pwc-archive/papers-with-abstracts huggingface dataset:
Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations
Authors: Zhenhua Zhou, Bozhen Jiang, Qin Wang
Date: 2025-05-10 00:00:00
Tasks: Kolmogorov-Arnold Networks, Load Forecasting, Transfer Learning

The rapid rise in electric vehicle (EV) adoption demands precise charging station load forecasting, challenged by long-sequence temporal dependencies and limited data in new facilities. This study proposes MIK-TST, a novel two-stage transfer learning framework integrating Mixer, Informer, and Kolmogorov-Arnold Networks (KAN). The Mixer fuses multi-source features, Informer captures long-range dependencies via ProbSparse attention, and KAN enhances nonlinear modeling with learnable activation functions. Pre-trained on extensive data and fine-tuned on limited target data, MIK-TST achieves 4% and 8% reductions in MAE and MSE, respectively, outperforming baselines on a dataset of 26 charging stations in Boulder, USA. This scalable solution enhances smart grid efficiency and supports sustainable EV infrastructure expansion.
Joint Parameterization of Hybrid Physics-Based and Machine Learning Li-Ion Battery Model
Authors: Jackson Fogelquist, Xinfan Lin
Date: 2025-05-10 00:00:00
Tasks: Diagnostic, parameter estimation

Electrochemical hybrid battery models have major potential to enable advanced physics-based control, diagnostic, and prognostic features for next-generation lithium-ion battery management systems. This is due to the physical significance of the electrochemical model, which is complemented by a machine learning model that compensates for output prediction errors caused by system uncertainties. While hybrid models have demonstrated robust output prediction performance under large system uncertainties, they are highly susceptible to the influence of uncertainties during parameter identification, which can compromise the physical significance of the model. To address this challenge, we present a parameter estimation framework that explicitly considers system uncertainties through a discrepancy function. The approach also incorporates a downsampling procedure to address the computational barriers associated with large time-series data sets, as are typical in the battery domain. The framework was validated in simulation, yielding several mean parameter estimation errors that were one order of magnitude smaller than those of the conventional least squares approach. While developed for the high-uncertainty, electrochemical hybrid modeling context, the estimation framework is applicable to all models and is presented in a generalized form.
Handling Pedestrian Uncertainty in Coordinating Autonomous Vehicles at Signal-Free Intersections
Authors: Filippos N. Tzortzoglou, Andreas A. Malikopoulos
Date: 2025-05-09 00:00:00
Tasks: Autonomous Vehicles

In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.
Development of Reduced Feeder and Load Models Using Practical Topological and Loading Data
Authors: Sameer Nekkalapu, Sushrut Thakar, Antos Cheeramban Varghese, Vijay Vittal, Bo Gong, Ken Brown
Date: 2025-05-09 00:00:00

Distribution feeder and load model reduction methods are essential for maintaining a good tradeoff between accurate representation of grid behavior and reduced computational complexity in power system studies. An effective algorithm to obtain a reduced order representation of the practical feeders using utility topological and loading data has been presented in this paper. Simulations conducted in this work show that the reduced feeder and load model of a utility feeder, obtained using the proposed method, can accurately capture contactor and motor stalling behaviors for critical events such as fault induced delayed voltage recovery.
Reconstructing Brain Causal Dynamics for Subject and Task Fingerprints using fMRI Time-series Data
Authors: Dachuan Song, Li Shen, Duy Duong-Tran, Xuan Wang
Date: 2025-05-09 00:00:00
Tasks: Graph Neural Network, Time Series

Purpose: Recently, there has been a revived interest in system neuroscience causation models, driven by their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, we present a novel method that leverages causal dynamics to achieve effective fMRI-based subject and task fingerprinting. Methods: By applying an implicit-explicit discretization scheme, we develop a two-timescale linear state-space model. Through data-driven identification of its parameters, the model captures causal signatures, including directed interactions among brain regions from a spatial perspective, and disentangled fast and slow dynamic modes of brain activity from a temporal perspective. These causal signatures are then integrated with: (i) a modal decomposition and projection method for model-based subject identification, and (ii) a Graph Neural Network (GNN) framework for learning-based task classification. Furthermore, we introduce the concept of the brain reachability landscape as a novel visualization tool, which quantitatively characterizes the maximum possible activation levels of brain regions under various fMRI tasks. Results: We evaluate the proposed approach using the Human Connectome Project dataset and demonstrate its advantage over non-causality-based methods. The obtained causal signatures are visualized and demonstrate clear biological relevance with established understandings of brain function. Conclusion: We verified the feasibility and effectiveness of utilizing brain causal signatures for subject and task fingerprinting. Additionally, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning
Authors: Haokun Yu, Jingyuan Zhou, Kaidi Yang
Date: 2025-05-09 00:00:00
Tasks: Decision Making, Privacy Preserving, Reinforcement Learning (RL)

