pwc-archive/papers-with-abstracts huggingface dataset:
Quantitative Indices for Improving Metro Load Curve, Using Distributed Generation
Authors: Masoud Behbahani, Alireza Fereidunian
Date: 2025-06-10 00:00:00
Tasks: Electrical Engineering

This paper promises the idea of using DG (Distributed Generation) to improve the Metro load curve. Public transportation systems are often based on gasoline and diesel. However, with the gradual development in usage of the Metro and monorail, a new load with heavy demand, inappropriate load curve and middle LF (Load factor) is added to the electricity grid. In addition to supply problem of this massive consumer, the Metro load curve is another problem, which has a relatively low LF. Furthermore, Metro load peak hours coincide with the peaks of national grid. Improvement of the load curve is well-known in electrical engineering literature, which depending on the type of load curve, offers general recommendations in three approaches; DSM (Demand Side Management), DS (Distributed Storage) and DG. In this paper, to achieve quantitative indices of improvement for Metro load curve using DG, firstly based on the analysis of volume and consumption pattern of the main loads in Metro, the typical load curve has been extracted. Using this curve, the result of using DG is shown by quantitative parameters which represent the significant improvement in load curve. These parameters can be used to calculate economic indicators such as initial cost and ROI (Return of Investment).
HabSim: Architecture for modelling disruptions, propagation, detection and repair in deep space habitats
Authors: Luca Vaccino, Alana K. Lund, Shirley J. Dyke, Mohsen Azimi, Ethan Vallerga
Date: 2025-06-10 00:00:00
Tasks: Computational Efficiency

Establishing long-term human settlements in deep space presents significant challenges. Harsh environmental conditions, such as extreme temperature fluctuations, micrometeorite impacts, seismic activity, and exposure to solar and cosmic radiation pose obstacles to the design and operation of habitat systems. Prolonged mission duration and the vast distances from Earth introduce further complications in the form of delayed communication and limited resources, making autonomy especially desirable. Enabling simulation of the consequences of disruptions and their propagation through the various habitat subsystems is important for the development of autonomous and resilient space habitats. While existing simulation tools can assist in modeling some of these aspects, the integration of damage propagation, detection and repair in a computational model is rarely considered. This paper introduces and demonstrates a simulation architecture designed to model these aspects efficiently. By combining physics-based and phenomenological models, our approach balances computational efficiency with model fidelity. Furthermore, by coordinating subsystems operating at different time scales, we achieve real-time simulation capabilities. After describing the architecture, we demonstrate its application within HabSim, a space habitat system model developed by the NASA-funded Resilient Extraterrestrial Habitat Institute (RETHi). In these scenarios we consider fire hazard propagation within a lunar habitat to illustrate both how our architecture supports the modeling of disruption propagation, detection, and repair in a simulation environment and how the HabSim model can be leveraged for through stochastic simulations to support resilience assessment. The architecture developed herein is efficient and scalable, enabling researchers to gain insight into resilience, autonomy and decision-making.
Distributed component-level modeling and control of energy dynamics in electric power systems
Authors: Hiya Gada, Rupamathi Jaddivada, Marija Ilic
Date: 2025-06-10 00:00:00

The widespread deployment of power electronic-based technologies is transforming modern power systems into fast, nonlinear, and heterogeneous systems. Conventional modeling and control approaches, rooted in quasi-static analysis and centralized control, are inadequate for these converter-dominated systems, which operate on fast timescales and involve proprietary models of diverse components. This paper adopts and extends a previously introduced energy space modeling framework grounded in energy conservation principles to address these challenges. We generalize the notion of a port interaction variable, which encodes energy exchange between interconnected, heterogeneous components in a unified and physically intuitive manner. A multilayered distributed control architecture is proposed, wherein the nonlinear physical dynamics of each component are lifted to a higher-level linear energy space through well-defined mappings. Distributed controllers are designed in this energy space using only local states and minimal neighbor information via port interaction variables. Two control designs, energy-based feedback linearizing control (FBLC) and sliding mode control (SMC), are proven to achieve asymptotic convergence to reference outputs. The approach is validated on two systems: an inverter-controlled RLC circuit and a synchronous generator connected to a load. In both cases, energy-based control improves transient response and reduces control effort.
Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland
Authors: Lorenzo Zapparoli, Alfredo Oneto, María Parajeles Herrera, Blazhe Gjorgiev, Gabriela Hug, Giovanni Sansavini
Date: 2025-06-10 00:00:00

