pwc-archive/papers-with-abstracts huggingface dataset:
An iterative tangential interpolation framework for model reduction of MIMO systems
Authors: Jared Jonas, Bassam Bamieh
Date: 2025-06-03 00:00:00

We consider model reduction of large-scale MIMO systems using tangential interpolation in the frequency domain. Our scheme is related to the recently-developed Adaptive Antoulas--Anderson (AAA) algorithm, which is an iterative algorithm that uses concepts from the Loewner framework. Our algorithm uses low-rank interpolation and iteratively adds interpolation points based on several criteria including minimizing maximum errors. We show there is freedom in the interpolation point selection method, leading to multiple algorithms that have trade-offs between computational complexity and approximation performance. We prove that a weighted \(H_2\) norm of a representative error system is monotonically decreasing as interpolation points are added. Finally, we provide computational results and some comparisons with prior works, demonstrating performance on par with standard model reduction methods.
Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark
Authors: Artur Grigorev, Khaled Saleh, Jiwon Kim, Adriana-Simona Mihaita
Date: 2025-06-03 00:00:00

Traffic incidents remain a critical public safety concern worldwide, with Australia recording 1,300 road fatalities in 2024, which is the highest toll in 12 years. Similarly, the United States reports approximately 6 million crashes annually, raising significant challenges in terms of a fast reponse time and operational management. Traditional response protocols rely on human decision-making, which introduces potential inconsistencies and delays during critical moments when every minute impacts both safety outcomes and network performance. To address this issue, we propose a novel Incident Response Benchmark that uses generative artificial intelligence to automatically generate response plans for incoming traffic incidents. Our approach aims to significantly reduce incident resolution times by suggesting context-appropriate actions such as variable message sign deployment, lane closures, and emergency resource allocation adapted to specific incident characteristics. First, the proposed methodology uses real-world incident reports from the Performance Measurement System (PeMS) as training and evaluation data. We extract historically implemented actions from these reports and compare them against AI-generated response plans that suggest specific actions, such as lane closures, variable message sign announcements, and/or dispatching appropriate emergency resources. Second, model evaluations reveal that advanced generative AI models like GPT-4o and Grok 2 achieve superior alignment with expert solutions, demonstrated by minimized Hamming distances (averaging 2.96-2.98) and low weighted differences (approximately 0.27-0.28). Conversely, while Gemini 1.5 Pro records the lowest count of missed actions, its extremely high number of unnecessary actions (1547 compared to 225 for GPT-4o) indicates an over-triggering strategy that reduces the overall plan efficiency.
Urban Visibility Hotspots: Quantifying Building Vertex Visibility from Connected Vehicle Trajectories using Spatial Indexing
Authors: Artur Grigorev, Adriana-Simona Mihaita
Date: 2025-06-03 00:00:00

Effective placement of Out-of-Home advertising and street furniture requires accurate identification of locations offering maximum visual exposure to target audiences, particularly vehicular traffic. Traditional site selection methods often rely on static traffic counts or subjective assessments. This research introduces a data-driven methodology to objectively quantify location visibility by analyzing large-scale connected vehicle trajectory data (sourced from Compass IoT) within urban environments. We model the dynamic driver field-of-view using a forward-projected visibility area for each vehicle position derived from interpolated trajectories. By integrating this with building vertex locations extracted from OpenStreetMap, we quantify the cumulative visual exposure, or ``visibility count'', for thousands of potential points of interest near roadways. The analysis reveals that visibility is highly concentrated, identifying specific ``visual hotspots'' that receive disproportionately high exposure compared to average locations. The core technical contribution involves the construction of a BallTree spatial index over building vertices. This enables highly efficient (O(logN) complexity) radius queries to determine which vertices fall within the viewing circles of millions of trajectory points across numerous trips, significantly outperforming brute-force geometric checks. Analysis reveals two key findings: 1) Visibility is highly concentrated, identifying distinct 'visual hotspots' receiving disproportionately high exposure compared to average locations. 2) The aggregated visibility counts across vertices conform to a Log-Normal distribution.
Online Detection and Mitigation of Robust Zero Dynamics Anomaly Behavior in MIMO Nonlinear Control Systems
Authors: Kosar Behnia, H. A. Talebi, Farzaneh Abdollahi
Date: 2025-06-03 00:00:00

