pwc-archive/papers-with-abstracts huggingface dataset:
Optimal configuration of cooperative stationary and mobile energy storage considering ambient temperature: A case for Winter Olympic Game
Authors: He Meng, Hongjie Jia, Tao Xu, Wei Wei, Yuhan Wu, Lemeng Liang, Shuqi Cai, Zuozheng Liu, Rujing Wang
Date: 2022-02-20 00:00:00

The international mega-event, such as the Winter Olympic Game, has been considered as one of the most carbon intensive activities worldwide. The commitment of fully renewable energy accommodation and utilization while ensuring the extreme high reliability has brought significant challenges on system operation due to the stochastic nature of the renewables. The battery energy storage system (BESS) composed of stationary energy storage system (SESS) and shared mobile energy storage system (MESS) can be utilized to meet the requirements of short-term load surges, renewable accommodation and emergency power supply for important loads during the mega-event. The BESS can continue to serve the venues electricity consumption to satisfy the carbon neutrality after the event. On the other hand, the low ambient temperature of Winter Olympic game has significant impact on BESSs degradation and performance which need to be integrated to the charging and discharging model of BESS. To this end, a joint two-stage optimal configuration method considering the ambient temperature of SESS and MESS has been developed to support the mega-event carbon neutrality, to reduce redundant BESS capacity allocation and improve the system life cycle cost-benefit. Simulation results have demonstrated the rationality and effectiveness of the collaborative operation of SESS and the MESS under various scenarios.
Evaluation of voltage magnitude based unbalance metric for low voltage distribution networks
Authors: Md Umar Hashmi, Arpan Koirala, Rickard Lundholm, Hakan Ergun, Dirk Van Hertem
Date: 2022-02-19 00:00:00

Voltage unbalance in distribution networks (DN) is expected to grow with increasing penetration of single-phase distributed generation and single-phase loads such as electric vehicle chargers. Unbalance mitigation will be a significant concern as voltage unbalance leads to increased losses, reduced motor and inverter efficiency, and becomes a limiting factor for DN operation. The true definition of the unbalance metric needs phasor measurements of network voltage and current. However, such phasor measurements are generally not available in real life and as such approximate definitions are widely used due to their simplicity. This work aims to compare the true voltage unbalance definition and approximate unbalance metrics derived from phase voltage magnitude, as phase voltage magnitudes are commonly measured by digital metering infrastructure. For the comparison, multi-period power flow simulations are performed for 161 Spanish distribution feeders with R/X ratios varying from 2.87 to 14.68. We observe that phase magnitude-based unbalance metrics reasonably approximate the true unbalance for higher R/X ratios with a varying load power factor in a DN. Furthermore, the approximate unbalance metrics slightly improve for a low DN power factor due to the increase in DN unbalance. Therefore, the phase magnitude-based unbalance metric can be utilized for approximating DN unbalance.
Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles*
Authors: Mohammad Bahrami, Hamidreza Jafarnejadsani
Date: 2022-02-19 00:00:00

A network of unmanned aerial vehicles (UAVs) provides distributed coverage, reconfigurability, and maneuverability in performing complex cooperative tasks. However, it relies on wireless communications that can be susceptible to cyber adversaries and intrusions, disrupting the entire network's operation. This paper develops model-based centralized and decentralized observer techniques for detecting a class of stealthy intrusions, namely zero-dynamics and covert attacks, on networked UAVs in formation control settings. The centralized observer that runs in a control center leverages switching in the UAVs' communication topology for attack detection, and the decentralized observers, implemented onboard each UAV in the network, use the model of networked UAVs and locally available measurements. Experimental results are provided to show the effectiveness of the proposed detection schemes in different case studies.
Exact Instability Margin Analysis and Minimum-Norm Strong Stabilization -- phase change rate maximization --
Authors: Shinji Hara, Chung-Yao Kao, Sei Zhen Khong, Tetsuya Iwasaki, Yutaka Hori
Date: 2022-02-19 00:00:00

