pwc-archive/papers-with-abstracts huggingface dataset:
Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference
Authors: Xiaoyan Qiu, Hang Zhang, Yiwei Qiu, Buxiang Zhou, Tianlei Zang, Ruomei Qi, Jin Lin, Jiepeng Wang
Date: 2022-03-08 00:00:00
Tasks: Bayesian Inference, parameter estimation

When directly coupled with fluctuating energy sources such as wind and photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen (P2H) system is required to operate flexibly by dynamically adjusting its hydrogen production rate. The flex-ibility characteristics, e.g., loading range and ramping rate, of an AEL system are significantly influenced by some parameters re-lated to the dynamic processes of the AEL system. These parame-ters are usually difficult to measure directly and may even change with time. To accurately evaluate the flexibility of an AEL system in online operation, this paper presents a Bayesian Inference-based Markov Chain Monte Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint probability distribu-tions of the estimated parameters are obtained as a byproduct, which provides valuable physical insight into the AEL systems. Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation method.
A Second-Order Reachable Sets Computation Scheme via a Cauchy-Type Variational Hamilton-Jacobi-Isaacs Equation
Authors: Lekan Molu, Ian Abraham, Sylvia Herbert
Date: 2022-03-08 00:00:00

Motivated by the scalability limitations of Eulerian methods for variational Hamilton-Jacobi-Isaacs (HJI) formulations that provide a least restrictive controller in problems that involve state or input constraints under a worst-possible disturbance, we introduce a second-order, successive sweep algorithm for computing the zero sublevel sets of a popular reachability value functional. Under sufficient HJI partial differential equation regularity and continuity assumption throughout the state space, we show that with state feedback control under the worst-possible disturbance, we can compute the state set that are reachable within a prescribed verification time bound.
Discrete Robust Control of Robot Manipulators using an Uncertainty and Disturbance Estimator
Authors: Ram Padmanabhan, Maithili Shetty, T. S. Chandar
Date: 2022-03-08 00:00:00

This article presents the design of a robust observer based on the discrete-time formulation of Uncertainty and Disturbance Estimator (UDE), a well-known robust control technique, for the purpose of controlling robot manipulators. The design results in a complete closed-loop, robust, controller--observer structure. The observer incorporates the estimate of the overall uncertainty associated with the plant, in order to mimic its dynamics, and the control law is generated using an auxiliary error instead of state tracking error. A detailed qualitative and quantitative stability analysis is provided, and simulations are performed on the two-link robot manipulator system. Further, a comparative study with well-known control strategies for robot manipulators is presented. The results demonstrate the efficacy of the proposed technique, with better tracking performance and lower control energy compared to other strategies.
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack
Authors: Mojtaba Dezvarei, Kevin Tomsovic, Jinyuan Stella Sun, Seddik M. Djouadi
Date: 2022-03-07 00:00:00
Tasks: Load Forecasting, State Estimation, Time Series, Time Series Analysis

Short-term load forecasting is an essential task that supports utilities to schedule generating sufficient power for balancing supply and demand, and can become an attractive target for cyber attacks. It has been shown that the power system state estimation is vulnerable to false data injection attacks. Similarly, false data injection on input variables can result in large forecast errors. The load forecasting system should have a protective mechanism to mitigate such attacks. One approach is to model physical system constraints that would identify anomalies. This study investigates possible constraints associated with a load forecasting application. Looking at regional forecasted loads, we analyze the relation between each zone through similarity measures used in time series in order to identify constraints. Comprehensive results for historical ERCOT load data indicate variation in the measures recognizing the existence of malicious action. Still, these static measures can not be considered an efficient index across different scenarios.
On a Continuous-Time Version of Willems' Lemma
Authors: Victor G. Lopez, Matthias A. Müller
Date: 2022-03-07 00:00:00
Tasks: LEMMA

In this paper, a method to represent every input-output trajectory of a continuous-time linear system in terms of previously collected data is presented. This corresponds to a continuous-time version of the well-known Willems' lemma. The result is obtained by sampling the continuous signals at regular intervals, and constructing Hankel-like structures that closely resemble their discrete-time counterparts. Then, it is shown how to use measured persistently excited data to design a time-varying vector of parameters that allows the generation of arbitrary piecewise differentiable trajectories. A class of input signals that satisfies the conditions for persistence of excitation is also provided.
Distributed Consensus of Stochastic Multi-agent Systems with Prescribed Performance Constraints
Authors: Pushpak Jagtap, Dimos V. Dimarogonas
Date: 2022-03-07 00:00:00

