pwc-archive/papers-with-abstracts huggingface dataset:
Deficient Excitation in Parameter Learning
Authors: Ganghui Cao, Shimin Wang, Martin Guay, Jinzhi Wang, Zhisheng Duan, Marios M. Polycarpou
Date: 2025-03-04 00:00:00

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper.
Pneumatically controlled non-magnetic, high-power, and low insertion loss RF switch
Authors: Chavalchart Herabut, Bryan Rangel Valle, Vikram D. Kodibagkar, Sung-Min Sohn
Date: 2025-03-04 00:00:00

While positive-intrinsic-negative (PIN) diodes are commonly used in radio frequency (RF) circuits, their use often degrades the signal-to-noise ratio (SNR) due to high insertion loss and interference from additional biasing circuit components, which is critical for SNR-prioritized applications. This work presents the design of a novel pneumatically controlled switch, called AeroSwitch, which serves as an RF switch alternative to PIN diodes by significantly reducing lossy elements and additional biasing circuitry, with a focus on the applicability of AeroSwitch for magnetic resonance imaging (MRI) RF switch implantation. AeroSwitch was assessed against the PIN diode using bench testing, revealing a slightly reduced average insertion loss of 0.1 dB and an enhancement in average isolation by 15 dB. The switches are incorporated into a capacitor switch array within an L-matching network. This matching network connects to a loop RF coil, serving as a high-impedance load. The quality (Q)-factors were evaluated compared to a PIN diode capacitor switch array matching network configured identically. Overall, the AeroSwitch demonstrated an average 62% improved Q-factor compared to the PIN diode. These findings indicate that AeroSwitch offers significant advantages over the PIN diode due to its lower loss and higher isolation, potentially improving SNR. Additionally, the temperature of the AeroSwitch, while operating at 100 watts, remained consistently below 30{\deg}C, suggesting its potential as a self-temperature-regulated high-power switch. Since the AeroSwitch is non-magnetic, has high-power capability, low insertion loss, requires no electric biasing, and uses no conductive wires, this switching technique is significantly beneficial for MRI applications and medical related other RF applications.
Detecting Unobservable Contingencies in Active Distribution Systems Using a Stochastic Hybrid Systems Approach
Authors: Erfan Mehdipour Abadi, Hamid Varmazyari, Masoud H. Nazari
Date: 2025-03-03 00:00:00

This paper introduces a distributed contingency detection algorithm for detecting unobservable contingencies in power distribution systems using stochastic hybrid system (SHS) models. We aim to tackle the challenge of limited measurement capabilities in distribution networks that restrict the ability to detect contingencies promptly. We incorporate the dynamics of distribution network connections, load feeders, PV, and battery energy storage system (BESS) hybrid resources into a fully correlated SHS model representing the distribution system as a randomly switching system between different structures during contingency occurrence. We show that jumps in the SHS model correspond to contingencies in the physical power grid. We propose a probing approach based on magnitude-modulation inputs (MaMI) to make contingencies detectable. The effectiveness of the proposed approach is validated through simulations on a sample distribution system.
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation
Authors: Yitao Bai, Sihan Zeng, Justin Romberg, Thinh T. Doan
Date: 2025-03-03 00:00:00
Tasks: Reinforcement Learning (RL)

We study policy evaluation problems in multi-task reinforcement learning (RL) under a low-rank representation setting. In this setting, we are given $N$ learning tasks where the corresponding value function of these tasks lie in an $r$-dimensional subspace, with $r<N$. One can apply the classic temporal-difference (TD) learning method for solving these problems where this method learns the value function of each task independently. In this paper, we are interested in understanding whether one can exploit the low-rank structure of the multi-task setting to accelerate the performance of TD learning. To answer this question, we propose a new variant of TD learning method, where we integrate the so-called truncated singular value decomposition step into the update of TD learning. This additional step will enable TD learning to exploit the dominant directions due to the low rank structure to update the iterates, therefore, improving its performance. Our empirical results show that the proposed method significantly outperforms the classic TD learning, where the performance gap increases as the rank $r$ decreases. From the theoretical point of view, introducing the truncated singular value decomposition step into TD learning might cause an instability on the updates. We provide a theoretical result showing that the instability does not happen. Specifically, we prove that the proposed method converges at a rate $\mathcal{O}(\frac{\ln(t)}{t})$, where $t$ is the number of iterations. This rate matches that of the standard TD learning.
Transition pathways to electrified chemical production within sector-coupled national energy systems
Authors: Patricia Mayer, Florian Joseph Baader, David Yang Shu, Ludger Leenders, Christian Zibunas, Stefano Moret, André Bardow
Date: 2025-03-03 00:00:00

