pwc-archive/papers-with-abstracts huggingface dataset:
The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram
Authors: Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran
Date: 2024-06-13 00:00:00

As urbanization advances, cities are expanding, leading to a more decentralized urban structure and longer average commuting durations. The construction of an urban expressway system emerges as a critical strategy to tackle this challenge. However, the traditional link-level network design method faces modeling and solution challenges when dealing with the large-scale expressway network design problem (ENDP). To address the challenges, this paper proposes an expressway network design method for multiple urban subregions based on the macroscopic fundamental diagram (MFD). Initially, a mixed road network traffic model that describes traffic dynamics of multiple subregions and candidate expressways is developed by integrating the MFD and the cell transmission model (CTM). Then, treating urban subregions and candidate expressways as route nodes in the mixed road network, a route choice model is established based on stochastic user equilibrium. Finally, a decision model for ENDP is proposed to minimize vehicle travel time under the construction budget constraint. The impact of financial investment and traffic demand on expressway network design schemes in the case study is explored separately. The simulation results indicate that during the initial stages of expressway planning, the construction of new expressways can significantly alleviate traffic congestion. However, as the expressway network expands further, the effectiveness of improving traffic conditions through new expressway construction gradually diminishes if traffic demand does not continue to increase. Additionally, variations in traffic demand between subregions result in different construction schemes, emphasizing the importance of adjusting budget allocations based on specific traffic demands.
Research on material identification of mobile phones falling to the ground
Authors: Xuesong Wang
Date: 2024-06-12 00:00:00

The failure mode of the phone falling has a lot to do with the ground material. At present, the research on ground material and mobile phone damage is generally carried out through experiments, which is extremely costly. This paper presents a method to identify the material of mobile phones falling on the ground. The method determines the material of the mobile phone falling to the ground according to the data of the mobile phone accelerometer and can obtain the ground material of the mobile phone falling through a large number of user data. By analyzing the physical process of mobile phone falling, the accelerometer data interval which can reflect the characteristics of falling is reasonably intercepted. And the data features that can reflect the collision are extracted. Finally, based on the fully connected neural network, the method of determining the material of mobile phones falling on the ground is developed. The experimental results show that the method has a high identification rate, and the average identification rate for different ground materials is 96.75%. Instead of relying on auxiliary devices, the identification process only relies on the phone's built-in acceleration sensor. This paper fills the gap in the field of mobile phone falling ground material identification. It has a great contribution to the collection of user drop data, the analysis of the drop process, and the improvement of mobile phone design defects.
Data-driven Thermal Modeling for Electrically Excited Synchronous Motors -- A Supervised Machine Learning Approach
Authors: Farzaneh Tatari, Davis Trapp, Jason Schneider, Mohsen Mirza Aligoudarzi
Date: 2024-06-13 00:00:00

This paper proposes a data-driven supervised machine learning (ML) for online thermal modeling of electrically excited synchronous motors (EESMs). EESMs are desired for EVs due to their high performance, efficiency, and durability at a relatively low cost. Therefore, obtaining precise EESM temperature estimations are significantly important, because online accurate temperature estimation can lead to EESM performance improvement and guaranteeing its safety and reliability. In this study, in addition to the default inputs' data, EESM losses data is leveraged to improve the performance of the proposed ML approach for thermal modeling. Exponentially weighted moving averages and standard deviations of the inputs are also incorporated in the learning process to consider the memory effect for modeling a dynamical thermal model. Using the experimental data of an EESM prototype, the performance of ordinary least squares (OLS) method is evaluated through a complete training, testing and cross-validation process. Finally, simulation results will provide the key performance metrics of OLS for EESM thermal modeling.
Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification
Authors: Jorge Espin, Yuichi Kajiura, Dong Zhang
Date: 2024-06-12 00:00:00
Tasks: Management, Model Predictive Control, parameter estimation

