pwc-archive/papers-with-abstracts huggingface dataset:
Classification of Urban Morphology with Deep Learning: Application on Urban Vitality
Authors: Wangyang Chen, Abraham Noah Wu, Filip Biljecki
Date: 2021-05-07 00:00:00

There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from the human's visual and intuitive perspective. We take the first step to bridge the gap by proposing a deep learning-based technique to automatically classify road networks into four classes on a visual basis. The method is implemented by generating an image of the street network (Colored Road Hierarchy Diagram), which we introduce in this paper, and classifying it using a deep convolutional neural network (ResNet-34). The model achieves an overall classification accuracy of 0.875. Nine cities around the world are selected as the study areas with their road networks acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through clustering on the percentage of each road network category. In the subsequent part of the paper, we focus on the usability of such classification: we apply our method in a case study of urban vitality prediction. An advanced tree-based regression model (LightGBM) is for the first time designated to establish the relationship between morphological indices and vitality indicators. The effect of road network classification is found to be small but positively associated with urban vitality. This work expands the toolkit of quantitative urban morphology study with new techniques, supporting further studies in the future.
Calibrated Click-Through Auctions: An Information Design Approach
Authors: Dirk Bergemann, Paul Duetting, Renato Paes Leme, Song Zuo
Date: 2021-05-19 00:00:00

We analyze the optimal information design in a click-through auction with fixed valuations per click, but stochastic click-through rates. While the auctioneer takes as given the auction rule of the click-through auction, namely the generalized second-price auction, the auctioneer can design the information flow regarding the click-through rates among the bidders. A natural requirement in this context is to ask for the information structure to be calibrated in the learning sense. With this constraint, the auction needs to rank the ads by a product of the bid and an unbiased estimator of the click-through rates, and the task of designing an optimal information structure is thus reduced to the task of designing an optimal unbiased estimator. We show that in a symmetric setting with uncertainty about the click-through rates, the optimal information structure attains both social efficiency and surplus extraction. The optimal information structure requires private (rather than public) signals to the bidders. It also requires correlated (rather than independent) signals, even when the underlying uncertainty regarding the click-through rates is independent. Beyond symmetric settings, we show that the optimal information structure requires partial information disclosure.
Multi-Frequency Phase Retrieval for Antenna Measurements
Authors: Josef Knapp, Alexander Paulus, Jonas Kornprobst, Uwe Siart, Thomas F. Eibert
Date: 2021-05-20 00:00:00
Tasks: All, Retrieval

Phase retrieval problems in antenna measurements arise when a reference phase cannot be provided to all measurement locations. Phase retrieval algorithms require sufficiently many independent measurement samples of the radiated fields to be successful. Larger amounts of independent data may improve the reconstruction of the phase information from magnitude-only measurements. We show how the knowledge of relative phases among the spectral components of a modulated signal at the individual measurement locations may be employed to reconstruct the relative phases between different measurement locations at all frequencies. Projection matrices map the estimated phases onto the space of fields possibly generated by equivalent antenna under test (AUT) sources at all frequencies. In this way, the phase of the reconstructed solution is not only restricted by the measurement samples at one frequency, but by the samples at allfrequencies simultaneously. The proposed method can increase the amount of independent phase information even if all probes are located in the far field of the AUT.
Speaker disentanglement in video-to-speech conversion
Authors: Dan Oneata, Adriana Stan, Horia Cucu
Date: 2021-05-20 00:00:00
Tasks: Disentanglement, Speech Synthesis

The task of video-to-speech aims to translate silent video of lip movement to its corresponding audio signal. Previous approaches to this task are generally limited to the case of a single speaker, but a method that accounts for multiple speakers is desirable as it allows to i) leverage datasets with multiple speakers or few samples per speaker; and ii) control speaker identity at inference time. In this paper, we introduce a new video-to-speech architecture and explore ways of extending it to the multi-speaker scenario: we augment the network with an additional speaker-related input, through which we feed either a discrete identity or a speaker embedding. Interestingly, we observe that the visual encoder of the network is capable of learning the speaker identity from the lip region of the face alone. To better disentangle the two inputs -- linguistic content and speaker identity -- we add adversarial losses that dispel the identity from the video embeddings. To the best of our knowledge, the proposed method is the first to provide important functionalities such as i) control of the target voice and ii) speech synthesis for unseen identities over the state-of-the-art, while still maintaining the intelligibility of the spoken output.
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech
Authors: Aashish Agarwal, Torsten Zesch
Date: 2021-05-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Selection bias, speech-recognition, Speech Recognition

