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Localizability and Distributed Localization of Sensor Networks using Relative Position Measurements

IFAC Proceedings Volumes 2013年第13期46卷 1-6页

作者： Yingfei Diao Zhiyun Lin Minyue Fu Huanshui Zhang School of Control Science and Engineering Shandong University Jinan Shandong 250061 P. R. China State Key Laboratory of Industrial Control Technology College of Electrical Engineering Zhejiang University 38 Zheda Road Hangzhou 310027 P. R. China School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia. State Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University 38 Zheda Road Hangzhou 310027 P. R. China State Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University 38 Zheda Road Hangzhou 310027 P. R. China

The paper introduces the localization problem of sensor networks using relative position measurements. It is assumed that relative positions are measured in local coordinate frames of individual sensors, for which different sensors may have different orientations of their local frames and the orientation errors with respect to the global coordinate frame are not known. A new necessary and sufficient condition is developed for localizability of such sensor networks that are modeled as directed graphs. That is, every sensor node should be 2-reachable from the anchor nodes. Moreover, for a localizable sensor network, a distributed, linear, and iterative scheme based on the graph Laplacian of the sensor network is developed to solve the coordinates of the sensor network in the global coordinate frame.

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An Embedded Software Fusion Power Model Based on Algorithm and Micro-architectural Level

An Embedded Software Fusion Power Model Based on Algorithm a...

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International Conference on Computational Intelligence and Security

作者： Bing Guo Yan Shen Xiao Bin Liu Qi Li Ji He Wang Yuan Sheng Wu College of Computer Science SiChuan University ChengDu China College of Control Engineering Chengdu University of Information and Technology Chengdu China

According to the power model based on algorithm complexity, we analyze the power characteristic on both the algorithm level and micro-architectural level. Therefore, we propose a fusion power model that combines both algorithm level and micro-architectural level. We extract the time complexity and space complexity on the algorithm level, the CPI(Cycles Per Instruction) on the micro-architectural level as the variables of this fusion model. We use HMSim, a high accurate power simulator based on ARM7TDMI instruction set, as the experimental platform. After measuring the power of the some selected benchmarks from HMSim, we develop a linear regression method to get the fusion model's coefficient. Simulation results show that the power is a linear function of both time complexity and CPI of a algorithm, but no direct relations with the space complexity of the algorithm, the relative error is below 4%, which proves the accurate of this fusion model.

关键词： Complexity theory Mathematical model Computational modeling Software algorithms Embedded software Power demand

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Optimal quarantining of wireless malware through power control

Optimal quarantining of wireless malware through power contr...

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Information Theory and Applications Workshop, ITA

作者： M. H. R. Khouzani Eitan Altman Saswati Sarkar School of Electrical and Systems Engineering University of Pennsylvania USA The French national Institute of research in computer science and control INRIA France

The topic of malware propagation in mobile wireless networks is gaining momentum among the research community, as actual vulnerabilities are revealed through recent outbreaks of worms. We introduce a defense strategy that quarantines the malware by reducing the communication range. This counter-measure faces us to a trade-off: reducing the communication range suppresses the spread of the malware, however, it also negatively affects the performance of the network as the end-to-end communication delay increases. We model the propagation of the malware as a deterministic epidemic. Using an optimal control framework, we select the optimal communication range that captures the above trade-off by minimizing a global cost function. Using Pontryagin's Maximum Principle, we derive structural characteristics of the optimal communication range as a function of time for two different cost functions.

关键词： Power control Mobile communication Wireless networks computer worms Optimal control computer networks Power engineering and energy Systems engineering and theory Mobile computing Cost function

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Design of a FPGA based position PI servo controller for a DC motor with dry friction

Design of a FPGA based position PI servo controller for a DC...

