pwc-archive/papers-with-abstracts huggingface dataset:
Spatial resolution of late reverberation in virtual acoustic environments
Authors: Christoph Kirsch, Josef Poppitz, Torben Wendt, Steven van de Par, Stephan D. Ewert
Date: 2021-06-30 00:00:00

Late reverberation involves the superposition of many sound reflections resulting in a diffuse sound field. Since the spatially resolved perception of individual diffuse reflections is impossible, simplifications can potentially be made for modelling late reverberation in room acoustics simulations with reduced spatial resolution. Such simplifications are desired for interactive, real-time virtual acoustic environments with applications in hearing research and for the evaluation of hearing supportive devices. In this context, the number and spatial arrangement of loudspeakers used for playback additionally affect spatial resolution. The current study assessed the minimum number of spatially evenly distributed virtual late reverberation sources required to perceptually approximate spatially highly resolved isotropic and anisotropic late reverberation and to technically approximate a spherically isotropic diffuse sound field. The spatial resolution of the rendering was systematically reduced by using subsets of the loudspeakers of an 86-channel spherical loudspeaker array in an anechoic chamber. It was tested whether listeners can distinguish lower spatial resolutions for the rendering of late reverberation from the highest achievable spatial resolution in different simulated rooms. Rendering of early reflections was kept fixed. The coherence of the sound field across a pair of microphones at ear and behind-the-ear hearing device distance was assessed to separate the effects of number of virtual sources and loudspeaker array geometry. Results show that between 12 and 24 reverberation sources are required.
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Authors: Yuma Koizumi, Shigeki Karita, Scott Wisdom, Hakan Erdogan, John R. Hershey, Llion Jones, Michiel Bacchiani
Date: 2021-06-30 00:00:00
Tasks: Computational Efficiency, Denoising, Prediction, Speech Enhancement

Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network, such as the Conv-TasNet architecture. In such systems, the denoising performance and computational efficiency are mainly affected by the structure of the mask prediction network. In this study, we aim to improve the sequential modeling ability of Conv-TasNet architectures by integrating Conformer layers into a new mask prediction network. To make the model computationally feasible, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers. We trained the model on 3,396 hours of noisy speech data, and show that (i) the use of linear complexity attention avoids high computational complexity, and (ii) our model achieves higher scale-invariant signal-to-noise ratio than the improved time-dilated convolution network (TDCN++), an extended version of Conv-TasNet.
Towards a generalized monaural and binaural auditory model for psychoacoustics and speech intelligibility
Authors: Thomas Biberger, Stephan D. Ewert
Date: 2021-06-29 00:00:00
Tasks: Decoder

Auditory perception involves cues in the monaural auditory pathways as well as binaural cues based on differences between the ears. So far auditory models have often focused on either monaural or binaural experiments in isolation. Although binaural models typically build upon stages of (existing) monaural models, only a few attempts have been made to extend a monaural model by a binaural stage using a unified decision stage for monaural and binaural cues. In such approaches, a typical prototype of binaural processing has been the classical equalization-cancelation mechanism, which either involves signal-adaptive delays and provides a single channel output or can be implemented with tapped delays providing a high-dimensional multichannel output. This contribution extends the (monaural) generalized envelope power spectrum model by a non-adaptive binaural stage with only a few, fixed output channels. The binaural stage resembles features of physiologically motivated hemispheric binaural processing, as simplified signal processing stages, yielding a 5-channel monaural and binaural matrix feature "decoder" (BMFD). The back end of the existing monaural model is applied to the 5-channel BMFD output and calculates short-time envelope power and power features. The model is evaluated and discussed for a baseline database of monaural and binaural psychoacoustic experiments from the literature.
Global Optimality of Inverter Dynamic Voltage Support
Authors: Yifei Guo, Bikash C. Pal, Rabih A. Jabr
Date: 2021-06-30 00:00:00

