pwc-archive/papers-with-abstracts huggingface dataset:
Islanding Strategy for Smart Grids Oriented to Resilience Enhancement and Its Power Supply Range Optimization
Authors: Yanhong Luo, Wenchao Meng, Xi Zhu, Andreas Elombo, Hu Rong, Bing Xie, Tianwen Zhang
Date: 2025-06-18 00:00:00
Tasks: Computational Efficiency

With the increasing prevalence of distributed generators, islanded operation based on distributed generation is considered a vital means to enhance the reliability and resilience of smart grids. This paper investigates the main factors in islanding partition of smart grids and establishes a mathematical model for islanding division. A method to determine the maximum power supply range of distributed energy resources (DERs) based on the reachability matrix and power circle algorithm is proposed to improve computational efficiency. A dynamic programming method based on breadth-first search (BFS) is used to solve the islanding partition scheme, and a region correction method is applied to modify the maximum power supply area by considering controllable loads and prioritizing critical load restoration, thereby enhancing system resilience. Finally, simulation results verify the effectiveness of the proposed algorithm in improving smart grid resilience.
A Force Feedback Exoskeleton for Teleoperation Using Magnetorheological Clutches
Authors: Zhongyuan Kong, Lei LI, Erwin Ang Tien Yew, Zirui Chen, Wenbo Li, Shiwu Zhang, Jian Yang, Shuaishuai Sun
Date: 2025-06-18 00:00:00

This paper proposes an upper-limb exoskeleton teleoperation system based on magnetorheological (MR) clutches, aiming to improve operational accuracy and enhance the immersive experience during lunar sampling tasks. Conventional exoskeleton teleoperation systems commonly employ active force feedback solutions, such as servo motors, which typically suffer from high system complexity and increased energy consumption. Furthermore, force feedback devices utilizing motors and gear reducers generally compromise backdrivability and pose safety risks to operators due to active force output. To address these limitations, we propose a semi-active force feedback strategy based on MR clutches. Dynamic magnetic field control enables precise adjustment of joint stiffness and damping, thereby providing smooth and high-resolution force feedback. The designed MR clutch exhibits outstanding performance across key metrics, achieving a torque-to-mass ratio (TMR) of 93.6 Nm/kg, a torque-to-volume ratio (TVR) of 4.05 x 10^5 Nm/m^3, and a torque-to-power ratio (TPR) of 4.15 Nm/W. Notably, the TMR represents an improvement of approximately 246% over a representative design in prior work. Experimental results validate the system's capability to deliver high-fidelity force feedback. Overall, the proposed system presents a promising solution for deep-space teleoperation with strong potential for real-world deployment in future missions.
Skew-Induced Insertion Loss Deviation (SILD) and FOM_SILD: Metrics for Quantifying P/N Skew Effects in High-Speed Channels
Authors: David Nozadze, Zurab Kiguradze, Amendra Koul, Mike Sapozhnikov
Date: 2025-06-18 00:00:00

The rise of AI workloads and growing data center demands have driven the need for ultra-high-speed interconnects exceeding 200 Gb/s. As unit intervals (UI) shrink, even a few picoseconds of P/N skew can degrade serializer-deserializer (SerDes) performance. Traditional methods for quantifying skew fall short in capturing its impact. We introduce two new metrics: 1) Skew-Induced Insertion Loss Deviation (SILD) and 2) its complementary Figure of Merit (FOM_SILD), analytically developed to assess P/N skew effects. Measured S-parameters confirm FOM_SILD reciprocity, while simulations of 224G PAM4 SerDes show strong correlation with bit error rate (BER) trends. This approach offers a robust framework for analyzing skew in next-generation ultra-high-speed interconnects.
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Authors: Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li
Date: 2025-06-18 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, Reinforcement Learning (RL)

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV's situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework's superior performance, robustness and adaptability.
Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
Authors: Seyed Moein Abtahi, Akramul Azim
Date: 2025-06-17 00:00:00
Tasks: Autonomous Vehicles