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Southern Conference on Programmable Logic

作者： Luis F. Castano Gustavo A. Osorio Perception and Intelligent Control Group Department of Electrical and Electronics Engineering and Computer Science Universidad Nacional de Colombia Manizales Colombia

In this paper we present the design, implementation and experimental validation of a FPGA based position servo controller for a DC motor with dry friction. VHDL and block diagram modules for trajectory generation, encoder signal decoding, PI controller and PWM control signal generation are described. The control system is implemented in the DE3 board of Terasic Technologies Inc using Quartus II environment of Altera Corporation. Servo system simulation was made using Simulink. The analytical non linear model for the anti-windup saturation on the integral control action, saturation on the actuator and dry friction is validated through experiments and simulations.

关键词： DC motors Servomotors Field programmable gate arrays Trajectory Shafts Digital signal processing Pulse width modulation

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Constrained Model Predictive control of High Dimensional Jump Markov Linear Systems

Constrained Model Predictive Control of High Dimensional Jum...

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American control Conference

作者： Jens Tonne Martin Jilg Olaf Stursberg Volkswagen AG Baunatal (Germany) Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel (Germany)

ISBN: (纸本)9781479917730

This paper proposes a model predictive control (MPC) approach for discrete-time jump Markov linear systems (JMLS) considering constraints on the inputs as well as on the expectancy of the states. Prediction equations for the first moment of the states are formulated, in which the dependencies on the inputs, on the expected values of disturbances, and on the current states are directly considered. For the computation of the matrices needed for predicting the first moment of the states, a recursive algorithm is presented. Finally, the prediction equations are used to formulate the MPC problem as a quadratic program (QP). Due to the recursive structure of the prediction equations and the formulation as a QP, the computational effort is low compared to existing approaches. Simulation results demonstrate the properties of the presented MPC approach and its capabilities of controlling large-scale JMLS online.

关键词： Markov processes Mathematical model Optimization Bismuth Probability distribution Predictive control Linear systems

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control of semiconductor manufacturing: curriculum development under an NSF combined research-curriculum grant

Control of semiconductor manufacturing: curriculum developme...

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American control Conference (ACC)

作者： P.P. Khargonekar F.L. Terry Electronics Manufacturing and Control Systems Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

In this paper, the authors describe their NSF sponsored research-curriculum program devoted to the topic of modeling and control of semiconductor manufacturing. The paper is focused for the most part on the curriculum... 详细信息

关键词： Semiconductor device manufacture Curriculum development Manufacturing processes Plasma materials processing Virtual manufacturing Process control Plasma applications Plasma displays Microelectronics Feedback control

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Partitioning methods used in DBS treatments analysis results

Partitioning methods used in DBS treatments analysis results

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International Joint Conference on Neural Networks (IJCNN)

作者： Oana Geman Cornel Turcu Department of Automatic Control and Computers Faculty of Electrical Engineering and Computer Science University Stefan cel Mare of Suceava Suceava Romania

Parkinson's disease is a neurodegenerative disorder and is associated with motor symptoms, including tremor. The DBS (Deep Brain Stimulation) involves electrode implantation into subcortical structures for long-term stimulation at frequencies greater than 100Hz. The mechanism by which chronic, electrical Deep Brain Stimulation with high frequency, suppresses tremor in Parkinson's disease is unknown, but might involve a gradual change in network properties controlling the generation of tremor. First, we performed linear and nonlinear analysis of the tremor signals to determine a set of parameters and rules for recognizing the behavior of the investigated patient and to characterize the typical responses for several forms of DBS. Second, we found patterns for homogeneous group for data reduction. We used Data Mining and Knowledge discovery techniques to reduce the number of data. Then, we found "clusters" the most well-known used and commonly partitioning methods used: K-means and K-medoids. To support such predictions, we develop a model of the tremor, to perform tests determining the DBS reducing the tremor or inducing tolerance and lesion if the stimulation is chronic.

