pwc-archive/papers-with-abstracts huggingface dataset:
UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation
Authors: Hashim A. Hashim, Abdelrahman E. E. Eltoukhy, Kyriakos G. Vamvoudakis
Date: 2023-08-25 00:00:00
Tasks: Sensor Fusion

This paper proposes a nonlinear stochastic complementary filter design for inertial navigation that takes advantage of a fusion of Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) technology ensuring semi-global uniform ultimate boundedness (SGUUB) of the closed loop error signals in mean square. The proposed filter estimates the vehicle's orientation, position, linear velocity, and noise covariance. The filter is designed to mimic the nonlinear navigation motion kinematics and is posed on a matrix Lie Group, the extended form of the Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$. The Lie Group based structure of the proposed filter provides unique and global representation avoiding singularity (a common shortcoming of Euler angles) as well as non-uniqueness (a common limitation of unit-quaternion). Unlike Kalman-type filters, the proposed filter successfully addresses IMU measurement noise considering unknown upper-bounded covariance. Although the navigation estimator is proposed in a continuous form, the discrete version is also presented. Moreover, the unit-quaternion implementation has been provided in the Appendix. Experimental validation performed using a publicly available real-world six-degrees-of-freedom (6 DoF) flight dataset obtained from an unmanned Micro Aerial Vehicle (MAV) illustrating the robustness of the proposed navigation technique. Keywords: Sensor-fusion, Inertial navigation, Ultra-wideband ranging, Inertial measurement unit, Stochastic differential equation, Stability, Localization, Observer design.
Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control
Authors: Erik Börve, Nikolce Murgovski, Leo Laine
Date: 2023-08-24 00:00:00
Tasks: Model Predictive Control, Prediction, Trajectory Planning, Trajectory Prediction

In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other. To tackle this problem, we present a novel framework that couples trajectory prediction and planning in multi-agent environments, using distributed model predictive control. A demonstration of our framework is presented in simulation, employing a trajectory planner using non-linear model predictive control. We analyze performance and convergence of our framework, subject to different prediction errors. The results indicate that the obtained locally optimal solutions are improved, compared with decoupled prediction and planning.
Decoupled Structure for Improved Adaptability of End-to-End Models
Authors: Keqi Deng, Philip C. Woodland
Date: 2023-08-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Domain Adaptation, Language Modelling, speech-recognition, Speech Recognition

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications. The E2E ASR model implicitly learns an internal language model (LM) which characterises the training distribution of the source domain, and the E2E trainable nature makes the internal LM difficult to adapt to the target domain with text-only data To solve this problem, this paper proposes decoupled structures for attention-based encoder-decoder (Decoupled-AED) and neural transducer (Decoupled-Transducer) models, which can achieve flexible domain adaptation in both offline and online scenarios while maintaining robust intra-domain performance. To this end, the acoustic and linguistic parts of the E2E model decoder (or prediction network) are decoupled, making the linguistic component (i.e. internal LM) replaceable. When encountering a domain shift, the internal LM can be directly replaced during inference by a target-domain LM, without re-training or using domain-specific paired speech-text data. Experiments for E2E ASR models trained on the LibriSpeech-100h corpus showed that the proposed decoupled structure gave 15.1% and 17.2% relative word error rate reductions on the TED-LIUM 2 and AESRC2020 corpora while still maintaining performance on intra-domain data.
Real-Time Simulations To Validate The Impact Of M-SSSC Devices On Protection Coordination In Power Systems
Authors: Sebastian Hincapie, John A. Calderon, Carlos E. Borda, Alejandro Duque, Pablo Macedo, Juan P. Gallego
Date: 2023-08-25 00:00:00

This paper analyzes the impact of the integration of the Modular Static Synchronous Series Compensator (M-SSSC) solution that will be deployed in the Santa Marta 220 kV (Colombian substation) by using real-time simulations with Hardware in the Loop (HIL) technique, over the protection coordination schemes in the area of influence. The results show that the M-SSSC devices can be successfully coordinated with the protection schemes, mitigating overloads through the transmission lines during steady state and contingencies, while avoiding potential harmful interactions with the grid and, most importantly, with protection devices. This behavior is achieved by using overcurrent detection logics implemented on M-SSSC devices that trip fast-acting Silicon Controlled Rectifiers (SCRs) that cease the voltage injection in less than 5 ms
Learning How to Price Charging in Electric Ride-Hailing Markets
Authors: Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
Date: 2023-08-25 00:00:00
Tasks: Multi-Armed Bandits

