pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources
Authors: Nils Müller, Samuel Chevalier, Carsten Heinrich, Kai Heussen, Charalampos Ziras
Date: 2021-10-08 00:00:00
Tasks: Decision Making, quantile regression, State Estimation, Uncertainty Quantification

The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading to network violations. DSOs require reliable and accurate low-voltage state estimation (LVSE) to improve awareness and mitigate such events. However, the influence of flexibility activations on LVSE has not been addressed yet. It remains unclear if flexibility-induced uncertainty can be reliably quantified to enable robust DSO decision-making. In this work, uncertainty quantification in LVSE is systematically investigated for multiple scenarios of input availability and flexibility utilization, using real data. For that purpose, a Bayesian neural network (BNN) is compared to quantile regression. Results show that frequent flexibility activations can significantly deteriorate LVSE performance, unless secondary substation measurements are available. Moreover, it is demonstrated that the BNN captures flexibility-induced voltage drops by dynamically extending the prediction interval during activation periods, and that it improves interpretability regarding the cause of uncertainty.
Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving
Authors: Flavia Sofia Acerbo, Mohsen Alirezaei, Herman Van der Auweraer, Tong Duy Son
Date: 2021-10-08 00:00:00
Tasks: Autonomous Driving, Imitation Learning

This paper presents a safe imitation learning approach for autonomous vehicle driving, with attention on real-life human driving data and experimental validation. In order to increase occupant's acceptance and gain drivers' trust, the autonomous driving function needs to provide a both safe and comfortable behavior such as risk-free and naturalistic driving. Our goal is to obtain such behavior via imitation learning of a planning policy from human driving data. In particular, we propose to incorporate barrier functions and smooth spline-based motion parametrization in the training loss function. The advantage is twofold: improving safety of the learning algorithm, while reducing the amount of needed training data. Moreover, the behavior is learned from highway driving data, which is collected consistently by a human driver and then processed towards a specific driving scenario. For development validation, a digital twin of the real test vehicle, sensors, and traffic scenarios are reconstructed toward high-fidelity and physics-based modeling technologies. These models are imported to simulation tools and co-simulated with the proposed algorithm for validation and further testing. Finally, we present experimental results and analyses, and compare with the conventional imitation learning technique (behavioral cloning) to justify the proposed development.
Charge capacity characteristics of a Lithium Nickel-Cobalt-Aluminium Oxide battery show fractional-derivative behavior
Authors: Marcus T. Wilson, Vance Farrow, Caleb Pyne, Jonathan Scott
Date: 2021-10-08 00:00:00

Batteries experience capacity offset where available charge depends on the rate at which this charge is drawn. In this work we analyze the capacity offset of a 4.8 A h lithium nickel-cobalt-aluminium oxide battery using an equivalent circuit model of a fractional capacitor in series with a resistor. In this case, the available charge, in theory, becomes infinite in the limit of infinitesimal rate. We show that the fractional properties of the capacitor can be extracted from the charge against rate plot. We then use a network of RC elements to represent the fractional capacitor in order to simulate the data with Matlab. We find that the fractional exponent alpha obtained in this way, 0.971, agrees with that obtained in a more traditional manner from an impedance versus frequency plot, although the fractional capacity does not. Such an approach demonstrates the importance of a fractional description for capacity offset even when an element is nearly a pure capacitor and is valuable for predictions of state-of-charge when low currents are drawn.
Discomfort Monitoring System for Residential Electrical Water Heater
Authors: Ziyad Almajali
Date: 2021-10-07 00:00:00
Tasks: energy management, Management

An approach is described in this work for detecting discomfort moments during electrical water heater daily usage. The approach employs chromatic analyzing sensors signals of electrical water heater systems for producing distinguishable mapping to characterize and identify selected discomfort moments. The preliminary results obtained indicate that it is possible to distinguish such events in a non-intrusive approach. Hot water comfort detection and classification intelligently through human behavior monitoring and analyses plays an important role in both, energy saving and energy management. The focus is on recording the discomfort situations followed by merging system outcomes with efficiency evaluation will be used to provide helpful recommendations for selecting appropriate operating strategy.
A fast co-simulation approach to vehicle/track interaction with finite element models of S&C
Authors: Demeng Fan, Michel Sebès, Emmanuel Bourgeois, Hugues Chollet, Cédric Pozzolini
Date: 2021-10-06 00:00:00

