G. Harley Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

关键词： Programmable control Adaptive control Optimal control Power system control Power systems Power system simulation PSCAD Power system modeling Flexible AC transmission systems Power grids

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