pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized State Estimation and Opacity Verification Based on Partially Ordered Observation Sequences
Authors: Dajiang Sun, Christoforos N. Hadjicostis, Zhiwu Li
Date: 2025-02-14 00:00:00
Tasks: State Estimation

In this paper, we investigate state estimation and opacity verification problems within a decentralized observation architecture. Specifically, we consider a discrete event system whose behavior is recorded by a set of observation sites. These sites transmit the partially ordered sequences of observations that they record to a coordinator whenever a \textit{synchronization} occurs. To properly analyze the system behavior from the coordinator's viewpoint, we first introduce the notion of a Complete Synchronizing Sequence structure (CSS structure), which concisely captures the state evolution of each system state upon different information provided by the observation sites. Based on the CSS structure, we then construct corresponding current-state and initial-state estimators for offline state estimation at the coordinator. When used to verify state-isolation properties under this decentralized architecture, the use of CSS structure demonstrates a significant reduction in complexity compared with existing approaches in the literature. In particular, we discuss how to verify initial-state opacity at the coordinator, as well as a novel opacity notion, namely current-state-at-synchronization opacity.
Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios with Foliage
Authors: Donggu Lee, Ozgur Ozdemir, Asokan Ram, Ismail Guvenc
Date: 2025-02-14 00:00:00

Unmanned aerial vehicles (UAVs) are expected to play a key role in 6G-enabled vehicular-to-everything (V2X) communications requiring high data rates, low latency, and reliable connectivity for mission-critical applications. Multi-input multi-output (MIMO) technology is essential for meeting these demands. However, UAV link performance is significantly affected by environmental factors such as signal attenuation, multipath propagation, and blockage from obstacles, particularly dense foliage in rural areas. In this paper, we investigate RF coverage and channel rank over UAV channels in foliage-dominated rural environments using ray tracing (RT) simulations. We conduct RT-based channel rank and RF coverage analysis over Lake Wheeler Field Labs at NC State University to examine the impact on UAV links. Custom-modeled trees are integrated into the RT simulations using NVIDIA Sionna, Blender, and Open Street Map (OSM) database to capture realistic blockage effects. Results indicate that tree-induced blockage impacts RF coverage and channel rank at lower UAV altitudes. We also propose a Kriging interpolation-based 3D channel rank interpolation scheme, leveraging the observed spatial correlation of channel rank in the given environments. The accuracy of the proposed scheme is evaluated using the mean absolute error (MAE) metric and compared against baseline interpolation methods. Finally, we compare the RT-based received signal strength (RSS) and channel rank results with real-world measurements from the NSF AERPAW testbed demonstrating reasonable consistency between simulation results and the measurements.
Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based Control and Cluster-Level Power Allocation
Authors: Guo Gan, Li Junhui, Mu Gang, Yan Gangui
Date: 2025-02-14 00:00:00

Battery Energy Storage Systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes capture rate (CR), release rate (RR), and capacity utilization rate (CUR), improving BESS performance. Compared to traditional day-ahead methods, the cycle-based approach enhances operational accuracy and reduces capacity waste, achieving a CUR increase from 75.1% to 79.9%. An innovative cluster-level power allocation method, leveraging an improved Particle Swarm Optimization (PSO) algorithm, is introduced. This strategy reduces daily energy loss by 174.21 kWh (3.7%) and increases BESS efficiency by 0.4%. Transient and steady-state energy loss components are analyzed, revealing that transient loss proportion decreases significantly as power depth increases, from 27.2% at 1 MW to 1.3% at 10 MW. Simulations based on a detailed Simulink/Simscape model validate these methods, demonstrating enhanced peak shaving effectiveness and prolonged BESS lifespan by reducing equivalent cycles. The study provides a robust framework for optimizing BESS performance and efficiency in real-world applications.
Safety Blind Spot in Remote Driving: Considerations for Risk Assessment of Connection Loss Fallback Strategies
Authors: Leon Johann Brettin, Niklas Braun, Robert Graubohm, Markus Maurer
Date: 2025-02-14 00:00:00