With the electrification of ride-hailing fleets, there will be a need to incentivize where and when the ride-hailing vehicles should charge. In this work, we assume that a central authority wants to control the distribution of the vehicles and can do so by selecting charging prices. Since there will likely be more than one ride-hailing company in the market, we model the problem as a single-leader multiple-follower Stackelberg game. The followers, i.e., the companies, compete about the charging resources under given prices provided by the leader. We present a learning algorithm based on the concept of contextual bandits that allows the central authority to find an efficient pricing strategy. We also show how the exploratory phase of the learning can be improved if the leader has some partial knowledge about the companies' objective functions. The efficiency of the proposed algorithm is demonstrated in a simulated case study for the city of Shenzhen, China.
Safe and Quasi-Optimal Autonomous Navigation in Environments with Convex Obstacles
Authors: Ishak Cheniouni, Soulaimane Berkane, Abdelhamid Tayebi
Date: 2023-08-25 00:00:00
Tasks: Autonomous Navigation

We propose a continuous feedback control strategy that steers a point-mass vehicle safely to a destination, in a quasi-optimal manner, in sphere worlds. The main idea consists in avoiding each obstacle via the shortest path on the cone's surface enclosing the obstacle and moving straight toward the target when the vehicle has a clear line of sight to the target location. In particular, almost global asymptotic stability of the target location is achieved in two-dimensional (2D) environments under a particular assumption on the obstacles configuration. We also propose a reactive (sensor-based) approach, suitable for real-time implementations in a priori unknown 2D environments with sufficiently curved convex obstacles, guaranteeing almost global asymptotic stability of the target location. Simulation results are presented to illustrate the effectiveness of the proposed approach.
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems
Authors: Xinliang Dai, Junyi Zhai, Yuning Jiang, Yi Guo, Colin N. Jones, Veit Hagenmeyer
Date: 2023-08-25 00:00:00
Tasks: Computational Efficiency, energy management, Management

This paper introduces a distributed operational solution for coordinating integrated transmission-distribution (ITD) systems regarding data privacy. To tackle the nonconvex challenges of AC optimal power flow (OPF) problems, our research proposes an enhanced version of the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). This proposed framework incorporates a second-order correction strategy and convexification, thereby enhancing numerical robustness and computational efficiency. The theoretical studies demonstrate that the proposed distributed algorithm operates the ITD systems with a local quadratic convergence guarantee. Extensive simulations on various ITD configurations highlight the superior performance of our distributed approach in terms of convergence speed, computational efficiency, scalability, and adaptability.
In-sector Compressive Beam Alignment for MmWave and THz Radios
Authors: Hamed Masoumi, Michel Verhaegen, Nitin Jonathan Myers
Date: 2023-08-25 00:00:00
Tasks: Compressive Sensing

Beam alignment is key in enabling millimeter wave and terahertz radios to achieve their capacity. Due to the use of large arrays in these systems, the common exhaustive beam scanning results in a substantial training overhead. Prior work has addressed this issue, by developing compressive sensing (CS)-based methods which exploit channel sparsity to achieve faster beam alignment. Unfortunately, standard CS techniques employ wide beams and suffer from a low signal-to-noise ratio (SNR) in the channel measurements. To solve this challenge, we develop an IEEE 802.11ad/ay compatible technique that takes an in-sector approach for CS. In our method, the angle domain channel is partitioned into several sectors, and the channel within the best sector is estimated for beam alignment. The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of the CS matrix for in-sector channel estimation. Our beam codebook illuminates distinct non-overlapping sectors and can be realized with low-resolution phased arrays. We show that the proposed codebook results in a higher received SNR than the state-of-the-art sector sweep codebooks. Furthermore, our optimized CS matrix achieves a better in-sector channel reconstruction than comparable benchmarks.
Model predictive control strategy in waked wind farms for optimal fatigue loads
Authors: Cheng Zhong, Yicheng Ding, Husai Wang, Jikai Chen, Jian Wang, Yang Li
Date: 2023-08-25 00:00:00
Tasks: Model Predictive Control

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.
Hybrid coupling rules for leaderless heterogeneous oscillators: uniform global asymptotic and finite-time synchronization
Authors: Simone Mariano, Riccardo Bertollo, Romain Postoyan, Luca Zaccarian
Date: 2023-08-25 00:00:00