Simulations of vehicle/track interaction (VTI) in switches and crossings (S\&C) require taking into account the complexity of their geometry. The VTI can be handled via a co-simulation process between a finite element (FE) model of the track and a multibody system (MBS) software. The objective of this paper is to reduce the computing effort in the co-simulation process. In the proposed approach, the VTI problem is solved inside the MBS software to reduce the computational effort in the track model as well as the flow of input/output between both modules. The FE code is used to supply the matrices of stiffness, damping and mass at the beginning of the simulation. An explicit time scheme is used with mass scaling. A good agreement is found between both approaches with a reduction of the computing time by a factor of 10. This new approach allows the optimisation of the design of S\&C in further studies.
Measurement of In-Circuit Common-Mode Impedance at the AC Input of a Motor Drive System
Authors: Zhenyu Zhao, Fei Fan, Arjuna Weerasinghe, Pengfei Tu, Kye Yak See
Date: 2021-10-06 00:00:00

The in-circuit common-mode (CM) impedance at the AC input of a motor drive system (MDS) provides valuable inputs for evaluating and estimating the CM electromagnetic interference (EMI) noise generated by the switching of power semiconductor devices in the MDS. This paper introduces a single-probe setup (SPS) with frequency-domain measurement to extract the in-circuit CM impedance of a MDS under its different operating modes. The SPS has the merits of non-contact measurement and simple structure.
Self-supervised Speaker Recognition with Loss-gated Learning
Authors: Ruijie Tao, Kong Aik Lee, Rohan Kumar Das, Ville Hautamäki, Haizhou Li
Date: 2021-10-08 00:00:00
Tasks: Self-Supervised Learning, Speaker Recognition

In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals. It is a known fact that a speaker recognition model doesn't always benefit from pseudo labels due to their unreliability. In this work, we observe that a speaker recognition network tends to model the data with reliable labels faster than those with unreliable labels. This motivates us to study a loss-gated learning (LGL) strategy, which extracts the reliable labels through the fitting ability of the neural network during training. With the proposed LGL, our speaker recognition model obtains a $46.3\%$ performance gain over the system without it. Further, the proposed self-supervised speaker recognition with LGL trained on the VoxCeleb2 dataset without any labels achieves an equal error rate of $1.66\%$ on the VoxCeleb1 original test set. Code has been made available at: https://github.com/TaoRuijie/Loss-Gated-Learning.
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
Authors: Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
Date: 2021-10-08 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Multimodal Activity Recognition

This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications.
Domain Decomposition Algorithms for Real-time Homogeneous Diffusion Inpainting in 4K
Authors: Niklas Kämper, Joachim Weickert
Date: 2021-10-08 00:00:00
Tasks: 4k

Inpainting-based compression methods are qualitatively promising alternatives to transform-based codecs, but they suffer from the high computational cost of the inpainting step. This prevents them from being applicable to time-critical scenarios such as real-time inpainting of 4K images. As a remedy, we adapt state-of-the-art numerical algorithms of domain decomposition type to this problem. They decompose the image domain into multiple overlapping blocks that can be inpainted in parallel by means of modern GPUs. In contrast to classical block decompositions such as the ones in JPEG, the global inpainting problem is solved without creating block artefacts. We consider the popular homogeneous diffusion inpainting and supplement it with a multilevel version of an optimised restricted additive Schwarz (ORAS) method that solves the local problems with a conjugate gradient algorithm. This enables us to perform real-time inpainting of 4K colour images on contemporary GPUs, which is substantially more efficient than previous algorithms for diffusion-based inpainting.
Physical Context and Timing Aware Sequence Generating GANs
Authors: Hayato Futase, Tomoki Tsujimura, Tetsuya Kajimoto, Hajime Kawarazaki, Toshiyuki Suzuki, Makoto Miwa, Yutaka Sasaki
Date: 2021-09-28 00:00:00

Generative Adversarial Networks (GANs) have shown remarkable successes in generating realistic images and interpolating changes between images. Existing models, however, do not take into account physical contexts behind images in generating the images, which may cause unrealistic changes. Furthermore, it is difficult to generate the changes at a specific timing and they often do not match with actual changes. This paper proposes a novel GAN, named Physical Context and Timing aware sequence generating GANs (PCTGAN), that generates an image in a sequence at a specific timing between two images with considering physical contexts behind them. Our method consists of three components: an encoder, a generator, and a discriminator. The encoder estimates latent vectors from the beginning and ending images, their timings, and a target timing. The generator generates images and the physical contexts at the beginning, ending, and target timing from the corresponding latent vectors. The discriminator discriminates whether the generated images and contexts are real or not. In the experiments, PCTGAN is applied to a data set of sequential changes of shapes in die forging processes. We show that both timing and physical contexts are effective in generating sequential images.
Superscalar Parallel Carrier Phase Recovery with Transmitter I/Q Imbalance Compensation
Authors: Daniel R. García, Mario R. Hueda
Date: 2021-10-08 00:00:00