As part of the overall goal of driverless road vehicles, remote driving is a major emerging field of research of its own. Current remote driving concepts for public road traffic often establish a fallback strategy of immediate braking to a standstill in the event of a connection loss. This may seem like the most logical option when human control of the vehicle is lost. However, our simulation results from hundreds of scenarios based on naturalistic traffic scenes indicate high collision rates for any immediate substantial deceleration to a standstill in urban settings. We show that such a fallback strategy can result in a SOTIF relevant hazard, making it questionable whether such a design decision can be considered acceptable. Therefore, from a safety perspective, we would call this problem a safety blind spot, as safety analyses in this regard seem to be very rare. In this article, we first present a simulation on a naturalistic dataset that shows a high probability of collision in the described case. Second, we discuss the severity of the resulting potential rear-end collisions and provide an even more severe example by including a large commercial vehicle in the potential collision.
Optimal and Coordinated Voltage Control: Case Study on a 132 kV Norwegian Grid Subsystem
Authors: Hugo Rodrigues de Brito, Daniel Simon Baltensperger, Kjetil Obstfelder Uhlen
Date: 2025-02-14 00:00:00

This work presents a framework for dynamic performance assessment of the higher layers in the hierarchical voltage regulation scheme, with case studies applied to specific areas of the Norwegian grid. Unlike the primary (PVR) level, the secondary (SVR) and tertiary (TVR) levels are not tuned to a single device at a time, handling instead several reactive power resources available within a control zone including generator units, static VAr compensators and others. Proper SVR-TVR coordination for realistic transmission systems is a challenging topic at the core of many ongoing discussions in voltage control literature. Special focus is placed on practical considerations from the system operator perspective, since this research is also aimed at simplifying daily control centre routines. Dynamic simulation results concern a 21-bus equivalent of a 132 kV network model that accurately represents a Norwegian grid subsystem. Case studies address daily grid operation with real-life load demand and wind power generation profiles, showing that the proposed strategy is effective not only to minimize total active power losses as much as possible within system-wide limitations, but also to maintain adequate voltage profiles and reactive power flows. Findings pertaining to this work showcase the benefits of applying hierarchical voltage regulation layers as an asset to day-to-day control center management of a realistic transmission network.
Reinforcement Learning based Constrained Optimal Control: an Interpretable Reward Design
Authors: Jingjie Ni, Fangfei Li, Xin Jin, Xianlun Peng, Yang Tang
Date: 2025-02-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This paper presents an interpretable reward design framework for reinforcement learning based constrained optimal control problems with state and terminal constraints. The problem is formalized within a standard partially observable Markov decision process framework. The reward function is constructed from four weighted components: a terminal constraint reward, a guidance reward, a penalty for state constraint violations, and a cost reduction incentive reward. A theoretically justified reward design is then presented, which establishes bounds on the weights of the components. This approach ensures that constraints are satisfied and objectives are optimized while mitigating numerical instability. Acknowledging the importance of prior knowledge in reward design, we sequentially solve two subproblems, using each solution to inform the reward design for the subsequent problem. Subsequently, we integrate reinforcement learning with curriculum learning, utilizing policies derived from simpler subproblems to assist in tackling more complex challenges, thereby facilitating convergence. The framework is evaluated against original and randomly weighted reward designs in a multi-agent particle environment. Experimental results demonstrate that the proposed approach significantly enhances satisfaction of terminal and state constraints and optimization of control cost.
Safe platooning control of connected and autonomous vehicles on curved multi-lane roads
Authors: Xiao Chen, Zhiqi Tang, Karl Henrik Johansson, Jonas Mårtensson
Date: 2025-02-14 00:00:00
Tasks: ARC, Autonomous Vehicles, Collision Avoidance

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the reference path regardless of the translational velocity, and a longitudinal control design for each vehicle to achieve the desired relative arc length and velocity with respect to its neighboring vehicle. The second novelty is exploiting the constructive barrier feedback as an additive term to the nominal tracking control, ensuring both lateral and longitudinal collision avoidance. This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller's performance. Consequently, our proposed control method enables safe platoon formation of vehicles on curved multi-lane roads, with theoretical guarantees for safety invariance and stability analysis. Simulation and experimental results on connected vehicles are provided to further validate the effectiveness of the proposed method.
LifeSaver: Predictive Load Limit Estimation for Transport Vehicles in Hilly Areas
Authors: Chanakya Rao, Vaibhav Chopra, Moksh Soni, Prashant Mishra
Date: 2025-02-14 00:00:00

