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real-time implementation of an intelligent algorithm for electric ship power system reconfiguration

Real-time implementation of an intelligent algorithm for ele...

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IEEE Electric Ship Technologies Symposium, ESTS

作者： Pinaki Mitra Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri USA

The naval electric ship is often subject to severe damages under battle conditions. The damages or faults might even affect the generators and as a result, critical loads might suffer from power deficiency which may lead to an eventual collapse of rest of the system. In order to serve the critical loads and maintain a proper power balance without excessive generation, the ship power system requires a fast reconfiguration of the remaining system under fault conditions. A fast intelligent algorithm using the small population based particle swarm optimization (SPPSO) for dynamic reconfiguration of the available generators and loads when a fault in the ship power system is detected is presented in this paper. SPPSO is a variant of PSO which operates with fewer particles and a regeneration concept, where new potential solutions are generated every few iterations. This concept of regeneration makes the algorithm fast and enhances its exploration capability to a large extent. The strength of the proposed reconfiguration strategy is first illustrated with Matlab results and then with a real-time implementation on a real time digital simulator and a digital signal processor.

关键词： real time systems Marine vehicles Power systems Signal processing algorithms Power system faults Power generation Power system dynamics Particle swarm optimization electrical fault detection Fault detection

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Generalized neuron based secure media access control protocol for wireless sensor networks

Generalized neuron based secure media access control protoco...

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IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM

作者： Raghavendra V. Kulkarni Ganesh K. Venayagamoorthy Abhishek V. Thakur Sanjay K. Madria Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Computer Science Missouri University of Science and Technology Rolla MO USA

Security plays a pivotal role in most applications of wireless sensor networks. It is common to find inadequately secure networks confined only to controlled environments. The issue of security in wireless sensor networks is a hot research topic for over a decade. This paper presents a compact generalized neuron (GN) based medium access protocol that renders a CSMA/CD network secure against denial-of-service attacks launched by adversaries. The GN enhances the security by constantly monitoring multiple parameters that reflect the possibility that an attack is launched by an adversary. Particle swarm optimization, a popular bio-inspired evolutionary-like optimization algorithm is used for training the GN. The wireless sensor network is simulated using Vanderbilt Prowler, a probabilistic wireless network simulator. Simulation results show that the choice of threshold suspicion parameter impacts on the tradeoff between network effectiveness and lifetime.

关键词： Media Access Protocol Neurons Wireless application protocol Wireless sensor networks Biomedical monitoring Routing Remote monitoring Condition monitoring Intelligent sensors Access protocols

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Fast discovery of spectrum opportunities in cognitive radio networks

Fast discovery of spectrum opportunities in cognitive radio ...

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2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2008

作者： Kim, Hyoil Shin, Kang G. Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2121 United States

ISBN: (纸本)9781424420179

We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning for secondary users (SUs) in cognitive radio networks (CRNs). Specifically, we propose an efficient sensing-sequence that incurs a small opportunity-discovery delay by considering (1) the probability that a spectrum band (or a channel) may be available at the time of sensing, (2) the duration of sensing on a channel, and (3) the channel capacity. We derive the optimal sensing-sequence for channels with homogeneous capacities, and a suboptimal sequence for channels with heterogeneous capacities for which the problem of finding the optimal sensing-sequence is shown to be NP-hard. To support the proposed sensing-sequence, we also propose a channel-management strategy that optimally selects and updates the list of backup channels. A hybrid of maximum likelihood (ML) and Bayesian inference is also introduced for flexible estimation of ON/OFF channel-usage patterns and prediction of channel availability when sensing produces infrequent samples. The proposed schemes are evaluated via in-depth simulation. For the scenarios we considered, the proposed suboptimal sequence is shown to achieve close-to-optimal performance, reducing the opportunity-discovery delay by up to 47% over an existing probability-based sequence. The hybrid estimation strategy is also shown to outperform the ML-only strategy by reducing the overall opportunity-discovery delay by up to 34%. ©2008 IEEE.