The rapid advancements in autonomous vehicle (AV) technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm (MLCA), a state-machine-based approach designed to reduce unnecessary lane changes, thereby enhancing both traffic safety and efficiency. The MLCA algorithm prioritizes maintaining lane stability unless safety-critical conditions necessitate a lane change. The algorithm's effectiveness was evaluated through simulations conducted on the SUMO platform, comparing its performance against established models, including LC2017 and MOBIL. Results demonstrate substantial reductions in lane changes and collisions, leading to smoother traffic flow and improved safety metrics. Additionally, the study highlights the MLCA's adaptability to various traffic densities and roadway configurations, showcasing its potential for wide-scale deployment in real-world AV systems. Future work aims to validate these findings in more complex scenarios using the CARLA simulator, which will enable the testing of the algorithm under more dynamic and high-fidelity conditions, such as urban traffic environments with diverse road users. Moreover, the integration of cybersecurity measures for vehicle-to-vehicle (V2V) communication will be explored to ensure robust and secure data exchange, further enhancing the reliability and safety of AV operations. This research contributes to the broader goal of developing intelligent traffic systems that optimize both individual vehicle performance and overall traffic network efficiency.
Mixed Traffic: A Perspective from Long Duration Autonomy
Authors: Filippos Tzortzoglou, Logan E. Beaver
Date: 2025-06-17 00:00:00
Tasks: Autonomous Vehicles

The rapid adoption of autonomous vehicle has established mixed traffic environments, comprising both autonomous and human-driven vehicles (HDVs), as essential components of next-generation mobility systems. Along these lines, connectivity between autonomous vehicles and infrastructure (V2I) is also a significant factor that can effectively support higher-level decision-making. At the same time, the integration of V2I within mixed traffic environments remains a timely and challenging problem. In this paper, we present a long-duration autonomy controller for connected and automated vehicles (CAVs) operating in such environments, with a focus on intersections where right turns on red are permitted. We begin by deriving the optimal control policy for CAVs under free-flow traffic. Next, we analyze crossing time constraints imposed by smart traffic lights and map these constraints to controller bounds using Control Barrier Functions (CBFs), with the aim to drive a CAV to cross the intersection on time. We also introduce criteria for identifying, in real-time, feasible crossing intervals for each CAV. To ensure safety for the CAVs, we present model-agnostic safety guarantees, and demonstrate their compatibility with both CAVs and HDVs. Ultimately, the final control actions are enforced through a combination of CBF constraints, constraining CAVs to traverse the intersection within the designated time intervals while respecting other vehicles. Finally, we guarantee that our control policy yields always a feasible solution and validate the proposed approach through extensive simulations in MATLAB.
Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems
Authors: Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
Date: 2025-06-17 00:00:00
Tasks: Computational Efficiency, Motion Planning

In multi-agent systems, signal temporal logic (STL) is widely used for path planning to accomplish complex objectives with formal safety guarantees. However, as the number of agents increases, existing approaches encounter significant computational challenges. Recognizing that many complex tasks require cooperation among multiple agents, we propose swarm STL specifications to describe the collective tasks that need to be achieved by a team of agents. Next, we address the motion planning problem for all the agents in two stages. First, we abstract a group of cooperating agents as a swarm and construct a reduced-dimension state space whose dimension does not increase with the number of agents. The path planning is performed at the swarm level, ensuring the safety and swarm STL specifications are satisfied. Then, we design low-level control strategies for agents within each swarm based on the path synthesized in the first step. The trajectories of agents generated by the two-step policy ensure satisfaction of the STL specifications. We evaluate our two-stage approach in both single-swarm and multi-swarm scenarios. The results demonstrate that all tasks are completed with safety guarantees. Compared to the baseline multi-agent planning approach, our method maintains computational efficiency as the number of agents increases, since the computational time scales with the number of swarms rather than the number of agents.
Modeling Uncertainty: From Simulink to Stochastic Hybrid Automata
Authors: Pauline Blohm, Felix Schulz, Lisa Willemsen, Anne Remke, Paula Herber
Date: 2025-06-17 00:00:00

Simulink is widely used in industrial design processes to model increasingly complex embedded control systems. Thus, their formal analysis is highly desirable. However, this comes with two major challenges: First, Simulink models often provide an idealized view of real-life systems and omit uncertainties such as, aging, sensor noise or failures. Second, the semantics of Simulink is only informally defined. In this paper, we present an approach to formally analyze safety and performance of embedded control systems modeled in Simulink in the presence of uncertainty. To achieve this, we 1) model different types of uncertainties as stochastic Simulink subsystems and 2) extend an existing formalization of the Simulink semantics based on stochastic hybrid automata (SHA) by providing transformation rules for the stochastic subsystems. Our approach gives us access to established quantitative analysis techniques, like statistical model checking and reachability analysis. We demonstrate the applicability of our approach by analyzing safety and performance in the presence of uncertainty for two smaller case studies.
Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother
Authors: Ondřej Skalský, Jakub Dokoupil
Date: 2025-06-17 00:00:00
Tasks: State Estimation, Transfer Learning