Lithium-ion (Li-ion) batteries are ubiquitous in modern energy storage systems, highlighting the critical need to comprehend and optimize their performance. Yet, battery models often exhibit poor parameter identifiability which hinders the development of effective battery management strategies and impacts their overall performance, longevity, and safety. This manuscript explores the integration of Fisher Information (FI) theory with Model Predictive Control (MPC) for battery charging. The study addresses the inherent hurdles in accurately estimating battery model parameters due to nonlinear dynamics and uncertainty. Our proposed method aims to ensure safe battery charging and enhance real-time parameter estimation capabilities by leveraging adaptive control strategies guided by FI metrics. Simulation results underscore the effectiveness of our approach in mitigating parameter identifiability issues, offering promising solutions for improving the control of batteries during safe charging process.
Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
Authors: Maonan Wang, YiRong Chen, Yuheng Kan, Chengcheng Xu, Michael Lepech, Man-on Pun, Xi Xiong
Date: 2024-06-12 00:00:00
Tasks: Traffic Signal Control

Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.
Efficient Communication and Powering for Smart Contact Lens with Resonant Magneto-Quasistatic Coupling
Authors: Sukriti Shaw, Mayukh Nath, Arunashish Datta, Shreyas Sen
Date: 2024-06-12 00:00:00

A two-coil wearable system is proposed for wireless communication and powering between a transmitter coil in a necklace and a receiver coil in a smart contact lens, where the necklace is invisible in contrast to coils embedded in wearables like spectacles or headbands. Magneto-quasistatic(MQS) field coupling facilitates communication between the transmitter in the necklace and the contact lens receiver, enabling AR/VR and health monitoring. As long as the receiver coil remains within the magnetic field generated by the transmitter, continuous communication is sustained through MQS field coupling despite the misalignments present. Resonant frequency tuning enhances system efficiency. The system's performance was tested for coil misalignments, showing a maximum path loss variation within $10 dB$ across scenarios, indicating robustness. Finite Element Method(FEM) analysis has been used to study the system for efficient wireless data transfer and powering. A communication channel capacity is $4.5 Mbps$ over a $1 MHz$ bandwidth. Simulations show negligible path loss differences with or without human tissues, as magnetic coupling remains unaffected at MQS frequencies below $30 MHz$ due to similar magnetic permeability of tissues and air. Therefore, the possibility of efficient communication and powering of smart contact lenses through a necklace is shown for the first time using resonant MQS coupling at an axial distance of $15cm$ and lateral distance of over $9cm$ to enable AR/VR and health monitoring on the contact lens.
Iterative method for real-time Hybrid testing: application to a cantilever beam with two interface degrees of freedom
Authors: Alessandra Vizzaccaro, Sandor Beregi, David A. W. Barton, Simon A. Neild
Date: 2024-06-12 00:00:00
Tasks: Cantilever Beam

In this paper, an iterative method for real-time hybrid testing (RTHT) is proposed. The method seeks to iteratively balance the interface conditions between the physical and numerical substructures by controlling the periodic demand of the actuators. It is then suitable for RTHT of structures undergoing a periodic response, e.g. structures excited at resonance. We demonstrate the capabilities of the method on a cantilever beam in bending motion with two degrees of freedom at the interface, which we use as a prototype for future testing of aircraft wings. We show that a number of challenges arise in these settings, such as the difficulty in measuring interface forces while controlling a continuous structure and the instability of the hybrid test for small time delays. Classical RTHT strategies could produce inaccurate or unstable outcomes, whereas the proposed method is able to attain very good interface synchronisation in a wide range of tested scenarios.
Characterization of Acoustic Losses in Interdigitated VHF to mmWave Piezoelectric M/NEMS Resonators
Authors: Luca Colombo, Gabriel Giribaldi, Ryan Tetro, Jack Guida, Walter Gubinelli, Luca Spagnuolo, Nicol Maietta, Siddhartha Ghosh, Matteo Rinaldi
Date: 2024-06-12 00:00:00

This work reports on a technology-agnostic and frequency-independent methodology combining a-priori modeling, Finite Element Analysis (FEA), and experimental results for the characterization of acoustic losses in interdigitated piezoelectric micro- and nano-electromechanical (M/NEMS) resonators. The proposed approach models the mechanical quality factor (Qm) and its dependency on piezoelectric (Qpiezo) and metal (Qmetal) acoustic losses, as a function of the mode of vibration dispersion characteristics. The model is finally experimentally validated by exploiting the intrinsic on-chip multifrequency manufacturability of interdigitated devices. A broad range of available resonator technologies, frequencies, and piezoelectric materials are investigated for this purpose, including bulk X-cut Lithium Niobate (XLN) leaky surface acoustic wave resonators operating at Ultra High Frequency (UHF), thin film XLN Lamb Wave resonators spanning between Very High Frequency (VHF) and the Ku band, and Aluminum Nitride (AlN) and scandium-doped AlN (ScAlN) cross-sectional lame' mode resonators ranging from the Ku to Ka band (mmWave).
A Set Cover Mapping Heuristic for Demand-Robust Fleet Size Vehicle Routing Problem with Time Windows and Compatibility Constraints
Authors: Jordan Makansi
Date: 2024-06-11 00:00:00