This paper addresses the problem of parameter privacy-preserving data sharing in coupled systems, where a data provider shares data with a data user but wants to protect its sensitive parameters. The shared data affects not only the data user's decision-making but also the data provider's operations through system interactions. To trade off control performance and privacy, we propose an interaction-aware privacy-preserving data sharing approach. Our approach generates distorted data by minimizing a combination of (i) mutual information, quantifying privacy leakage of sensitive parameters, and (ii) the impact of distorted data on the data provider's control performance, considering the interactions between stakeholders. The optimization problem is formulated into a Bellman equation and solved by a particle filter reinforcement learning (RL)-based approach. Compared to existing RL-based methods, our formulation significantly reduces history dependency and efficiently handles scenarios with continuous state space. Validated in a mixed-autonomy platoon scenario, our method effectively protects sensitive driving behavior parameters of human-driven vehicles (HDVs) against inference attacks while maintaining negligible impact on fuel efficiency.
Zero Dynamics Attack Detection and Isolation in Cyber-Physical Systems with Event-triggered Communication
Authors: Ali Eslami, Khashayar Khorasani
Date: 2025-05-09 00:00:00
Tasks: Cyber Attack Detection

This paper investigates the problem of Zero Dynamics (ZD) cyber-attack detection and isolation in Cyber-Physical Systems (CPS). By utilizing the notion of auxiliary systems with event-based communications, we will develop a detection mechanism capable of detecting and isolating the ZD cyber-attack even when the attackers have full knowledge of the dynamics of the auxiliary system and can launch False Data Injection (FDI) attacks on all the communication channels. More specifically, we will utilize a self-triggering rule for the communication channels connecting the auxiliary system with the Command & Control (C&C) center, leveraging its properties to detect the ZD cyber-attack. Finally, the effectiveness and capabilities of our approach are verified and demonstrated through simulation case studies.
On the Potential of Electrified Supply Chains to Provide Long Duration Demand Flexibility
Authors: Rina Davila Severiano, Constance Crozier, Mark O Malley
Date: 2025-05-09 00:00:00

Demand flexibility can offset some of the variability introduced on the supply-side by variable renewable generation. However, most efforts (e.g. control of residential vehicle charging) focus on short durations -- typically on the scale of minutes to hours. This paper investigates whether a fully electrified supply chain (transport and manufacturing) could provide demand flexibility over longer durations, exploiting the latency that typically exists between the processing of raw material to the delivery of finished product. Using a case study of the cement industry along the East Coast of the United States, we demonstrate that electrified supply chains could shift gigawatt-hours (GWh) of electricity demand for durations of more than a week, largely following wind power variability. Furthermore, we show that this occurs using low levels of carbon taxing (below $50/tn), at which battery storage is not economically viable. A sensitivity analysis shows potential to provide flexibility in all considered cost scenarios, although where the flexibility comes from can change (e.g. transport vs manufacturing). We show that today's cost of electrified heavy goods vehicles are the most significant parameter -- with substantially lower costs yielding a more demand-flexible supply chain.
Design and Application of Energy-saving Sub-Optimal Sliding Mode Control
Authors: Michael Ruderman
Date: 2025-05-09 00:00:00

