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Contaminant Event Monitoring in intelligent Buildings Using a Multi-zone Formulation *

IFAC Proceedings Volumes 2012年第20期45卷 492-497页

作者： Michalis P. Michaelides Vasso Reppa Christos Panayiotou Marios Polycarpou Department of Electrical Engineering and Information Technologies Cyprus University of Technology KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants’ comfort, health, productivity and safety. These events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. Furthermore, we demonstrate how the problem of monitoring the indoor air quality in intelligent buildings against the presence of contaminant sources fits the usual framework of fault detection, isolation, identification and accommodation. Specifically, we develop a multi-zone formulation using state space equations that enables the use of fault diagnosis and fault tolerant control techniques for monitoring contaminant events inside the building environment. We demonstrate our proposed formulation for the problem of isolating multiple contaminant sources using an estimation scheme in a nine zone building setting.

关键词： intelligent buildings indoor air quality contaminant event monitoring multi-zone model fault isolation

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Distributed Sensor Fault Detection and Isolation for Nonlinear Uncertain systems *

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IFAC Proceedings Volumes 2012年第20期45卷 1077-1082页

作者： Vasso Reppa Marios M. Polycarpou Christos G. Panayiotou KIOS Research Center for Intelligent Systems and Networks Electrical & Computer Engineering Department University of Cyprus Nicosia 1678 Cyprus

This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. The proposed architecture is based on the design of local SFDI modules, which monitor the healthy operation of a set of sensors and aim to detect the faults in this set, using a dedicated nonlinear observer scheme. The multiple sensor fault isolation procedure is further enhanced by deriving a combinatorial decision logic that processes information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of its robustness with respect to the modeling uncertainties and conditions for ensuring fault detectability and isolability.

关键词： Fault detection fault isolation sensor faults nonlinear systems uncertain dynamic systems

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3D ray tracing for device-independent fingerprint-based positioning in WLANs

3D ray tracing for device-independent fingerprint-based posi...

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2012 9th Workshop on Positioning, Navigation and Communication, WPNC'12

作者： Raspopoulos, Marios Laoudias, Christos Kanaris, Loizos Kokkinis, Akis Panayiotou, Christos G. Stavrou, Stavros Sigint Solutions Ltd. Nicosia Cyprus KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus Cyprus Faculty of Pure and Applied Sciences Open University of Cyprus Cyprus

ISBN: (纸本)9781467314398

We study the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to make the overall process device-independent. RSS data collection might be a tedious and time-consuming process and also the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a more attractive and efficient way to generate radiomaps. Moreover, traditional fingerprint-based methods lead to radiomaps which are restricted to the device used to generate the radiomap and fail to provide acceptable performance when different devices are considered. We address both challenges by exploiting 3D RT-generated radiomaps and using linear data transformation to match the characteristics of various devices. We evaluate the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device. © 2012 IEEE.

关键词： Ray tracing

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A Distributed Architecture for Sensor Fault Detection and Isolation using Adaptive Approximation

A Distributed Architecture for Sensor Fault Detection and Is...

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International Joint Conference on Neural Networks

作者： Vasso Reppa Marios M. Polycarpou Christos G. Panayiotou KIOS Research Center for Intelligent Systems and Networks Department of Electrical & Computer Engineering University of Cyprus

ISBN: (纸本)9781467314886

This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, an adaptive approximation technique is used for learning online the modeling uncertainty. Then, local SFDI modules are designed using a dedicated nonlinear observer scheme. The fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of its robustness with respect to the modeling uncertainties and conditions for ensuring fault detectability and isolability.

关键词： Defect detection uncertain systems approximation Self tuning fault isolation model uncertainty

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Stochastic search strategies in 2D using agents with limited perception

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IFAC Proceedings Volumes 2012年第22期45卷 650-654页

作者： David Puljiz Maja Varga Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne 1015 Lausanne Switzerland

Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rather limited, search strategies are usually simplistic and stochastic. And yet such simple search strategies result in high efficiency in finding potential targets in limited time intervals. Similar problems with perception of the environment and limited computational power and sensory inputs are observed on the mobile robots used in search scenarios. In spite of their limitations, such robotic systems usually perform deterministic search strategies which heavily depend on environmental cues and sensory input. The research question we are addressing in this paper is which stochastic search strategy has the best performance in terms of area coverage and can stochastic search strategies outperform deterministic search strategies.

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Machine learning with operational costs

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Theja Tulabandhula Cynthia Rudin Google MPI for Intelligent Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA MIT Sloan School of Management and Operations Research Center Massachusetts Institute of Technology Cambridge MA

This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm's objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization.

