pwc-archive/papers-with-abstracts huggingface dataset:
Requirements for Mass Adoption of Assistive Listening Technology by the General Public
Authors: Thomas B. Kaufmann, Mehdi Foroogozar, Julie Liss, Visar Berisha
Date: 2023-03-04 00:00:00

Assistive listening systems (ALSs) dramatically increase speech intelligibility and reduce listening effort. It is very likely that essentially everyone, not only individuals with hearing loss, would benefit from the increased signal-to-noise ratio an ALS provides in almost any listening scenario. However, ALSs are rarely used by anyone other than people with severe to profound hearing losses. To date, the reasons for this poor adoption have not been systematically investigated. The authors hypothesize that the reasons for poor adoption of assistive listening technology include (1) an inability to use personally owned receiving devices, (2) a lack of high-fidelity stereo sound, (3) receiving devices not providing an unoccluded listening experience, (4) distortion from alignment delay and (5) a lack of automatic connectivity to an available assistive listening audio signal. We propose solutions to each of these problems in an effort to pave the way for mass adoption of assistive listening technology by the general public.
Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile
Authors: Cunzhi Zhao, Xingpeng Li
Date: 2023-03-06 00:00:00
Tasks: energy management, Management, Scheduling

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed.
Social herding in mean field games
Authors: Deepanshu Vasal
Date: 2023-03-06 00:00:00

In this paper, we consider a mean field model of social behavior where there are an infinite number of players, each of whom observes a type privately that represents her preference, and publicly observes a mean field state of types and actions of the players in the society. The types (and equivalently preferences) of the players are dynamically evolving. Each player is fully rational and forward-looking and makes a decision in each round t to buy a product. She receives a higher utility if the product she bought is aligned with her current preference and if there is a higher fraction of people who bought that product (thus a game of strategic complementarity). We show that for certain parameters when the weight of strategic complementarity is high, players eventually herd towards one of the actions with probability 1 which is when each player buys a product irrespective of her preference.
Probabilistic Game-Theoretic Traffic Routing
Authors: Emilio Benenati, Sergio Grammatico
Date: 2023-03-06 00:00:00

We examine the routing problem for self-interested vehicles using stochastic decision strategies. By approximating the road latency functions and a non-linear variable transformation, we frame the problem as an aggregative game. We characterize the approximation error and we derive a new monotonicity condition for a broad category of games that encompasses the problem under consideration. Next, we propose a semi-decentralized algorithm to calculate the routing as a variational generalized Nash equilibrium and demonstrate the solution's benefits with numerical simulations. In the particular case of potential games, which emerges for linear latency functions, we explore a receding-horizon formulation of the routing problem, showing asymptotic convergence to destinations and analysing closed-loop performance dependence on horizon length through numerical simulations.
Multi-RIS-aided Wireless Communications in Real-world: Prototyping and Field Trials
Authors: Rujing Xiong, Jianan Zhang, Xuehui Dong, Zhengyu Wang, Junshuo Liu, Wei Yang, Tiebin Mi, Wenbo Huang, Robert Caiming Qiu
Date: 2023-03-06 00:00:00
Tasks: Management

The performance of multiple reconfigurable intelligent surfaces (RISs) receives limited attention in previous studies. This article fills this research gap by investigating the capabilities of multiple RISs in real-world networks. We propose a simplified yet highly scalable sandwich architecture for implementing one-bit unit cells, with the flexibility to accommodate multi-bit unit cells. To effectively control multiple RISs, we present a cost-effective remote-controlling scheme and develop a cloud-based RIS management system. Through a series of four field trials, we demonstrate the effectiveness of multi-hop routing schemes in establishing reliable links. Our experiments reveal significant improvements in signal strength and data transmission in multi-RIS-aided Wi-Fi and commercial 5G networks. Furthermore, we investigate the power scaling law of RIS-aided beamforming and provide insights into the roles of the later nodes in multi-hop relay chains.
Probability of Line of Sight Evaluation in Urban Environments using 3D Simulator
Authors: Abdul Saboor, Evgenii Vinogradov, Zhuangzhuang Cui, Sofie Pollin
Date: 2023-03-06 00:00:00