This paper is concerned with a new optimization problem named "phase change rate maximization" for single-input-single-output linear time-invariant systems. The problem relates to two control problems, namely robust instability analysis against stable perturbations and minimum-norm strong stabilization. We define an index of the instability margin called "robust instability radius (RIR)" as the smallest $H_\infty$-norm of a stable perturbation that stabilizes a given unstable system. This paper has two main contributions. It is first shown that the problem of finding the exact RIR via the small-gain condition can be transformed into the problem of maximizing the phase change rate at the peak frequency with a phase constraint. Then, we show that the maximum is attained by a constant or a first-order all-pass function and derive conditions, under which the RIR can be exactly characterized, in terms of the phase change rate. Two practical applications are provided to illustrate the utility of our results.
Model Order Reduction for Water Quality Dynamics
Authors: Shen Wang, Ahmad F. Taha, Ankush Chakrabarty, Lina Sela, Ahmed Abokifa
Date: 2021-02-22 00:00:00
Tasks: model, Model Predictive Control

A state-space representation of water quality dynamics describing disinfectant (e.g., chlorine) transport dynamics in drinking water distribution networks has been recently proposed. Such representation is a byproduct of space- and time-discretization of the PDE modeling transport dynamics. This results in a large state-space dimension even for small networks with tens of nodes. Although such a state-space model provides a model-driven approach to predict water quality dynamics, incorporating it into model-based control algorithms or state estimators for large networks is challenging and at times intractable. To that end, this paper investigates model order reduction (MOR) methods for water quality dynamics with the objective of performing post-reduction feedback control. The presented investigation focuses on reducing state-dimension by orders of magnitude, the stability of the MOR methods, and the application of these methods to model predictive control.
A Soft-Switching Single-Stage AC-DC Converter
Authors: Farid Naghavi, Hamid Toliyat
Date: 2022-02-18 00:00:00

Partial resonance high frequency AC link converters are recognized to provide soft-switching, single-stage power conversion. They are highly reliable due to elimination of DC capacitors. Previous work has focused on proof of concept topologies and design requirements. In this work, a current regulated AC-DC converter from this family of converters is presented. The control design and grid integration requirements are discussed. An active damping method using grid current feedback is implemented to mitigate the filter resonance oscillations. The current control is implemented in the synchronous frame using voltage oriented control which enable power factor and power flow control. The result of converter operation and its control loops performances are presented
Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review
Authors: Jie Zhu, Said Easa, Kun Gao
Date: 2022-02-18 00:00:00
Tasks: Autonomous Vehicles

On-ramp merging areas are typical bottlenecks in the freeway network, since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper presents a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field. The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows, and merging into multilane freeways. Relevant literature is reviewed regarding the required technologies, control decision level, applied methods, and impacts on traffic performance. More importantly, we identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.
Flow-level Coordination of Connected and Autonomous Vehicles in Multilane Freeway Ramp Merging Areas
Authors: Jie Zhu, Ivana Tasic, Xiaobo Qu
Date: 2022-02-18 00:00:00
Tasks: Autonomous Vehicles

On-ramp merging areas are deemed to be typical bottlenecks for freeway networks due to the intensive disturbances induced by the frequent merging, weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles (CAVs), benefited from their capabilities of real-time communication and precise motion control, hold an opportunity to promote ramp merging operation through enhanced cooperation. The existing CAV cooperation strategies are mainly designed for single-lane freeways, although multilane configurations are more prevailing in the real-world. In this paper, we present a flow-level CAV coordination strategy to facilitate merging operation in multilane freeways. The coordination integrates lane-change rules between mainstream lanes, proactive creation of large merging gaps, and platooning of ramp vehicles for enhanced benefits in traffic flow stability and efficiency. The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions. The impacts of tunable model parameters on the produced control plan are discussed in detail. The efficiency of the proposed multilane strategy is demonstrated in a micro-simulation environment. The results show that the coordination can substantially improve the overall ramp merging efficiency and prevent recurrent traffic congestions, especially under high traffic volume conditions.
Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networks
Authors: Ognjen Stanojev, Yi Guo, Petros Aristidou, Gabriela Hug
Date: 2022-02-18 00:00:00

