Robust fault detection for networked systems with distributed sensors

Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the robust fault detection problem for a class of discrete-time networked systems with distributed sensors. Since the bandwidth of the communication channel is limited, packets from different sensors may be dropped with different missing rates during the transmission. Therefore, a diagonal matrix is introduced to describe the multiple packet dropout phenomenon and the parameter uncertainties are supposed to reside in a polytope. The aim is to design a robust fault detection filter such that, for all probabilistic packet dropouts, all unknown inputs and admissible uncertain parameters, the error between the residual (generated by the fault detection filter) and the fault signal is made as small as possible. Two parameter-dependent approaches are proposed to obtain less conservative results. The existence of the desired fault detection filter can be determined from the feasibility of a set of linear matrix inequalities that can be easily solved by the efficient convex optimization method. A simulation example on a networked three-tank system is provided to illustrate the effectiveness and applicability of the proposed techniques.This work was supported by national 973 project under Grants 2009CB320602 and 2010CB731800, and the NSFC under Grants 60721003 and 60736026

Event-based recursive distributed filtering over wireless sensor networks

In this technical note, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Send-on-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.This work is supported by National Basic Research Program of China (973 Program) under Grant 2010CB731800, National Natural Science Foundation of China under Grants 61210012, 61290324, 61473163 and 61273156, and Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Jiangsu and Economics of China under Grant JSEB201301

Event-based H∞ consensus control of multi-agent systems with relative output feedback: The finite-horizon case

In this technical note, the H∞ consensus control problem is investigated over a finite horizon for general discrete time-varying multi-agent systems subject to energy-bounded external disturbances. A decentralized estimation-based output feedback control protocol is put forward via the relative output measurements. A novel event-based mechanism is proposed for each intelligent agent to utilize the available information in order to decide when to broadcast messages and update control input. The aim of the problem addressed is to co-design the time-varying controller and estimator parameters such that the controlled multi-agent systems achieve consensus with a disturbance attenuation level γ over a finite horizon [0,T]. A constrained recursive Riccati difference equation approach is developed to derive the sufficient conditions under which the H∞ consensus performance is guaranteed in the framework of event-based scheme. Furthermore, the desired controller and estimator parameters can be iteratively computed by resorting to the Moore-Penrose pseudo inverse. Finally, the effectiveness of the developed event-based H∞ consensus control strategy is demonstrated in the numerical simulation

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid

Algorithms for Visualizing Phylogenetic Networks

We study the problem of visualizing phylogenetic networks, which are extensions of the Tree of Life in biology. We use a space filling visualization method, called DAGmaps, in order to obtain clear visualizations using limited space. In this paper, we restrict our attention to galled trees and galled networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

General discussion on energy saving

Author name used in this publication: K. W. E. ChengRefereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

GPS calibrated ad-hoc localization for geosocial networking

LNCS v. 6406 is conference proceedings of UIC 2010Cost-effective localization for large-scale Geosocial networking service is a challenging issue in urban environment. This paper studies an ad-hoc localization technique which takes advantages of short-range interchanged location information for calibrating the location of mobile users carrying non-GPS mobile phones. We demonstrate by simulation that a small percentage of GPS-enabled mobile phones can greatly enable the localization of other non-GPS pedestrians in the urban environment. Based on the proposed localization technique, we implement a location-aware social networking tool called Mobile Twitter, similar to the microblogging service of Twitter, for fast propagation of social events happening in surroundings. Evaluation shows the our localization algorithm can achieve better accuracy of the location estimation and wider coverage as compared with the Amorphous algorithm and the Monte Carlo Localization (MCL) method. Moreover, we show that the Mobile Twitter implemented on an Android mobile phone is power-efficient in real-life usage scenarios. © 2010 Springer-Verlag.postprintThe 7th International Conference on Ubiquitous Intelligence and Computing (UIC) 2010, Xi'an, China, 26-29 October 2010. In Lecture Notes in Computer Science, 2010, v. 6406, p. 52-6

A semantic context management framework on mobile device

We present a semantic context management framework named ContextTorrent, which can make various types of context information be semantically searchable and sharable among local and remote context-aware applications. We implement this framework on the Google Android platform with its elegant application support. An open source RDF parser has been extended to effectively get RDF triples from files or over the network. Three embedded database systems were evaluated for storing ontology represented contexts in the resource-constrained mobile devices. We use the FOAF ontology schema and a synthetic data set of up to 2500 records to evaluate the context query and storage performance. Ordinary context queries can be replied instantaneously.published_or_final_versionThe 6th IEEE International Conference on Embedded Software and Systems (ICESS 2009), Zhejiang, China, 25-27 May 2009. In Proceedings of the 6th ICESS, 2009, p. 331-33

Sensitivity of activated human lymphocytes to cyclosporine and its metabolites

Alloreactive T cells generated as clones from mixed lymphocyte cultures, or propagated from heart or liver transplant biopsies, were tested for secondary proliferation measured in the primed lymphocyte test in the presence of Cyclosporine A and metabolites fractionated from human bile. Significant differences were observed in Cyclosporine A sensitivity between various cell cultures ranging as high as 100-fold. The liver is the primary site of Cyclosporine A metabolism, which yields a number of hydroxylated and N-dimethylated derivatives that are eventually secreted into the bile. Bile was collected from adult liver transplant patients on Cyclosporine A therapy and following extraction with diethyl ether, separated by high pressure liquid chromatography. Thirteen fractions were tested for their effect on lymphocyte proliferation in concanavalin A activation, mixed lymphocyte cultures and primed lymphocyte test assays. The strongest immunosuppressive effect was found with fraction 8, which contained metabolite M17, which has a single hydroxylation in position 1. Only three other fractions 9, 10, and 13, which contained metabolites M1, M18, and M21, respectively, exhibited immunosuppressive activity, albeit much lower than that of Cyclosporine A. Differences in Cyclosporine A sensitivity among alloreactive T cells followed similar patterns with Cyclosporius A metabolites. Thus, the assessment of the Cyclosporine A effect must consider differences in drug sensitivity of lymphocytes involved in transplant immunity and the generation of metabolites with immunosuppressive activity. © 1988

Minimum-variance recursive filtering over sensor networks with stochastic sensor gain degradation: Algorithms and performance analysis

This paper is concerned with the minimum variance filtering problem for a class of time-varying systems with both additive and multiplicative stochastic noises through a sensor network with a given topology. The measurements collected via the sensor network are subject to stochastic sensor gain degradation, and the gain degradation phenomenon for each individual sensor occurs in a random way governed by a random variable distributed over the interval [0, 1]. The purpose of the addressed problem is to design a distributed filter for each sensor such that the overall estimation error variance is minimized at each time step via a novel recursive algorithm. By solving a set of Riccati-like matrix equations, the parameters of the desired filters are calculated recursively. The performance of the designed filters is analyzed in terms of the boundedness and monotonicity. Specifically, sufficient conditions are obtained under which the estimation error is exponentially bounded in mean square. Moreover, the monotonicity property for the error variance with respect to the sensor gain degradation is thoroughly discussed. Numerical simulations are exploited to illustrate the effectiveness of the proposed filtering algorithm and the performance of the developed filter.This work was supported by the National Natural Science Foundation of China under Grants 61490701, 61210012, 61290324, 61473163, 61522309, and 61273156; in part by the Tsinghua University Initiative Scientific Research Program; and in part by the Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Finance and Economics of China under Grant JSEB201301; and in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China