This paper presents a methodology to detect robust zero dynamics anomaly behavior and mitigate the impacts in general multi-input multi-output (MIMO) nonlinear systems. The proposed method guarantees the resiliency and stability of the closed-loop system without relying on an accurate dynamical model. The presented method operates in two stages. First, it measures the difference between the system input and that of the model as a residual signal to detect the anomaly behavior. After detecting the attack, a recovery signal is generated to restore the system to its nominal condition. In this stage, a neural network model is used to estimate the anomaly signal and recover the closed-loop system. The weights of the neural network model are updated online using adaptation rules without needing prior data for training. The accuracy and performance of the proposed methods are verified by simulating various scenarios on a fourtank system.
Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
Authors: Salah Eddine Choutri, Boualem Djehiche, Prajwal Chauhan, Saif Eddin Jabari
Date: 2025-06-03 00:00:00
Tasks: Scheduling

We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional backpressure algorithms leverage queue differentials to regulate data flow and maintain network stability. In this work, we extend the backpressure framework by incorporating a mean-field term into the cost functional, capturing the global behavior of the system alongside local dynamics. The resulting model utilizes the strengths of non-cooperative mean-field type games, enabling nodes to make decentralized decisions based on both individual queue states and system mean-field effects while accounting for stochastic network interactions. By leveraging the interplay between backpressure dynamics and mean-field coupling, the approach balances local optimization with global efficiency. Numerical simulations demonstrate the efficacy of the proposed method in handling congestion and scheduling in large-scale WSNs.
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Authors: Tom Danino, Nahum Shimkin
Date: 2025-06-03 00:00:00
Tasks: Ensemble Learning, Multi-agent Reinforcement Learning, SMAC, SMAC+

Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with decentralized ensemble learning, incorporating several key contributions. The main component in our scheme is a selective exploration method that leverages ensemble kurtosis. We extend the global decomposed critic with a diversity-regularized ensemble of individual critics and utilize its excess kurtosis to guide exploration toward high-uncertainty states and actions. To improve sample efficiency, we train the centralized critic with a novel truncated variation of the TD($\lambda$) algorithm, enabling efficient off-policy learning with reduced variance. On the actor side, our suggested algorithm adapts the mixed samples approach to MARL, mixing on-policy and off-policy loss functions for training the actors. This approach balances between stability and efficiency and outperforms purely off-policy learning. The evaluation shows our method outperforms state-of-the-art baselines on standard MARL benchmarks, including a variety of SMAC II maps.
On dual-rate consensus under transmission delays
Authors: David Umsonst, Mina Ferizbegovic
Date: 2025-06-03 00:00:00

In this paper, we investigate the problem of dual-rate consensus under transmission delays, where the control updates happen at a faster rate than the measurements being received. We assume that the measurements are delayed by a fixed delay and show that for all delays and rates, the system reaches a consensus if and only if the communication graph of the agents is connected and the control gain is chosen in a specific interval. Based on these results we dive deeper into the convergence properties and investigate how the convergence changes when we change the rate for sending measurements. We observe that in certain cases there exists a sweet spot for choosing the sampling rate of the measurements, which can improve the convergence to the consensus point. We then formulate an optimization problem to find a sampling rate to improve the convergence speed and provide a necessary and sufficient condition for the existence of a finite optimizer of this problem. Our results are verified with numerical simulations.
Target Sensing Performance in Disaster-Specific ISAC Networks
Authors: Ahmet Burak Ozyurt, John S. Thompson
Date: 2025-06-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

As sixth-generation (6G) wireless technology emerges, integrated sensing and communication (ISAC) networks offer significant potential for enhancing real-time monitoring in disaster areas. However, existing ISAC approaches often fail to address the unique challenges of dynamic and cluttered disaster areas, resulting in limited sensing coverage and interruptions in sensing service. To address these limitations, this work proposes a mobile ISAC network specifically designed for disaster scenarios. By leveraging stochastic geometry, we derive closed-form expressions for sensing coverage and introduce a novel performance metric to evaluate sensing service continuity. Simulation results validate the analytical derivations and offer key insights into network design.
Quantized Dissipative Uncertain Model for Fractional T_S Fuzzy systems with Time_Varying Delays Under Networked Control System
Authors: Muhammad Shamrooz Aslam, Hazrat Bilal, Sumeera Shamrooz
Date: 2025-06-03 00:00:00
Tasks: Quantization

This paper addressed with the quantized dissipative uncertain problem for delayed fractional T_S Fuzzy system for event_triggered networked systems (E_NS), where the extended dissipativity analysis combines the H infinity, dissipativity, L2 and L infinity and passivity performance in a unified frame. To attain the high efficiency for available channel resources, measurement size decrease mechanism and event_triggered scheme (ETS) are proposed. Firstly, we present the ETS in which signal is transmitted through the channel with logical function then logarithmic quantization methodology is implemented for size reduction. Then, we transfer the original delayed fractional T_S fuzzy systems with the effect of quantization under ETS as induced communications delays. Furthermore, by employing the associative Lyapunov functional method in terms of linear matrix inequalities, adequate conditions for asymptotical stability is given. Moreover, we also construct the design fuzzy model for state space filtering system. At last, a truck_trailer model is given to show the effectiveness of the proposed strategy.
Recursive Privacy-Preserving Estimation Over Markov Fading Channels
Authors: Jie Huang, Fanlin Jia, Xiao He
Date: 2025-06-03 00:00:00
Tasks: Privacy Preserving, State Estimation