We propose a superscalar parallel two-stage carrier phase recovery architecture to improve the performance of optical coherent receivers in the presence of Tx I/Q imbalance, Tx I/Q skew, and laser frequency fluctuations.
Multi-resolution Dynamic Mode Decomposition for Damage Detection in Wind Turbine Gearboxes
Authors: Paolo Climaco, Jochen Garcke, Rodrigo Iza-Teran
Date: 2021-10-08 00:00:00

We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus is the condition monitoring of wind turbine gearboxes under varying load conditions, in particular irregular and stochastic wind fluctuations. We analyze data stemming from a simulated vibration response of a simple nonlinear gearbox model in a healthy and damaged scenario and under different wind conditions. With mrDMD applied on time-delay snapshots of the sensor data, we can extract components in these vibration signals that highlight features related to damage and enable its identification. A comparison with Fourier analysis, Time Synchronous Averaging and Empirical Mode Decomposition shows the advantages of the proposed mrDMD-based data analysis approach for damage detection.
DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Blind Detection
Authors: Xin Zhong, Chen Chen, Shu Fu, Zhihong Zeng, Min Liu
Date: 2021-10-08 00:00:00

Generalized optical multiple-input multiple-output (GOMIMO) techniques have been recently shown to be promising for high-speed optical wireless communication (OWC) systems. In this paper, we propose a novel deep learning-aided GOMIMO (DeepGOMIMO) framework for GOMIMO systems, where channel state information (CSI)-free blind detection can be enabled by employing a specially designed deep neural network (DNN)-based MIMO detector. The CSI-free blind DNN detector mainly consists of two modules: one is the pre-processing module which is designed to address both the path loss and channel crosstalk issues caused by MIMO transmission, and the other is the feed-forward DNN module which is used for joint detection of spatial and constellation information by learning the statistics of both the input signal and the additive noise. Our simulation results clearly verify that, in a typical indoor 4 $\times$ 4 MIMO-OWC system using both generalized optical spatial modulation (GOSM) and generalized optical spatial multiplexing (GOSMP) with unipolar non-zero 4-ary pulse amplitude modulation (4-PAM) modulation, the proposed CSI-free blind DNN detector achieves near the same bit error rate (BER) performance as the optimal joint maximum-likelihood (ML) detector, but with much reduced computational complexity. Moreover, since the CSI-free blind DNN detector does not require instantaneous channel estimation to obtain accurate CSI, it enjoys the unique advantages of improved achievable data rate and reduced communication time delay in comparison to the CSI-based zero-forcing DNN (ZF-DNN) detector.
WLS Design of ARMA Graph Filters using Iterative Second-Order Cone Programming
Authors: Darukeesan Pakiyarajah, Chamira U. S. Edussooriya
Date: 2021-10-08 00:00:00

We propose a weighted least-square (WLS) method to design autoregressive moving average (ARMA) graph filters. We first express the WLS design problem as a numerically-stable optimization problem using Chebyshev polynomial bases. We then formulate the optimization problem with a nonconvex objective function and linear constraints for stability. We employ a relaxation technique and convert the nonconvex optimization problem into an iterative second-order cone programming problem. Experimental results confirm that ARMA graph filters designed using the proposed WLS method have significantly improved frequency responses compared to those designed using previously proposed WLS design methods.
Joint Normality Test Via Two-Dimensional Projection
Authors: Sara Elbouch, Olivier Michel, Pierre Comon
Date: 2021-10-08 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Valence Prediction

Extensive literature exists on how to test for normality, especially for identically and independently distributed (i.i.d) processes. The case of dependent samples has also been addressed, but only for scalar random processes. For this reason, we have proposed a joint normality test for multivariate time-series, extending Mardia's Kurtosis test. In the continuity of this work, we provide here an original performance study of the latter test applied to two-dimensional projections. By leveraging copula, we conduct a comparative study between the bivariate tests and their scalar counterparts. This simulation study reveals that one-dimensional random projections lead to notably less powerful tests than two-dimensional ones.
A Compact Size 5G Hairpin Bandpass Filter with Multilayer Coupled Line
Authors: Qazwan Abdullah, Ömer Aydoğdu, Adeeb Salh, Nabil Farah, Md Hairul Nizam Talib, Taha Sadeq, Mohammed A. A. Al-Mekhalfi, Abdu Saif
Date: 2021-10-07 00:00:00
Tasks: Dielectric Constant

