pwc-archive/papers-with-abstracts huggingface dataset:
Indoor Fusion Positioning Based on "IMU-Ultrasonic-UWB" and Factor Graph Optimization Method
Authors: Fengyun Zhang, Jia Li, Xiaoqing Zhang, Shukai Duan, Shuang-Hua Yang
Date: 2025-03-17 00:00:00
Tasks: Sensor Fusion

This paper presents a high-precision positioning system that integrates ultra-wideband (UWB) time difference of arrival (TDoA) measurements, inertial measurement unit (IMU) data, and ultrasonic sensors through factor graph optimization. To overcome the shortcomings of standalone UWB systems in non-line-of-sight (NLOS) scenarios and the inherent drift associated with inertial navigation, we developed a novel hybrid fusion framework. First, a dynamic covariance estimation mechanism is incorporated, which automatically adjusts measurement weights based on real-time channel conditions. Then, a tightly-coupled sensor fusion architecture is employed, utilizing IMU pre-integration theory for temporal synchronization. Finally, a sliding-window factor graph optimization backend is utilized, incorporating NLOS mitigation constraints. Experimental results in complex indoor environments show a 38\% improvement in positioning accuracy compared to conventional Kalman filter-based approaches, achieving a 12.3 cm root mean square (RMS) error under dynamic motion conditions. The system maintains robust performance even with intermittent UWB signal availability, down to a 40\% packet reception rate, effectively suppressing IMU drift through multi-modal constraint fusion. This work offers a practical solution for applications that require reliable indoor positioning in GPS-denied environments.
RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems
Authors: Roozbeh Siyadatzadeh, Mohsen Ansari, Muhammad Shafique, Alireza Ejlali
Date: 2025-03-16 00:00:00

Embedded systems power many modern applications and must often meet strict reliability, real-time, thermal, and power requirements. Task replication can improve reliability by duplicating a task's execution to handle transient and permanent faults, but blindly applying replication often leads to excessive overhead and higher temperatures. Existing design-time methods typically choose the number of replicas based on worst-case conditions, which can waste resources under normal operation. In this paper, we present RL-TIME, a reinforcement learning-based approach that dynamically decides the number of replicas according to actual system conditions. By considering both the reliability target and a core-level Thermal Safe Power (TSP) constraint at run-time, RL-TIME adapts the replication strategy to avoid unnecessary overhead and overheating. Experimental results show that, compared to state-of-the-art methods, RL-TIME reduces power consumption by 63%, increases schedulability by 53%, and respects TSP 72% more often.
Equivalent-Circuit Thermal Model for Batteries with One-Shot Parameter Identification
Authors: Myisha A. Chowdhury, Qiugang Lu
Date: 2025-03-16 00:00:00

Accurate state of temperature (SOT) estimation for batteries is crucial for regulating their temperature within a desired range to ensure safe operation and optimal performance. The existing measurement-based methods often generate noisy signals and cannot scale up for large-scale battery packs. The electrochemical model-based methods, on the contrary, offer high accuracy but are computationally expensive. To tackle these issues, inspired by the equivalentcircuit voltage model for batteries, this paper presents a novel equivalent-circuit electro-thermal model (ECTM) for modeling battery surface temperature. By approximating the complex heat generation inside batteries with data-driven nonlinear (polynomial) functions of key measurable parameters such as state-of-charge (SOC), current, and terminal voltage, our ECTM is simplified into a linear form that admits rapid solutions. Such simplified ECTM can be readily identified with one single (one-shot) cycle data. The proposed model is extensively validated with benchmark NASA, MIT, and Oxford battery datasets. Simulation results verify the accuracy of the model, despite being identified with one-shot cycle data, in predicting battery temperatures robustly under different battery degradation status and ambient conditions.
Routing Guidance for Emerging Transportation Systems with Improved Dynamic Trip Equity
Authors: Ting Bai, Anni Li, Gehui Xu, Christos G. Cassandras, Andreas A. Malikopoulos
Date: 2025-03-16 00:00:00

In this paper, we present a dynamic routing guidance system that optimizes route recommendations for individual vehicles within an emerging transportation system while enhancing travelers' trip equity. We develop a framework to quantify trip quality and equity in a dynamic travel environment, providing new insights into how routing guidance influences equity in road transportation. Our approach enables real-time routing by incorporating both monitored and anticipated traffic congestion. We provide conditions that ensure achieving perfect trip equity for all travelers in a free-flow network. Finally, simulation studies on 1,000 vehicles traversing an urban road network in Boston demonstrate that our proposed method improves trip equity by approximately 11.4\% compared to the shortest-route strategy. In addition, the results reveal that our approach redistributes travel costs across vehicle types through route optimization, contributing to a more equitable transportation system.
Fast data augmentation for battery degradation prediction
Authors: Weihan Li, Harshvardhan Samsukha, Bruis van Vlijmen, Lisen Yan, Samuel Greenbank, Simona Onori, Venkat Viswanathan
Date: 2025-03-16 00:00:00
Tasks: Data Augmentation, Prediction

Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field, is time- and resource-intensive. Here, we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications. These datasets encompass various chemistries and realistic conditions, including cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery. Our results show that it is possible to reduce cell-testing efforts by at least 50% by substituting synthetic data into an existing dataset. This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.
Iterative Motion Planning in Multi-agent Systems with Opportunistic Communication under Disturbance
Authors: Neelanga Thelasingha, Agung Julius, James Humann, James Dotterweich
Date: 2025-03-16 00:00:00
Tasks: Motion Planning

In complex multi-agent systems involving heterogeneous teams, uncertainty arises from numerous sources like environmental disturbances, model inaccuracies, and changing tasks. This causes planned trajectories to become infeasible, requiring replanning. Further, different communication architectures used in multi-agent systems give rise to asymmetric knowledge of planned trajectories across the agents. In such systems, replanning must be done in a communication-aware fashion. This paper establishes the conditions for synchronization and feasibility in epistemic planning scenarios introduced by opportunistic communication architectures. We also establish conditions on task satisfaction based on quantified recoverability of disturbances in an iterative planning scheme. We further validate these theoretical results experimentally in a UAV--UGV task assignment problem.
A Unified Approach to Enforce Non-Negativity Constraint in Neural Network Approximation for Optimal Voltage Regulation (preprint)
Authors: Jiaqi Wu, Jingyi Yuan, Yang Weng, Guangwen Wang
Date: 2025-03-16 00:00:00
Tasks: Negation

Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neural networks (ICNN) for "optimal" control without relying on a physical model. While ICNNs offer theoretical guarantees based on restrictive assumptions of non-negative neural network parameters, can one improve the approximation power with an extra step on negative duplication of inputs? We show that such added mirroring step fails to improve accuracy, as a linear combination of the original input and duplicated input is equivalent to a linear operation of ICNN's input without duplication. While this design can not improve performance, we propose a unified approach to embed the non-negativity constraint as a regularized optimization of the neural network, contrary to the existing methods, which added a loosely integrated second step for post-processing on parameter negation. Our integration directly ties back-propagation to simultaneously minimizing the approximation error while enforcing the convexity constraints. Numerical experiments validate the issues of the mirroring method and show that our integrated objective can avoid problems such as unstable training and non-convergence existing in other methods for optimal control.
Computing Modes of Instability of Parameterized Nonlinear Systems for Vulnerability Assessment
Authors: Jinghan Wang, Michael W. Fisher
Date: 2025-03-16 00:00:00
Tasks: Numerical Integration, Vulnerability Detection

Engineered systems naturally experience large disturbances that can disrupt desired operation because the system may fail to recover to a stable equilibrium point. It is valuable to determine the mechanism of instability when the system is subject to a particular finite-time disturbance, because this information can be used to improve vulnerability detection, and to design controllers to reduce vulnerability. Often there exists a particular unstable equilibrium point on the region of attraction boundary of the stable equilibrium point such that the unstable eigenvector of the Jacobian at this unstable equilibrium point represents the mode of instability for the disturbance. Unfortunately, it is challenging to find this mode of instability, especially in high dimensional systems, because it is computationally intractable to obtain this particular unstable equilibrium point. This paper develops a novel algorithm for numerically computing the mode of instability for parameterized nonlinear systems without identifying the particular unstable equilibrium point, resulting in a computationally efficient method. The key idea is to first consider the setting where the system recovers, and to average the Jacobian along the system trajectory from the post-disturbance state up until the Jacobian becomes stable. As the system approaches inability to recover, the averaged Jacobians converge to the Jacobian at the particular unstable equilibrium point, and can be used to extract the unstable eigenvector representing the mode of instability. Convergence guarantees are provided for computing the mode of instability, both for the theoretical setting in continuous time, and for the proposed algorithm which relies on numerical integration. Numerical examples illustrate the successful application of the method to identify the mechanism of instability in power systems subject to temporary short circuits.
Power Swing Trajectory Influenced by Virtual Impedance-Based Current-Limiting Strategy
Authors: Yanshu Niu, Zhe Yang, Bikash C. Pal
Date: 2025-03-15 00:00:00