The integration of Non-Terrestrial Networks (NTNs) into 6G networks is one of the most promising ways to achieve significant improvements in capacity, reliability, and global coverage. The design of NTN heavily relies on using channel models. In this paper, we propose two easy-to-use simulators for estimating the Line-of-Sight (LoS) probability $\mathbf{P_{LoS}}$ in a 3D urban environment. The first simulator is a 3D city simulator that employs simplified Ray Tracing (RT), while the second one is a lightweight geometry-based simulator that generates only the relevant buildings between users and Unmanned Aerial Vehicles (UAVs). Using these simulators, we assess the accuracy of existing models for $\mathbf{P_{LoS}}$ estimation and examine $\mathbf{P_{LoS}}$ for different UAV heights, user-UAV distances, and azimuth/elevation angles. We conclude that 1) existing models overestimate $\mathbf{P_{LoS}}$, resulting in overoptimistic NTN performance predictions, 2) nodes location (including azimuth and elevation angles) is an important factor influencing $\mathbf{P_{LoS}}$, however, this influence is not captured by the existing models.
A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound
Authors: T. M. J. T. Baltussen, M. Goutham, M. Menon, S. G. Garrow, M. Santillo, S. Stockar
Date: 2023-03-06 00:00:00

Autonomous mobile robots enable increased flexibility of manufacturing systems. The design and operating strategy of such a fleet of robots requires careful consideration of both fixed and operational costs. In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a Branch & Bound (B&B) algorithm to find the globally optimal solution to the Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW).The metaheuristic and exact algorithms are implemented in a parallel hybrid optimization algorithm where the metaheuristic rapidly finds feasible solutions that provide candidate upper bounds for the B&B algorithm. The MCTS additionally provides a candidate fleet composition to initiate the B&B search. Experiments show that the proposed approach results in significant improvements in computation time and convergence to the optimal solution.
Generalized Filippov solutions for systems with prescribed-time convergence
Authors: Richard Seeber
Date: 2023-03-06 00:00:00

Dynamical systems with prescribed-time convergence sometimes feature a right-hand side exhibiting a singularity at the prescribed convergence time instant. In an open neighborhood of this singularity, classical absolutely continuous Filippov solutions may fail to exist, preventing indefinite continuation of such solutions. This note introduces a generalized Filippov solution definition based on the notion of generalized absolute continuity in the restricted sense. Conditions for the continuability of such generalized solutions are presented and it is shown, in particular, that generalized Filippov solutions of systems with an equilibrium that is attractive, in prescribed time or otherwise, can always be continued indefinitely. The results are demonstrated by applying them to a prescribed-time controller for a perturbed second-order integrator chain.
Worst-case error bounds for the super-twisting differentiator in presence of measurement noise
Authors: Richard Seeber
Date: 2023-03-06 00:00:00

The super-twisting differentiator, also known as the first-order robust exact differentiator, is a well known sliding mode differentiator. In the absence of measurement noise, it achieves exact reconstruction of the time derivative of a function with bounded second derivative. This note proposes an upper bound for its worst-case differentiation error in the presence of bounded measurement noise, based on a novel Lipschitz continuous Lyapunov function. It is shown that the bound can be made arbitrarily tight and never exceeds the true worst-case differentiation error by more than a factor of two. A numerical simulation illustrates the results and also demonstrates the non-conservativeness of the proposed bound.
Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation
Authors: Marta Vanin, Tom Van Acker, Reinhilde D'hulst, Dirk Van Hertem
Date: 2023-03-06 00:00:00
Tasks: State Estimation

State estimation allows to monitor power networks, exploiting field measurements to derive the most likely grid state. In the literature, measurement errors are usually assumed to follow zero-mean Gaussian distributions; however, it has been shown that this assumption often does not hold. One such example is when considering pseudo-measurements. In distribution networks, a significant amount of pseudo-measurements might be necessary, due to the scarcity of real-time measurements. In this paper, a state estimator is presented which allows to model measurement uncertainty with any continuous distribution, without approximations. This becomes possible by writing state estimation as a general maximum-likelihood estimation-based constrained optimization problem. To realistically describe distribution networks, three-phase unbalanced power flow equations are used. Results are presented that illustrate the differences in accuracy and computational effort between different uncertainty modeling methods, for the IEEE European Low Voltage Test Feeder.
Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities
Authors: Julien Leprince, Amos Schledorn, Daniela Guericke, Dominik Franjo Dominkovic, Henrik Madsen, Wim Zeiler
Date: 2023-03-06 00:00:00
Tasks: Stochastic Optimization

