pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Modulation Current for Gain-Switching Lasers
Authors: Alex Borisevich
Date: 2023-10-23 00:00:00

This paper formally shows that an exponentially rising current is optimal in terms of resistive ohmic loss for driving a semiconductor laser into the gain-switching mode. A metric to quantify the quality of laser operation that measures the similarity of a generated optical pulse to the delta function is proposed. Several circuit implementations to approximate exponentially rising current are developed, including using a driver circuit with BJT output stage, a network of RLC circuits, and a saturating inductor. An experimental comparison between a state-of-the-art sinewave resonant driver circuit and a directly driven laser is performed that favors the latest variant of the driver.
An Electric Vehicle Control Strategy to Mitigate Load Altering Attacks Against Power Grids
Authors: Mohammad Ali Sayed, Mohsen Ghafouri, Ribal Atallah, Mourad Debbabi, Chadi Assi
Date: 2023-10-23 00:00:00

Due to growing environmental concerns, the world's governments have been encouraging the shift of the transportation sector towards the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially and are expected to continue growing further which will add a large EV charging load to the power grid. To this end, this paper presents an EV-based defense mechanism against Load-Altering (LA) attacks targeting the grid. The developed mechanism utilizes H-infinity controllers and Linear Matrix Inequalities (LMIs) to mitigate LA attacks. After the controller synthesis and presentation of the attack scenarios, we demonstrate the effectiveness and success of our defense mechanism against the three known types of LA attacks. The scenarios include three 800 MW LA attacks against the New England 39-bus grid. The results demonstrate how our EV-based mitigation scheme eliminates the attack impacts and maintains the grid's stability in face of an unknown persisting attack.
Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator
Authors: Tushar Desai, Federico Oliva, Riccardo M. G. Ferrari, Daniele Carnevale
Date: 2023-10-22 00:00:00

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation are vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Due to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model.
Piezoelectric Sensors for Real-time Monitoring and Quality Control in Additive Manufacturing
Authors: Rashid T. Momin
Date: 2023-10-22 00:00:00

Within the ever-evolving landscape of engineering, particularly in the dynamic domain of additive In manufacturing, a pursuit of precision and excellence in production processes takes centre stage. This research , This paper serves to give a comprehensive understanding of piezoelectric sensors, a topic that is both academically engaging and of practical significance, catering to both seasoned experts and those newly venturing into the field. Additive manufacturing, lauded for its groundbreaking potential, underscores the imperative of rigorous quality control. This introduces piezoelectric sensors, devices that may be unfamiliar to many but possess considerable potential. This paper embarks on a methodical journey, commencing with an introductory elucidation of the piezoelectric effect. It then advances to the vital role of piezoelectric sensors in real-time monitoring and quality control, unveiling their potential and relevance for newcomers and seasoned professionals alike. This research, structured systematically from fundamental principles to pragmatic applications, presents findings that are not only academically informative but also represent a substantial stride towards achieving precision and high-quality manufacturing processes in the engineering field.
Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach
Authors: Tushar Desai, Riccardo M. G. Ferrari
Date: 2023-10-22 00:00:00
Tasks: Decoder

Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mechanisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence-to-sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.
Constructive barrier feedback for collision avoidance in leader-follower formation control
Authors: Zhiqi Tang, Rita Cunha, Tarek Hamel, Carlos Silvestre
Date: 2023-10-22 00:00:00
Tasks: Collision Avoidance

This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we propose an elegant decentralized controller as the sum of a nominal tracking controller and the constructive barrier feedback for leader-follower formations under a directed single-spanning tree graph topology. The key ingredient is the use of divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring robots without compromising the nominal controller's performance. Compared to traditional barrier function-based optimization controllers, the proposed constructive barrier feedback avoids feasibility issues and results in more computationally efficient control algorithms with systematic equilibrium analysis.
Dynamic Smooth Sliding Control Applied to UAV Trajectory Tracking
Authors: Alessandro Jacoud Peixoto, Wenderson G. Serrantola, Fernando Lizarralde
Date: 2023-10-21 00:00:00