When evaluating the performance of automatic speech recognition models, usually word error rate within a certain dataset is used. Special care must be taken in understanding the dataset in order to report realistic performance numbers. We argue that many performance numbers reported probably underestimate the expected error rate. We conduct experiments controlling for selection bias, gender as well as overlap (between training and test data) in content, voices, and recording conditions. We find that content overlap has the biggest impact, but other factors like gender also play a role.
Short Circuit and Arc Flash Study on a Microgrid Facility
Authors: K. E. K. Schmitt, C. A. Negri, S. Daneshvardehnavi, S. Bayne, M. Giesselmann
Date: 2021-05-20 00:00:00
Tasks: ARC

Arc flash is one of the main hazards when operating an electrical facility. Without correct Personal Protective Equipment, the operator can be subjected to severe including fatal injuries. By code, facilities are required to properly label their electrical equipment that may be accessed by any operator. While energized, the operator proximity to the equipment can provide the necessary potential for an arc flash accident. The labels are mainly responsible for displaying the equipment short circuit and arc flash levels and the minimum PPE level required to operate it. These electrical hazard aspects become more critical in testbed facilities, usually located inside research centers and universities, where the electrical equipment is more frequently accessed by students and researchers. This paper develops complete modeling of a real microgrid testbed facility to perform short circuit and arc flash studies with the main goal to label the devices accessed by the facility researchers.
Model Reduction for Inverters with Current Limiting and Dispatchable Virtual Oscillator Control
Authors: Olaoluwapo Ajala, Minghui Lu, Brian Johnson, Sairaj Dhople, Alejandro Domínguez-García
Date: 2021-05-20 00:00:00

This paper outlines reduced-order models for grid-forming virtual-oscillator-controlled inverters with nested current- and voltage-control loops, and current-limiting action for over-current protection. While a variety of model-reduction methods have been proposed to tame complexity in inverter models, previous efforts have not included the impact of current-reference limiting. In addition to acknowledging the current-limiting action, the reduced-order models we outline are tailored to networks with resistive and inductive interconnecting lines. Our analytical approach is centered on a smooth function approximation for the current-reference limiter, participation factor analysis to identify slow- and fast-varying states, and singular perturbation to systematically eliminate the fast states. Computational benefits and accuracy of the reduced-order models are benchmarked via numerical simulations that compare them to higher-order averaged and switched models.
Data-driven Unknown-input Observers and State Estimation
Authors: Mustafa Sahin Turan, Giancarlo Ferrari-Trecate
Date: 2021-05-20 00:00:00
Tasks: Cyber Attack Detection, LEMMA, State Estimation

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs. In this paper, we provide a novel data-driven UIO based on behavioral system theory and the result known as Fundamental Lemma proposed by Jan Willems and coworkers. We give necessary and sufficient conditions on the data collected from the system for the existence of a UIO providing asymptotically converging state estimates, and propose a purely data-driven algorithm for their computation. Even though we focus on UIOs, our results also apply to the standard case of completely known inputs. As an example, we apply the proposed method to distributed state estimation in DC microgrids and illustrate its potential for cyber-attack detection.
An Accelerated Stackelberg Game Approach for Distributed Energy Resource Aggregator participating in Energy and Reserve Markets Considering Security Check
Authors: Zhijun Shen, Mingbo Liu, Lixin Xu, Wentian Lu
Date: 2021-05-20 00:00:00