The multilayer structure is a promising technique used to minimize the size of planar microstrip filters. In the flexible design and incorporation of other microwave components, multilayer band-pass filter results in better and enhanced dimensions. This paper introduces a microstrip fifth-generation (5G) low-frequency band of 2.52-2.65 GHz using a parallel-coupled line (PCL) Bandpass filter and multilayer (ML) hairpin bandpass filter. The targeted four-pole resonator has a center frequency of 2.58 GHz with a bandwidth of 130 MHz. The filters are designed with a 0.1 dB passband ripple with a Chebyshev response. The hairpin-line offers compact filter design structures. Theoretically, they can be obtained by bending half-wavelength resonator resonators with parallel couplings into a "U" shape. The proposed configuration of the parallel-coupled line resonator is used to design the ML band-pass filter. The FR4 substrate with a dielectric constant ({\epsilon}r) of 4.3 and 1.6 mm thickness was used. A comparative analysis between the simulated insertion loss and the reflection coefficient of substrates RO3003 and FR4 was performed to validate the efficiency of the proposed filter design. Simulation of PCL filter is accomplished using computer simulation technology (CST) and an advanced design system (ADS) software. The PCL Bandpass filter was experimentally validated and a total tally between simulation results and measured results were achieved demonstrating a well-measured reflection coefficient. The simulated results obtained by the hairpin ML bandpass filter show that the circuit performs well in terms of Scattering(S) parameters and the filter size is significantly reduced.
A Short Note on the Exact Counting of Tree-Child Networks
Authors: Michael Fuchs, Hexuan Liu, Guan-Ru Yu
Date: 2021-10-08 00:00:00
Tasks: Relation

Tree-child networks are an important network class which are used in phylogenetics to model reticulate evolution. In a recent paper, Pons and Batle (2021) conjectured a relation between tree-child networks and certain words. In this short note, we prove their conjecture for the (important) class of one-component tree-child networks.
ShinyCOPASI: a web-based exploratory interface for COPASI models
Authors: Abhishekh Gupta, Pedro Mendes
Date: 2021-10-07 00:00:00

COPASI is a popular application for simulation and analysis of biochemical networks and their dynamics. While this software is widely used, it works as a standalone application and until now it was not possible for users to interact with its models through the web. We built ShinyCOPASI, a web-based application that allows COPASI models to be explored through a web browser. ShinyCOPASI was written in R with the CoRC package, which provides a high-level R API for COPASI, and the Shiny package to expose it as a web application. The web view provided by ShinyCOPASI follows a similar interface to the standalone COPASI and allows users to explore the details of a model, as well as running a subset of the tasks available in COPASI from within a browser. A generic version allows users to load model files from their computer, while another one pre-loads a specific model from the server and may be useful to provide web access to published models. The application is available at: http://shiny.copasi.org/; and the source code is at: https://github.com/copasi/shinyCOPASI.
Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data
Authors: Nicolas Franco, Pietro Coletti, Lander Willem, Leonardo Angeli, Adrien Lajot, Steven Abrams, Philippe Beutels, Christel Faes, Niel Hens
Date: 2021-10-08 00:00:00

Several important aspects related to SARS-CoV-2 transmission are not well known due to a lack of appropriate data. However, mathematical and computational tools can be used to extract part of this information from the available data, like some hidden age-related characteristics. In this paper, we present a method to investigate age-specific differences in transmission parameters related to susceptibility to and infectiousness upon contracting SARS-CoV-2 infection. More specifically, we use panel-based social contact data from diary-based surveys conducted in Belgium combined with the next generation principle to infer the relative incidence and we compare this to real-life incidence data. Comparing these two allows for the estimation of age-specific transmission parameters. Our analysis implies the susceptibility in children to be around half of the susceptibility in adults, and even lower for very young children (preschooler). However, the probability of adults and the elderly to contract the infection is decreasing throughout the vaccination campaign, thereby modifying the picture over time.
The Shape of Phylogenies Under Phase-Type Distributed Times to Speciation and Extinction
Authors: Albert Ch. Soewongsono, Barbara R. Holland, Małgorzata M. O'Reilly
Date: 2021-10-08 00:00:00

Phylogenetic trees are widely used to understand the evolutionary history of organisms. Tree shapes provide information about macroevolutionary processes. However, macroevolutionary models are unreliable for inferring the true processes underlying empirical trees. Here, we propose a flexible and biologically plausible macroevolutionary model for phylogenetic trees where times to speciation or extinction events are drawn from a Coxian phase-type (PH) distribution. First, we show that different choices of parameters in our model lead to a range of tree balances as measured by Aldous' $\beta$ statistic. In particular, we demonstrate that it is possible to find parameters that correspond well to empirical tree balance. Next, we provide a natural extension of the $\beta$ statistic to sets of trees. This extension produces less biased estimates of $\beta$ compared to using the median $\beta$ values from individual trees. Furthermore, we derive a likelihood expression for the probability of observing any tree with branch lengths under a model with speciation but no extinction. Finally, we illustrate the application of our model by performing both absolute and relative goodness-of-fit tests for two large empirical phylogenies (squamates and angiosperms) that compare models with Coxian PH distributed times to speciation with models that assume exponential or Weibull distributed waiting times. In our numerical analysis, we found that, in most cases, models assuming a Coxian PH distribution provided the best fit.