pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Target Tracking based on Localization with Linear Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked Estimation Approach
Authors: Mohammadreza Doostmohammadian, Themistoklis Charalambous
Date: 2024-12-22 00:00:00

This paper considers target tracking based on a beacon signal's time-difference-of-arrival (TDOA) to a group of cooperating sensors. The sensors receive a reflected signal from the target where the time-of-arrival (TOA) renders the distance information. The existing approaches include: (i) classic centralized solutions which gather and process the target data at a central unit, (ii) distributed solutions which assume that the target data is observable in the dense neighborhood of each sensor (to be filtered locally), and (iii) double time-scale distributed methods with high rates of communication/consensus over the network. This work, in order to reduce the network connectivity in (i)-(ii) and communication rate in (iii), proposes a distributed single time-scale technique, which can also handle heterogeneous constant data-exchange delays over the static sensor network. This work assumes only distributed observability (in contrast to local observability in some existing works categorized in (ii)), i.e., the target is observable globally over a (strongly) connected network. The (strong) connectivity further allows for survivable network and $q$-redundant observer design. Each sensor locally shares information and processes the received data in its immediate neighborhood via local linear-matrix-inequalities (LMI) feedback gains to ensure tracking error stability. The same gain matrix works in the presence of heterogeneous delays with no need of redesigning algorithms. Since most existing distributed estimation scenarios are linear (based on consensus), many works use linearization of the existing nonlinear TDOA measurement models where the output matrix is a function of the target position.
GLARE: Google Apps Arabic Reviews Dataset
Authors: Fatima AlGhamdi, Reem Mohammed, Hend Al-Khalifa, Areeb Alowisheq
Date: 2024-12-16 00:00:00
Tasks: Feature Engineering

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.
An Incremental Clustering Baseline for Event Detection on Twitter
Authors: Marjolaine Ray, Qi Wang, Frédérique Mélanie-Becquet, Thierry Poibeau, Béatrice Mazoyer
Date: 2024-12-16 00:00:00
Tasks: Clustering, Event Detection, Sentence, Sentence Embeddings

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.
A reaction network approach to modeling carbon dioxide removal systems
Authors: Noel Fortun, Piolo Gaspar, Editha Jose, Angelyn Lao, Eduardo Mendoza, Luis Razon
Date: 2024-12-23 00:00:00

This paper focuses on what we call Reaction Network Cardon Dioxide Removal (RNDCR) framework to analyze several proposed negative emissions technologies (NETs) so as to determine when present-day Earth carbon cycle system would exhibit multistationarity (steady-state multiplicity) or possibly monostationarity, that will in effect lower the rising earth temperature. Using mathematical modeling based on the techniques of chemical reaction network theory (CRNT), we propose an RNDCR system consisting of the Anderies subnetwork, fossil fuel emission reaction, the carbon capture subnetwork and the carbon storage subnetwork. The RNCDR framework analysis was done in the cases of two NETs: Bioenergy with Carbon Capture and Storage and Afforestation/Reforestation. It was found out that these two methods of carbon dioxide removal are almost similar with respect to their network properties and their capacities to exhibit multistationarity and absolute concentration robustness in certain species.
Accuracy and robust early detection of short-circuit faults in single-cell lithium battery
Authors: Chengzhong Zhang, Hongyu Zhao, Wenjie Zhang
Date: 2024-12-23 00:00:00

Effective early-stage detection of internal short circuit in lithium-ion batteries is crucial to preventing thermal runaway. This report proposes an effective approach to address this challenging issue, in which the current change, state of charge and resistance are considered simultaneously to depict the voltage differential envelope curve. The envelope naturally utilizes the inherent physical information of the battery and accounts for error interference, providing a high-precision range for battery voltage fluctuations under any operating conditions. This study validates the algorithm using data from 10 fault intervals under dynamic operating condition. The results demonstrate that the algorithm achieves 100% accuracy and responds rapidly, enabling timely detection of early-stage internal short circuit faults in batteries. Compared to signal processing-based and neural network methods, the proposed approach offers significant advantages in both accuracy and practicality, making it highly relevant for the safe application and widespread adoption of lithium-ion batteries.
Resilience Dynamics in Coupled Natural-Industrial Systems: A Surrogate Modeling Approach for Assessing Climate Change Impacts on Industrial Ecosystems
Authors: William Farlessyost, Shweta Singh
Date: 2024-12-22 00:00:00

Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors
Leveraging Neural Networks to Optimize Heliostat Field Aiming Strategies in Concentrating Solar Power Tower Plants
Authors: Antonio Alcántara, Pablo Diaz-Cachinero, Alberto Sánchez-González, Carlos Ruiz
Date: 2024-12-22 00:00:00

Concentrating Solar Power Tower (CSPT) plants rely on heliostat fields to focus sunlight onto a central receiver. Although simple aiming strategies, such as directing all heliostats to the receivers equator, can maximize energy collection, they often result in uneven flux distributions that lead to hotspots, thermal stresses, and reduced receiver lifetimes. This paper presents a novel, data-driven approach that integrates constraint learning, neural network-based surrogates, and mathematical optimization to overcome these challenges. The methodology learns complex heliostat-to-receiver flux interactions from simulation data, constructing a surrogate model that is embedded into a tractable optimization framework. By maximizing a tailored quality score that balances energy collection and flux uniformity, the approach yields smoothly distributed flux profiles and mitigates excessive thermal peaks. An iterative refinement process, guided by the trust region and progressive data sampling, ensures the surrogate model improves the obtained solution by exploring new spaces during the iterations. Results from a real CSPT case study demonstrate that the proposed approach surpasses conventional heuristic methods, offering flatter flux distributions and safer thermal conditions without a substantial loss in overall energy capture.
NMPC and Deep Learning-Based Vibration Control of Satellite Beam Antenna Dynamics Using PZT Actuators and Sensors
Authors: Sean Kalaycioglu, Daniel Ding
Date: 2024-12-21 00:00:00
Tasks: Deep Learning, Model Predictive Control

This paper presents a novel approach for vibration control of satellite-based flexible beam-type antennas using Nonlinear Model Predictive Control (NMPC) and Deep Learning techniques. The developed control system leverages piezoelectric (PZT) actuators and sensors to manage the coupled attitude and structural dynamics of the satellite, improving precision and stability. We propose a detailed coupled dynamics model that integrates both satellite attitude and beam structural dynamics, considering the effects of PZT-based actuators. Through MATLAB/Simulink simulations, we demonstrate the effectiveness of the combined NMPC and Deep Learning framework in reducing structural vibrations, achieving faster response times, and enhancing overall control accuracy. The results indicate that the proposed system provides a robust solution for controlling flexible beam-type satellite antennas in space environments.
Fixed-Time Input-to-State Stability for Singularly Perturbed Systems via Composite Lyapunov Functions
Authors: Michael Tang, Miroslav Krstic, Jorge Poveda
Date: 2024-12-21 00:00:00

We study singularly perturbed systems that exhibit input-to-state stability (ISS) with fixed-time properties in the presence of bounded disturbances. In these systems, solutions converge to the origin within a time frame independent of initial conditions when undisturbed, and to a vicinity of the origin when subjected to bounded disturbances. First, we extend the traditional composite Lyapunov method, commonly applied in singular perturbation theory to analyze asymptotic stability, to include fixed-time ISS. We demonstrate that if both the reduced system and the boundary layer system exhibit fixed-time ISS, and if certain interconnection conditions are met, the entire multi-time scale system retains this fixed-time ISS characteristic, provided the separation of time scales is sufficiently pronounced. Next, we illustrate our findings via analytical and numerical examples, including a novel application in fixed-time feedback optimization for dynamic plants with slowly varying cost functions.
Non-asymptotic Error Analysis of Subspace Identification for Deterministic Systems
Authors: Shuai Sun
Date: 2024-12-21 00:00:00
Tasks: State Space Models