This paper investigates the dynamic voltage support (DVS) control of inverter-based resources (IBRs) under voltage sags to enhance the low-voltage ride-through performance. We first revisit the prevalent droop control from an optimization perspective to elaborate on why it usually suffers from suboptimality. Then, we formulate the DVS problem as an optimization program that maximizes the positive-sequence voltage magnitude at the point of common coupling (PCC) subject to the current, active power, and stability constraints. The program is inherently nonconvex due to the active power limits, of which the global optimality is not guaranteed by off-the-shelf solvers. In this context, we perform the optimality analysis to explore the global optimum analytically. It is found that the unique global optimum has three scenarios/stages (S1--S3), which depends on the specific relationship among grid voltage, grid strength, as well as physical limits of IBRs. The closed-form solutions in S1 and S3 are derived and the optimality conditions for S2 are provided, which guarantees the optimality and compatibility with the fast real-time control. We implement the optimum with a grid-connected photovoltaic (PV) power plant by integrating a DVS controller. Dynamic simulations are carried out under different scenarios to test our proposal and compare it with other existing methods. Additionally, the robustness of optimality against model errors is discussed and numerically demonstrated.
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
Authors: Yu Xing, Benjamin Gravell, Xingkang He, Karl Henrik Johansson, Tyler Summers
Date: 2021-06-30 00:00:00

The paper studies identification of linear systems with multiplicative noise from multiple-trajectory data. An algorithm based on the least-squares method and multiple-trajectory data is proposed for joint estimation of the nominal system matrices and the covariance matrix of the multiplicative noise. The algorithm does not need prior knowledge of the noise or stability of the system, but requires only independent inputs with pre-designed first and second moments and relatively small trajectory length. The study of identifiability of the noise covariance matrix shows that there exists an equivalent class of matrices that generate the same second-moment dynamic of system states. It is demonstrated how to obtain the equivalent class based on estimates of the noise covariance. Asymptotic consistency of the algorithm is verified under sufficiently exciting inputs and system controllability conditions. Non-asymptotic performance of the algorithm is also analyzed under the assumption that the system is bounded. The analysis provides high-probability bounds vanishing as the number of trajectories grows to infinity. The results are illustrated by numerical simulations.
Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
Authors: Jochen Stiasny, Samuel Chevalier, Spyros Chatzivasileiadis
Date: 2021-06-30 00:00:00

In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly learn the solutions of power system dynamics. In contrast to the limitations of classical model order reduction approaches, commonly used to accelerate time-domain simulations, PINNs can universally approximate any continuous function with an arbitrary degree of accuracy. One of the novelties of this paper is that we avoid the need for any training data. We achieve this by incorporating the governing differential equations and an implicit Runge-Kutta (RK) integration scheme directly into the training process of the PINN; through this approach, PINNs can predict the trajectory of a dynamical power system at any discrete time step. The resulting Runge-Kutta-based physics-informed neural networks (RK-PINNs) can yield up to 100 times faster evaluations of the dynamics compared to standard time-domain simulations. We demonstrate the methodology on a single-machine infinite bus system governed by the swing equation. We show that RK-PINNs can accurately and quickly predict the solution trajectories.
Zames-Falb Multipliers: don't panic
Authors: Matthew C. Turner
Date: 2021-06-30 00:00:00

Zames-Falb multipliers are mathematical constructs which can be used to prove stability of so-called Lur'e systems: systems that consist of a feedback interconnection of a linear element and a static nonlinear element. The main advantage of Zames-Falb multipliers is that they enable "passivity"-like results to be obtained but with a level of conservatism much lower than \emph{pure} passivity results. However, some of the papers describing the development of the Zames-Falb multiplier machinery are somewhat abstruse and not entirely clear. This article attempts to provide a relatively simple construction of Zames and Falb's main results which will hopefully be understandable to most graduate-level control engineers.
Frequency-Constrained Resilient Scheduling of Microgrid: A Distributionally Robust Approach
Authors: Zhongda Chu, Ning Zhang, Fei Teng
Date: 2021-06-30 00:00:00
Tasks: Scheduling