This paper focuses on the problem of distributed consensus control of multi-agent systems while considering two main practical concerns (i) stochastic noise in the agent dynamics and (ii) predefined performance constraints over evolutions of multi-agent systems. In particular, we consider that each agent is driven by a stochastic differential equation with state-dependent noise which makes the considered problem more challenging compared to non-stochastic agents. The work provides sufficient conditions under which the proposed timevarying distributed control laws ensure consensus in expectation and almost sure consensus of stochastic multi-agent systems while satisfying prescribed performance constraints over evolutions of the systems in the sense of the qth moment. Finally, we demonstrate the effectiveness of the proposed results with a numerical example.
Role of limiting dispersal on metacommunity stability and persistence
Authors: Snehasish Roy Chowdhury, Ramesh Arumugam, Wei Zou, V. K. Chandrasekar, D. V. Senthilkumar
Date: 2022-03-08 00:00:00

The role of dispersal on the stability and synchrony of a metacommunity is a topic of considerable interest in theoretical ecology. Dispersal is known to promote both synchrony, which enhances the likelihood of extinction, and spatial heterogeneity, which favors the persistence of the population. Several efforts have been made to understand the effect of diverse variants of dispersal in the spatially distributed ecological community. Despite the environmental change strongly affect the dispersal, the effects of controlled dispersal on the metacommunity stability and their persistence remain unknown. We study the influence of limiting the immigration using two patch prey-predator metacommunity at both local and spatial scales. We find that the spread of the inhomogeneous stable steady states (asynchronous states) decreases monotonically upon limiting the predator dispersal. Nevertheless, at the local scale, the spread of the inhomogeneous steady states increases up to a critical value of the limiting factor, favoring the metacommunity persistence, and then starts decreasing for further decrease in the limiting factor with varying local interaction. Interestingly, limiting the prey dispersal promotes inhomogeneous steady states in a large region of the parameter space, thereby increasing the metacommunity persistence both at spatial and local scales. Further, we show similar qualitative dynamics in an entire class of complex networks consisting of a large number of patches. We also deduce various bifurcation curves and stability condition for the inhomogeneous steady states, which we find to agree well with the simulation results. Thus, our findings on the effect of the limiting dispersal can help to develop conservation measures for ecological community.
The Concept of Force in Population Dynamics
Authors: John Hayward, Paul A. Roach
Date: 2022-03-08 00:00:00

The area of population dynamics has a rich history of the development and analysis of models of biological and social phenomena using ordinary differential equations. This paper describes a method for understanding the influence one variable exerts on another in such models as a force, with the relative effects of these forces providing a narrative explanation of the curvature in variable behaviour. Using the stock/flow form of a model, a symbolic notation is developed that identifies the forces with the causal pathways of the model's feedback loops. A force is measured by its impact, defined as the ratio of acceleration to rate of change, computed by differentiation along its associated pathway between variables. Different phases of force dominance are determined to enhance the standard stability analysis of the models, providing an explanation of model behaviour in Newtonian mechanical terms. The concepts developed are applied to well-known models from mathematical biology: the Spruce Budworm model, where force dominance identifies scenarios that give clarity to intervention points; and the Lotka-Volterra predator-prey model where the analysis highlights the importance of dissipative forces in achieving stability. Conclusions are drawn on the explanatory power of this approach, with suggestions made for future work.
On Robust Inference in Time Series Regression
Authors: Richard T. Baillie, Francis X. Diebold, George Kapetanios, Kun Ho Kim, Aaron Mora
Date: 2022-03-08 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Time Series Regression

Least squares regression with heteroskedasticity consistent standard errors ("OLS-HC regression") has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time series environments via heteroskedasticity and autocorrelation consistent standard errors ("OLS-HAC regression"). First, in plausible time-series environments, OLS parameter estimates can be inconsistent, so that OLS-HAC inference fails even asymptotically. Second, most economic time series have autocorrelation, which renders OLS parameter estimates inefficient. Third, autocorrelation similarly renders conditional predictions based on OLS parameter estimates inefficient. Finally, the structure of popular HAC covariance matrix estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HAC-based hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided by the use of a simple and easily-implemented dynamic regression procedure, which we call DURBIN. We demonstrate the advantages of DURBIN with detailed simulations covering a range of practical issues.
When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume
Authors: Francis X. Diebold, Glenn D. Rudebusch, Maximilian Goebel, Philippe Goulet Coulombe, Boyuan Zhang
Date: 2022-03-08 00:00:00

Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to atmospheric carbon dioxide concentration and models relating sea ice directly to time. The resulting "carbon-trend" and "time-trend" projections are mutually consistent and predict a nearly ice-free summer Arctic Ocean by the mid-2030s with an 80% probability. Moreover, the carbon-trend projections show that global adoption of a lower carbon path would likely delay the arrival of a seasonally ice-free Arctic by only a few years.
Populist Discourse and Entrepreneurship: The Role of Political Ideology and Institutions
Authors: Daniel L. Bennett, Christopher J. Boudreaux, Boris N. Nikolaev
Date: 2022-03-08 00:00:00

Using institutional economic theory as our guiding framework, we develop a model to describe how populist discourse by a nation's political leader influences entrepreneurship. We hypothesize that populist discourse reduces entrepreneurship by creating regime uncertainty concerning the future stability of the institutional environment, resulting in entrepreneurs anticipating higher future transaction costs. Our model highlights two important factors that moderate the relationship. First, is the strength of political checks and balances, which we hypothesize weakens the negative relationship between populist discourse and entrepreneurship by providing entrepreneurs with greater confidence that the actions of a populist will be constrained. Second, the political ideology of the leader moderates the relationship between populist discourse and entrepreneurship. The anti-capitalistic rhetoric of left-wing populism will create greater regime uncertainty than right-wing populism, which is often accompanied by rhetoric critical of free trade and foreigners but also supportive of business interests. The effect of centrist populism, which is accompanied by a mix of contradictory and often moderate ideas that make it difficult to discern future transaction costs, will have a weaker negative effect on entrepreneurship than either left-wing or right-wing populism. We empirically test our model using a multi-level design and a dataset comprised of more than 780,000 individuals in 33 countries over the period 2002-2016. Our analysis largely supports our theory regarding the moderating role of ideology. Still, surprisingly, our findings suggest that the negative effect of populism on entrepreneurship is greater in nations with stronger checks and balances.
Cooperation and punishment mechanisms in uncertain and dynamic networks
Authors: Edoardo Gallo, Yohanes E. Riyanto, Nilanjan Roy, Tat-How Teh
Date: 2022-03-08 00:00:00

This paper examines experimentally how reputational uncertainty and the rate of change of the social environment determine cooperation. Reputational uncertainty significantly decreases cooperation, while a fast-changing social environment only causes a second-order qualitative increase in cooperation. At the individual level, reputational uncertainty induces more leniency and forgiveness in imposing network punishment through the link proposal and removal processes, inhibiting the formation of cooperative clusters. However, this effect is significant only in the fast-changing environment and not in the slow-changing environment. A substitution pattern between network punishment and action punishment (retaliatory defection) explains this discrepancy across the two social environments.
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets
Authors: Kai Riemer, Matthieu Toulemonde, Jipeng Yan, Marcelo Lerendegui, Eleanor Stride, Peter D. Weinberg, Christopher Dunsby, Meng-Xing Tang
Date: 2022-03-08 00:00:00
Tasks: Super-Resolution

Perfusion by the microcirculation is key to the development, maintenance and pathology of tissue. Its measurement with high spatiotemporal resolution is consequently valuable but remains a challenge in deep tissue. Ultrasound Localization Microscopy (ULM) provides very high spatiotemporal resolution but the use of microbubbles requires low contrast agent concentrations, a long acquisition time, and gives little control over the spatial and temporal distribution of the bubbles. The present study is the first to demonstrate Acoustic Wave Sparsely-Activated Localization Microscopy (AWSALM) and fast-AWSALM for in vivo super-resolution ultrasound imaging, offering contrast on demand and vascular selectivity. Three different formulations of sono-switchable contrast agents were tested. We demonstrate their use with ultrasound mechanical indices well within recommended safety limits to enable fast on-demand sparse switching at very high agent concentrations. We produce super-localization maps of the rabbit renal vasculature with acquisition times between 5.5 s and 0.25 s, and an 4-fold improvement in spatial resolution. We present the unique selectivity of AWSALM in visualizing specific vascular branches and downstream microvasculature, and we show super-localized kidney structures in systole and diastole with fast-AWSALM. In conclusion we demonstrate the feasibility of fast and selective measurement of microvascular dynamics in vivo with subwavelength resolution using ultrasound and sono-switchable nanodroplets.
Human Biometric Signals Monitoring based on WiFi Channel State Information using Deep Learning
Authors: Moyu Liu, Zihuai Lin, Pei Xiao, Wei Xiang
Date: 2022-03-08 00:00:00