关键词： Satellite broadcasting Clustering algorithms Time series analysis Correlation Algorithm design and analysis Mathematical model Software algorithms

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Reliability Prediction Based on SVR and Improved SCSO

Reliability Prediction Based on SVR and Improved SCSO

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International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Hongyue Liu Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Reliability prediction can provide basis for the identification of potential improvement area, cost control, and mission reliability assessment, etc. However, for complex equipment, there are many reliability influencing factors and incomplete knowledge of failure causes, which lead to a significant disparity between the predicted outcomes and the real values for traditional reliability prediction methods. To address the above issues, this research paper introduces an approach that utilizes the Support Vector Regression (SVR) model and Sand Cat Swarm Optimization (SCSO). To begin with, the sliding window technique is employed on the historical reliability data to generate time series samples, with the 5 adjacent data as sample data, and the sixth as label of the sample, and train SVR model on these samples; Second, the SVR model parameters are optimized using the ISCSO algorithm to obtain the optimal combination of parameters. In the testing stage, firstly, historical reliability data was used to predicted future data by the model, and the predicted data are then added to the sequence to form new samples, thus old data are discarded and new data are predicted continuously to realize continuous reliability prediction. Finally, the algorithm proposed in this paper is validated on a diesel engine reliability dataset. The algorithm proposed in this paper demonstrates its superiority through the Normalized Root Mean Squared Error (NRMSE) evaluation. The NRMSE of SVR-ISCSO is 8.86E-05, showcasing a remarkable 99.24% year-on-year decrease compared to the standard SVR. Additionally, it exhibits a 5.86% year-on-year decrease compared to SVR-SCSO, further validating the effectiveness of the proposed algorithm.

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An Object Frame Knowledge Representation Approach for Fault Diagnosis Expert System

An Object Frame Knowledge Representation Approach for Fault ...

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International Conference on Future computer sciences and Application (ICFCSA)

作者： Bailin Liu Mingye Duan Gang Zhao School of Computer Science and Engineering Xi'an Technological University Xi'an China North Automatic Control Technique Institute Taiyuan China

Knowledge quality and usability is core to a fault diagnosis system. Frame knowledge technique has been used to represent knowledge in many information and expert systems. To simplify the complexity of knowledge representation in fault diagnosis expert system, a novel object-frame knowledge representation approach based on hierarchical model was proposed. In this approach, domain specific knowledge is expressed by combinations of object-frames. An Object-frame is composed by relevant state-object, test-object and rule-object or repair-object. Production rules are used to connect relevant objects' states. General features of object-frame and inference algorithm are introduced. Object-frame based knowledge items are stored in SQL database, inference engine performs the inference operation of knowledge using forward chaining strategy, implements reasoning, finds the cause of faults and gives repair suggestion driven by test data. Inference interpretation completes the task of explanation, which improved the clarity of reasoning. The advantage of this method is that we do not need knowledge representation language support. Experimental results show that the method proposed is effective, which improved the fault diagnosis and maintenance for a meteorological vehicle system.

关键词： Fault diagnosis Knowledge representation Expert systems Maintenance engineering Cognition Engines

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Convex relaxation approach to the identification of the Wiener-Hammerstein model

Convex relaxation approach to the identification of the Wien...

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IEEE Conference on Decision and control

作者： Kin Cheong Sou Alexandre Megretski Luca Daniel Department of Automatic Control Lund Institute of Technology Sweden Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology USA

ISBN: (纸本)9781424431236

In this paper, an input/output system identification technique for the Wiener-Hammerstein model and its feedback extension is proposed. In the proposed framework, the identification of the nonlinearity is non-parametric. The identification problem can be formulated as a non-convex quadratic program (QP). A convex semidefinite programming (SDP) relaxation is then formulated and solved to obtain a sub-optimal solution to the original non-convex QP. The convex relaxation turns out to be tight in most cases. Combined with the use of local search, high quality solutions to the Wiener-Hammerstein identification can frequently be found. As an application example, randomly generated Wiener-Hammerstein models are identified.

关键词： System identification Quadratic programming Output feedback Power system modeling Piecewise linear techniques Polynomials Gaussian noise Least squares approximation Maximum likelihood estimation Noise measurement

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