With increasing distributed energy resoures (DERs) integration, the strategic behavior of a DER aggregator in electricity markets will significantly affect the secure operation of the distribution system. In this paper, the interactions among the DER aggregator, energy and reserve markets, and distribution system are investigated through a single-leader-multi-follower Stackelberg game model with the DER aggregator as the leader and the independent system operator and distribution system operator as the followers. To guarantee the operation security of the distribution system, security check problems under three different scenarios are involved in the follower level, which is linearized using a mixed-integer linearized power flow model. Then, using the strong duality theorem, the proposed model is converted into a bi-level mixed-integer linear (BMILP) programming model with only mixed-integer linear follower-level problems. Next, an accelerated relaxation-based bi-level reformulation and decomposition algorithm is proposed to solve the BMILP problem. Finally, case studies are carried out on a constructed integrated transmission and distribution (T&D) system and a practical integrated T&D system to verify the effectiveness of the proposed model and algorithm. The simulation results indicate that the available downward reserve of the DER aggregator will decrease with the security limitation of the distribution system.
A Combination 5-DOF Active Magnetic Bearing For Energy Storage Flywheel
Authors: Xiaojun Li, Alan Palazzolo, Zhiyang Wang
Date: 2021-03-14 00:00:00

Conventional active magnetic bearing (AMB) systems use several separate radial and thrust bearings to provide a 5 degree of freedom (DOF) levitation control. This paper presents a novel combination 5-DOF active magnetic bearing (C5AMB) designed for a shaft-less, hub-less, high-strength steel energy storage flywheel (SHFES), which achieves doubled energy density compared to prior technologies. As a single device, the C5AMB provides radial, axial, and tilting levitations simultaneously. In addition, it utilizes low-cost and more available materials to replace silicon steels and laminations, which results in reduced costs and more convenient assemblies. Apart from the unique structure and the use of low magnetic grade material, other design challenges include shared flux paths, large dimensions, and relatively small air gaps. The finite element method (FEM) is too computationally intensive for early-stage analysis. An equivalent magnetic circuit method (EMCM) is developed for modeling and analysis. Nonlinear FEM is then used for detailed simulations. Both permanent magnets (PM) and electromagnetic control currents provide the weight-balancing lifting force. During the full-scale prototype testing, the C5AMB successfully levitates a 5440 kg and 2 m diameter flywheel at an air gap of 1.14 mm. Its current and position stiffnesses are verified experimentally.
Deep learning in physics: a study of dielectric quasi-cubic particles in a uniform electric field
Authors: Zhe Wang, Claude Guet
Date: 2021-05-11 00:00:00
Tasks: Dielectric Constant

Solving physics problems for which we know the equations, boundary conditions and symmetries can be done by deep learning. The constraints can be either imposed as terms in a loss function or used to formulate a neural ansatz. In the present case study, we calculate the induced field inside and outside a dielectric cube placed in a uniform electric field, wherein the dielectric mismatch at edges and corners of the cube makes accurate calculations numerically challenging. The electric potential is expressed as an ansatz incorporating neural networks with known leading order behaviors and symmetries and the Laplace's equation is then solved with boundary conditions at the dielectric interface by minimizing a loss function. The loss function ensures that both Laplace's equation and boundary conditions are satisfied everywhere inside a large solution domain. We study how the electric potential inside and outside a quasi-cubic particle evolves through a sequence of shapes from a sphere to a cube. The neural network being differentiable, it is straightforward to calculate the electric field over the whole domain, the induced surface charge distribution and the polarizability. The neural network being retentive, one can efficiently follow how the field changes upon particle's shape or dielectric constant by iterating from any previously converged solution. The present work's objective is two-fold, first to show how an a priori knowledge can be incorporated into neural networks to achieve efficient learning and second to apply the method and study how the induced field and polarizability change when a dielectric particle progressively changes its shape from a sphere to a cube.
Intelligent-Metasurface-Assisted Full-Duplex Wireless Communications
Authors: Sajjad Taravati, George V. Eleftheriades
Date: 2021-05-19 00:00:00