The recently introduced energy-saving extension of the sub-optimal sliding mode control (SOSMC), which is known in the literature for the last two and half decades, incorporates a control-off mode that allows for saving energy during the finite-time convergence process. This novel energy-saving algorithm (denoted by ES-SOSMC) assumes the systems with relative degree two between the sliding variable and the switching control with a bounded magnitude, while the matched upper-bounded perturbations are not necessarily continuous. The design and practical application of the ES-SOSMC are the subject of this chapter. A method for parameterizing the ES-SOSMC through a constrained minimization of the energy cost function is recalled which guarantees the total energy consumption is lower than that of the conventional SOSMC. Also the residual steady-state oscillations (chattering), occurring when additional (actuator) dynamics are taken into account, are addressed. An application example for scanning and machining a rough surface, both of which require a stiff position control in contact with a moving surface, demonstrates practical suitability of the control. Here, ES-SOSMC is compared with SOSMC by showing an equivalent tracking and stabilization performance and evaluating the energy-saving operation with respect to a fuel consumption norm.
Shortlisting Protection Configurations for HVDC Grids and Electrical Energy Hubs
Authors: Merijn Van Deyck, Geraint Chaffey, Dirk Van Hertem
Date: 2025-05-09 00:00:00

This paper proposes a methodology for shortlisting protection system configurations for large HVDC switching stations, which are expected in multiterminal HVDC grids and electrical energy hubs (or energy islands). This novel approach focuses on the configuration of protection equipment and the arrangement of lines and converters in various protection zones, instead of expert decisions on protection strategies based on numerous simulations. A graph-based approach that allows high-level evaluation of possible DC fault impacts is presented. This fault impact evaluation method can evaluate many possible protection configurations allowing the selection of less obvious choices, as experts cannot consider all possible configurations, especially when the switching station size increases. A filtering process is applied to reduce the number of possible configurations based on multiple protection performance metrics which are evaluated for different power flow scenarios. The results for these performance metrics can be compared for configurations with different numbers of HVDC circuit breakers to assess the benefit of increasing the amount of protection equipment in different network topologies. It is also shown that, through continued filtering using additional performance metrics or fault scenarios, the number of possible breaker, cable and converter configurations can be further reduced, leading to a protection design that is well suited for many operational scenarios. The results of the shortlisting process provide insights on the required number of HVDC circuit breakers to limit fault impacts to a given value. Moreover, observed trends in the results could, in future studies, contribute to new design principles and priorities, allowing system developers to more effectively design HVDC protection systems for different operational scenarios and possible investment levels.
Integrating Building Thermal Flexibility Into Distribution System: A Privacy-Preserved Dispatch Approach
Authors: Shuai Lu, Zeyin Hou, Wei Gu, Yijun Xu
Date: 2025-05-09 00:00:00
Tasks: Computational Efficiency

The inherent thermal storage capacity of buildings brings considerable thermal flexibility to the heating/cooling loads, which are promising demand response resources for power systems. It is widely believed that integrating the thermal flexibility of buildings into the distribution system can improve the operating economy and reliability of the system. However, the private information of the buildings needs to be transferred to the distribution system operator (DSO) to achieve a coordinated optimization, bringing serious privacy concerns to users. Given this issue, we propose a novel privacy-preserved optimal dispatch approach for the distribution system incorporating buildings. Using it, the DSO can exploit the thermal flexibility of buildings without accessing their private information, such as model parameters and indoor temperature profiles. Specifically, we first develop an optimal dispatch model for the distribution system integrating buildings, which can be extended to other storage-like flexibility resources. Second, we reveal that the privacy-preserved integration of buildings is a joint privacy preservation problem for both parameters and state variables and then design a privacy-preserved algorithm based on transformation-based encryption, constraint relaxation, and constraint extension techniques. Besides, we implement a detailed privacy analysis for the proposed method, considering both semi-honest adversaries and external eavesdroppers. Case studies demonstrate the accuracy, privacy-preserved performance, and computational efficiency of the proposed method.
Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer
Authors: Tjeerd Ickenroth, Max van Haren, Johan Kon, Max van Meer, Jilles van Hulst, Tom Oomen
Date: 2025-05-09 00:00:00

Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.
DeepSync: A Learning Framework for Pervasive Localization using Code Synchronization on Compressed Cellular Spectrum
Authors: Aritrik Ghosh, Nakul Garg, Nirupam Roy
Date: 2025-05-09 00:00:00
Tasks: Template Matching

