pwc-archive/papers-with-abstracts huggingface dataset:
Sizing of Energy Storage System for Virtual Inertia Emulation
Authors: Mohamed Abuagreb, Ahmed Abuhussein, Saif alZahir
Date: 2022-01-17 00:00:00

The infusion of renewable energy sources into the conventional synchronous generation system decreases the overall system inertia and negatively impacts the stability of its primary frequency response. The lowered inertia is due to the absence of inertia in some of the renewable energy-based systems. To maintain the stability of the system, we need to keep the frequency in the permissible limits and maintain low rotational inertia. Some authors in the literature have used the virtual synchronous generators (VSG) as a solution to this problem. Although the VSG based distributed recourses (DER) exhibits the characteristics and behavior of synchronous generators (SG) such as inertia, frequency droop functions and damping but it does not optimally solve the question of frequency stability. This paper presents a solution for these problems via an empirical model that sizes the Battery Energy Storage System (BESS) required for the inertia emulation and damping control. The tested system consists of a Photovoltaic (PV) based VSG that is connected to a 9-Bus grid and the simulation experiments are carried out using EMTP software. The VSG transient response is initiated by a symmetric fault on the grid side. Our simulations show the battery energy sizing required to emulate the virtual inertia corresponding to several design parameters, i.e., the droop gain, K{\omega}, the droop coefficient, Kd, and the VSG time constant Ta.
Potential Game-Based Decision-Making for Autonomous Driving
Authors: Mushuang Liu, Ilya Kolmanovsky, H. Eric Tseng, Suzhou Huang, Dimitar Filev, Anouck Girard
Date: 2022-01-16 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making

Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (e.g., autonomous vehicles (AVs), human drivers, and pedestrians) and the computational load needed to evaluate these interactions. This paper develops two general potential game based frameworks, namely, finite and continuous potential games, for decision-making in autonomous driving. The two frameworks account for the AVs' two types of action spaces, i.e., finite and continuous action spaces, respectively. We show that the developed frameworks provide theoretical guarantees, including 1) existence of pure-strategy Nash equilibria, 2) convergence of the Nash equilibrium (NE) seeking algorithms, and 3) global optimality of the derived NE (in the sense that both self- and team- interests are optimized). In addition, we provide cost function shaping approaches to constructing multi-agent potential games in autonomous driving. Moreover, two solution algorithms, including self-play dynamics (e.g., best response dynamics) and potential function optimization, are developed for each game. The developed frameworks are then applied to two different traffic scenarios, including intersection-crossing and lane-changing in highways. Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms. It is shown that both developed frameworks are practical (i.e., computationally efficient), reliable (i.e., resulting in satisfying driving performances in diverse scenarios and situations), and robust (i.e., resulting in satisfying driving performances against uncertain behaviors of the surrounding vehicles) for real-time decision-making in autonomous driving.
When Small Gain Meets Small Phase
Authors: Di Zhao, Wei Chen, Li Qiu
Date: 2022-01-16 00:00:00
Tasks: LEMMA

In this paper, we investigate the feedback stability of multiple-input multiple-output linear time-invariant systems with combined gain and phase information. To begin with, we explore the stability condition for a class of so-called easily controllable systems, which have small phase at low frequency ranges and low gain at high frequency. Next, we extend the stability condition via frequency-wise gain and phase combination, based on which a mixed small gain and phase condition with necessity, called a small vase theorem, is then obtained. Furthermore, the fusion of gain and phase information is investigated by a geometric approach based on the Davis-Wielandt shell. Finally, for the purpose of efficient computation and controller synthesis, we present a bounded & sectored real lemma, which gives state-space characterization of combined gain and phase properties based on a triple of linear matrix inequalities.
Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach
Authors: Yang Li, Bo Feng, Bin Wang, Shuchao Sun
Date: 2022-01-15 00:00:00

In order to improve the penetration of renewable energy resources for distribution networks, a joint planning model of distributed generations (DGs) and energy storage is proposed for an active distribution network by using a bi-level programming approach in this paper. In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices. To solve this model, an improved binary particle swarm optimization (IBPSO) algorithm based on chaos optimization is developed, and the optimal joint planning is achieved through alternating iterations between the two levels. The simulation results on the PG & E 69-bus distribution system demonstrate that the presented approach manages to reduce the planning deviation caused by the uncertainties of DG outputs and remarkably improve the voltage profile and operational economy of distribution systems.
Using Voltage Phasor Control to Avoid Distribution Network Constraint Violations
Authors: Keith Moffat, Alexandra von Meier
Date: 2022-01-15 00:00:00