A Bayesian knowledge transfer mechanism that leverages external information to improve the performance of the Kalman fixed-lag interval smoother (FLIS) is proposed. Exact knowledge of the external observation model is assumed to be missing, which hinders the direct application of Bayes' rule in traditional transfer learning approaches. This limitation is overcome by the fully probabilistic design, conditioning the targeted task of state estimation on external information. To mitigate the negative impact of inaccurate external data while leveraging precise information, a latent variable is introduced. Favorably, in contrast to a filter, FLIS retrospectively refines past decisions up to a fixed time horizon, reducing the accumulation of estimation error and consequently improving the performance of state inference. Simulations indicate that the proposed algorithm better exploits precise external knowledge compared to a similar technique and achieves comparable results when the information is imprecise.
Network-Independent Incremental Passivity Conditions for Grid-Forming Inverter Control
Authors: Jared Miller, Maitraya Avadhut Desai, Xiuqiang He, Roy S. Smith, Gabriela Hug
Date: 2025-06-17 00:00:00

Grid-forming inverters control the power transfer between the AC and DC sides of an electrical grid while maintaining the frequency and voltage of the AC side. This paper focuses on ensuring large-signal stability of an electrical grid with inverter-interfaced renewable sources. We prove that the Hybrid-Angle Control (HAC) scheme for grid-forming inverters can exhibit incremental passivity properties between current and voltage at both the AC and DC ports. This incremental passivity can be certified through decentralized conditions. Inverters operating under HAC can, therefore, be connected to other passive elements (e.g. transmission lines) with an immediate guarantee of global transient stability regardless of the network topology or parameters. Passivity of Hybrid Angle Control is also preserved under small-signal (linearized) analyses, in contrast to conventional proportional droop laws that are passivity-short at low frequencies. Passivity and interconnected-stability properties are demonstrated through an example case study.
Techno-economic optimization of hybrid steam-electric energy systems with excess heat utilization and reserve market participation
Authors: Olav Galteland, Jacob Hadler-Jacobsen, Hanne Kauko
Date: 2025-06-17 00:00:00

This study investigates the economic viability and optimal configuration of a hybrid industrial energy system combining an electrode boiler, steam accumulator, and battery energy storage system (BESS). This study optimizes system operation for a specific configuration to minimize net energy costs, defined as energy costs minus profits from price arbitrage and reserve markets. The optimization uses load shifting, peak shaving, and frequency containment reserve market participation with hourly 2024 data from Norway and Germany. Net present value (NPV) analysis was performed to determine the most cost-efficient energy storage configurations. The results show that current investment costs favor steam accumulators over BESS in both countries. However, a reduction in BESS cost will make batteries economically competitive, particularly in Germany, where high price volatility and power-based grid tariffs provide stronger incentives for load shifting and peak shaving. Participation in the FCR market accounted for a 17% and 7% reduction of the net energy costs in Norway and Germany, respectively. Utilization of excess heat, through inlet water preheating, further reduced the net energy costs. Sensitivity analyses confirm that investment costs, especially for BESS, strongly influence optimal system design. These findings offer guidance for industrial flexibility investments across diverse electricity markets.
Nonlinear Control of a Quadrotor UAV Using Backstepping-Based Sliding Mode Technique
Authors: Thien Nhan Vo
Date: 2025-06-17 00:00:00

This paper presents the development of a sliding mode controller using the backstepping approach. The controller is employed to synthesize tracking errors and Lyapunov functions. A novel state-space representation is formulated by incorporating the dynamics of the quadrotor and accounting for non-holonomic constraints. The proposed sliding mode controller effectively addresses system nonlinearities and improves tracking of predefined trajectories. Simulation results are presented graphically to demonstrate the controller's performance.
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Authors: Nikhil Pawar, Guilherme Vieira Hollweg, Akhtar Hussain, Wencong Su, Van-Hai Bui
Date: 2025-06-17 00:00:00
Tasks: Drift Detection, Feature Engineering, Time Series, Time Series Forecasting

Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction performance. To address this challenge, this study proposes an adaptive forecasting framework that integrates drift detection with targeted model retraining to compensate for drift effects. The framework utilizes the Pruned Exact Linear Time (PELT) algorithm to identify drift points within the feature space of time series data. Once drift intervals are detected, selective retraining is applied to prediction models using Multilayer Perceptron (MLP) and Lasso Regressor architectures, allowing the models to adjust to changing data patterns. The effectiveness of the proposed approach is demonstrated on two datasets: a real-world dataset containing electricity consumption and HVAC system data, and a synthetic financial dataset designed to test cross-domain applicability. Initial baseline models were developed without drift detection using extensive feature engineering. After integrating drift-aware retraining, the MLP model achieved a 44% reduction in mean absolute error (MAE) and a 39% increase in R^2 on the real-world dataset, while even greater improvements were observed on the synthetic financial dataset. Similar enhancements were achieved with the Lasso Regressor. These results highlight the robustness and generalizability of incorporating drift detection and adaptive retraining to sustain forecasting accuracy across diverse domains.
Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles
Authors: Hengyu Liu, Yanhong Luo, Congcong Wu, Yin Guan, Ahmed Lotfy Elrefai, Andreas Elombo, Si Li, Sahban Wael Saeed Alnaser, Mingyu Yan
Date: 2025-06-17 00:00:00
Tasks: Scheduling