关键词： covering numbers decision theory optimization statistical learning theory

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Towards a semantically enhanced control architecture

Towards a semantically enhanced control architecture

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IEEE International Symposium on intelligent Control (ISIC)

作者： Georgios M. Milis Christos G. Panayiotou Marios M. Polycarpou KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus Cyprus

The design of feedback control systems typically assumes a fixed number of sensors and actuators. However, in today's large-scale complex systems there is often a need to replace faulty sensors/actuators with new ones or to deploy additional sensors of possibly different characteristics compared to the old sensors. In such cases, it may be necessary to deactivate, redesign and reconfigure the feedback control system, which implies system interruptions and significant economic costs. This paper provides a first step towards a semantically enhanced feedback control architecture, where it is possible to automatically reconfigure the sensor/actuator components without having to redesign the feedback control law. The proposed architecture uses ontology-based semantic mediation. This paper focuses on the sensor case and utilizes a simple example to illustrate the approach. It is emphasized that the proposed architecture will be even more beneficial for achieving interoperability in the new paradigm of system of systems where systems may be added or removed and the expectation is that the overall system should continue to operate optimally.

关键词： Temperature measurement Temperature sensors Semantics Temperature control Sensor systems

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Semi-supervised learning using greedy max-cut

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Jun Wang Tony Jebara Shih-Fu Chang Google MPI for Intelligent Systems IBM T.J. Watson Research Center Yorktown Heights NY Department of Computer Science Columbia University New York NY Department of Electrical Engineering Columbia University New York NY

Graph-based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems, particularly in agnostic settings when no parametric information or other prior knowledge is available about the data distribution. Given the constructed graph represented by a weight matrix, transductive inference is used to propagate known labels to predict the values of all unlabeled vertices. Designing a robust label diffusion algorithm for such graphs is a widely studied problem and various methods have recently been suggested. Many of these can be formalized as regularized function estimation through the minimization of a quadratic cost. However, most existing label diffusion methods minimize a univariate cost with the classification function as the only variable of interest. Since the observed labels seed the diffusion process, such univariate frameworks are extremely sensitive to the initial label choice and any label noise. To alleviate the dependency on the initial observed labels, this article proposes a bivariate formulation for graph-based SSL, where both the binary label information and a continuous classification function are arguments of the optimization. This bivariate formulation is shown to be equivalent to a linearly constrained Max-Cut problem. Finally an efficient solution via greedy gradient Max-Cut (GGMC) is derived which gradually assigns unlabeled vertices to each class with minimum connectivity. Once convergence guarantees are established, this greedy Max-Cut based SSL is applied on both artificial and standard benchmark data sets where it obtains superior classification accuracy compared to existing state-of-the-art SSL methods. Moreover, GGMC shows robustness with respect to the graph construction method and maintains high accuracy over extensive experiments with various edge linking and weighting schemes.

关键词： bivariate formulation graph transduction greedy max-cut mixed integer programming semi-supervised learning

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Fractional Order Dynamic Modeling Of Crowd Pedestrians

Fractional Order Dynamic Modeling Of Crowd Pedestrians

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第五届国际自动控制联合会分数阶导数及其应用会议

作者： Ke-Cai Cao Caibin Zeng Dan Stuart YangQuan Chen Nanjing University of Posts and Telecommunications Nanjing210003 China Center for Self-Organizing and Intelligent Systems (CSOIS)Department of Electrical and Computer Engineering 4160 Old MainHill Utah State University (USU) Logan UT 84322-4160 U School of Sciences South China University of TechnologyGuangzhou 510640 China Center for Self-Organizing and Intelligent Systems (CSOIS)Department of Electrical and Computer Engineering 4160 Old Main Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering 4160 Old Main Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering 4160 Old Main

＜正＞Motivated by the complexity of spatial-temporal patterns,fractional order model of crowd dynamics are proposed in this paper based on the popular Lighthill-Whitham-Richards Model（LWR） model for traffic *** to previous models for traffic systems and models for crowd pedestrian system,space andtime characters are included and powerful

关键词： Fractional Order LWR Model Crowd Pedestrian Dynamics Fractal Derivative. Pedestrians Fractional calculus fractional order Fractals Modeling time characteristic

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The Effect of Parameter and Measurement Uncertainties on Hybrid State Estimation

The Effect of Parameter and Measurement Uncertainties on Hyb...

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IEEE Power and Energy Society General Meeting

作者： Markos Asprou Elias Kyriakides Mihaela Albu KIOS Research Center for Intelligent Systems and Networks and the Department of Electrical and Computer Engineering University of Cyprus Cyprus. Department of Electrical Engineering "Politehnica" University of Bucharest 060042 Bucharest Romania

ISBN: (纸本)9781467327299

State estimation constitutes the cornerstone of a Supervisory Control and Data Acquisition (SCADA) system, providing the operating situation of the power system in consecutive time intervals. With the incorporation of the Synchronized Measurement Technology (SMT) in the power system measurements layer, the state estimation is among the applications whose performance is improved considerably. In this paper, two sources of uncertainty that deteriorate the performance of a hybrid state estimator, that uses both synchronized phasor measurements and conventional measurements, are examined. Specifically, the measurement uncertainty and the uncertainty arising from the limited knowledge of the transmission line parameters are considered. These uncertainties are associated with the inputs of the hybrid state estimator and their effect on the overall accuracy of the process is investigated. The methodology is tested using the IEEE 14 and 118 bus systems.

关键词： Hybrid state estimator conventional measurements measurement uncertainty parameter uncertainty synchronized phasor measurements.

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