In industrial applications, the presence of moving machinery, vehicles, and personnel, contributes to the dynamic nature of the wireless channel. This time variability induces channel fading, which can be effectively modeled using a Markov fading channel (MFC). In this paper, we investigate the problem of secure state estimation for systems that communicate over a MFC in the presence of an eavesdropper. The objective is to enable a remote authorized user to accurately estimate the states of a dynamic system, while considering the potential interception of the sensor's packet through a wiretap channel. To prevent information leakage, a novel co-design strategy is established, which combines a privacy-preserving mechanism with a state estimator. To implement our encoding scheme, a nonlinear mapping of the innovation is introduced based on the weighted reconstructed innovation previously received by the legitimate user. Corresponding to this encoding scheme, we design a recursive privacy-preserving filtering algorithm to achieve accurate estimation. The boundedness of estimation error dynamics at the legitimate user's side is discussed and the divergence of the eavesdropper's estimation error is analyzed, which demonstrates the effectiveness of our co-design strategy in ensuring secrecy. Furthermore, a simulation example of a three-tank system is provided to demonstrate the effectiveness and feasibility of our privacy-preserving estimation method.
Unit Commitment with Cost-Oriented Temporal Resolution
Authors: Junyi Tao, Ran Li, Salvador Pineda
Date: 2025-06-03 00:00:00
Tasks: Bilevel Optimization, Clustering, Scheduling

Time-adaptive unit commitment (UC) has recently been investigated to reduce the scheduling costs by flexibly varying the temporal resolution, which is usually determined by clustering the net load patterns. However, there exists a misalignment between cost and net load patterns due to the discrete start-up costs and out-of-merit-order dispatch triggered by ramping and other constraints. The optimal time-adaptive resolution cannot be completely captured by clustering-based method. This paper proposes a cost-oriented method to address this misalignment by a novel bilevel optimization approach that is efficiently solved through a heuristic greedy algorithm. The impact of varying temporal resolution on the final scheduling costs are tested, based on which the temporal resolution is heuristically updated, achieving significant cost reduction without increasing the number of temporal periods. Subsequently, an improved discretized Adam optimization method together with offline warm start and online refinement strategy is proposed to efficiently search for the better temporal resolution configuration. Results show that the proposed cost-oriented UC temporal resolution determination method achieves enhanced cost efficiency.
Data-assimilated model-informed reinforcement learning
Authors: Defne E. Ozan, Andrea Nóvoa, Georgios Rigas, Luca Magri
Date: 2025-06-02 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies by interacting with the system, typically requiring observations of the full physical state. In practice, sensors often provide only partial and noisy measurements (observations) of the system. The objective of this paper is to develop a framework that enables the control of chaotic systems with partial and noisy observability. The proposed method, data-assimilated model-informed reinforcement learning (DA-MIRL), integrates (i) low-order models to approximate high-dimensional dynamics; (ii) sequential data assimilation to correct the model prediction when observations become available; and (iii) an off-policy actor-critic RL algorithm to adaptively learn an optimal control strategy based on the corrected state estimates. We test DA-MIRL on the spatiotemporally chaotic solutions of the Kuramoto-Sivashinsky equation. We estimate the full state of the environment with (i) a physics-based model, here, a coarse-grained model; and (ii) a data-driven model, here, the control-aware echo state network, which is proposed in this paper. We show that DA-MIRL successfully estimates and suppresses the chaotic dynamics of the environment in real time from partial observations and approximate models. This work opens opportunities for the control of partially observable chaotic systems.
SIL Allocation for Mitigation Safety Functions
Authors: Hamid Jahanian
Date: 2025-06-02 00:00:00

SIL (Safety Integrity Level) allocation plays a pivotal role in evaluating the significance of Safety Functions (SFs) within high-risk industries. The outcomes of a SIL allocation study determine the design specifications necessary to uphold the Probability of Failure on Demand (PFD) below permissible limits, thus managing risk effectively. While extensive research has focused on SIL allocation for preventive SFs, there is a noticeable gap in attention towards mitigation SFs. To address this gap, this paper discusses the shortcomings of current methods and proposes a new approach to overcome them. The principles of the proposed method are substantiated by detailed mathematical formulation and the practical application of the method is demonstrated through a case study in a road tunnel project.
Second-Order-Cone Formulations of Power Flow for Topology Optimization
Authors: Noah Rhodes, James Luedkte, Line Roald
Date: 2025-06-02 00:00:00