The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.
Linguistic Ordered Weighted Averaging based deep learning pooling for fault diagnosis in a wastewater treatment plant
Authors: Alicia Beneyto-Rodriguez, Gregorio I. Sainz-Palmero, Marta Galende-Hernández, María J. Fuente
Date: 2025-06-10 00:00:00
Tasks: Fault Diagnosis

Nowadays, water reuse is a serious challenge to help address water shortages. Here, the wastewater treatment plants (WWTP) play a key role, and its proper operation is mandatory. So, fault diagnosis is a key activity for these plants. Their high complexity and large-scale require of smart methodologies for that fault diagnosis and safety operation. All these large-scale and complex industrial processes are monitored, allowing the data collection about the plant operation, so data driven approaches for fault diagnosis can be applied. A popular approach to fault diagnosis is deep learning-based methodologies. Here, a fault diagnosis methodology is proposed for a WWTP using a new linguistic Ordered Weighted Averaging (OWA) pooling based Deep Convolutional Neural Network (DCNN) and a sliding and overlapping time window. This window slides over input data based on the monitoring sampling time, then the diagnosis is carried out by the linguistic OWA pooling based DCNN. This alternative linguistic pooling uses well-known linguistic OWA quantifiers, which permit terms such as \textsl{Most, AtLeast, etc.}, supplying new intuitive options for the pooling tasks. This sliding time window and the OWA pooling based network permit a better and earlier fault diagnosis, at each sampling time, using a few monitoring samples and a fewer learning iterations than DCNN standard pooling. Several linguistic OWA operators have been checked with a benchmark for WWTPs. A set of 5 fault types has been used, taking into account 140 variables sampled at 15 minutes time intervals. The performance has been over $91\%$ for $Accuracy$, $Recall$ or $F1-Score$, and better than other competitive methodologies. Moreover, these linguistic OWA operators for DCNN pooling have shown a better performance than the standard \textsl{Max} and \textsl{Average} options.
Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
Authors: Yang Lv, Rong Hu, Bin Qian, Jian-Bo Yang
Date: 2025-06-10 00:00:00
Tasks: Q-Learning, Scheduling

The steelmaking continuous casting scheduling problem (SCCSP) is a critical and complex challenge in modern steel production, requiring the coordinated assignment and sequencing of steel charges across multiple production stages. Efficient scheduling not only enhances productivity but also significantly reduces energy consumption. However, both traditional heuristics (e.g., two-stage local search) and recent metaheuristics often struggle to adapt to the dynamic characteristics of practical SCCSP instances. To address these limitations, this paper introduces a novel Q learning based hierarchical cooperative local search framework, termed HierC_Q, aimed at minimizing the weighted sum of the maximum completion time and the average waiting time in SCCSP. The core contributions of HierC_Q are twofold. First, considering the intrinsic coupling properties of the SCCSP, a dedicated reward function is proposed based on a novel coupling measure (CM), guiding the exploration process towards promising regions of the solution space. Second, a hierarchical architecture is devised, comprising two distinct tiers: the learn to improve (L2I) tier and the "disturb to renovate" (D2R) tier. The L2I tier performs deep exploitation within promising regions using two independent Q-learning-based local search frameworks (QLSFs) tailored for subproblems, along with a synergy QLSF designed for the main problem. To enhance the effectiveness of local search, a validity evaluation approach and a speed-up evaluation method are also intro-duced, grounded in a detailed study of the problem's structure. Meanwhile, the D2R tier incorporates a perturbation and construction based solution renewal strategy to mitigate the risk of premature convergence. The superiority and effectiveness of HierC_Q are demonstrated through extensive comparisons with eleven local search frameworks and nine state-of-the-art algorithms.
The Invariant Zonotopic Set-Membership Filter for State Estimation on Groups
Authors: Tao Li, Yi Li, Lulin Zhang, Jiuxiang Dong
Date: 2025-06-10 00:00:00
Tasks: State Estimation