The subspace identification method (SIM) has been extensively employed in the identification of discrete-time multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. This paper focuses on the analysis of perturbation errors for the system matrices in state-space models and the corresponding system poles, under two unified SIMs, based on a single finite-length input/output sample trajectory. Specifically, we derive non-asymptotic upper bounds on these errors, providing a unified perspective across various SIM variants. Furthermore, we prove that SIMs become ill-conditioned for MIMO systems when the state-to-output dimensionality ratio $n/m$ is large, regardless of system parameters. Finally, numerical experiments are conducted to validate the non-asymptotic results and the ill-conditionedness of SIMs.
Vehicle Rebalancing Under Adherence Uncertainty
Authors: Avalpreet Singh Brar, Rong Su, Gioele Zardini
Date: 2024-12-21 00:00:00

Ride hailing systems suffer from spatial-temporal supply demand imbalance due to drivers operating in independent, and uncoordinated manner. Several fleet rebalancing models have been proposed that can provide repositioning recommendations to idle standing drivers with the objective of maximizing service rate or minimizing customer waiting time. Existing models assume complete adherence by the drivers which leads to limited practical implementation of these models. A novel taxi rebalancing model and a procedure to compute recommendations for repositioning of taxis are proposed which accounts for uncertainties in adherence arising from taxi driver's preferences and the evolving confidence of the driver in the system due to outcomes of repositioning recommendations. Extensive simulations using NYC taxi dataset showed that the proposed model can lead to 6.3% higher allocation rates, 11.8% higher driver profits, 4.5% higher demand fulfillment, and 14% higher confidence of drivers in the rebalancing system, as compared to a state-of-the-art rebalancing model that is agnostic to the taxi driver preferences and confidence in the system.
Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information
Authors: Max Langtry, Ruchi Choudhary
Date: 2024-12-20 00:00:00
Tasks: Decision Making

Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for improved designs with less compromise to be identified, reducing the cost of decarbonizing energy usage. However, the building monitoring required to reduce load uncertainty is costly. This study quantifies the economic benefit of practical building monitoring for supporting energy system design decisions, to determine if its benefits outweigh its cost. Value of Information analysis (VoI) is a numerical framework for quantifying the benefit of uncertainty reduction to support decision making. An extension of the framework, termed 'On-Policy' VoI, is proposed, which admits complex decision making tasks where decision policies are required. This is applied to a case study district energy system design problem, where a Linear Program model is used to size solar-battery systems and grid connection capacity under uncertain building loads, modelled using historic electricity metering data. Load uncertainty is found to have a significant impact on both system operating costs (\pm30%) and the optimal system design (\pm20%). However, using building monitoring is found to reduce overall costs by less than 2% on average, less than the cost of measurement, and is therefore not economically worthwhile. This provides the first numerical evidence to support the sufficiency of using standard building load profiles for energy system design. Further, reducing only uncertainty in mean load is found to provide all available decision support benefit, meaning using hourly measurement data provides no benefit for energy retrofit design.
PyBOP: A Python package for battery model optimisation and parameterisation
Authors: Brady Planden, Nicola E. Courtier, Martin Robinson, Agriya Khetarpal, Ferran Brosa Planella, David A. Howey
Date: 2024-12-20 00:00:00

The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, including the electrochemical and equivalent circuit models provided by the popular open-source PyBaMM package. Using the same approaches, PyBOP can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. PyBOP facilitates optimisation with a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line estimation and control, and design optimisation, accelerating battery research and development.
Tutorial Problems for Nonsmooth Dynamics and Optimal Control: Ski Jumping and Accelerating a Bike Without Pedaling
Authors: Julian Golembiewski, Timm Faulwasser
Date: 2024-12-20 00:00:00

Nonsmooth phenomena, such as abrupt changes, impacts, and switching behaviors, frequently arise in real-world systems and present significant challenges for traditional optimal control methods, which typically assume smoothness and differentiability. These phenomena introduce numerical challenges in both simulation and optimization, highlighting the need for specialized solution methods. Although various applications and test problems have been documented in the literature, many are either overly simplified, excessively complex, or narrowly focused on specific domains. On this canvas, this paper proposes two novel tutorial problems that are both conceptually accessible and allow for further scaling of problem difficulty. The first problem features a simple ski jump model, characterized by state-dependent jumps and sliding motion on impact surfaces. This system does not involve control inputs and serves as a testbed for simulating nonsmooth dynamics. The second problem considers optimal control of a special type of bicycle model. This problem is inspired by practical techniques observed in BMX riding and mountain biking, where riders accelerate their bike without pedaling by strategically shifting their center of mass in response to the track's slope.
FTISS Adaptive Bearing-Only Formation Tracking Control with Unknown Disturbance Rejection
Authors: Hong Liang Cheah, Mohammad Deghat
Date: 2024-12-20 00:00:00