In order to prevent the potential frequency instability due to the high Power Electronics (PE) penetration under an unintentional islanding event, this paper presents a novel microgrid scheduling model which explicitly models the system frequency dynamics as well as the long/short term uncertainty associated with renewable energy resources and load. Synthetic Inertia (SI) control is applied to regulating the active power output of the Inverter-Based Generators (IBGs) to support the post-islanding frequency evaluation. The uncertainty associated with the noncritical load shedding is explicitly modeled based on the distributionally robust formulation to ensure resilient operation during islanding events. The resulted frequency constraints are derived analytically and reformulated into Second-Order Cone (SOC) form, which are further incorporated into the microgrid scheduling model, enabling optimal SI provision of Renewable Energy Sources (RESs) from the micorgrid perspective. With the SOC relaxation of the AC power flow constraints, the overall problem is constructed as a mixed-integer SOC Programming (MISOCP). The effectiveness of the proposed model is demonstrated based on modified IEEE 14-bus system.
Bilateral Market for Distribution-level Coordination of Flexible Resources using Volttron
Authors: Mohammad Ostadijafari, Juan Carlos Bedoya, Anamika Dubey, Chen-Ching Liu
Date: 2021-06-29 00:00:00

Increasing penetrations of distributed energy resources (DERs) and responsive loads (RLs) in the electric power distribution systems calls for a mechanism for joint supply-demand coordination. Recently, several transactive/bilateral coordination mechanisms have been proposed for the distribution-level coordination of flexible resources. Implementing a transactive market coordination approach requires a secure, reliable, and computationally efficient multi-agent platform. An example of such a platform is VOLTTRON, developed by the Pacific Northwest National Laboratories (PNNL). The VOLTTRON platform allows the market actors to exchange information and execute proper control actions in a decentralized way. This paper aims to provide a proof-of-concept of the transactive market coordination approach via a small-scale demonstration on the VOLTTRON platform. The steps needed to implement the proposed market architecture using virtual machines and VOLTTRON are thoroughly described, and illustrative examples are provided to show the market-clearing process for different scenarios.
A Semantic Model for Interacting Cyber-Physical Systems
Authors: Benjamin Lion, Farhad Arbab, Carolyn Talcott
Date: 2021-06-29 00:00:00
Tasks: model

We propose a component-based semantic model for Cyber-Physical Systems (CPSs) wherein the notion of a component abstracts the internal details of both cyber and physical processes, to expose a uniform semantic model of their externally observable behaviors expressed as sets of sequences of observations. We introduce algebraic operations on such sequences to model different kinds of component composition. These composition operators yield the externally observable behavior of their resulting composite components through specifications of interactions of the behaviors of their constituent components, as they, e.g., synchronize with or mutually exclude each other's alternative behaviors. Our framework is expressive enough to allow articulation of properties that coordinate desired interactions among composed components within the framework, also as component behavior. We demonstrate the usefulness of our formalism through examples of coordination properties in a CPS consisting of two robots interacting through shared physical resources.
Alternating Direction Method of Multiplier-Based Distributed Planning Model for Natural Gas, Electricity Network, and Regional Integrated Energy Systems
Authors: Ang Xuan, Yang Qiu, Yang Liu, Xin Sun
Date: 2021-06-29 00:00:00