The invariant filtering theory based on the group theory has been successful in statistical filtering methods. However, there exists a class of state estimation problems with unknown statistical properties of noise disturbances, and it is worth discussing whether the invariant observer still has performance advantages. In this paper, considering the problem of state estimation with unknown but bounded noise disturbances, an Invariant Zonotopic Set-Membership Filter (InZSMF) method on groups is innovatively proposed, which extends the invariant filtering theory to the field of non-statistical filtering represented by set-membership filtering. Firstly, the InZSMF method transforms the state space from the traditional Euclidean vector space to the Lie group space to construct group affine discrete systems with unknown but bounded noise uncertainty defined by the zonotope on groups. Secondly, the nonlinear observer on the group is defined and the corresponding linearized estimation error is derived. Then, two observer gain tuning algorithms under the InZSMF method are proposed, respectively, the pole configuration method and the F-radius optimization method. Finally, through simulation experiments, it is shown that the InZSMF state estimation method is generally superior to the traditional Zonotopic Set-Membership Filter (ZSMF) state estimation method. Especially, when the initial estimations are imprecise, the convergence speed of state estimation, the accuracy of set-membership center estimation, and the average interval area of zonotopic estimation of the InZSMF method are significantly better than those of the ZSMF method.
Predictive reinforcement learning based adaptive PID controller
Authors: Chaoqun Ma, Zhiyong Zhang
Date: 2025-06-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support training. Findings: Experimental results show that the PRL-PID controller achieves superior stability and tracking accuracy in nonlinear, unstable, and strongly coupled systems, consistently outperforming existing RL-tuned PID methods while maintaining excellent robustness and adaptability across diverse operating conditions. Originality/Value: By adopting predictive learning, the proposed PRL-PID integrates system model priors into data-driven control, enhancing both the control framework's training efficiency and the controller's stability. As a result, PRL-PID provides a balanced blend of model-based and data-driven approaches, delivering robust, high-performance control.
Theoretical Foundations of Waste Factor and Waste Figure with Applications to Fixed Wireless Access and Relay Systems
Authors: Nurullah Sevim, Mostafa Ibrahim, Sabit Ekin, Theodore S. Rappaport
Date: 2025-06-10 00:00:00

The exponential rise in energy consumption across wireless communication systems, particularly in anticipation of next-generation wireless systems, necessitates rigorous frameworks for evaluating and optimizing energy efficiency. This paper revisits and expands the concept of the Waste Factor (W), or Waste Figure (WF) in decibel scale, as a unifying metric that captures both utilized and wasted power in cascaded communication systems. Building upon its foundation in system-level power modeling, we integrate the Waste Factor into a refined formulation of the Consumption Factor (CF), the ratio of data rate to total consumed power, linking it directly to Shannon's theoretical limit on energy per bit. This analysis introduces additive energy waste into the classical energy-per-bit derivation through the Waste Factor term. We derive closed-form expressions for energy-per-bit expenditure in both direct and relay-assisted links and develop a decision rule to determine which communication path is more energy efficient under given conditions. While not modeled explicitly, Reflective Intelligent Surfaces (RIS) can be interpreted as a special case of relay-based architectures within this unified formulation, suggesting broader applicability of the Waste Factor framework to emerging 6G use cases. The framework is then extended to a Fixed Wireless Access (FWA) scenario, where uplink and downlink asymmetries, traffic directionality, and component inefficiencies are jointly considered to analyze energy-optimal deployment strategies.
Compact Amplified Laser Power Stabilization Using Robust Active Disturbance Rejection Control with Sensor Noise Decoupling
Authors: Yanpei Shi, JingXuan Zhang, Zhuo Shi, Chenyao Zhang, Yuze Guo, Rui Feng
Date: 2025-06-10 00:00:00

Laser power instability, encompassing random jitter and slow drift, severely limits the performance of optically pumped magnetometers (OPMs) in detecting ultra-weak magnetic fields, especially in large-scale OPM arrays for magnetoencephalography. Although a unified amplified laser (AL) architecture improves integration, fluctuations in the pump beam progressively degrade performance across all channels, exacerbated by environmental disturbances and system uncertainties. To address this challenge, this paper presents a compact AL power stabilization approach based on an innovative dual-loop active disturbance rejection control (DLADRC) strategy, while integrating a comprehensive quantitative stability analysis through novel exponential decay estimates for extended state observers (ESOs) and control error dynamics. As validated through physical experimental results, the proposed method significantly improves AL's long-term stability with sensor noise decoupling, achieving an over 85.7% reduction in 1-hour power instability and a tenfold decrease in Allan variance for correlation times 10^2 s--10^3 s, compared to standard ADRC. Crucially, the strategy demonstrates robust effectiveness across diverse operating scenarios, enabling AL-based OPM systems to achieve their full potential in high-sensitivity biomagnetic field detection.
Learning event-triggered controllers for linear parameter-varying systems from data
Authors: Renjie Ma, Su Zhang, Wenjie Liu, Zhijian Hu, Peng Shi
Date: 2025-06-10 00:00:00
Tasks: LEMMA