The chemical industry's transition to net-zero greenhouse gas (GHG) emissions is particularly challenging due to the carbon inherently contained in chemical products, eventually released to the environment. Fossil feedstock-based production can be replaced by electrified chemical production, combining carbon capture and utilization (CCU) with electrolysis-based hydrogen. However, electrified chemical production requires vast amounts of clean electricity, leading to competition in our sector-coupled energy systems. In this work, we investigate the pathway of the chemical industry towards electrified production within the context of a sector-coupled national energy system's transition to net-zero emissions. Our results show that the sectors for electricity, low-temperature heat, and mobility transition before the chemical industry due to the required build-up of renewables, and to the higher emissions abatement of heat pumps and battery electric vehicles. To achieve the net-zero target, the energy system relies on clean energy imports to cover 41\% of its electricity needs, largely driven by the high energy requirements of a fully electrified chemical industry. Nonetheless, a partially electrified industry combined with dispatchable production alternatives provides flexibility to the energy system by enabling electrified production when renewable electricity is available. Hence, a partially electrified, diversified chemical industry can support the integration of intermittent renewables, serving as a valuable component in net-zero energy systems.
Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System
Authors: Xuehui Dong, Kai Wan, Shuangyang Li, Robert Caiming Qiu, Giuseppe Caire
Date: 2025-03-03 00:00:00
Tasks: compressed sensing, Compressive Sensing

Fluid Antenna System (FAS) unlocks unprecedented flexibility in wireless channel optimization through spatial reconfigurability. However, its practical deployment is hindered by the coupled challenges posed by high-dimensional channel estimation and real-time position optimization. This paper bridges wireless propagation physics with compressed sensing theory to address these challenges through three aspects. First, we establish a group-sparse recovery framework for space-frequency characteristics (SFC) in FAS, formally characterizing leakage-induced sparsity degradation from limited aperture and bandwidth as a structured group-sparsity problem. By deriving dictionary-adapted group restricted isometry property (D-GRIP), we prove tight recovery bounds for a convex $\ell_1/\ell_2$-mixed norm optimization formulation that preserves leakage-aware sparsity patterns. Second, we develop a Descending Correlation Group Orthogonal Matching Pursuit (DC-GOMP) algorithm that systematically relaxes leakage constraints to reduce subcoherence. This approach enables robust FSC recovery with accelerated convergence and superior performance compared to conventional compressive sensing methods like OMP or GOMP. Third, we formulate spatial equalization (SE) as a mixed-integer linear programming (MILP) problem, ensuring optimality through the branch-and-bound method. To achieve real-time implementability while maintaining near-optimal performance, we complement this with a greedy algorithm. Simulation results demonstrate the proposed channel estimation algorithm effectively resolves energy misallocation and enables recovery of weak details, achieving superior recovery accuracy and convergence rate. The SE framework suppresses deep fading phenomena and reduces hardware deployment overhead while maintaining equivalent link reliability.
A Lagrangian-Informed Long-Term Dispatch Policy for Coupled Hydropower and Photovoltaic Systems
Authors: Eliza Cohn, Ning Qi, Upmanu Lall, Bolun Xu
Date: 2025-03-03 00:00:00

This paper presents a long-term dispatch framework for coupled hydropower and floating photovoltaic systems. We introduce a temporal decomposition algorithm based on partial Lagrangian relaxation to address long-term water contract constraints. We derive a real-time, non-anticipatory dispatch policy based on water contract pricing. Our framework is evaluated with a case study using real-world hydrology and power system data from Lake Mead and Lake Powell, on the Colorado River, demonstrating competitive performance against commercial solvers for both linearized and nonlinear reservoir models. We conduct a sensitivity analysis on transmission capacity, electricity price and uncertainty scenarios, showing that the operational performance is significantly impacted by the transmission capacity and electricity prices while remaining relatively robust under uncertainty scenarios.
Local Rational Modeling for Identification Beyond the Nyquist Frequency: Applied to a Prototype Wafer Stage
Authors: Max van Haren, Lennart Blanken, Koen Classens, Tom Oomen
Date: 2025-03-03 00:00:00

Fast-rate models are essential for control design, specifically to address intersample behavior. The aim of this paper is to develop a frequency-domain non-parametric identification technique to estimate fast-rate models of systems that have relevant dynamics and allow for actuation above the Nyquist frequency of a slow-rate output. Examples of such systems include vision-in-the-loop systems. Through local rational models over multiple frequency bands, aliased components are effectively disentangled, particularly for lightly-damped systems. The developed technique accurately determines non-parametric fast-rate models of systems with slow-rate outputs, all within a single identification experiment. Finally, the effectiveness of the technique is demonstrated through experiments conducted on a prototype wafer stage used for semiconductor manufacturing.
Stability Analysis and Intervention Strategies on a Coupled SIS Epidemic Model with Polar Opinion Dynamics
Authors: Qiulin Xu, Tatsuya Masada, Hideaki Ishii
Date: 2025-03-03 00:00:00