Regional integrated energy system coupling with multienergy devices, energy storage devices, and renewable energy devices has been regarded as one of the most promising solutions for future energy systems. Planning for existing natural gas and electricity network expansion, regional integrated energy system locations, or system equipment types and capacities are urgent problems in infrastructure development. This article employs a joint planning model to address these; however, the joint planning model ignores the potential ownerships by three agents, for which investment decisions are generally made by different investors. In this work, the joint planning model is decomposed into three distributed planning subproblems related to the corresponding stakeholders, and the alternating direction method of multipliers is adopted to solve the tripartite distributed planning problem. The effectiveness of the planning model is verified on an updated version of the Institute of Electrical and Electronics Engineers (IEEE) 24-bus electric system, the Belgian 20-node natural gas system, and three assumed integrated energy systems. Simulation results illustrate that a distributed planning model is more sensitive to individual load differences, which is precisely the defect of the joint planning model. Moreover, the algorithm performance considering rates of convergence and the impacts of penalty parameters is further analyzed
The energy revolution: cyber physical advances and opportunities for smart local energy systems
Authors: Nandor Verba, Elena Gaura, Stephen McArthur, George Konstantopoulos, Jianzhoug Wu, Zhong Fan, Dimitrios Athanasiadis, Pablo Rodolfo Baldivieso Monasterios, Euan Morris, Jeffrey Hardy
Date: 2021-06-29 00:00:00

We have designed a two-stage, 10-step process to give organisations a method to analyse small local energy systems (SLES) projects based on their Cyber Physical System components in order to develop future-proof energy systems. SLES are often developed for a specific range of use cases and functions, and these match the specific requirements and needs of the community, location or site under consideration. During the design and commissioning, new and specific cyber physical architectures are developed. These are the control and data systems that are needed to bridge the gap between the physical assets, the captured data and the control signals. Often, the cyber physical architecture and infrastructure is focused on functionality and the delivery of the specific applications. But we find that technologies and approaches have arisen from other fields that, if used within SLES, could support the flexibility, scalability and reusability vital to their success. As these can improve the operational data systems then they can also be used to enhance predictive functions If used and deployed effectively, these new approaches can offer longer term improvements in the use and effectiveness of SLES, while allowing the concepts and designs to be capitalised upon through wider roll-out and the offering of commercial services or products.
Optimal Background Correction in Double Quantum Coherence Electron Spin Resonance Spectroscopy for Accurate Data Analysis
Authors: Aritro Sinha Roy
Date: 2021-06-29 00:00:00

Electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS) is used in protein 3D structure determination. However, the accuracy of the signal analysis depends heavily on the background correction process. In this work, we derive the functional forms of double quantum coherence (DQC) ESR signal in typical frozen samples of micro-molar concentration, quantifying both the intramolecular and the background contributions.
Reinfection and low cross-immunity as drivers of epidemic resurgence under high seroprevalence: a model-based approach with application to Amazonas, Brazil
Authors: Edilson F. Arruda, Dayse H. Pastore, Claudia M. Dias, Fabricio O. Ourique
Date: 2021-06-30 00:00:00

This paper introduces a new multi-strain epidemic model with reinfection and cross-immunity to provide insights into the resurgence of the COVID-19 epidemic in an area with reportedly high seroprevalence due to a largely unmitigated outbreak: the state of Amazonas, Brazil. Although high seroprevalence could have been expected to trigger herd immunity and prevent further waves in the state, we have observed persistent levels of infection after the first wave and eventually the emergence of a second viral strain just before an augmented second wave. Our experiments suggest that the persistent levels of infection after the first wave may be due to reinfection, whereas the higher peak at the second wave can be explained by the emergence of the second variant and a low level of cross-immunity between the original and the second variant. Finally, the proposed model provides insights into the effect of reinfection and cross-immunity on the long-term spread of an unmitigated epidemic.
Energy security: key concepts, components, and change of the paradigm
Authors: Julia Edigareva, Tatiana Khimich, Oleg Antonov, Jesus Gonzalez
Date: 2021-06-30 00:00:00

The authors of the study conduct a legal analysis of the concept of energy security. Energy is vital for sustainable development, and sustainability is not only at the heart of development, but also economic, environmental, social and military policies. To ensure the sustainability of the policy, 'security' seems to be a mandatory goal to achieve. The article critically assesses the change in the energy paradigm.
Quantal Response Equilibrium and Rationalizability: Inside the Black Box
Authors: Shuige Liu, Fabio Maccheroni
Date: 2021-06-30 00:00:00