Grid-forming (GFM) inverter-based resources (IBRs) can emulate the external characteristics of synchronous generators (SGs) through appropriate control loop design. However, in systems with GFM IBRs, the apparent impedance trajectory under current limitation differs significantly from that of SG-based systems due to the limited overcurrent capability of power electronic devices. This difference challenges the power swing detection functions of distance relays designed for SG-based systems. This paper presents a theoretical analysis of the apparent impedance trajectory over a full power swing cycle under two typical current-limiting strategies: variable virtual impedance (VI) and adaptive VI. The analysis reveals that the trajectory under VI current-limiting strategies differs significantly from that of a conventional SG. The results also indicate that the control parameters affect the characteristics of the trajectory. In addition, the new trajectories challenge conventional power swing detection functions, increasing the risk of malfunction. Furthermore, the implementation of VI leads to a deterioration in system stability. The theoretical analysis is further validated through simulations on the MATLAB/Simulink platform.
Lithium-ion Battery Capacity Prediction via Conditional Recurrent Generative Adversarial Network-based Time-Series Regeneration
Authors: Myisha A. Chowdhury, Gift Modekwe, Qiugang Lu
Date: 2025-03-15 00:00:00
Tasks: Generative Adversarial Network, Prediction, Time Series

Accurate capacity prediction is essential for the safe and reliable operation of batteries by anticipating potential failures beforehand. The performance of state-of-the-art capacity prediction methods is significantly hindered by the limited availability of training data, primarily attributed to the expensive experimentation and data sharing restrictions. To tackle this issue, this paper presents a recurrent conditional generative adversarial network (RCGAN) scheme to enrich the limited battery data by adding high-fidelity synthetic ones to improve the capacity prediction. The proposed RCGAN scheme consists of a generator network to generate synthetic samples that closely resemble the true data and a discriminator network to differentiate real and synthetic samples. Long shortterm memory (LSTM)-based generator and discriminator are leveraged to learn the temporal and spatial distributions in the multivariate time-series battery data. Moreover, the generator is conditioned on the capacity value to account for changes in battery dynamics due to the degradation over usage cycles. The effectiveness of the RCGAN is evaluated across six batteries from two benchmark datasets (NASA and MIT). The raw data is then augmented with synthetic samples from the RCGAN to train LSTM and gate recurrent unit (GRU) models for capacity prediction. Simulation results show that the models trained with augmented datasets significantly outperform those trained with the original datasets in capacity prediction.
Formation Control of Multi-agent System with Local Interaction and Artificial Potential Field
Authors: Luoyin Zhao, Zheping Yan, Yuqing Wang, Raye Chen-Hua Yeow
Date: 2025-03-15 00:00:00
Tasks: Collision Avoidance

A novel local interaction control method (LICM) is proposed in this paper to realize the formation control of multi-agent system (MAS). A local interaction leader follower (LILF) structure is provided by coupling the advantages of information consensus and leader follower frame, the agents can obtain the state information of the leader by interacting with their neighbours, which will reduce the communication overhead of the system and the dependence on a single node of the topology. In addition, the artificial potential field (APF) method is introduced to achieve obstacle avoidance and collision avoidance between agents. Inspired by the stress response of animals, a stress response mechanism-artificial potential field (SRM-APF) is proposed, which will be triggered when the local minimum problem of APF occurs. Ultimately, the simulation experiments of three formation shapes, including triangular formation, square formation and hexagonal formation, validate the effectiveness of the proposed method.
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework
Authors: Ertuğrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar
Date: 2025-03-15 00:00:00
Tasks: Federated Learning, State Space Models

This paper presents FedAlign, a Federated Learning (FL) framework particularly designed for System Identification (SYSID) tasks by aligning state representations. Local workers can learn State-Space Models (SSMs) with equivalent representations but different dynamics. We demonstrate that directly aggregating these local SSMs via FedAvg results in a global model with altered system dynamics. FedAlign overcomes this problem by employing similarity transformation matrices to align state representations of local SSMs, thereby establishing a common parameter basin that retains the dynamics of local SSMs. FedAlign computes similarity transformation matrices via two distinct approaches: FedAlign-A and FedAlign-O. In FedAlign-A, we represent the global SSM in controllable canonical form (CCF). We apply control theory to analytically derive similarity transformation matrices that convert each local SSM into this form. Yet, establishing global SSM in CCF brings additional alignment challenges in multi input - multi output SYSID as CCF representation is not unique, unlike in single input - single output SYSID. In FedAlign-O, we address these alignment challenges by reformulating the local parameter basin alignment problem as an optimization task. We determine the parameter basin of a local worker as the common parameter basin and solve least square problems to obtain similarity transformation matrices needed to align the remaining local SSMs. Through the experiments conducted on synthetic and real-world datasets, we show that FedAlign outperforms FedAvg, converges faster, and provides improved stability of the global SSM thanks to the efficient alignment of local parameter basins.
Fast Critical Clearing Time Calculation for Power Systems with Synchronous and Asynchronous Generation
Authors: Xuezao Wang, Yijun Xu, Wei Gu, Kai Liu, Shuai Lu, Mert Korkali, Lamine Mili
Date: 2025-03-15 00:00:00