关键词： Bayesian networks

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ARCMA: Attack-resilient collaborative message authentication in Wireless Sensor Networks 08

ARCMA: Attack-resilient collaborative message authentication...

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4th International Conference on Security and Privacy in Communication Networks, SecureComm'08

作者： Cho, Min-Gyu Shin, Kang G. Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2121 United States

ISBN: (纸本)9781605582412

Data Centric Storage (DCS) is a well-known data storage and query processing mechanism for Wireless Sensor Networks (WSNs), storing sensed data or their metadata at pre-specified locations. Queries issued by mobile users are sent to, and processed at, such storage nodes. However, securing DCS is very difficult because WSNs usually operate in an unattended environment and hence are subject to node-capture attacks. Even after capturing a single node, an attacker may be able to subvert the entire system by using the keying material extracted from the captured node. To remedy/alleviate the above problem, we propose Attack-Resilient Collaborative Message Authentication (ARCMA), in which sensor nodes collaboratively authenticate messages to be sent to, or received from, remote nodes. In ARCMA, each node belongs to one of k groups, and constructs an Authentication Tree (AT) which is formed with k nodes, each from a distinct group. Each node collaborates with the other nodes in its AT to authenticate messages. We propose two heuristics, called MIN and OPT, to construct ATs. Our analysis shows that the security of ARCMA does not degrade until the attacker capture k or more nodes. We also evaluate the overhead of constructing ATs and the cost of authenticating messages using ATs. Copyright 2008 ACM.

关键词： Authentication

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Embedded neural network for fire classification using an array of gas sensors

Embedded neural network for fire classification using an arr...

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作者： Bashyal, Shishir Venayagamoorthy, Ganesh Kumar Paudel, Bandana Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical Engineering Missouri University of Science and Technology United States Smart Engineering Systems Laboratory Systems Engineering Department Missouri University of Science and Technology United States

ISBN: (纸本)9781424419630

Fire is one of the most common hazards in US households. In 2006 alone, 2705 people were killed due to fire in building structures. 74% of the deaths result from fires in homes with no smoke alarms or no working smoke alarms while surveys report that 96% of all homes have at least one smoke alarm. This study discusses the development of a fire sensing system that is not only capable of detecting fire in its early stage but also of classifying the fire based on the smell of the smoke in the environment. By using an array of sensors along with a neural network for sensor pattern recognition, an impressive result is obtained. Instead of confining the ANN to a PC based application, this work extends the implementation of the neural network fire classifier in a general purpose microcontroller. The result is a low cost intelligent embedded fire classifier that can be used in real life situations for fire hazards minimization, for example this intelligent fire classifier can be used for the development of a smart extinguisher that detects the fire, classifies it and then uses appropriate extinguishing material required for extinguishing the particular class of fire. ©2008 IEEE.

关键词： Fires

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Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks

Fast Discovery of Spectrum Opportunities in Cognitive Radio ...

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Hyoil Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

关键词： Sensors Channel capacity Delay Algorithm design and analysis Availability Channel estimation Maximum likelihood estimation

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Network-centric localization in MANETs based on particle swarm optimization

Network-centric localization in MANETs based on particle swa...

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2008 IEEE Swarm Intelligence Symposium, SIS 2008

作者： Kulkarni, Raghavendra V. Venayagamoorthy, Ganesh K. Miller, Ann Dagli, Cihan H. Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO United States Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO United States Department of Systems Engineering and Engineering Management Missouri University of Science and Technology Rolla MO United States

ISBN: (纸本)9781424427055

There exist several application scenarios of mobile ad hoc networks (MANET) in which the nodes need to locate a target or surround it. Severe resource constraints in MANETs call for energy efficient target localization and collaborative navigation. Centralized control of MANET nodes is not an attractive solution due to its high network utilization that can result in congestions and delays. In nature, many colonies of biological species (such as a flock of birds) can achieve effective collaborative navigation without any centralized control. Particle swarm optimization (PSO), a popular swarm intelligence approach that models social dynamics of a biological swarm is proposed in this paper for network-centric target localization in MANETs that are enhanced by mobile robots. Simulation study of two application scenarios is conducted. While one scenario focuses on quick target localization, the other alms at convergence of MANET nodes around the target. Reduction of swarm size during PSO search is proposed for accelerated convergence. The results of the study show that the proposed algorithm is effective in network-centric collaborative navigation. Emergence of converging behavior of MANET nodes is observed. ©2008 IEEE.