Nonlinear dynamical behaviours in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop the data-driven control strategies for event-triggered LPV systems with stability verifications. First, we provide the theoretical analysis of ${\theta}$-persistence of excitation for LPV systems, which leads to the feasible data-based representations. Then, in terms of the available perturbed data, we derive the stability certificates for event-triggered LPV systems with the aid of Petersen's lemma in the sense of robust control, resulting in the computationally tractable semidefinite programmings, the feasible solutions of which yields the optimal gain schedulings. Besides, we generalize the data-driven eventtriggered LPV control methods to the scenario of reference trajectory tracking, and discuss the robust tracking stability accordingly. Finally, we verify the effectiveness of our theoretical derivations by numerical simulations.
Standard LSParameter Estimators Ensure Finite Convergence Time for Linear Regression Equations Under an Interval Excitation Assumption
Authors: Romeo Ortega, Jose Guadalupe Romero, Stanislav Aranovskiy, Gang Tao
Date: 2025-06-09 00:00:00
Tasks: parameter estimation

In this brief note we recall the little-known fact that, for linear regression equations (LRE) with intervally excited (IE) regressors, standard Least Square (LS) parameter estimators ensure finite convergence time (FCT) of the estimated parameters. The convergence time being equal to the time length needed to comply with the IE assumption. As is well-known, IE is necessary and sufficient for the identifiability of the LRE-hence, it is the weakest assumption for the on-or off-line solution of the parameter estimation problem.
A 40.68-MHz, 200-ns-Settling Active Rectifier and TX-Side Load Monitoring for Minimizing Radiated Power in Biomedical Implants
Authors: Ronald Wijermars, Yi-Han Ou-Yang, Sijun Du, Dante Gabriel Muratore
Date: 2025-06-09 00:00:00

This letter describes a 40.68 MHz wireless power transfer receiver for implantable applications focused on minimizing tissue heating. The system features a novel power radiated efficiency optimization strategy and a fast-settling active rectifier that maintains high efficiency during load and link variations required for downlink communication. The power radiated efficiency optimization explicitly reduces tissue heating while enabling transmitter-side load monitoring for closed-loop control. The active rectifier was fabricated in 40nm CMOS and achieves a voltage conversion ratio of 93.9% and a simulated power conversion efficiency of 90.1% in a 0.19 $mm^2$ area, resulting in a 118 mW/$mm^2$ power density while integrating the resonance and filter capacitors. The worst-case settling of the on- and off-delay compensation in the active rectifier is 200 ns, which is the fastest reported to date.
Pseudo-random sequences for low-cost operando impedance measurements of Li-ion batteries
Authors: Jussi Sihvo, Noël Hallemans, Ai Hui Tan, David A. Howey, Stephen. R. Duncan, Tomi Roinila
Date: 2025-06-09 00:00:00
Tasks: Management

Operando impedance measurements are promising for monitoring batteries in the field. In this work, we present pseudo-random sequences for low-cost operando battery impedance measurements. The quadratic-residue ternary sequence and direct-synthesis ternary sequence exhibit specific properties related to eigenvectors of the discrete Fourier transform matrix that allow computationally efficient compensation for drifts and transients in operando impedance measurements. We describe the application of pseudo-random sequences and provide the data processing required to suppress drift and transients, validated on simulations. Finally, we perform experimental operando impedance measurements on a Li-ion battery cell during fast-charging, demonstrating the applicability of the proposed method. It's low-cost hardware requirements, fast measurements, and simple data-processing make the method practical for embedding in battery management systems.
Extended Version of "Distributed Adaptive Resilient Consensus Control for Uncertain Nonlinear Multiagent Systems Against Deception Attacks"
Authors: Mengze Yu, Wei Wang, Jiaqi Yan
Date: 2025-06-09 00:00:00

This paper studies distributed resilient consensus problem for a class of uncertain nonlinear multiagent systems susceptible to deception attacks. The attacks invade both sensor and actuator channels of each agent. A specific class of Nussbaum functions is adopted to manage the attack-incurred multiple unknown control directions. Additionally, a general form of these Nussbaum functions is provided, which helps to ease the degeneration of output performance caused by Nussbaum gains. Then, by introducing finite-time distributed reference systems and local-error-based dynamic gains, we propose a novel distributed adaptive backstepping-based resilient consensus control strategy. We prove that all the closed-loop signals are uniformly bounded under attacks, and output consensus errors converge in finite time to a clearly-defined residual set whose size can be reduced by tuning control parameters, which is superior to existing results. Simulation results display the effectiveness of the proposed controllers.