The limited radio-frequency spectrum is a fundamental factor in the design of wireless communication systems. The ever increasing number of wireless devices and systems has led to a crowded spectrum and increased the demand for versatile and multi-functional full-duplex wireless apparatuses. Recently, dynamic and intelligent metasurfaces are explored as a prominent technological solution to the current paradigm of spectrum scarcity by opportunistically sharing the spectrum with various users. In general, intelligent metasurfaces are dynamic, ultra-compact, multi-functional and programmable structures which are capable of both reciprocal and nonreciprocal signal wave transmissions in a full-duplex manner. The controllability and programmability of such metasurfaces are governed through the dc bias and occasionally a radio-frequency (RF) modulation applied to the the active components of the unit cells of the metasurface, e.g., diodes and transistors. This article shows that such intelligent metasurfaces can enhance the performance of wireless communications systems thanks to their unique features such as real-time signal coding, nonreciprocal-beam radiation, nonreciprocal beamsteering amplification, and advanced pattern-coding multiple access communication.
Compact Uniform Circular Quarter-Wavelength Monopole Antenna Arrays with Wideband Decoupling and Matching Networks
Authors: Jonas Kornprobst, Thomas J. Mittermaier, Raimund A. M. Mauermayer, Gerhard F. Hamberger, Matthias G. Ehrnsperger, Bernhard Lehmeyer, Michel T. Ivrlac, Ulrik Imberg, Thomas F. Eibert, Josef A. Nossek
Date: 2021-05-19 00:00:00

Two novel decoupling and matching networks (DMNs) in microstrip technology for three-element uniform circular arrays (UCAs) are investigated and compared to a more conventional DMN approach with simple neutralization lines. The array elements are coaxially-fed quarter-wavelength monopole antennas over a finite groundplane. Three-element arrays are considered since UCAs with an odd number of elements are able to provide an almost constant maximum array factor over the whole azimuthal angular range. The new designs are explained from a theoretical point of view and their implementations are compared to four- and three-elements UCAs without DMN in terms of decoupling and matching bandwidth as well as beamforming capabilities. In addition to excellent decoupling and matching below -16 dB, a broader bandwidth is obtained by the two DMNs. The reasons for the enhanced bandwidth are similar in both cases: By introducing several circuit elements offering additional degrees of freedom, matching of the monopole input impedances at different frequencies becomes feasible. One of the presented designs offers a larger bandwidth, while the other design is able to provide a better total efficiency. Scattering parameters, radiation patterns, beamforming capabilities, and enhanced gain are all verified by measurements over the operating bandwidth.
A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media
Authors: BiCheng Yan, Dylan Robert Harp, Bailian Chen, Rajesh Pawar
Date: 2021-04-30 00:00:00
Tasks: Deep Learning

In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3D heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.
A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
Authors: Hewei Tang, Pengcheng Fu, Christopher S. Sherman, Jize Zhang, Xin Ju, François Hamon, Nicholas A. Azzolina, Matthew Burton-Kelly, Joseph P. Morris
Date: 2021-05-09 00:00:00
Tasks: Decision Making, Management, Seismic Inversion, Uncertainty Quantification

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast history matching-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat three-dimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.
Covid-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts
Authors: Nicolas Franco
Date: 2020-09-07 00:00:00

Following the spread of the COVID-19 pandemic and pending the establishment of vaccination campaigns, several non pharmaceutical interventions such as partial and full lockdown, quarantine and measures of physical distancing have been imposed in order to reduce the spread of the disease and to lift the pressure on healthcare system. Mathematical models are important tools for estimating the impact of these interventions, for monitoring the current evolution of the epidemic at a national level and for estimating the potential long-term consequences of relaxation of measures. In this paper, we model the evolution of the COVID-19 epidemic in Belgium with a deterministic age-structured extended compartmental model. Our model takes special consideration for nursing homes which are modelled as separate entities from the general population in order to capture the specific delay and dynamics within these entities. The model integrates social contact data and is fitted on hospitalisations data (admission and discharge), on the daily number of COVID-19 deaths (with a distinction between general population and nursing home related deaths) and results from serological studies, with a sensitivity analysis based on a Bayesian approach. We present the situation as in November 2020 with the estimation of some characteristics of the COVID-19 deduced from the model. We also present several mid-term and long-term projections based on scenarios of reinforcement or relaxation of social contacts for different general sectors, with a lot of uncertainties remaining.