In this paper, we introduce Voltage Phasor Control (VPC), also known as Phasor Based Control, as a novel way of implementing Optimal Power Flow (OPF). Unlike conventional OPF, in which the power flow optimization broadcasts power injections, the VPC power flow optimization broadcasts voltage phasor setpoints to feedback controllers distributed throughout the network which respond to disturbances in real time. In this paper, we demonstrate that VPC can actively manage distributed energy resources to avoid voltage magnitude and line flow constraint violations in power distribution networks. We provide sensitivities and bounds that quantify how the distributed voltage phasor feedback control reduces the effect of disturbances on the network voltages and line flows. Using simulations, we compare the performance of VPC with the related but more conventional Voltage Magnitude Control (VMC). The sensitivities, bounds, and simulations provide implementation insights, which we highlight, for how distribution network operators can use VPC and VMC to avoid constraint violations.
Skydiving Technique Analysis from a Control Engineering Perspective: Developing a Tool for Aiding Motor Learning
Authors: Anna Clarke, Per Olof Gutman
Date: 2022-01-15 00:00:00

This study offers an interdisciplinary approach to movement technique analysis, designed to deal with intensive interaction between an environment and a trainee. The free-fall stage of skydiving is investigated, when aerial maneuvers are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process of body flight is hard and protracted since the required movements are not similar to our daily movement repertoire, and often counter-intuitive. The proposed method can provide a valuable insight into the subject's learning process and may be used by coaches to identify potentially successful technique changes. The main novelty is that instead of comparing directly the trainee's movements to a template or a movement pattern, extracted from a top-rated athlete in the field, we offer an independent way of technique analysis. We incorporate design tools of automatic control theory to link the trainee's movement patterns to specific performance characteristics. This makes it possible to suggest technique modifications that provide the desired performance improvement, taking into account the individual body parameters and constraints. Representing the performed maneuvers in terms of a dynamic response of closed-loop control system offers an unconventional insight into the motor equivalence problem. The method is demonstrated on three case studies of aerial rotation of skilled, less-skilled, and elite skydivers.
Design and Operation of Hybrid Multi-Terminal Soft Open Points using Feeder Selector Switches for Flexible Distribution System Interconnection
Authors: Matthew Deakin, Phil C. Taylor, Janusz Bialek, Wenlong Ming
Date: 2022-01-15 00:00:00

Distribution systems will require new cost-effective solutions to provide network capacity and increased flexibility to accommodate Low Carbon Technologies. To address this need, we propose the Hybrid Multi-Terminal Soft Open Point (Hybrid MT-SOP) to efficiently provide distribution system interconnection capacity. Each leg of the Hybrid MT-SOP has an AC/DC converter connected in series with a bank of AC switches (Feeder Selector Switches) to allow the converter to connect to any of the feeders at a node. Asymmetric converter sizing is shown to increase feasible power transfers by up to 50% in the three-terminal case, whilst a conic mixed-integer program is formulated to optimally select the device configuration and power transfers. A case study shows the Hybrid MT-SOP increasing utilization of the converters by more than one third, with a 13% increase in system loss reduction as compared to an equally-sized MT-SOP.
Submarine Cable Network Design for Regional Connectivity
Authors: Tianjiao Wang, Zengfu Wang, Bill Moran, Moshe Zukerman
Date: 2022-01-15 00:00:00
Tasks: Steiner Tree Problem

This paper optimizes path planning for a trunkand-branch topology network in an irregular 2-dimensional manifold embedded in 3-dimensional Euclidean space with application to submarine cable network planning. We go beyond our earlier focus on the costs of cable construction (including labor, equipment and materials) together with additional cost to enhance cable resilience, to incorporate the overall cost of branching units (again including material, construction and laying) and the choice of submarine cable landing stations, where such a station can be anywhere on the coast in a connected region. These are important issues for the economics of cable laying and significantly change the model and the optimization process. We pose the problem as a variant of the Steiner tree problem, but one in which the Steiner nodes can vary in number, while incurring a penalty. We refer to it as the weighted Steiner node problem. It differs from the Euclidean Steiner tree problem, where Steiner points are forced to have degree three; this is no longer the case, in general, when nodes incur a cost. We are able to prove that our algorithm is applicable to Steiner nodes with degree greater than three, enabling optimization of network costs in this context. The optimal solution is achieved in polynomialtime using dynamic programming.
Robust Safe Control Synthesis with Disturbance Observer-Based Control Barrier Functions
Authors: Ersin Daş, Richard M. Murray
Date: 2022-01-15 00:00:00