The large-scale access of electric vehicles to the power grid not only provides flexible adjustment resources for the power system, but the temporal uncertainty and distribution complexity of their energy interaction pose significant challenges to the economy and robustness of the micro-energy network. In this paper, we propose a multi-time scale rolling optimization scheduling method for micro-energy networks considering the access of electric vehicles. In order to solve the problem of evaluating the dispatchable potential of electric vehicle clusters, a charging station aggregation model was constructed based on Minkowski summation theory, and the scattered electric vehicle resources were aggregated into virtual energy storage units to participate in system scheduling. Integrate price-based and incentive-based demand response mechanisms to synergistically tap the potential of source-load two-side regulation; On this basis, a two-stage optimal scheduling model of day-ahead and intra-day is constructed. The simulation results show that the proposed method reduces the scale of "preventive curtailment" due to more accurate scheduling, avoids the threat of power shortage to the safety of the power grid, and has more advantages in the efficiency of new energy consumption. At the same time, intra-day scheduling significantly reduces economic penalties and operating costs by avoiding output shortages, and improves the economy of the system in an uncertain forecasting environment.
From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks
Authors: Weijie Yuan, Yuanhao Cui, Jiacheng Wang, Fan Liu, Geng Sun, Tao Xiang, Jie Xu, Shi Jin, Dusit Niyato, Sinem Coleri, Sumei Sun, Shiwen Mao, Abbas Jamalipour, Dong In Kim, Mohamed-Slim Alouini, Xuemin Shen
Date: 2025-06-14 00:00:00
Tasks: Integrated sensing and communication, ISAC, Semantic Communication

In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nodes that enable seamless operation in complex, dynamic, and mission-critical environments. Different from the conventional aerial communication systems, LAWN's distinctive feature is its tight integration of functional planes in which multiple functionalities continually reshape themselves to operate safely and efficiently in the low-altitude sky. With the LAWN, we discuss several enabling technologies, such as integrated sensing and communication (ISAC), semantic communication, and fully-actuated control systems. Finally, we identify potential applications and key cross-layer challenges. This article offers a comprehensive roadmap for future research and development in the low-altitude airspace.
Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition
Authors: Zhicheng Zhang, Yoshihiko Susuki, Atsushi Okazaki
Date: 2025-06-17 00:00:00
Tasks: Diagnostic

Convective features-here represented as warm bubble-like patterns-reveal essential, high-level information about how short-term weather dynamics evolve within a high-dimensional state space. We introduce a data-driven framework that uncovers transient dynamics captured by Koopman modes responsible for these structures and traces their emergence, growth, and decay. Our approach incorporates the sparsity-promoting dynamic mode decomposition into the framework of Koopman mode decomposition, yielding a few number of selected modes whose sparse amplitudes highlight dominant transient structures. By tuning the sparsity weight, we balance reconstruction accuracy and model complexity. We illustrate the methodology on weather simulations, using the magnitude of velocity and vorticity fields as distinct observable datasets. The resulting sparse dominant Koopman modes capture the transient evolution of bubble-like pattern and can reduce the dimensionality of the weather system model, offering an efficient surrogate for diagnostic and forecasting tasks.
A Stochastic Differential Equation Framework for Modeling Queue Length Dynamics Inspired by Self-Similarity
Authors: Shakib Mustavee, Shaurya Agarwal, Arvind Singh
Date: 2025-06-16 00:00:00

This article develops a stochastic differential equation (SDE) for modeling the temporal evolution of queue length dynamics at signalized intersections. Inspired by the observed quasiperiodic and self-similar characteristics of the queue length dynamics, the proposed model incorporates three properties into the SDE: (i) mean reversion with periodic mean, (ii) multiplicative noise, and (iii) fractional Brownian motion. It replicates key statistical features observed in real data, including the probability distribution function (PDF) and PSD of queue lengths. To our knowledge, this is the first equation-based model for queue dynamics. The proposed approach offers a transparent, data-consistent framework that may help inform and enhance the design of black-box learning algorithms with underlying traffic physics.