We investigate the engineering scenario where the objective is to synchronize heterogeneous oscillators in a distributed fashion. The internal dynamics of each oscillator are general enough to capture their time-varying natural frequency as well as physical couplings and unknown bounded terms. A communication layer is set in place to allow the oscillators to exchange synchronizing coupling actions through a tree-like leaderless network. In particular, we present a class of hybrid coupling rules depending only on local information to ensure uniform global practical or asymptotic synchronization, which is impossible to obtain by using the Kuramoto model customarily used in the literature. We further show that the synchronization set can be made uniformly globally prescribed finite-time stable by selecting the coupling function to be discontinuous at the origin. Novel mathematical tools on non-pathological functions and set-valued Lie derivatives are developed to carry out the stability analysis. The effectiveness of the approach is illustrated in simulations where we apply our synchronizing hybrid coupling rules to models of power grids previously used in the literature.
Experimental Evaluation of Bird Strikes in Urban Air Mobility
Authors: Aditya Devta, Dr. Isabel C. Metz, Prof. Dr. Sophie F. Armanini
Date: 2023-08-23 00:00:00

Advanced mobility concepts such as Urban Air Mobility are emerging in full swing. In that concept, a safe and efficient aviation transportation system will use highly automated aircraft that will transport passengers or cargo at low altitudes within and between metropolitan regions. To accomplish these missions, new types of aircraft which are sometimes known as air taxis are being developed. A successful integration of these aircraft into existing airspace is complicated and needs to take into account various aspects. One of these is the risk of wildlife strikes which is predicted to be higher in case of air taxis. The proposed operational cruising altitude of air taxis is lower resulting in higher probability of collision as these are the altitudes where birds typically fly. Additionally, air taxis are smaller in size and have lower certification requirements compared to conventional aircraft. As a result, the severity of damaging bird strikes is higher. To assess the risk and formulate suitable regulations, an extensive analysis is required providing more quantitative insight into the bird strike challenge. Therefore, a theoretical model of bird strike to quantify the impact force by considering different bird and aircraft related parameters was developed previously. This paper aims to validate this theoretical model experimentally. It presents a methodology for implementing an experimental setup, allowing for the theoretical impact force model to be fully validated. A test matrix containing seven test cases, nine test scenarios and 135 iterations is formulated to conduct the bird strike experiment and the influencing parameters are considered for theoretical model verification. The paper closes with the presentation of the experimental results for validating the theoretical model which indicate 92.89 % conformance of experimental results with the theoretical model.
Asynchronous Sensor System for Collecting Detailed Data on the Environment and Resource Consumption in Smart City
Authors: Sergey Surnov, Igor Bychkovskiy, Grigory Surnov, Nikolay Surnov
Date: 2023-08-19 00:00:00

This article expands on the ideas presented in arXiv:1910.08759. The article demonstrates that within a unified monitoring system, cities can collect not only detailed resource consumption data but also information on the environmental conditions under a common set of rules. A method for constructing asynchronous sensor monitoring systems for controlled parameters in Smart City is proposed. The controlled parameters include: resource consumption in apartment buildings (electricity, cold and hot water, heat, gas); indoor and outdoor air pollution indicators (carbon monoxide, nitrogen oxides, hydrocarbons, dust, heavy metals, radiation levels, etc.); meteorological parameters (air temperature and humidity, atmospheric pressure, wind speed and direction). In an asynchronous sensor monitoring system, an event occurs when the value of a controlled parameter changes by a specified amount. This enables adjusting the granularity of the collected data. More detailed data contains more information and is therefore more valuable. Transitioning from traditional synchronous systems, where the value of a controlled parameter is recorded at set time intervals, to asynchronous systems allows for the abandonment of complex "smart meters" and the use of extremely simple and inexpensive sensors. Standardizing the data transmission protocol for all types of controlled parameters and reducing the cost of the most widespread equipment in the system -- sensors -- leads to lower expenses for creating and operating a monitoring system. Lower costs and increased value of collected data enable the potential opening of a new market -- a market for data on resource consumption and environmental parameters in Smart City, attracting private businesses to this area and accelerating the energy transition towards a global economy with zero greenhouse gas emissions.
Outbreak-size distributions under fluctuating rates
Authors: Jason Hindes, Luis Mier-y-Teran-Romero, Ira B. Schwartz, Michael Assaf
Date: 2023-08-25 00:00:00