The transportation of essential goods in moun- tainous regions faces severe logistical challenges and frequent disruptions. To mitigate these difficulties, transport companies often overload trucks, which, though cost-saving, significantly heightens the risk of accidents and mechanical failures. This paper presents the development of a device that detects over- loaded and insecurely fastened loads on trucks and commercial vehicles. Using advanced load sensors, the device offers real- time monitoring of cargo weight distribution, alerting drivers and authorities to unsafe conditions. The initial prototype utilised two basic load cells and an Arduino microcontroller. The second version was enhanced with four load cells and extended sensors. This version was tested by placing an electric golf cart onto the prototype. Various loads were then added to the cart in different orientations to assess whether the system could accurately detect improper or excessive load conditions.
A scalable, gradient-stable approach to multi-step, nonlinear system identification using first-order methods
Authors: Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa
Date: 2024-09-16 00:00:00

This paper presents three main contributions to the field of multi-step system identification. First, drawing inspiration from Neural Network (NN) training, it introduces a tool for solving identification problems by leveraging first-order optimization and Automatic Differentiation (AD). The proposed method exploits gradients with respect to the parameters to be identified and leverages Linear Parameter-Varying (LPV) sensitivity equations to model gradient evolution. Second, it demonstrates that the computational complexity of the proposed method is linear in both the multi-step horizon length and the parameter size, ensuring scalability for large identification problems. Third, it formally addresses the "exploding gradient" issue: via a stability analysis of the LPV equations, it derives conditions for a reliable and efficient optimization and identification process for dynamical systems. Simulation results indicate that the proposed method is both effective and efficient, making it a promising tool for future research and applications in nonlinear system identification and non-convex optimization.
Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies
Authors: Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa
Date: 2025-02-14 00:00:00
Tasks: parameter estimation

Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-uniform observations, i.e., data scenarios that include missing measurements, multiple runs, or aggregated observations. The goal is to provide a general approach for accurately recovering the overall dynamics of possibly nonlinear systems, allowing the capture of the system behavior over time from non-uniform observations. The proposed approach exploits prior knowledge by integrating domain-specific, interpretable, physical principles with black-box approximators, proving significant flexibility and adaptability in handling different types of non-uniform measurements, and addressing the limitations of traditional linear and black-box methods. The description of this novel framework is supported by a theoretical study on the effect of non-uniform observations on the accuracy of parameter estimation. Specifically, we demonstrate the existence of upper bounds on the parametric error resulting from missing measurements and aggregated observations. Then, the effectiveness of the approach is demonstrated through two case studies. These include a practical application with missing samples, i.e., the identification of a continuous stirred-tank reactor using real data, and a simulated Lotka-Volterra system under aggregated observations. The results highlight the ability of the framework to robustly estimate the system parameters and to accurately reconstruct the model dynamics despite the availability of non-uniform measurements.
Stretching Rubber, Not Budgets: Accurate Parking Utilization on a Shoestring
Authors: Christopher K. Allsup
Date: 2025-02-14 00:00:00
Tasks: Scheduling

Effective parking management is essential for ensuring accessibility, safety, and convenience in master-planned communities, particularly in active adult neighborhoods experiencing rapid growth. Accurately assessing parking utilization is a crucial first step in planning for future demand, but data collection methods can be costly and labor-intensive. This paper presents a low-cost yet highly accurate methodology for measuring parking utilization using road tubes connected to portable traffic counters from JAMAR Technologies, Inc. By integrating results from JAMAR's analysis tool with custom Python scripting, the methodology enables precise parking lot counts through parameter optimization and automated error correction. The system's efficiency allows for scalable deployment without significant manual observation, reducing both costs and disruptions to daily operations. Using Tellico Village as a case study, this research demonstrates that community planners can obtain actionable parking insights on a limited budget, empowering them to make informed decisions about capacity expansion, traffic flow improvements, and facility scheduling. The findings underscore the feasibility of leveraging cost-effective technology to optimize infrastructure planning and ensure long-term resident satisfaction as communities grow.
Power System Electromagnetic Transient Stability: an Analysis Based on Convergent Hamiltonian
Authors: Xinyuan Jiang, Constantino M. Lagoa, Yan Li
Date: 2025-02-13 00:00:00