To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessary system scope simplifications. This has resulted in disconnected system scales, namely, building occupants and smart-city energy networks. This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning thanks to a holistic approach. The intent is to assess the aggregated impact of user behavior stochasticities on optimal urban energy planning. To this end, a stochastic energy community sizing and operation problem is designed, encompassing multi-level utilities founded on energy hub concepts for improved energy and carbon emission efficiencies. To secure the scalability of our approach, an organic spatial problem distribution suitable for field deployment is put forth, validated by a proof of concept. Uncertainty factors affecting urban energy planning are particularly examined through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on historical measurements a typical Dutch energy community composed of 41 residential buildings is designed. Results disclose a fast-converging distributed stochastic problem, where boilers are showcased as the preferred heating utility, and distributed renewable energy and storage systems were identified as unprofitable for the community. Occupant behavior was particularly exposed as the leading uncertainty factor impacting energy community planning. This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies.
Angle-Constrained Formation Control under Directed Non-Triangulated Sensing Graphs (Extended Version)
Authors: Kun Li, Zhixi Shen, Gangshan Jing, Yongduan Song
Date: 2023-03-06 00:00:00

Angle-constrained formation control has attracted much attention from control community due to the advantage that inter-edge angles are invariant under uniform translations, rotations, and scalings of the whole formation. However, almost all the existing angle-constrained formation control methods are limited to undirected triangulated sensing graphs. In this paper, we propose an angle-constrained formation control approach under a Leader-First Follower sensing architecture, where the sensing graph is directed and non-triangulated. Both shape stabilization and maneuver control are achieved under arbitrary initial configurations of the formation. During the formation process, the control input of each agent is based on relative positions from its neighbors measured in the local reference frame and wireless communications among agents are not required. We show that the proposed distributed formation controller ensures global exponential stability of the desired formation for an nagent system. Furthermore, it is interesting to see that the convergence rate of the whole formation is solely determined by partial specific angles within the target formation. The effectiveness of the proposed control algorithms is illustrated by carrying out experiments both in simulation environments and on real robotic platforms.
Multipath Effects on Frequency-Locked Loops (FLLs) and FLL-derived Doppler Measurements
Authors: Liangchun Xu, Jason Rife
Date: 2023-03-05 00:00:00

This paper investigates the impact of non-line-of-sight (NLOS) and multipath signals on frequency-locked loops (FLLs), which are commonly used to obtain Doppler shift measurements for velocity estimation in radio navigation. A salient result is that, in the absence of a direct signal, a single NLOS signal does not corrupt Doppler observables if the angle-of-arrival (AOA) is known. Another striking result is that, in a multipath environment involving two signals, the arctangent discriminator (averaged over the beat frequency) tracks only the higher amplitude signal. Our investigation has particular significance for radio navigation in urban environments, either using global navigation satellite system (GNSS) signals or ground-based cellular signals.
Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition
Authors: Aitor R. Gomez, Mohamad Al Ahdab
Date: 2023-03-05 00:00:00
Tasks: Gravitational Wave Detection

This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors $<1\mu$rad in a timely manner.
Resilient Strong Structural Controllability in Networks using Leaky Forcing in Graphs
Authors: Waseem Abbas
Date: 2023-03-04 00:00:00

This paper studies the problem of selecting input nodes (leaders) to make networks strong structurally controllable despite misbehaving nodes and edges. We utilize a graph-based characterization of network strong structural controllability (SSC) in terms of zero forcing in graphs, which is a dynamic coloring of nodes. We consider three types of misbehaving nodes and edges that disrupt the zero forcing process in graphs, thus, deteriorating the network SSC. Then, we examine a leader selection guaranteeing network SSC by ensuring the accuracy of the zero forcing process, despite $k$ misbehaving nodes/edges. Our main result shows that a network is resilient to $k$ misbehaving nodes/edges under one threat model if and only if it is resilient to the same number of failures under the other threat models. Thus, resilience against one threat model implies resilience against the other models. We then discuss the computational aspects of leader selection for resilient SSC and present a numerical evaluation.
Reduced-Order Model Predictive Control of a Fish Schooling Model
Authors: Masaki Ogura, Naoki Wakamiya
Date: 2023-03-04 00:00:00
Tasks: model, Model Predictive Control