In a complex real-time operating environment, external disturbances and uncertainties adversely affect the safety, stability, and performance of dynamical systems. This paper presents a robust stabilizing safety-critical controller synthesis framework with control Lyapunov functions (CLFs) and control barrier functions (CBFs) in the presence of disturbance. A high-gain input observer method is adapted to estimate the time-varying unmodelled dynamics of the CBF with an error bound using the first-order time derivative of the CBF. This approach leads to an easily tunable low order disturbance estimator structure with a design parameter as it utilizes only the CBF constraint. The estimated unknown input and associated error bound are used to ensure robust safety and exponential stability by formulating a CLF-CBF quadratic program. The proposed method is applicable to both relative degree one and higher relative degree CBF constraints. The efficacy of the proposed approach is demonstrated using a numerical simulations of an adaptive cruise control system and a Segway platform with an external disturbance.
Indirect Adaptive Control of Nonlinearly Parameterized Nonlinear Dissipative Systems
Authors: Romeo Ortega, Rafael Cisneros, Lei Wang, Arjan van der Schaft
Date: 2022-01-15 00:00:00

In this note we address the problem of indirect adaptive (regulation or tracking) control of nonlinear, input affine dissipative systems. It is assumed that the supply rate, the storage and the internal dissipation functions may be expressed as nonlinearly parameterized regression equations where the mappings (depending on the unknown parameters) satisfy a monotonicity condition -- this encompasses a large class of physical systems, including passive systems. We propose to estimate the system parameters using the "power-balance" equation, which is the differential version of the classical dissipation inequality, with a new estimator that ensures global, exponential, parameter convergence under the very weak assumption of interval excitation of the power-balance equation regressor. To design the indirect adaptive controller we make the standard assumption of existence of an asymptotically stabilizing controller that depends -- possibly nonlinearly -- on the unknown plant parameters, and apply a certainty-equivalent control law. The benefits of the proposed approach, with respect to other existing solutions, are illustrated with examples.
Retina organoids: Window into the biophysics of neuronal systems
Authors: Katja A. Salbaum, Elijah R. Shelton, Friedhelm Serwane
Date: 2022-01-18 00:00:00

With a kind of magnetism, the human retina draws the eye of neuroscientist and physicist alike. It is attractive as a self-organizing system, which forms as a part of the central nervous system via biochemical and mechanical cues. The retina is also intriguing as an electro-optical device, converting photons into voltages to perform on-the-fly filtering before the signals are sent to our brain. Here, we consider how the advent of stem cell derived in vitro analogs of the retina, termed retina organoids, opens up an exploration of the interplay between optics, electrics, and mechanics in a complex neuronal network, all in a Petri dish. This review presents state-of-the-art retina organoid protocols by emphasizing links to the biochemical and mechanical signals of in vivo retinogenesis. Electrophysiological recording of active signal processing becomes possible as retina organoids generate light sensitive and synaptically connected photoreceptors. Experimental biophysical tools provide data to steer the development of mathematical models operating at different levels of coarse-graining. In concert, they provide a means to study how mechanical factors guide retina self-assembly. In turn, this understanding informs the engineering of mechanical signals required to tailor the growth of neuronal network morphology. Tackling the complex developmental and computational processes in the retina requires an interdisciplinary endeavor combining experiment and theory, physics, and biology. The reward is enticing: in the next few years, retina organoids could offer a glimpse inside the machinery of simultaneous cellular self-assembly and signal processing, all in an in vitro setting.
Discrete scaling and criticality in a chain of adaptive excitable integrators
Authors: Mario Martinez-Saito
Date: 2022-01-18 00:00:00

We describe a chain of unidirectionally coupled adaptive excitable elements slowly driven by a stochastic process from one end and open at the other end, as a minimal toy model of unresolved irreducible uncertainty in a system performing inference through a hierarchical model. Threshold potentials adapt slowly to ensure sensitivity without being wasteful. Activity and energy are released as intermittent avalanches of pulses with a discrete scaling distribution largely independent of the exogenous input form. Subthreshold activities and threshold potentials exhibit Lorentzian temporal spectra, with a power-law range determined by position in the chain. Subthreshold bistability closely resembles empirical measurements of intracellular membrane potential. We suggest that critical cortical cascades emerge from a trade-off between metabolic power consumption and performance requirements in a critical world, and that the temporal scaling patterns of brain electrophysiological recordings ensue from weighted linear combinations of subthreshold activities and pulses from different hierarchy levels.
Contact-mediated signaling enables disorder-driven transitions in cellular assemblies
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Sitabhra Sinha
Date: 2022-01-17 00:00:00