关键词： Multipurpose robots

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A differential evolution approach to optimal generator maintenance scheduling of the nigerian power system

A differential evolution approach to optimal generator maint...

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IEEE General Meeting Power& Energy Society

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

The goal of optimal generator maintenance scheduling is to evolve optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. In this paper, the differential evolution (DE), an evolutionary computation algorithm that utilizes the differential information to guide its further search, is applied to effectively solve the generator maintenance scheduling (GMS) optimization problem. The proposed method can handle mixed integer discrete continuous optimization problems. Results are presented with the DE algorithm on two different case studies for Nigerian power system.

关键词： Maintenance engineering Power systems Optimization Optimal scheduling Power system dynamics Generators Evolution (biology)

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Distributed Resource Kernels: OS Support for End-To-End Resource Isolation

Distributed Resource Kernels: OS Support for End-To-End Reso...

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IEEE Symposium on real-time and Embedded Technology and Applications

作者： Karthik Lakshmanan Raj Rajkumar Real Time and Multimedia Systems Laboratory Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

The notion of resource reservation for obtaining real-time scheduling guarantees and enforcement of resource usage has gained strong support in recent years. However, much work on resource reservation has primarily focused on single-processor systems. In this paper, we propose the distributed resource kernel frame wo rk to deploy distributed real-time applications with end-to-end timing constraints, and to efficiently enforce and monitor their usage. Modern distributed real-time systems host multiple applications, where each application can span two or more processors. Timing bugs in one distributed application can affect the timing properties of other applications in the system. Our framework introduces the abstraction of a distributed resource container as an isolated virtual operating environment for a distributed real-time application. We have implemented this framework by extending our open-source single- node Linux/RK platform (R. Rajkumar et al., 1998). A deployment and monitoring tool called dMon is also provided. We evaluate the framework's ability to provide timing guarantees by stress-testing the system using the Distributed Hartstone benchmarks. An audio processing pipeline is then used to illustrate the temporal isolation support provided by the Distributed RK framework. The distributed container abstraction can also be extended in the future to support security and fault-tolerance attributes.

关键词： Kernel Timing Containers real time systems computer bugs Open source software Linux Pipelines Security Fault tolerance

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Comparison of DE and PSO for generator maintenance scheduling

Comparison of DE and PSO for generator maintenance schedulin...

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IEEE Swarm Intelligence Symposium, SIS

作者： Y. Yare G. K. Venayagamoorthy Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

This paper presents a comparison of a differential evolution (DE) algorithm and a modified discrete particle swarm optimization (MDPSO) algorithm for generating optimal preventive maintenance schedules for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. The DE, an evolutionary technique and an optimization algorithm utilizes the differential information to guide its further search, and can handle mixed integer discrete continuous optimization problems. Discrete particle swarm optimization (DPSO) is known to effectively solve large scale multi-objective optimization problems and has been widely applied in power systems. Both the DE and MDPSO are applied to solve a multi-objective generator maintenance scheduling (GMS) optimization problem. The two algorithms generate feasible and optimal solutions and overcome the limitations of the conventional methods including extensive computational effort, which increases exponentially as the size of the problem increases. The proposed methods are tested, validated and compared on the Nigerian power system.

关键词： Power system reliability Scheduling algorithm Particle swarm optimization Power systems Power generation Preventive maintenance Power system economics Power generation economics Large-scale systems Processor scheduling

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