This paper investigates the spread of infectious diseases within a networked community by integrating epidemic transmission and public opinion dynamics. We propose a novel discrete-time networked SIS (Susceptible-Infectious-Susceptible) epidemic model coupled with opinion dynamics that includes stubborn agents with biased views. The model captures the interplay between perceived and actual epidemic severity, offering insights into epidemic dynamics in socially interconnected environments. We introduce the SIS-opinion reproduction number to assess epidemic severity and analyze conditions for disease eradication and the global stability of endemic equilibria. Additionally, we explore opinion-based intervention strategies, providing a framework for policymakers to design effective prevention measures. Numerical examples are provided to illustrate our theoretical findings and the model's practical implications.
Online Learning of Nonlinear Parametric Models under Non-smooth Regularization using EKF and ADMM
Authors: Lapo Frascati, Alberto Bemporad
Date: 2025-03-03 00:00:00

This paper proposes a novel combination of extended Kalman filtering (EKF) with the alternating direction method of multipliers (ADMM) for learning parametric nonlinear models online under non-smooth regularization terms, including l1 and l0 penalties and bound constraints on model parameters. For the case of linear time-varying models and non-smoothconvex regularization terms, we provide a sublinear regret bound that ensures the proper behavior of the online learning strategy. The approach is computationally efficient for a wide range of regularization terms, which makes it appealing for its use in embedded control applications for online model adaptation. We show the performance of the proposed method in three simulation examples, highlighting its effectiveness compared to other batch and online algorithms.
Velocity-Aware Statistical Analysis of Peak AoI for Ground and Aerial Users
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2025-03-03 00:00:00

In this paper, we present a framework to analyze the impact of user velocity on the distribution of the peak age-of-information (PAoI) for both ground and aerial users by using the dominant interferer-based approximation. We first approximate the SINR meta distribution for the uplink transmission using the distances between the serving base station (BS) and each of the user of interest and the dominant interfering user, which is the interferer that provides the strongest average received power at the tagged BS. We then analyze the spatio-temporal correlation coefficient of the conditional success probability by studying the correlation between the aforementioned two distances. Finally, we choose PAoI as a performance metric to showcase how spatio-temporal correlation or user velocity affect system performance. Our results reveal that ground users exhibit higher spatio-temporal correlations compared to aerial users, resulting in a more pronounced impact of velocity on system performance, such as joint probability of the conditional success probability and distribution of PAoI. Furthermore, our work demonstrates that the dominant interferer-based approximation for the SINR meta distribution delivers good matching performance in complex scenarios, such as Nakagami-m fading model, and it can also be effectively utilized in computing spatio-temporal correlation, as this approximation is derived from the distances to the serving BS and the dominant interferer.
Levels of Spacecraft Autonomy
Authors: Daniel Baker, Sean Phillips
Date: 2025-03-02 00:00:00

As autonomous systems are being developed in multiple industries it has been recognized that a phased approach is needed both for technical development and user acceptance. Partially in response, the automotive and aircraft industries have published five or six Levels of Autonomy in an attempt to broadly characterize the amount of autonomy technology that is present in any particular vehicle. We have developed a similar six Levels of Spacecraft Autonomy for similar purposes. Here, we propose extending these ideas to spacecraft constellations, clusters, swarms, ground stations for spacecraft, and strategic collaboration among space and ground assets. We intend these levels of autonomy to be used to characterize and describe high-level capabilities for any given satellite for education and communication with lawmakers, government officials, and the general public. All of the proposed levels mimic those of the automotive and aircraft levels in an attempt to maintain consistency and a common understanding with our intended audiences.
Spacecraft Autonomy Levels
Authors: Daniel Baker, Sean Phillips
Date: 2025-03-02 00:00:00

As autonomous systems are being developed in multiple industries it has been recognized that a phased approach is needed both for technical development and user acceptance. Partially in response, the automotive and aircraft industries have published five or six Levels of Autonomy in an attempt to broadly characterize the amount of autonomy technology that is present in any particular vehicle. We propose six Spacecraft Autonomy Levels to characterize and describe the high-level autonomous capabilities for any given satellite. Our proposed spacecraft autonomy levels parallel those of the automotive and aircraft levels in an attempt to maintain consistency and a common understanding. We see these autonomy levels as useful in education and communication with lawmakers, government officials, and the general public.
Unified Power Flow Model for Bipolar HVDC Grids in Unbalanced Operation
Authors: Chandra Kant Jat, Francesco Giacomo Puricelli, Jef Beerten, Hakan Ergun, Dirk Van Hertem
Date: 2025-03-02 00:00:00