This paper proposes a finite-time input-to-state stable (FTISS) bearing-only formation control law that rejects unknown constant disturbances. Unlike existing finite-time bearing-based formation control laws, which typically rely on the availability of a global coordinate frame and some information about the disturbances, our approach requires only local bearing vector measurements and does not necessitate the alignment of agent coordinate frames. The proposed control law guarantees that formation control errors converge to a neighborhood of zero in finite time, and subsequently converge to zero asymptotically. We first address the scenario where leaders are stationary and then extend the results to leaders moving with a constant velocity. Simulation and experimental results are presented to validate the effectiveness of the proposed control law.
Asynchronous Vector Consensus over Matrix-Weighted Networks
Authors: P Raghavendra Rao, Pooja Vyavahare
Date: 2024-12-20 00:00:00

We study the distributed consensus of state vectors in a discrete-time multi-agent network with matrix edge weights using stochastic matrix convergence theory. We present a distributed asynchronous time update model wherein one randomly selected agent updates its state vector at a time by interacting with its neighbors. We prove that all agents converge to same state vector almost surely when every edge weight matrix is positive definite. We study vector consensus in cooperative-competitive networks with edge weights being either positive or negative definite matrices and present a necessary and sufficient condition to achieve bipartite vector consensus in such networks. We study the network structures on which agents achieve zero consensus. We also present a convergence result on nonhomogenous matrix products which is of independent interest in matrix convergence theory. All the results hold true for the synchronous time update model as well in which all agents update their states simultaneously.
Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
Authors: Bang Nguyen, Mayank Panwar, Rob Hovsapian, Yashodhan Agalgaonkar
Date: 2024-12-20 00:00:00
Tasks: energy management, Load Forecasting, Management

Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.
Advanced Control Strategy to Compensate Power Sharing Error and DC Circulating Current in Parallel Single-Phase Inverters
Authors: Quoc Nam Trinh, Bang Nguyen, Rob Hovsapian
Date: 2024-12-20 00:00:00

This paper proposes an advanced control strategy to eliminate both current sharing error and DC circulating current caused by line impedance mismatched and measurement errors in islanded AC microgrid system. The proposed adaptive virtual impedance scheme is developed with the aid of a low bandwidth communication and a simple PI compensator to adaptively adjust the virtual impedance value so that active and reactive power will be shared equally among inverters. Meanwhile, the DC offset in output voltage measurement is detected by using the voltage ripple information of DC link voltage of inverter. The proposed control strategy can be implemented directly without any pre-knowledge of the feeder impedances. The proposed compensation scheme is simple and easy to implement, and it also does not require extra hardware circuit or sensors. The effectiveness of the proposed solution is verified by simulation and experimental results of 6 kVA microgrid system.
Effects of Line Dynamics on Stability Margin to Hopf Bifurcation in Grid-Forming Inverters
Authors: Sushobhan Chatterjee, Sijia Geng
Date: 2024-12-19 00:00:00
Tasks: Sensitivity

This paper studies the parameter sensitivity of grid-forming inverters to Hopf bifurcations to address oscillatory instability. An analytical expression for the sensitivity of the stability margin is derived based on the normal vector to the bifurcation hypersurface. We identify the most effective control parameters through comprehensive analysis. In particular, the impacts of line dynamics on the stability margin to Hopf bifurcation are investigated. The results indicate that the feedforward gain in the voltage control loop is the most effective parameter for enhancing the stability margin. Furthermore, it is observed that line dynamics introduce a uniform reduction in the stability margin across all parameters. However, the reduction is generally small for most parameters except for the voltage-reactive power droop gain, which shows a more pronounced effect.