We study the effect of noisy infection (contact) and recovery rates on the distribution of outbreak sizes in the stochastic SIR model. The rates are modeled as Ornstein-Uhlenbeck processes with finite correlation time and variance, which we illustrate using outbreak data from the RSV 2019-2020 season in the US. In the limit of large populations, we find analytical solutions for the outbreak-size distribution in the long-correlated (adiabatic) and short-correlated (white) noise regimes, and demonstrate that the distribution can be highly skewed with significant probabilities for large fluctuations away from mean-field theory. Furthermore, we assess the relative contribution of demographic and reaction-rate noise on the outbreak-size variance, and show that demographic noise becomes irrelevant in the presence of slowly varying reaction-rate noise but persists for large system sizes if the noise is fast. Finally, we show that the crossover to the white-noise regime typically occurs for correlation times that are on the same order as the characteristic recovery time in the model.
Grover Search for Portfolio Selection
Authors: A. Ege Yilmaz, Stefan Stettler, Thomas Ankenbrand, Urs Rhyner
Date: 2023-08-24 00:00:00

We present explicit oracles designed to be used in Grover's algorithm to match investor preferences. Specifically, the oracles select portfolios with returns and standard deviations exceeding and falling below certain thresholds, respectively. One potential use case for the oracles is selecting portfolios with the best Sharpe ratios. We have implemented these algorithms using quantum simulators.
GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables
Authors: Kejin Wu, Sayar Karmakar, Rangan Gupta
Date: 2023-08-25 00:00:00
Tasks: Econometrics, Prediction

In this work, we explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation with the possible inclusion of exogenous variables. From an applied point-of-view, extra knowledge such as fundamentals- and sentiments-based information could be beneficial to improve the prediction accuracy of market volatility if they are incorporated into the forecasting process. In the classical approach, these models including exogenous variables are typically termed GARCHX-type models. Being a Model-free prediction method, NoVaS has generally shown more accurate, stable and robust (to misspecifications) performance than that compared to classical GARCH-type methods. This motivates us to extend this framework to the GARCHX forecasting as well. We derive the NoVaS transformation needed to include exogenous covariates and then construct the corresponding prediction procedure. We show through extensive simulation studies that bolster our claim that the NoVaS method outperforms traditional ones, especially for long-term time aggregated predictions. We also provide an interesting data analysis to exhibit how our method could possibly shed light on the role of geopolitical risks in forecasting volatility in national stock market indices for three different countries in Europe.
Spatial and Spatiotemporal Volatility Models: A Review
Authors: Philipp Otto, Osman Doğan, Süleyman Taşpınar, Wolfgang Schmid, Anil K. Bera
Date: 2023-08-24 00:00:00
Tasks: Time Series

Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, if two locations are in close proximity, they can exhibit similar volatilities. In this paper, we aim to provide a comprehensive review of the recent literature on spatial and spatiotemporal volatility models. We first briefly review time series volatility models and their multivariate extensions to motivate their spatial and spatiotemporal counterparts. We then review various spatial and spatiotemporal volatility specifications proposed in the literature along with their underlying motivations and estimation strategies. Through this analysis, we effectively compare all models and provide practical recommendations for their appropriate usage. We highlight possible extensions and conclude by outlining directions for future research.
Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data
Authors: Tesary Lin, Avner Strulov-Shlain
Date: 2023-08-25 00:00:00
Tasks: Decision Making, Selection bias

We study how choice architecture that companies deploy during data collection influences consumers' privacy valuations. Further, we explore how this influence affects the quality of data collected, including both volume and representativeness. To this end, we run a large-scale choice experiment to elicit consumers' valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14-22% compared to opt-in, while a \$0-50 price anchor decreases valuations by 37-53% compared to a \$50-100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence negatively correlates with consumers' average valuation. We find that conventional frame optimization practices that maximize the volume of data collected can have opposite effects on its representativeness. A bias-exacerbating effect emerges when consumers' privacy valuations and frame effects are negatively correlated. On the other hand, a volume-maximizing frame may also mitigate the bias by getting a high percentage of consumers into the sample data, thereby improving its coverage. We demonstrate the magnitude of the volume-bias trade-off in our data and argue that it should be a decision-making factor in choice architecture design.
Parental Health Penalty on Adult Children's Employment: Gender Difference and Long-Term Consequence
Authors: Jiayi Wen, Haili Huang
Date: 2023-08-25 00:00:00

This paper examines the long-term gender-specific impacts of parental health shocks on adult children's employment in China. We build up an inter-temporal cooperative framework to analyze household work decisions in response to parental health deterioration. Then employing an event-study approach, we establish a causal link between parental health shocks and a notable decline in female employment rates. Male employment, however, remains largely unaffected. This negative impact shows no abatement up to eight years that are observable by the sample. These findings indicate the consequence of "growing old before getting rich" for developing countries.