The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators. Since the principal providers of ancillary services are being phased out, new flexibility and reserve providers are needed. The proliferation of Distributed Energy Resources (DERs) in modern distribution networks has opened new possibilities for distribution system operators, enabling them to fill the market gap by harnessing the DER flexibility. This paper introduces a novel centralized MPC-based controller that enables the concurrent provision of voltage support, primary and secondary frequency control by adjusting the setpoints of a heterogeneous group of DERs in active distribution grids. The input-multirate control framework is used to accommodate the distinct timescales and provision requirements of each ancillary service and to ensure that the available resources are properly allocated. Furthermore, an efficient way for incorporating network constraints in the formulation is proposed, where network decomposition is applied to a linear power flow formulation together with network reduction. In addition, different timescale dynamics of the employed DERs and their capability curves are included. The performance of the proposed controller is evaluated on several case studies via dynamic simulations of the IEEE 33-bus system.
Transition Matrix Representation of Trees with Transposed Convolutions
Authors: Jaemin Yoo, Lee Sael
Date: 2022-02-22 00:00:00

How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making irreversible decisions. However, searching for a tree structure that gives the best balance between the performance and the interpretability remains a challenging task. In this paper, we propose TART (Transition Matrix Representation with Transposed Convolutions), our novel generalized tree representation for optimal structural search. TART represents a tree model with a series of transposed convolutions that boost the speed of inference by avoiding the creation of transition matrices. As a result, TART allows one to search for the best tree structure with a few design parameters, achieving higher classification accuracy than those of baseline models in feature-based datasets.
Deep learning-guided weighted averaging for signal dropout compensation in diffusion-weighted imaging of the liver
Authors: Fasil Gadjimuradov, Thomas Benkert, Marcel Dominik Nickel, Tobit Führes, Marc Saake, Andreas Maier
Date: 2022-02-20 00:00:00

Purpose: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal diffusion-weighted imaging (DWI) resulting from cardiac motion. Methods: Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed which aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, termed Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the Apparent Diffusion Coefficient (ADC) and noise characteristics. Results: In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to signal-to-noise ratio (SNR) due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters. Conclusion: This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Since DLAWA follows a retrospective approach, no changes to the acquisition are required.
Dynamic Survival Analysis for non-Markovian Epidemic Models
Authors: Francesco Di Lauro, Wasiur R. KhudaBukhsh, Istvan Z. Kiss, Eben Kenah, Max Jensen, Grzegorz A. Rempala
Date: 2022-02-21 00:00:00
Tasks: parameter estimation, Survival Analysis

We present a new method for analyzing stochastic epidemic models under minimal assumptions. The method, dubbed DSA, is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of PDE may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from the FMD in the United Kingdom and the COVID-19 in India show good accuracy and confirm method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modeling, analyzing and interpreting epidemic data with the help of the DSA approach.
Long Run Risk in Stationary Structural Vector Autoregressive Models
Authors: Christian Gourieroux, Joann Jasiak
Date: 2022-02-18 00:00:00
Tasks: Time Series, Time Series Analysis, Unity

This paper introduces a local-to-unity/small sigma process for a stationary time series with strong persistence and non-negligible long run risk. This process represents the stationary long run component in an unobserved short- and long-run components model involving different time scales. More specifically, the short run component evolves in the calendar time and the long run component evolves in an ultra long time scale. We develop the methods of estimation and long run prediction for the univariate and multivariate Structural VAR (SVAR) models with unobserved components and reveal the impossibility to consistently estimate some of the long run parameters. The approach is illustrated by a Monte-Carlo study and an application to macroeconomic data.
Commuting to work and gender-conforming social norms: evidence from same-sex couples
Authors: Sonia Oreffice, Dario Sansone
Date: 2022-02-21 00:00:00

We analyze work commute time by sexual orientation of partnered or married individuals, using the American Community Survey 2008-2019. Women in same-sex couples have a longer commute to work than working women in different-sex couples, whereas the commute to work of men in same-sex couples is shorter than the one of working men in different-sex couples, also after controlling for demographic characteristics, partner characteristics, location, fertility, and marital status. These differences are particularly stark among married couples with children: on average, about 3 minutes more one-way to work for married mothers in same-sex couples, and almost 2 minutes less for married fathers in same-sex couples, than their corresponding working parents in different-sex couples. These gaps among men and women amount to 50 percent, and 100 percent, respectively, of the gender commuting gap estimated in the literature. Within-couple gaps in commuting time are also significantly smaller in same-sex couples. We interpret these differences as evidence that it is gender-conforming social norms boosted by parenthood that lead women in different-sex couples to specialize into jobs with a shorter commute while their male partners or spouses hold jobs with a longer commute.
The geographic proximity effect on domestic cross-sector vis-a-vis intra-sector research collaborations
Authors: Giovanni Abramo, Francesca Apponi, Ciriaco Andrea D'Angelo
Date: 2022-02-18 00:00:00