Transient stability is crucial to the reliable operation of power systems. Existing theories rely on the simplified electromechanical models, substituting the detailed electromagnetic dynamics of inductor and capacitor with their impedance representations. However, this simplification is inadequate for the growing penetration of fast-switching power electronic devices. Attempts to extend the existing theories to include electromagnetic dynamics lead to overly conservative stability conditions. To tackle this problem more directly, we study the condition under which the power source and dissipation in the electromagnetic dynamics tend to balance each other asymptotically. This is equivalent to the convergence of the Hamiltonian (total stored energy) and can be shown to imply transient stability. Using contraction analysis, we prove that this property holds for a large class of time-varying port-Hamiltonian systems with (i) constant damping matrix and (ii) strictly convex Hamiltonian. Then through port-Hamiltonian modeling of the electromagnetic dynamics, we obtain that the synchronized steady state of the power system is globally stable if it exists. This result provides new insights into the reliable operation of power systems. The proposed theory is illustrated in the simulation results of a two-machine system.
Performance of energy harvesters with parameter mismatch
Authors: Tomasz Burzyński, Piotr Brzeski, Przemysław Perlikowski
Date: 2025-02-12 00:00:00

This study explores the impact of parameter mismatch on the stability of cross-well motion in energy harvesters, using a basin stability metric. Energy harvesters, essential for converting ambient energy into electricity, increasingly incorporate multi-well systems to enhance efficiency. However, these systems are sensitive to initial conditions and parameter variations, which can affect their ability to sustain optimal cross-well motion -- a state associated with maximum power output. Our analysis compared four harvester types under varying levels of parameter mismatch, assessing resilience of the devices to parameter variations. By identifying safe operating ranges within the excitation parameter space, this study provides practical guidance for designing robust, stable harvesters capable of maintaining cross-well motion despite parameter uncertainties. These insights contribute to advancing the reliability of energy harvesting devices in real-world applications where parameter mismatches are inevitable.
$Λ$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum
Authors: Davide Piras, Laura Herold, Luisa Lucie-Smith, Eiichiro Komatsu
Date: 2025-02-13 00:00:00

Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a variational autoencoder (VAE) trained to compress cosmic microwave background (CMB) temperature power spectra. We consider a broad range of $\Lambda$CDM and beyond-$\Lambda$CDM cosmologies with an additional early dark energy (EDE) component. We show that these spectra can be compressed into 5 ($\Lambda$CDM) or 8 (EDE) independent latent parameters, as expected when using temperature power spectra alone, and which reconstruct spectra at an accuracy well within the Planck errors. These latent parameters have a physical interpretation in terms of well-known features of the CMB temperature spectrum: these include the position, height and even-odd modulation of the acoustic peaks, as well as the gravitational lensing effect. The VAE also discovers one latent parameter which entirely isolates the EDE effects from those related to $\Lambda$CDM parameters, thus revealing a previously unknown degree of freedom in the CMB temperature power spectrum. We further showcase how to place constraints on the latent parameters using Planck data as typically done for cosmological parameters, obtaining latent values consistent with previous $\Lambda$CDM and EDE cosmological constraints. Our work demonstrates the potential of a data-driven reformulation of current beyond-$\Lambda$CDM phenomenological models into the independent degrees of freedom to which the data observables are sensitive.
ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data
Authors: Hamed Valizadegan, Miguel J. S. Martinho, Jon M. Jenkins, Joseph D. Twicken, Douglas A. Caldwell, Patrick Maynard, Hongbo Wei, William Zhong, Charles Yates, Sam Donald, Karen A. Collins, David Latham, Khalid Barkaoui, Michael L. Calkins, Kylee Carden, Nikita Chazov, Gilbert A. Esquerdo, Tristan Guillot, Vadim Krushinsky, Grzegorz Nowak, Benjamin V. Rackham, Amaury Triaud, Richard P. Schwarz, Denise Stephens, Chris Stockdale, Cristilyn N. Watkins, Francis P. Wilkin
Date: 2025-02-13 00:00:00
Tasks: Diagnostic, Transfer Learning

We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives. To further enhance performance, we leverage multi-source training by combining high-quality labeled data from the Kepler space telescope with TESS data. This approach mitigates the impact of TESS's noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce new TESS catalog containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7,330 as planet candidates, with the remainder classified as false positives. These 7,330 planet candidates correspond to 1,868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1,797 out of the 2,506 TOIs previously labeled as planet candidates in ExoFOP are classified as planet candidates by ExoMiner++. This reduction in plausible candidates combined with the excellent ranking quality of ExoMiner++ allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.