We study the demand-robust fleet size vehicle routing problem with time windows and compatibility constraints. Unlike traditional robust optimization, which considers uncertainty in the data, demand-robust optimization considers uncertainty in which constraints must be satisfied. This paper is the first to solve a practical demand-robust optimization problem at large scale. We present an MILP formulation and also propose a heuristic that maps the problem to set cover in polynomial time. We show that under modest assumptions the relative difference in time complexity from a standard branch-and-bound algorithm to the proposed heuristic scales exponentially with the size of the problem. We evaluate our heuristic using a simulation case study on the Solomon benchmark instances for a variety of practical problem sizes, and compare with Gurobi. The empirical approximation ratio remains below 2.0.
Active Sidestick Control Integration for Enhanced Aircraft Flight Envelope Protection
Authors: Çağrı Ege Altunkaya, Fatih Erol, Akın Çatak, Volkan Mert, Pierluigi Capone, Şükrü Akif Ertürk, Emre Koyuncu
Date: 2024-06-11 00:00:00

The design of Envelope and Pilot-Induced Oscillation (PIO) Protection Features, and Failure Cases detection and prevention using Active Control Sidestick (ACS) is a challenging task. While helping the pilot to respect the envelope limitations also in failure scenarios and, therefore, increasing mission effectiveness, these features may have a significant impact on the aircraft's agility. ACS characteristics are investigated in an integrated environment. A set of effective and flexible control laws based on Incremental Nonlinear Dynamic Inversion have been developed in a state-of-the-art aircraft simulation model and coupled with a two-ways communication with the selected ACS. The model can run in real-time in a fixed-based simulator composed of representative cockpit and out-of-the-window.
Stabilization of a Quadrotor via Energy Shaping
Authors: M. Reza J. Harandi, Babak Salamat, Gerhard Elsbacher
Date: 2024-06-11 00:00:00

Stabilization of a quadrotor without a controller based on cascade structure is a challenging problem. Besides, due to the dynamics and the number of underactuation, an energy shaping controller has not been designed in 3D for a quadrotor. This paper presents a novel solution to the potential energy shaping problem for a quadrotor utilizing the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) approach. For the first time, we extend the solution of PDEs from the 2D case to the full 3D scenario. This advancement seems to be a significant step forward for stabilization of underactuated aerial vehicles without a cascade controller. The results are verified via simulation on a typical quadrotor.
Regularizing Numerical Extremals Along Singular Arcs: A Lie-Theoretic Approach
Authors: Arthur Castello Branco de Oliveira, Milad Siami, Eduardo D. Sontag
Date: 2024-06-11 00:00:00
Tasks: ARC

Numerical ``direct'' approaches to time-optimal control often fail to find solutions that are singular in the sense of the Pontryagin Maximum Principle, performing better when searching for saturated (bang-bang) solutions. In previous work by one of the authors, singular solutions were shown to exist for the time-optimal control problem for fully actuated mechanical systems under hard torque constraints. Explicit formulas, based on a Lie theoretic analysis of the problem, were given for singular segments of trajectories, but the global structure of solutions remains unknown. In this work, we review the aforementioned framework, and show how to effectively combine these formulas with the use of general-purpose optimal control software packages. By using the explicit formula given by the theory in the intervals where the numerical solution enters a singular arc, we not only obtain an algebraic expression for the control in that interval but we are also able to remove artifacts present in the numerical solution. In this way, the best features of numerical algorithms and theory complement each other and provide a better picture of the global optimal structure. We illustrate the technique on a two degree of freedom robotic arm example, using two distinct optimal control numerical software packages running on different programming languages.
Bistability in filamentous actin through monomer-sequestration of an effector species
Authors: Panayiotis Foteinopoulos, Bela M. Mulder
Date: 2024-06-13 00:00:00