Geographic proximity is acknowledged to be a key factor in research collaborations. Specifically, it can work as a possible substitute for institutional proximity. The present study investigates the relevance of the "proximity" effect for different types of national research collaborations. We apply a bibliometric approach based on the Italian 2010-2017 scientific production indexed in the Web of Science. On such dataset, we apply statistical tools for analyzing if and to what extent geographical distance between co-authors in the byline of a publication varies across collaboration types, scientific disciplines, and along time. Results can inform policies aimed at effectively stimulating cross-sector collaborations, and also bear direct practical implications for research performance assessments.
A Dutch book argument for belief consistency
Authors: Emiliano Catonini
Date: 2022-02-21 00:00:00
Tasks: counterfactual

An agent progressively learns about a state of the world. A bookmaker is ready to offer one bet after every new discovery. I say that the agent is Dutch-booked when she is willing to accept every single bet, but her expected payoff is negative under each state, where the expected payoff is computed with the objective probabilities of different discoveries conditional on the state. I introduce a rule of coherence among beliefs after counterfactual discoveries that is necessary and sufficient to avoid being Dutch-booked. This rule characterizes an agent who derives all her beliefs with Bayes rule from the same Lexicographic Conditional Probability System (Blume, Brandenburger and Dekel, 1991).
Equilibria of Attacker-Defender Games
Authors: Zsombor Z. Méder, Carsten K. W. de Dreu, Jörg Gross
Date: 2022-02-21 00:00:00

Attempts at predatory capture may provoke a defensive response that reduces the very value of the predated resource. We provide a game-theoretic analysis of simultaneous-move, two-player Attacker-Defender games that model such interactions. When initial endowments are equal, Attackers win about a third of such games in equilibrium. Under power disparities, Attackers become particularly aggressive when they are approximately one-third poorer than Defenders. With non-conflictual outside options Attackers become exceptionally aggressive when their opponent has access to high-benefit, low-cost production, and refrain from attack most when they are unilaterally provided with a high-benefit, high-cost production option.
Monopoly, Product Quality, and Flexible Learning
Authors: Jeffrey Mensch, Doron Ravid
Date: 2022-02-21 00:00:00

A seller offers a buyer a schedule of transfers and associated product qualities. After observing this schedule, the buyer chooses a flexible costly signal about his type. We show it is without loss to focus on a class of mechanisms that compensate the buyer for his learning costs. Using these mechanisms, we prove that when marginal costs of quality are strictly increasing, quality always lies strictly below the efficient level at all types strictly below the highest possible type under the prior. When learning costs are sufficiently steep, it follows the monopolist optimum distorts quality downward even "at the top" of the buyer's chosen signal structure. Moreover, when marginal costs of quality are constant, interior buyer types obtain interior qualities. These results stand in contrast to the exogenous information case, which features "no distortion at the top" when marginal production costs are increasing, and serving the maximal quality to all types above some threshold when marginal product costs are constant. We also show that when learning costs are steep, the monopolist finds it optimal to offer a simple menu that contains at most two purchasing options. Under appropriate curvature conditions, offering a single purchasing option is optimal.
Model-Based Reconstruction for Collimated Beam Ultrasound Systems
Authors: Abdulrahman Alanazi, Singanallur Venkatakrishnan, Hector Santos-Villalobos, Gregery Buzzard, Charles Bouman
Date: 2022-02-20 00:00:00
Tasks: model

Collimated beam ultrasound systems are a novel technology for imaging inside multi-layered structures such as geothermal wells. Such systems include a transmitter and multiple receivers to capture reflected signals. Common algorithms for ultrasound reconstruction use delay-and-sum (DAS) approaches; these have low computational complexity but produce inaccurate images in the presence of complex structures and specialized geometries such as collimated beams. In this paper, we propose a multi-layer, ultrasonic, model-based iterative reconstruction algorithm designed for collimated beam systems. We introduce a physics-based forward model to accurately account for the propagation of a collimated ultrasonic beam in multi-layer media and describe an efficient implementation using binary search. We model direct arrival signals, detector noise, and a spatially varying image prior, then cast the reconstruction as a maximum a posteriori estimation problem. Using simulated and experimental data we obtain significantly fewer artifacts relative to DAS while running in near real time using commodity compute resources.