This paper aims to connect epistemic and behavioral game theory by examining the epistemic foundations of quantal response equilibrium (QRE) in static games. We focus on how much information agents possess about the probability distributions of idiosyncratic payoff shocks, in addition to the standard assumptions of rationality and common belief in rationality. When these distributions are transparent, we obtain a solution concept called $\Delta^p$-rationalizability, which includes action distributions derived from QRE; we also give a condition under which this relationship holds true in reverse. When agents only have common belief in the monotonicity of these distributions (for example, extreme value distributions), we obtain another solution concept called $\Delta^M$-rationalizability, which includes action distributions derived from rank-dependent choice equilibrium, a parameter-free variant of QRE. Our solution concepts also provide insights for interpreting experimental and empirical data.
Behavioral Mistakes Support Cooperation in an N-Person Repeated Public Goods Game
Authors: Jung-Kyoo Choi, Jun Sok Huhh
Date: 2021-06-30 00:00:00

This study investigates the effect of behavioral mistakes on the evolutionary stability of the cooperative equilibrium in a repeated public goods game. Many studies show that behavioral mistakes have detrimental effects on cooperation because they reduce the expected length of mutual cooperation by triggering the conditional retaliation of the cooperators. However, this study shows that behavioral mistakes could have positive effects. Conditional cooperative strategies are either neutrally stable or are unstable in a mistake-free environment, but we show that behavioral mistakes can make \textit{all} of the conditional cooperative strategies evolutionarily stable. We show that behavioral mistakes stabilize the cooperative equilibrium based on the most intolerant cooperative strategy by eliminating the behavioral indistinguishability between conditional cooperators in the cooperative equilibrium. We also show that mistakes make the tolerant conditional cooperative strategies evolutionarily stable by preventing the defectors from accumulating the free-rider's advantages. Lastly, we show that the behavioral mistakes could serve as a criterion for the equilibrium selection among cooperative equilibria.
Dynamic Imaging using Deep Bi-linear Unsupervised Regularization (DEBLUR)
Authors: Abdul Haseeb Ahmed, Prashant Nagpal, Mathews Jacob
Date: 2021-06-30 00:00:00

Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to regularize the recovery. The quality of the recovered images depend on the specific priors. Motivated by deep image prior, we introduce a novel bilinear model whose factors are represented using convolutional neural networks (CNNs). The CNN parameters are learned from the undersampled data off the same subject. To reduce the run time and to improve performance, we initialize the CNN parameters. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to low-rank and SToRM reconstructions.
mmWave Spatial-Temporal Single Harmonic Switching Transmitter Arrays for High back-off Beamforming Efficiency
Authors: Zhehao Yu, Xuyang Lu, Changzhan Gu, Suresh Venkatesh, Junfa Mao
Date: 2021-06-30 00:00:00

This paper presents a spatial-temporal single harmonic switching (STHS) transmitter array architecture with enhanced efficiency in the power back-off (PBO) region. STHS is an electromagnetic and circuit co-designed and jointly optimized transmitter array that realizes beamforming and back-off power generation at the same time. The temporal dimension is originally added in STHS to achieve back-off efficiency enhancement, which can be combined with conventional power back-off enhancement methods such as Doherty amplifiers and envelope tracking. The design is validated through a simulation of a two-stage power amplifier in 65-nm CMOS at 77 GHz, which achieves a peak drain efficiency (DE) of 24.2%, a 22% DE at 3-dB PBO, 16% DE at 6-dB PBO, and 10.2% at 9-dB PBO. The efficiency exhibits a 57% improvement at 3-dB PBO, 100% improvement at 6-dB PBO, and 190% improvement at 9-dB PBO compared with class A/B amplifier.