Optimization problems that involve topology optimization in scenarios with large scale outages, such as post-disaster restoration or public safety power shutoff planning, are very challenging to solve. Using simple power flow representations such as DC power flow or network flow models results in low quality solutions which requires significantly higher-than-predicted load shed to become AC feasible. Recent work has shown that formulations based on the Second Order Cone (SOC) power flow formulation find very high quality solutions with low load shed, but the computational burden of these formulations remains a significant challenge. With the aim of reducing computational time while maintaining high solution quality, this work explores formulations which replace the conic constraints with a small number of linear cuts. The goal of this approach is not to find an exact power flow solution, but rather to identify good binary decisions, where the power flow can be resolved after the binary variables are fixed. We find that a simple reformulation of the Second Order Cone Optimal Power Shutoff problem can greatly improve the solution speed, but that a full linearization of the SOC voltage cone equation results in an overestimation of the amount of power that can be delivered to loads.
Bregman Centroid Guided Cross-Entropy Method
Authors: Yuliang Gu, Hongpeng Cao, Marco Caccamo, Naira Hovakimyan
Date: 2025-06-02 00:00:00
Tasks: Diversity, Model-based Reinforcement Learning

The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature convergence in multimodal landscapes. In this work, we propose Bregman Centroid Guided CEM ($\mathcal{BC}$-EvoCEM), a lightweight enhancement to ensemble CEM that leverages $\textit{Bregman centroids}$ for principled information aggregation and diversity control. $\textbf{$\mathcal{BC}$-EvoCEM}$ computes a performance-weighted Bregman centroid across CEM workers and updates the least contributing ones by sampling within a trust region around the centroid. Leveraging the duality between Bregman divergences and exponential family distributions, we show that $\textbf{$\mathcal{BC}$-EvoCEM}$ integrates seamlessly into standard CEM pipelines with negligible overhead. Empirical results on synthetic benchmarks, a cluttered navigation task, and full MBRL pipelines demonstrate that $\textbf{$\mathcal{BC}$-EvoCEM}$ enhances both convergence and solution quality, providing a simple yet effective upgrade for CEM.
Optimal Coordination of Flexible DERs in Local Energy and Flexibility Markets to Ensure Social Equity
Authors: Niloofar Pourghaderi, Milad Kabirifar, Payman Dehghanian
Date: 2025-06-02 00:00:00
Tasks: Management

Local electricity markets offer a promising solution for integrating renewable energy sources and other distributed energy resources (DERs) into distribution networks. These markets enable the effective utilization of flexible resources by facilitating coordination among various agents. Beyond technical and economic considerations, addressing social equity within these local communities is critical and requires dedicated attention in market-clearing frameworks. This paper proposes a social equity-based market-clearing framework for the optimal management of DERs' energy and flexibility within local communities. The proposed framework incorporates consumers' energy burden to ensure fair pricing in energy market clearance. Furthermore, to ensure equity during unbalanced operating conditions, flexible resources are managed in the local flexibility market, ensuring that all participants can trade power fairly under network disturbances. The model is formulated as a second-order cone programming (SOCP) optimization and validated on the IEEE 33-bus test distribution network.
Hybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
Authors: Ian Walter, Jitesh H. Panchal, Philip E. Paré
Date: 2025-06-02 00:00:00
Tasks: parameter estimation

We propose a hybrid spreading process model to capture the dynamics of demand for software-based products. We introduce discontinuous jumps in the state to model sudden surges in demand that can be seen immediately after a product update is released. After each update, the modeled demand evolves according to a continuous-time susceptible-infected-susceptible (SIS) epidemic model. We identify the necessary and sufficient conditions for estimating the hybrid model's parameters for an arbitrary finite number of sequential updates. We verify the parameter estimation conditions in simulation, and evaluate how the estimation of these parameters is impacted by the presence of observation and process noise. We then validate our model by applying our estimation method to daily user engagement data for a regularly updating software product, the live-service video game `Apex Legends.'
Generalized Super-Twisting Observer for a class of interconnected nonlinear systems with uncertainties
Authors: Rania Tafat, Jaime A. Moreno, Stefan Streif
Date: 2025-06-02 00:00:00

The Generalized Super-Twisting Observer (GSTO) is extended for a strongly observable class of nonlinearly interconnected systems with bounded uncertainties/perturbations. A nonsmooth strong Lyapunov function is used to prove the finite-time convergence of the proposed observer to the true system's trajectories, in the presence of the uncertainties. A case study on the interaction between two food production systems is presented, comparing the proposed observer with the High Gain observer. The results emphasize the critical role of the GSTO's discontinuous term in achieving exact estimation.