In the near future, point-to-point High Voltage Direct Current (HVDC) systems are expected to evolve into multi-terminal and meshed HVDC grids, predominantly adopting a bipolar HVDC configuration. Normally, bipolar HVDC systems operate in balanced mode, i.e., near zero current flows through metallic or ground return. However, bipolar HVDC systems can also be operated in an unbalanced mode in case of a single converter pole or line conductor outage. A steady-state analysis of the unbalanced DC network requires solving a power flow problem including various converter control modes, as the steady-state behavior of the converters is governed by their control modes. This paper presents a comprehensive and unified power flow model for the balanced and unbalanced operation of bipolar HVDC grids, including various converter control modes on the AC and DC sides of the converter, in a hybrid AC/DC system. It extends the basic control modes, developed for monopolar HVDC grids, to support the balanced as well as unbalanced operation of bipolar HVDC grids. Additionally, an AC-droop control, which defines a droop relationship between voltage magnitude and reactive power at the AC side of the converter, is incorporated into the modeling of bipolar HVDC systems. The functionality of the proposed model is demonstrated through a test case, and the power flow results are validated using PSCAD simulations. The impact of converter control modes on post-contingency system states is also investigated for single-pole contingencies. The proposed model is implemented as an open-source tool in the Julia/JuMP framework, where larger test cases demonstrate the robustness of the model and tool.
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control System
Authors: Yuhang Zhang, Zhiyao Zhang, Junyi Ji, Marcos Quiñones-Grueiro, William Barbour, Derek Gloudemans, Gergely Zachár, Clay Weston, Gautam Biswas, Daniel B. Work
Date: 2025-03-02 00:00:00
Tasks: Multi-agent Reinforcement Learning

This article presents the first field deployment of a multi-agent reinforcement learning (MARL) based variable speed limit (VSL) control system on Interstate 24 (I-24) near Nashville, Tennessee. We design and demonstrate a full pipeline from training MARL agents in a traffic simulator to a field deployment on a 17-mile segment of I-24 encompassing 67 VSL controllers. The system was launched on March 8th, 2024, and has made approximately 35 million decisions on 28 million trips in six months of operation. We apply an invalid action masking mechanism and several safety guards to ensure real-world constraints. The MARL-based implementation operates up to 98% of the time, with the safety guards overriding the MARL decisions for the remaining time. We evaluate the performance of the MARL-based algorithm in comparison to a previously deployed non-RL VSL benchmark algorithm on I-24. Results show that the MARL-based VSL control system achieves a superior performance. The accuracy of correctly warning drivers about slowing traffic ahead is improved by 14% and the response delay to non-recurrent congestion is reduced by 75%. The preliminary data shows that the VSL control system has reduced the crash rate by 26% and the secondary crash rate by 50%. We open-sourced the deployed MARL-based VSL algorithm at https://github.com/Lab-Work/marl-vsl-controller.
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing
Authors: Xiaohan Li, Sebastian Pattinson
Date: 2025-03-02 00:00:00
Tasks: Q-Learning, Uncertainty Quantification, Zero-Shot Learning

Defects in extrusion additive manufacturing remain common despite its prevalent use. While numerous AI-driven approaches have been proposed to improve quality assurance, the inherently dynamic nature of the printing process poses persistent challenges. Regardless of how comprehensive the training dataset is, out-of-distribution data remains inevitable. Consequently, deterministic models often struggle to maintain robustness and, in some cases, fail entirely when deployed in new or slightly altered printing environments. This work introduces an agent that dynamically adjusts flow rate and temperature setpoints in real time, optimizing process control while addressing bottlenecks in training efficiency and uncertainty management. It integrates a vision-based uncertainty quantification module with a reinforcement learning controller, using probabilistic distributions to describe printing segments. While the underlying networks are deterministic, these evolving distributions introduce adaptability into the decision-making process. The vision system classifies material extrusion with a certain level of precision, generating corresponding distributions. A deep Q-learning controller interacts with a simulated environment calibrated to the vision system precision, allowing the agent to learn optimal actions while demonstrating appropriate hesitation when necessary. By executing asynchronous actions and applying progressively tightened elliptical reward shaping, the controller develops robust, adaptive control strategies that account for the coupling effects between process parameters. When deployed with zero-shot learning, the agent effectively bridges the sim-to-real gap, correcting mild and severe under- and over-extrusion reliably. Beyond extrusion additive manufacturing, this scalable framework enables practical AI-driven quality assurance across various additive manufacturing processes.