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Using micro-climate sensing to enhance RF localization in assisted living environments

Using micro-climate sensing to enhance RF localization in as...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Anthony Rowe Zane Starr Raj Rajkumar Real-Time and Multimedia Systems Laboratory Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

In this paper, we propose micro-climate sensing as an effective means of enhancing conventional RF-based localization. Our system targets people-tracking applications in dynamic indoor environments, such as nursing homes, hospitals and office spaces that require simple deployment and where conventional RF-based tracking using signal strengths alone is very likely to suffer from time-varying signal attenuation and inevitable changes in the environment over time such as new furniture arrangements, people traffic, changing obstacle patterns etc. To help mitigate these effects, we use time-synchronized windows of sensor samples to dynamically associate a mobile node with its nearest beacon nodes. Comparisons and matches are always relative to the ambient attributes at the time of localization, and hence our technique automatically evolves with environmental changes. We consider this property of localization techniques to be a significant contribution and a necessary requirement for any long- lived localization system. In assisted-living environments, sensor networks likely already have basic sensors to collect contextual information about users and to monitor the environment. We propose using light, humidity, temperature and audio data samples over a short window of time to model the micro-climate of a beacon node. Using microclimate matching in conjunction with RF signal strength decreases the worst-case localization error significantly by a factor of more than 3 (from 25 m to 8 m) while making the system more resilient to environment changes. Microclimate data helps ensure at least room level location tracking even in buildings like hospitals with many rooms in close proximity.

关键词： Radio frequency Sensor phenomena and characterization Hospitals Biomedical monitoring Indoor environments Medical services Target tracking time varying systems Attenuation Humidity

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Identification of Induction Machines Stator Currents with Generalized Neurons

Identification of Induction Machines Stator Currents with Ge...

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2007 IEEE Power engineering Society Conference and Exposition in Africa (IEEE PES PowerAfrica 2007), part 2

作者： Jing Huang Ganesh K. Venayagamoorthy Keith Corzine Real-Time Power and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Missouri Rolla MO USA

A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system.

关键词： Induction machines Stators Neurons Multilayer perceptrons Neural networks Multi-layer neural network Feedforward neural networks Neural network hardware Particle swarm optimization Algorithm design and analysis

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Optimal scheduling of generator maintenance using modified discrete particle swarm optimization

Optimal scheduling of generator maintenance using modified d...

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iREP Symposium on Bulk Power System Dynamics and Control

作者： Yusuf Yare Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

This paper presents a modified discrete particle swarm optimization (PSO) based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. While GA and other analytical methods might suffer from premature convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. PSO is known to effectively solve large scale multi-objective optimization problems. Here, a modified discrete PSO approach is proposed for the GMS optimization problem in order to overcome the limitations of the conventional methods and come up with a feasible and an optimal solution.

关键词： Optimal scheduling Particle swarm optimization Power system reliability Power generation Preventive maintenance Power system economics Power generation economics Power system analysis computing Convergence Large-scale systems

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A Novel Impedance Measurement Technique for Power Electronic Systems

A Novel Impedance Measurement Technique for Power Electronic...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： Peng Xiao Ganesh Venayagamoorthy Keith Corzine Real-Time Power and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Missouri Rolla Rolla MO USA

When designing and building power systems that contain power electronic switching sources and loads, system integrators must consider the frequency-dependent impedance characteristics at an interface to ensure system stability. Stability criteria have been developed in terms of source and load impedance for both dc and ac systems and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online tests which lead to many disadvantages. The impedance identification method proposed in this paper greatly reduces online test time by modeling the system with recurrent neural networks. The recurrent networks are trained with measured signals from the system with only one injection. The measurement and identification processes for dc and three-phase ac interfaces are developed. Simulation tests demonstrate the effectiveness of this new technique.

关键词： Impedance measurement Power electronics Power system stability Stability analysis Frequency measurement System testing Admittance Power system measurements Recurrent neural networks Power system simulation

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Self-healing control with multifunctional gate drive circuits for power converters

Self-healing control with multifunctional gate drive circuit...

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2007 IEEE Industry Applications Conference 42nd Annual Meeting, IAS

作者： Peng, Xiao Venayagamoorthy, Ganesh K. Corzine, Keith A. Woodley, Robert Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla United States 21st Century Systems Inc. 199 E 4th St. Ft. Leonard Wood MO United States

ISBN: (纸本)1424403642

Many commercial and military transport systems have fault diagnostic functions implemented to help protect the device when a severe fault occurs. However, most present systems do not contain prognostics capability which would allow operators to observe an unhealthy system component in its prefault condition. In industry applications, scheduled downtime can result in considerable cost avoidance. The next technology step is self-healing system components which observe not only potential problems, but can also take steps to continue operation under abnormal conditions - whether due to long-term normal wear-and-tear or sudden combat damage. In this paper, current and voltage information using the double-layer gate drive concept is fed to intelligent networks to identify the type of fault and its location. These intelligent networks are based on unsupervised and supervised learning networks (self-organizing maps and learning vector quantization networks respectively). The proposed concept allows the reconfiguration of the electric machinery system for continued normal operation of the machine. This paper presents an intelligent health monitoring and self-healing control strategy for a multi-phase multilevel motor drive under various types of faults. © 2007 IEEE.

