Probability-guaranteed set-membership state estimation for polynomially uncertain linear time-invariant systems

2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksConventional deterministic set-membership (SM) estimation is limited to unknown-but-bounded uncertainties. In order to exploit distributional information of probabilistic uncertainties, a probability-guaranteed SM state estimation approach is proposed for uncertain linear time-invariant systems. This approach takes into account polynomial dependence on probabilistic uncertain parameters as well as additive stochastic noises. The purpose is to compute, at each time instant, a bounded set that contains the actual state with a guaranteed probability. The proposed approach relies on the extended form of an observer representation over a sliding window. For the offline observer synthesis, a polynomial-chaos-based method is proposed to minimize the averaged H2 estimation performance with respect to probabilistic uncertain parameters. It explicitly accounts for the polynomial uncertainty structure, whilst most literature relies on conservative affine or polytopic overbounding. Online state estimation restructures the extended observer form, and constructs a Gaussian mixture model to approximate the state distribution. This enables computationally efficient ellipsoidal calculus to derive SM estimates with a predefined confidence level. The proposed approach preserves time invariance of the uncertain parameters and fully exploits the polynomial uncertainty structure, to achieve tighter SM bounds. This improvement is illustrated by a numerical example with a comparison to a deterministic zonotopic method.Peer ReviewedPostprint (author's final draft

First Demonstration of a Scintillating Xenon Bubble Chamber for Detecting Dark Matter and Coherent Elastic Neutrino-Nucleus Scattering

A 30-g xenon bubble chamber, operated at Northwestern University in June and November 2016, has for the first time observed simultaneous bubble nucleation and scintillation by nuclear recoils in a superheated liquid. This chamber is instrumented with a CCD camera for near-IR bubble imaging, a solar-blind photomultiplier tube to detect 175-nm xenon scintillation light, and a piezoelectric acoustic transducer to detect the ultrasonic emission from a growing bubble. The time of nucleation determined from the acoustic signal is used to correlate specific scintillation pulses with bubble-nucleating events. We report on data from this chamber for thermodynamic "Seitz" thresholds from 4.2 to 15.0 keV. The observed single- and multiple-bubble rates when exposed to a $^{252}$Cf neutron source indicate that, for an 8.3-keV thermodynamic threshold, the minimum nuclear recoil energy required to nucleate a bubble is $19\pm6$ keV (1$\sigma$ uncertainty). This is consistent with the observed scintillation spectrum for bubble-nucleating events. We see no evidence for bubble nucleation by gamma rays at any of the thresholds studied, setting a 90% C.L. upper limit of $6.3\times10^{-7}$ bubbles per gamma interaction at a 4.2-keV thermodynamic threshold. This indicates stronger gamma discrimination than in CF$_3$I bubble chambers, supporting the hypothesis that scintillation production suppresses bubble nucleation by electron recoils while nuclear recoils nucleate bubbles as usual. These measurements establish the noble-liquid bubble chamber as a promising new technology for the detection of weakly interacting massive particle dark matter and coherent elastic neutrino-nucleus scattering.Comment: 6 pages, 4 figures. Published versio

Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images

The limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged scene covers a large distance range. In order to compensate for this problem, image fusion has been exploited for combining images captured with different camera settings, thus yielding a higher quality all-in-focus image. Since most current approaches for image fusion rely on maximizing the spatial frequency of the composed image, the fusion process is sensitive to noise. In this paper, a new algorithm for computing the all-in-focus image from a sequence of images captured with a low depth-of-field camera is presented. The proposed approach adaptively fuses the different frames of the focus sequence in order to reduce noise while preserving image features. The algorithm consists of three stages: 1) focus measure; 2) selectivity measure; 3) and image fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-the-art all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the proposed scheme even for high levels of noise

Can we always get the entanglement entropy from the Kadanoff-Baym equations? The case of the T-matrix approximation

We study the time-dependent transmission of entanglement entropy through an out-of-equilibrium model interacting device in a quantum transport set-up. The dynamics is performed via the Kadanoff-Baym equations within many-body perturbation theory. The double occupancy $< \hat{n}_{R \uparrow} \hat{n}_{R \downarrow} >$, needed to determine the entanglement entropy, is obtained from the equations of motion of the single-particle Green's function. A remarkable result of our calculations is that $< \hat{n}_{R \uparrow} \hat{n}_{R \downarrow} >$ can become negative, thus not permitting to evaluate the entanglement entropy. This is a shortcoming of approximate, and yet conserving, many-body self-energies. Among the tested perturbation schemes, the $T$-matrix approximation stands out for two reasons: it compares well to exact results in the low density regime and it always provides a non-negative $< \hat{n}_{R \uparrow} \hat{n}_{R \downarrow} >$. For the second part of this statement, we give an analytical proof. Finally, the transmission of entanglement across the device is diminished by interactions but can be amplified by a current flowing through the system.Comment: 6 pages, 6 figure

Water demand estimation and outlier detection from smart meter data using classification and Big Data methods

Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. On the one hand, this paper aims at predicting water demand and detect suspicious behaviors – e.g. a leak, a smart meter break down or even a fraud – by extracting water consumption patterns. On the other hand, the main contribution of this paper, a software framework, based on Big Data techniques, is presented to tackle the barriers of traditional data storage and data analysis since the volume of AMR data collected by Water Utilities is enormous and it is continuously growing because this technology is expanding .Peer ReviewedPostprint (author’s final draft

Control-oriented thermal modeling methodology for water-cooled PEM fuel-cell-based systems

In this paper, a new control-oriented modeling methodology for the thermal dynamics of water-cooled Proton Exchange Membrane Fuel Cells (PEMFCs) is presented and validated. This methodology is not only useful for control applications, but also can be used for predicting the temperature variation across the stack, allowing to monitor its operation. The methodology has been validated in a real 600-W, 20-cells, water cooled PEMFC, with encouraging results for both the stationary and the transient states. Results show that the proposed methodology is accurate and suitable for control purposes.Peer Reviewe

Control-oriented thermal modeling methodology for water-cooled PEM fuel-cell-based systems

In this paper, a new control-oriented modeling methodology for the thermal dynamics of water-cooled Proton Exchange Membrane Fuel Cells (PEMFCs) is presented and validated. This methodology is not only useful for control applications, but also can be used for predicting the temperature variation across the stack, allowing to monitor its operation. The methodology has been validated in a real 600-W, 20-cells, water cooled PEMFC, with encouraging results for both the stationary and the transient states. Results show that the proposed methodology is accurate and suitable for control purposes.Peer Reviewe

Distinctive dielectric properties of nematic liquid crystal dimers

We provide an overview of the effect of the molecular structure on the dielectric properties of dimers exhibiting nematic and twist-bend nematic phases with special focus on how the conformational distribution changes are reflected by the dielectric behaviour. Nematic dimers show distinctive dielectric properties which differ from those of archetypical nematic liquid crystals, as for example, unusual temperature dependence of the static permittivity or dielectric spectra characterised by two low-frequency relaxation processes with correlated strengths. The interpretation of such characteristic behaviour requires that account is taken of the effect of molecular flexibility on the energetically favoured molecular shapes. The anisotropic nematic interactions greatly influence the conformational distribution. Dielectric behaviour can be used to track those conformational changes due to dependence of the averaged molecular dipole moment on the averaged molecular shape. Results for a number of dimers are compared and analysed on the basis of the influence of details of the molecular structure, using a recently developed theory for the dielectric properties of dimers.Postprint (author's final draft

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201