The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs). This penetration threatens the dynamic stability of the modern power system. To assess the latter, we resort to the critical clearing time (CCT) as a stability index, which is typically computed through a large number of time-domain simulations. This is especially true for CIG-embedded power systems, where the complexity of the model is further increased. To alleviate the computing burden, we developed a trajectory sensitivity-based method for assessing the CCT in power systems with synchronous and asynchronous generators. This allows us to obtain the CCT cost-effectively. The simulation results reveal the excellent performance of the proposed method.
Dynamic IRS Allocation for Spectrum-Sharing MIMO Communication and Radar Systems
Authors: Daniyal Munir, Atta Ullah, Danish Mehmood Mughal, Min Young Chung, Hans D. Schotten
Date: 2025-03-15 00:00:00

This paper investigates the use of intelligent reflecting surfaces (IRS) to assist cellular communications and radar sensing operations in a communications and sensing setup. The IRS dynamically allocates reflecting elements to simultaneously localize a target and assist a user's communication. To achieve this, we propose a novel optimization framework that jointly addresses beamforming design and IRS element allocation. Specifically, we formulate a Weighted Minimum Mean Square Error (WMMSE)-based approach that iteratively optimizes the transmit and receive beamforming vectors, IRS phase shifts, and element allocation. The allocation mechanism adaptively balances the number of IRS elements dedicated to communication and sensing subsystems by leveraging the signal-to-noise-plus-interference-ratio (SINR) between the two. The proposed solution ensures efficient resource utilization while maintaining performance trade-offs. Numerical results demonstrate significant improvements in both communication and sensing SINRs under varying system parameters.
Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation
Authors: Tongxuan Tian, Haoyang Li, Bo Ai, Xiaodi Yuan, Zhiao Huang, Hao Su
Date: 2025-03-15 00:00:00
Tasks: Model Predictive Control, State Estimation

Manipulating deformable objects like cloth is challenging due to their complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions, which complicate state estimation and dynamics modeling. Prior work has struggled with robust cloth state estimation, while dynamics models, primarily based on Graph Neural Networks (GNNs), are limited by their locality. Inspired by recent advances in generative models, we hypothesize that these expressive models can effectively capture intricate cloth configurations and deformation patterns from data. Building on this insight, we propose a diffusion-based generative approach for both perception and dynamics modeling. Specifically, we formulate state estimation as reconstructing the full cloth state from sparse RGB-D observations conditioned on a canonical cloth mesh and dynamics modeling as predicting future states given the current state and robot actions. Leveraging a transformer-based diffusion model, our method achieves high-fidelity state reconstruction while reducing long-horizon dynamics prediction errors by an order of magnitude compared to GNN-based approaches. Integrated with model-predictive control (MPC), our framework successfully executes cloth folding on a real robotic system, demonstrating the potential of generative models for manipulation tasks with partial observability and complex dynamics.
A Profit Sharing Mechanism for Coordinated Power Traffic System
Authors: Tianyu Sima, Mingyu Yan, Jianfeng Wen, Wensheng Luo, Mariusz Malinowski
Date: 2025-03-15 00:00:00
Tasks: Scheduling

During the scheduling process, the traffic network operator (TNO) and the distribution network operator (DNO) act noncooperatively. Under the TNO management, the distribution of charging loads may exacerbate the local supply demand imbalance in the power distribution network (PDN), which negatively impacts the economic operation of the PDN. This paper proposes a profitsharing mechanism based on the principle of incentive compatibility for coordinating the traffic network (TN) and the PDN to minimize the operation cost of PDN. Under this mechanism, the scheduling process of the power traffic system is divided into two stages. At the prescheduling stage, the TNO allocates traffic flow and charging loads without considering the operation of the PDN, after which the DNO schedules and obtains the original cost. At the rescheduling stage, the DNO shares part of benefits of the optimal operation to the TNO to redispatch the EV charging to obtain a more effective charging plan, thus minimize the overall cost of PDN. Then, a bilevel model is developed to simulate the operation of the power traffic system with the proposed sharing scheme and identify the best sharing ratio. Finally, numerical results demonstrate that the PDN can achieve the minimum total cost and simultaneously the TN can also benefit from the proposed profit sharing mechanism.