关键词： Power converters

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Swarm Intelligence for Transmission System Control

Swarm Intelligence for Transmission System Control

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IEEE General Meeting Power& Energy Society

作者： G. K. Venayagamoorthy R. G. Harley Real Time Power and Intelligent Systems 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

Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems.

关键词： Particle swarm optimization Control systems Power system analysis computing Power systems Power system stability Birds Marine animals Educational institutions Ant colony optimization Nonlinear control systems

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A Quantum Algorithm for Finding Minimum Exclusive-Or Expressions

A Quantum Algorithm for Finding Minimum Exclusive-Or Express...

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IEEE computer Society Annual Symposium on VLSI

作者： M. Sampson D. Voudouris G. Papakonstantinou Department of Electrical and Computer Engineering Division of Computer Science Computing Systems Laboratory National and Technical University of Athens Greece

ISBN: (纸本)9780769528960;0769528961

This paper presents a quantum algorithm for minimizing both exclusive-or sum of complex terms (ESCT) and exclusive-or sum of products (ESOP) expressions. The proposed algorithm, QMin, takes advantage of the inherent massive parallelism of quantum circuits. The ESCT expressions produced by QMin are presented in the related bibliography as an attractive architecture for implementing reversible and quantum circuits.

关键词： Quantum computing Circuits Quantum mechanics computer architecture Boolean functions computer science Input variables Minimization methods Laboratories Parallel processing

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Making the power grid more intelligent

Making the power grid more intelligent

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iREP Symposium on Bulk Power System Dynamics and Control

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

Summary form only given. This paper focuses on the applications of intelligent techniques for improving the performances of the power system controllers. Intelligent control techniques lay the foundation of the next generation of nonlinear controllers and have the advantage of further improving the controller's performance by incorporating heuristics and expert knowledge into its design. Most of these techniques are independent of any mathematical model of the power system, which proves to be a considerable advantage.

关键词： Power grids Power system modeling Control systems Power system dynamics Power system control Power systems Power system reliability Intelligent networks Humans Three-term control

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Optimal Control of Class of Non-linear Plants Using Artificial Immune Systems: Application of the Clonal Selection Algorithm

Optimal Control of Class of Non-linear Plants Using Artifici...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： S.A. Panimadai Ramaswamy G.K. Venayagamoorthy S.N. Balakrishnan Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Department of Mechanical AeroSpace and Engineering Mechanics University of Missouri Rolla MO USA

The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear plants which are affine in control. The clonal selection algorithm is adapted for optimal control. A new mutation operator that operates on real values and one that aids in fast convergence is developed in this paper. AIS is used to obtain constant coefficient Kalman gain matrices. The validation and evaluation of the results thus obtained are carried out by comparing with standard and the widely used State Dependent Algebraic Riccati Equation (SDARE) method for the non-linear plants. In case of non-linear systems with hard state constraints, the SDARE formulation requires the use of mathematically involved expressions to incorporate these state constraints. However, the modified clonal selection algorithm developed in this paper has been used with hardly any changes to incorporate the hard state constraints and obtain the Kalman gain matrix.

关键词： Optimal control Artificial immune systems Immune system Kalman filters Riccati equations Protection Organisms Pathogens Computational intelligence Systems engineering and theory

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Adaptive Critic Design Based Neuro-Fuzzy Controller for a Static Compensator in a Multimachine Power System

Adaptive Critic Design Based Neuro-Fuzzy Controller for a St...

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IEEE General Meeting Power& Energy Society

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

Summary form only given. This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming (ADHDP), a member of the adaptive critic designs (ACD) family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming, and can effectively perform in the presence of noise and uncertainties. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system in order to provide voltage support during the steady state performance of the power system and also improve the dynamic stability of the network during faults and disturbances. Two multimachine power systems are considered in this study: a 10-bus 2-generator system and a 45-bus 10-generator system, where the latter is a section of the Brazilian power grid. Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbances.

关键词： Programmable control Adaptive control Control systems STATCOM Power systems Power system dynamics Power system stability Optimal control Automatic voltage control Power system faults

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