Pervasive localization is essential for continuous tracking applications, yet existing solutions face challenges in balancing power consumption and accuracy. GPS, while precise, is impractical for continuous tracking of micro-assets due to high power requirements. Recent advances in non-linear compressed spectrum sensing offer low-power alternatives, but existing implementations achieve only coarse positioning through Received Signal Strength Indicator (RSSI) measurements. We present DeepSync, a deep learning framework that enables precise localization using compressed cellular spectrum. Our key technical insight lies in formulating sub-sample timing estimation as a template matching problem, solved through a novel architecture combining temporal CNN encoders for multi-frame processing with cross-attention mechanisms. The system processes non-linear inter-modulated spectrum through hierarchical feature extraction, achieving robust performance at SNR levels below -10dB -- a regime where conventional timing estimation fails. By integrating real cellular infrastructure data with physics-based ray-tracing simulations, DeepSync achieves 2.128-meter median accuracy while consuming significantly less power than conventional systems. Real-world evaluations demonstrate 10x improvement over existing compressed spectrum approaches, establishing a new paradigm for ultra-low-power localization.
A Feedback Control Framework for Incentivised Suburban Parking Utilisation and Urban Core Traffic Relief
Authors: Abdul Baseer Satti, James Saunderson, Wynita Griggs, S. M. Nawazish Ali, Nameer Al Khafaf, Saman Ahmadi, Mahdi Jalili, Jakub Marecek, Robert Shorten
Date: 2025-05-09 00:00:00
Tasks: Discrete Choice Models

Urban traffic congestion, exacerbated by inefficient parking management and cruising for parking, significantly hampers mobility and sustainability in smart cities. Drivers often face delays searching for parking spaces, influenced by factors such as accessibility, cost, distance, and available services such as charging facilities in the case of electric vehicles. These inefficiencies contribute to increased urban congestion, fuel consumption, and environmental impact. Addressing these challenges, this paper proposes a feedback control incentivisation-based system that aims to better distribute vehicles between city and suburban parking facilities offering park-and-charge/-ride services. Individual driver behaviours are captured via discrete choice models incorporating factors of importance to parking location choice among drivers, such as distance to work, public transport connectivity, charging infrastructure availability, and amount of incentive offered; and are regulated through principles of ergodic control theory. The proposed framework is applied to an electric vehicle park-and-charge/-ride problem, and demonstrates how predictable long-term behaviour of the system can be guaranteed.
Connected C-Core Hybrid SRMs for EV Applications
Authors: Gholamreza Davarpanah, Sajjad Mohammadi
Date: 2025-05-09 00:00:00

This paper proposes a new class of permanent magnet-assisted three-phase switched reluctance motors (PM-SRMs) designed to achieve significantly higher torque density for electric vehicle (EV) propulsion systems. Eight distinct motor topologies are systematically investigated, including a non-PM baseline design, three innovative PM arrangement strategies, and two optimized rotor/stator teeth configurations (22-pole and 26-pole variants). The study presents analytical models including magnetic equivalent circuits (MECs), detailed operating principles, and generalized design formulations that account for both electromagnetic and structural considerations. A key contribution is the introduction of the point-of-conversion (PoC) concept, which optimizes PM placement by minimizing magnetic path reluctance. Comparative analysis demonstrates torque density improvements over conventional SRMs and existing PM-assisted designs while maintaining structural robustness. Experimental validation confirms that the proposed 24/22 configuration with inter-phase PMs delivers higher torque per PM volume compared to state-of-the-art designs. The findings provide insights for EV motor designers seeking to balance performance, cost, and reliability.
Robust Management of Airport Security Queues Considering Passenger Non-compliance with Chance-Constrained Optimization
Authors: Shangqing Cao, Aparimit Kasliwal, Huangyi Zheng, Masoud Reihanifar, Francesc Robuste, Mark Hansen
Date: 2025-05-09 00:00:00
Tasks: Management

The long waiting time at airport security has become an emergent issue as demand for air travel continues to grow. Not only does queuing at security cause passengers to miss their flights, but also reduce the amount of time passengers spend at the airport post-security, potentially leading to less revenue for the airport operator. One of the key issues to address to reduce waiting time is the management of arrival priority. As passengers on later flights can arrive before passengers on earlier flights, the security system does not always process passengers in the order of the degree of urgency. In this paper, we propose a chance-constrained optimization model that decides in which time slot passengers should be recommended to arrive. We use chance constraints to obtain solutions that take the uncertainty in passenger non-compliance into account. The experimental results, based on a sample day of flight schedules at the Barcelona airport, show a reduction of 85% in the total waiting time. Compared to the deterministic case, in which passengers are assumed to fully comply with the recommendations, we see a 30% increase in the reduction of the total waiting time. This highlights the importance of considering variation in passenger compliance in the management of airport security queues.