We show that when cells communicate by contact-mediated interactions, heterogeneity in cell shapes and sizes leads to qualitatively distinct collective behavior in the tissue. For inter-cellular coupling that implements lateral inhibition, such disorder-driven transitions can substantially alter the asymptotic pattern of differentiated cells by modulating their fate choice through changes in the neighborhood geometry. In addition, when contact-induced signals influence inherent cellular oscillations, disorder leads to the emergence of functionally relevant partially-ordered dynamical states.
Single-shot blind deconvolution with coded aperture
Authors: Hideyuki Muneta, Ryoichi Horisaki, Yohei Nishizaki, Makoto Naruse, Jun Tanida
Date: 2022-01-17 00:00:00

In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA). In this method, we utilize the CA, inserted on the pupil plane, as support constraints in blind deconvolution. Not only an object but also a point spread function of turbulence are estimated from a single captured image by a reconstruction algorithm with the CA support. The proposed method is demonstrated by a simulation and an experiment in which point sources are recovered under severe turbulence.
Bayesian inference of spatial and temporal relations in AI patents for EU countries
Authors: Krzysztof Rusek, Agnieszka Kleszcz, Albert Cabellos-Aparicio
Date: 2022-01-18 00:00:00
Tasks: Bayesian Inference

In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction between countries or explain the rapidly growing trends in AI patents. For spatial analysis Poisson regression is used to explain collaboration between a pair of countries measured by the number of common patents. Through Bayesian inference, we estimated the strengths of interactions between countries in the EU and the rest of the world. In particular, a significant lack of cooperation has been identified for some pairs of countries. Alternatively, an inhomogeneous Poisson process combined with the logistic curve growth accurately models the temporal behaviour by an accurate trend line. Bayesian analysis in the time domain revealed an upcoming slowdown in patenting intensity.
The Time-Varying Multivariate Autoregressive Index Model
Authors: G. Cubadda, S. Grassi, B. Guardabascio
Date: 2022-01-18 00:00:00
Tasks: model, Model Selection

Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model. Keywords: Large datasets, Multivariate Autoregressive Index models, Stochastic volatility, Bayesian VARs.
Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement
Authors: Brett R. Gordon, Robert Moakler, Florian Zettelmeyer
Date: 2022-01-18 00:00:00
Tasks: Marketing

Despite their popularity, randomized controlled trials (RCTs) are not always available for the purposes of advertising measurement. Non-experimental data is thus required. However, Facebook and other ad platforms use complex and evolving processes to select ads for users. Therefore, successful non-experimental approaches need to "undo" this selection. We analyze 663 large-scale experiments at Facebook to investigate whether this is possible with the data typically logged at large ad platforms. With access to over 5,000 user-level features, these data are richer than what most advertisers or their measurement partners can access. We investigate how accurately two non-experimental methods -- double/debiased machine learning (DML) and stratified propensity score matching (SPSM) -- can recover the experimental effects. Although DML performs better than SPSM, neither method performs well, even using flexible deep learning models to implement the propensity and outcome models. The median RCT lifts are 29%, 18%, and 5% for the upper, middle, and lower funnel outcomes, respectively. Using DML (SPSM), the median lift by funnel is 83% (173%), 58% (176%), and 24% (64%), respectively, indicating significant relative measurement errors. We further characterize the circumstances under which each method performs comparatively better. Overall, despite having access to large-scale experiments and rich user-level data, we are unable to reliably estimate an ad campaign's causal effect.
A Nearly Similar Powerful Test for Mediation
Authors: Kees Jan van Garderen, Noud van Giersbergen
Date: 2020-12-21 00:00:00
Tasks: Sociology

This paper derives a new powerful test for mediation that is easy to use. Testing for mediation is empirically very important in psychology, sociology, medicine, economics and business, generating over 100,000 citations to a single key paper. The no-mediation hypothesis $H_{0}:\theta_{1}\theta _{2}=0$ also poses a theoretically interesting statistical problem since it defines a manifold that is non-regular in the origin where rejection probabilities of standard tests are extremely low. We prove that a similar test for mediation only exists if the size is the reciprocal of an integer. It is unique, but has objectionable properties. We propose a new test that is nearly similar with power close to the envelope without these abject properties and is easy to use in practice. Construction uses the general varying $g$-method that we propose. We illustrate the results in an educational setting with gender role beliefs and in a trade union sentiment application.