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

Oscillatory thermopower of carbon chains: First-principles calculations

We investigate the thermoelectric transport through carbon chains connected by two Al leads. Using a Landauer-Buttiker-like formula, we calculate the thermopower and thermoconductance of Al-Cn-Al from first principles. We find that the charge transfer plays an important role in the thermoelectric transport. Because of the charge transfer, the thermopower changes sign for even-odd number of carbon atoms. The thermopower and electric conductance as a function of the gate voltage also exhibit oscillatory behaviors with a phase difference of pi/2.published_or_final_versio

Evolution of superconductivity in K2-xFe4+ySe5: Spectroscopic studies of X-ray absorption and emission.

This study investigates the evolution of superconductivity in K2-xFe4+ySe5 using temperature-dependent X-ray absorption and resonant inelastic X-ray scattering techniques. Magnetization measurements show that polycrystalline superconducting (SC) K1.9Fe4.2Se5 has a critical temperature (T c) of ∼31 K with a varying superconducting volume fraction, which strongly depends on its synthesis temperature. An increase in Fe-structural/vacancy disorder in SC samples with more Fe atoms occupying vacant 4d sites is found to be closely related to the decrease in the spin magnetic moment of Fe. Moreover, the nearest-neighbor Fe-Se bond length in SC samples exceeds that in the non-SC (NS) sample, K2Fe4Se5, which indicates a weaker hybridization between the Fe 3d and Se 4p states in SC samples. These results clearly demonstrate the correlations among the local electronic and atomic structures and the magnetic properties of K2-xFe4+ySe5 superconductors, providing deeper insight into the electron pairing mechanisms of superconductivity

Optimal density of radial major roads in a two-dimensional monocentric city with endogenous residential distribution and housing prices

postprin

Uncertainty sampling for action recognition via maximizing expected average precision

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Recognizing human actions in video clips has been an important topic in computer vision. Sufficient labeled data is one of the prerequisites for the good performance of action recognition algorithms. However, while abundant videos can be collected from the Internet, categorizing each video clip is time-consuming. Active learning is one way to alleviate the labeling labor by allowing the classifier to choose the most informative unlabeled instances for manual annotation. Among various active learning algorithms, uncertainty sampling is arguably the most widely-used strategy. Conventional uncertainty sampling strategies such as entropy-based methods are usually tested under accuracy. However, in action recognition Average Precision (AP) is an acknowledged evaluation metric, which is somehow ignored in the active learning community. It is defined as the area under the precision-recall curve. In this paper, we propose a novel uncertainty sampling algorithm for action recognition using expected AP. We conduct experiments on three real-world action recognition datasets and show that our algorithm outperforms other uncertainty-based active learning algorithms

The politics of linking educational research, policy, and practice: The case of improving educational quality in Ghana, Guatemala and Mali

This paper examines the political dimension of the educational research undertaken by Ghanian,\ud Guatemalan, and Malian teams as part of the 1991 to 1996, USAID-funded “Improving Educational\ud Quality” (IEQ) project. The following questions are addressed: (a) why were (or were not) aspects\ud of the educational reforms studied by the researchers; (b) why were (or were not) research findings\ud used in decision-makingabout the educationalpolicies and practices associatedwith the reforms; and\ud (c) why were particular institutional arrangements and funding levels constituted for the research and\ud dialogue activity. In offering answers to these questions, attentionis paid to local, national, and global\ud power relations and resource distributions

Mapping the city scale anthropogenic heat emissions from buildings in Kuala Lumpur through a top-down and a bottom-up approach

The warming urban climates increase the building energy consumption by changing the heating/cooling loads of the buildings. On the other hand, building induced anthropogenic heat emissions can also contribute to the urban heating, creating a warming feedback loop. Such impact is more profound in the (sub)tropical and hot/arid context, where Air Conditioning (AC) systems are widely used. A better understanding of the building energy consumption and its contribution to urban heating can therefore help mitigate urban heating. To this end, we aim to estimate building energy use and induced heat emissions in Kuala Lumpur, Malaysia, using both a bottom-up strategy based on building energy modelling and a top-down strategy based on national scale energy inventory. We further integrate the building energy model with measured diurnal temperature profiles at different land use areas, to discuss the impact of urban heat island (UHI) on energy use, and potential mitigation strategies through different urban morphologies. The estimated energy use obtained via both bottom-up and the top-down approaches were within the range of actual energy use from case studies available for Kuala Lumpur. It also highlights the need to adapt multi-scale strategies to mitigate the building energy use, and the associated impacts on the UHI

Transmission spectra and valley processing of graphene and carbon nanotube superlattices with inter-valley coupling

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GLProbs: Aligning multiple sequences adaptively

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