Filamentous actin, a species of dynamic protein polymers, is one of the main components of the cytoskeleton of eukaryotic cells. We formulate a class of models that predict the possibility of bistable steady states in populations of dynamic actin filaments. They are built upon a basic model of actin dynamics that includes severing and capping in the presence of a finite actin monomer pool. The key additional ingredient is the presence of a single species of effector molecules that is partially sequestered to an inactive state by binding to free G-actin. In its unbound active state, this effector species can \emph{enhance} the rate of nucleation of filamentous actin or its growth speed, or \emph{inhibit} the activity of capping or severing proteins. Using an explicit analytical solution of the basic actin dynamics model, we show that bistability is predicted to occur in all of the proposed models. We verify these predictions using particle-based stochastic simulations. In addition, we show that switching between the two stable states can be achieved by transient manipulation of the free G-actin pool size.
Mental intervention in quantum scattering of ions without violating conservation laws
Authors: Johann Summhammer
Date: 2024-06-12 00:00:00

There have been several proposals in the past that mind might influence matter by exploiting the randomness of quantum events. Here, calculations are presented how mental selection of quantum mechanical scattering directions of ions in the axon hillock of neuronal cells could influence diffusion and initiate an action potential. Only a few thousand ions would need to be affected. No conservation laws are violated, but a momentary and very small local decrease of temperature should occur, consistent with a quantum mechanically possible but extremely improbable evolution. An estimate of the concurrent violation of the second law of thermodynamics is presented. Some thoughts are given to how this hypothesized mental intervention could be tested.
Jackknife inference with two-way clustering
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2024-06-13 00:00:00
Tasks: Clustering

For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-way CRVEs based on the cluster jackknife. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a simple software package, twowayjack for Stata, that implements our recommended variance estimator.
Positive and negative word of mouth in the United States
Authors: Shawn Berry
Date: 2024-06-12 00:00:00

Word of mouth is a process by which consumers transmit positive or negative sentiment to other consumers about a business. While this process has long been recognized as a type of promotion for businesses, the value of word of mouth is questionable. This study will examine the various correlates of word of mouth to demographic variables, including the role of the trust of business owners. Education level, region of residence, and income level were found to be significant predictors of positive word of mouth. Although the results generally suggest that the majority of respondents do not engage in word of mouth, there are valuable insights to be learned.
HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning
Authors: Francesco Audrino, Jonathan Chassot
Date: 2024-06-12 00:00:00

We investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1,455 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model's performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting.
Did Harold Zuercher Have Time-Separable Preferences?
Authors: Jay Lu, Yao Luo, Kota Saito, Yi Xin
Date: 2024-06-12 00:00:00

This paper proposes an empirical model of dynamic discrete choice to allow for non-separable time preferences, generalizing the well-known Rust (1987) model. Under weak conditions, we show the existence of value functions and hence well-defined optimal choices. We construct a contraction mapping of the value function and propose an estimation method similar to Rust's nested fixed point algorithm. Finally, we apply the framework to the bus engine replacement data. We improve the fit of the data with our general model and reject the null hypothesis that Harold Zuercher has separable time preferences. Misspecifying an agent's preference as time-separable when it is not leads to biased inferences about structure parameters (such as the agent's risk attitudes) and misleading policy recommendations.
How & Why To Use Audience Segmentation to Maximize (Listener) Demand Across Digital Music Portfolio
Authors: Kobi Abayomi
Date: 2024-06-13 00:00:00
Tasks: Time Series

Digital delivery of songs has radically changed the way people can enjoy music, the sort of music available for listening, and the manner by which rights holders are compensated for their contributions to songs. Listeners enjoy an unlimited potpourri of sounds, uniquely free of incremental acquisition or switching costs which have been replaced by subscription or rentier fees. This regime shift has revealed listening patterns governed by affinity, boredom, attention budget, etc.: instantaneous, dynamic, organic or programmatic song selection. This regime shift in demand availability -- with the commensurate translation of revenue implications -- deprecates current orthodoxy for content curation. The impulse to point-of-sale model is insufficient in a regime where demand revenue is proportional to demand affinity and each are strongly dependent time series processes. We explore strategies & implications -- which are generalizable to any media rights holding firm -- from a prediction & optimization point of view for two straightforward demand models.