In this paper, we first present a single-input, multiple-output convolutional neural network that can estimate both heart rate and respiration rate simultaneously by exploiting the underlying link between heart rate and respiration rate. The inputs to the neural network are the amplitude and phase of channel state information collected by a pair of WiFi devices. Our WiFi-based technique addresses privacy concerns and is adapt- able to a variety of settings. This system overall accuracy for the heart and respiration rate estimation can reach 99.109% and 98.581%, respectively. Furthermore, we developed and analyzed two deep learning-based neural network classification algorithms for categorizing four types of sleep stages: wake, rapid eye movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep sleep. This system overall classification accuracy can reach 95.925%
Amplitude-Constrained Constellation and Reflection Pattern Designs for Directional Backscatter Communications Using Programmable Metasurface
Authors: Wei Wang, Bincheng Zhu, Yongming Huang, Wei zhang
Date: 2022-03-08 00:00:00

The large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and interring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.
Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data
Authors: Wenhao Luo, Yee Hui Lee, Lai Fern Ow, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel
Date: 2022-03-08 00:00:00
Tasks: Clustering, GPR

3D mapping of tree roots is a popular ground-penetrating radar (GPR) application. In real field tests, the recognition of tree roots suffers due to noisey reflection patterns from subsurface targets that are not of interest, such as rocks, cavities, soil unevenness, etc. A Slice-Connection Clustering Algorithm (SCC) is applied to separate the regions of interest from each other in a reconstructed 3D image. The proposed method can successfully recognize the radar signatures of the roots and distinguish roots from other objects. Meanwhile, most noise radar features are ignored through our method. The final 3D mapping of the radargram obtained by the method can be used to estimate the location and extension trend of the tree roots. The effectiveness of the proposed system is tested on real GPR data.
Dense Urban Outdoor-Indoor Coverage from 3.5 to 28 GHz
Authors: Dipankar Shakya, Dmitry Chizhik, Jinfeng Du, Reinaldo A. Valenzuela, Theodore S. Rappaport
Date: 2022-03-08 00:00:00

In the US, people spend 87% of their time indoors and have an average of four connected devices per person (in 2020). As such, providing indoor coverage has always been a challenge but becomes even more difficult as carrier frequencies increase to mmWave and beyond. This paper investigates the outdoor and outdoor-indoor coverage of an urban network comparing globally standardized building penetration models and implementing models to corresponding scenarios. The glass used in windows of buildings in the grid plays a pivotal role in determining the outdoor-to-indoor propagation loss. For 28 GHz with 1 W/polarization transmit power in the urban street grid, the downlink data rates for 90% of outdoor users are estimated at over 250 Mbps. In contrast, 15% of indoor users are estimated to be in outage, with SNR $<-$3 dB when base stations are 400 m apart with one-fifth of the buildings imposing high penetration loss ($\sim$ 35 dB). At 3.5 GHz, base stations may achieve over 250 Mbps for 90% indoor users if 400 MHz bandwidth with 100 W/polarization transmit power is available. The methods and models presented can be used to facilitate decisions regarding the density and transmit power required to provide high data rates to majority users in urban centers.
Sub-Terahertz Channel Measurements and Characterization in a Factory Building
Authors: Shihao Ju, Yunchou Xing, Ojas Kanhere, Theodore S. Rappaport
Date: 2022-03-08 00:00:00
Tasks: Super-Resolution

Sub-Terahertz (THz) frequencies between 100 GHz and 300 GHz are being considered as a key enabler for the sixth-generation (6G) wireless communications due to the vast amounts of unused spectrum. The 3rd Generation Partnership Project (3GPP) included the indoor industrial environments as a scenario of interest since Release 15. This paper presents recent sub-THz channel measurements using directional horn antennas of 27 dBi gain at 142 GHz in a factory building, which hosts equipment manufacturing startups. Directional measurements with co-polarized and cross-polarized antenna configurations were conducted over distances from 6 to 40 meters. Omnidirectional and directional path loss with two antenna polarization configurations produce the gross cross-polarization discrimination (XPD) with a mean of 27.7 dB, which suggests that dual-polarized antenna arrays can provide good multiplexing gain for sub-THz wireless systems. The measured power delay profile and power angular spectrum show the maximum root mean square (RMS) delay spread of 66.0 nanoseconds and the maximum RMS angular spread of 103.7 degrees using a 30 dB threshold, indicating the factory scenario is a rich-scattering environment due to a massive number of metal structures and objects. This work will facilitate emerging sub-THz applications such as super-resolution sensing and positioning for future smart factories.