We study the problem of model predictive control (MPC) for the fish schooling model proposed by Gautrais et al. (Annales Zoologici Fennici, 2008). The high nonlinearity of the model attributed to its attraction/alignment/repulsion law suggests the need to use MPC for controlling the fish schooling's motion. However, for large schools, the hybrid nature of the law can make it numerically demanding to perform finite-horizon optimizations in MPC. Therefore, this paper proposes reducing the fish schooling model for numerically efficient MPC; the reduction is based on using the weighted average of the directions of individual fish in the school. We analytically show how using the normalized eigenvector centrality of the alignment-interaction network can yield a better reduction by comparing reduction errors. We confirm this finding on the weight and numerical efficiency of the MPC with the reduced-order model by numerical simulations. The proposed reduction allows us to control a school with up to 500 individuals. Further, we confirm that reduction with the normalized eigenvector centrality allows us to improve the control accuracy by factor of five when compared to that using constant weights.
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach
Authors: Xinyue Wang, Haiwang Zhong, Guanglun Zhang, Guangchun Ruan, Yiliu He, Zekuan Yu
Date: 2023-03-04 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

With the increasing proportion of renewable energy in the generation side, it becomes more difficult to accurately predict the power generation and adapt to the large deviations between the optimal dispatch scheme and the day-ahead scheduling in the process of real-time dispatch. Therefore, it is necessary to conduct look-ahead dispatches to revise the operation plan according to the real-time status of the power grid and reliable ultra-short-term prediction. Application of traditional model-driven methods is often limited by the scale of the power system and cannot meet the computational time requirements of real-time dispatch. Data-driven methods can provide strong online decision-making support abilities when facing large-scale systems, while it is limited by the quantity and quality of the training dataset. This paper proposes a model-informed reinforcement learning approach for look-ahead AC optimal power flow. The reinforcement learning model is first formulated based on the domain knowledge of economic dispatch, and then the physics-informed neural network is constructed to enhance the reliability and efficiency. At last, the case study based on the SG 126-bus system validates the accuracy and efficiency of the proposed approach.
On the Use of Safety Critical Control for Cyber-Physical Security in the Smart Grid
Authors: Amr S. Mohamed, Mohsen Khalaf, Deepa Kundur
Date: 2023-03-03 00:00:00
Tasks: Decision Making

The tight coupling between communication and control in cyber-physical systems is necessary to enable the complex regulation required to operate these systems. Unfortunately, cyberattackers can exploit network vulnerabilities to compromise communication and force unsafe decision-making and dynamics. If a cyberattack is not detected and isolated in a timely manner, the control process must balance adhering to the received measurement signals to maintain system operation and ensuring that temporary compromise of the signals does not force unsafe dynamics. For this purpose, we present and employ a safety critical controller based on control barrier functions to mitigate attacks against load frequency control in smart power grids. We validate the paper's findings using simulation on a high-fidelity testbed.
Multiverse Predictions for Habitability: Origin of Life Scenarios
Authors: McCullen Sandora, Vladimir Airapetian, Luke Barnes, Geraint F. Lewis, Ileana Pérez-Rodríguez
Date: 2023-03-05 00:00:00

If the origin of life is rare and sensitive to the local conditions at the site of its emergence, then, using the principle of mediocrity within a multiverse framework, we may expect to find ourselves in a universe that is better than usual at creating these necessary conditions. We use this reasoning to investigate several origin of life scenarios to determine whether they are compatible with the multiverse, including the prebiotic soup scenario, hydrothermal vents, delivery of prebiotic material from impacts, and panspermia. We find that most of these scenarios induce a preference toward weaker-gravity universes, and that panspermia and scenarios involving solar radiation or large impacts as a disequilibrium source are disfavored. Additionally, we show that several hypothesized habitability criteria which are disfavored when the origin of life is not taken into account become compatible with the multiverse, and that the emergence of life and emergence of intelligence cannot both be sensitive to disequilibrium production conditions.