This paper proposes a sliding mode controller with smooth control effort for a class of nonlinear plants. The proposed controller is created by allowing some constant parameters of the earlier smooth sliding control (SSC) to vary as a function of the output tracking error, improving the control chattering alleviation in practical implementations. Furthermore, during the sliding mode, the new scheme can synthesize a range of controllers, such as fixed gain PI controllers and approximations of the standard Super-Twisting Algorithm (STA), as well as, the variable gain Super-Twisting Algorithm (VGSTA). A complete closed-loop stability analysis is provided. In addition, realistic simulation results with an unmanned aerial vehicle (UAV) model, incorporating aerodynamic effects and internal closed-loop controllers, are obtained and validated via experiments with a commercial hexacopter.
Feasibility-Guided Safety-Aware Model Predictive Control for Jump Markov Linear Systems
Authors: Zakariya Laouar, Qi Heng Ho, Rayan Mazouz, Tyler Becker, Zachary N. Sunberg
Date: 2023-10-21 00:00:00
Tasks: Model Predictive Control, State Estimation

In this paper, we present a controller framework that synthesizes control policies for Jump Markov Linear Systems subject to stochastic mode switches and imperfect mode estimation. Our approach builds on safe and robust methods for Model Predictive Control (MPC), but in contrast to existing approaches that either optimize without regard to feasibility or utilize soft constraints that increase computational requirements, we employ a safe and robust control approach informed by the feasibility of the optimization problem. We formulate and encode finite horizon safety for multiple model systems in our MPC design using Control Barrier Functions (CBFs). When subject to inaccurate hybrid state estimation, our feasibility-guided MPC generates a control policy that is maximally robust to uncertainty in the system's modes. We evaluate our approach on an orbital rendezvous problem and a six degree-of-freedom hexacopter under several scenarios and benchmarks to demonstrate the utility of the framework. Results indicate that the proposed technique of maximizing the robustness horizon, and the use of CBFs for safety awareness, improve the overall safety and performance of MPC for Jump Markov Linear Systems.
Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2023-10-21 00:00:00
Tasks: regression

Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which is almost always unacceptable for practical scenarios. In this paper, a new identification law is proposed to overcome this drawback and guarantee asymptotic convergence of the unknown parameters estimation error to zero in case the mentioned additive perturbation meets special averaging conditions. Such law is successfully applied to state reconstruction problem. Theoretical results are illustrated by numerical simulations.
Physics-informed Neural Network Modelling and Predictive Control of District Heating Systems
Authors: Laura Boca de Giuli, Alessio La Bella, Riccardo Scattolini
Date: 2023-10-21 00:00:00
Tasks: Model Predictive Control

This paper addresses the data-based modelling and optimal control of District Heating Systems (DHSs). Physical models of such large-scale networked systems are governed by complex nonlinear equations that require a large amount of parameters, leading to potential computational issues in optimizing their operation. A novel methodology is hence proposed, exploiting operational data and available physical knowledge to attain accurate and computationally efficient DHSs dynamic models. The proposed idea consists in leveraging multiple Recurrent Neural Networks (RNNs) and in embedding the physical topology of the DHS network in their interconnections. With respect to standard RNN approaches, the resulting modelling methodology, denoted as Physics-Informed RNN (PI-RNN), enables to achieve faster training procedures and higher modelling accuracy, even when reduced-dimension models are exploited. The developed PI-RNN modelling technique paves the way for the design of a Nonlinear Model Predictive Control (NMPC) regulation strategy, enabling, with limited computational time, to minimize production costs, to increase system efficiency and to respect operative constraints over the whole DHS network. The proposed methods are tested in simulation on a DHS benchmark referenced in the literature, showing promising results from the modelling and control perspective.
Robust Model Predictive Control for Enhanced Fast Charging on Electric Vehicles through Integrated Power and Thermal Management
Authors: Qiuhao Hu, Mohammad Reza Amini, Ashley Wiese, Ilya Kolmanovsky, Jing Sun
Date: 2023-10-21 00:00:00
Tasks: Management, Model Predictive Control

This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV). A multi-objective model predictive control (MPC) framework is developed to optimize the fast charging performance while enforcing the constraints in the power and thermal loops. The approach takes into account the coupling of the battery and cabin thermal management. The case study of a commercial EV demonstrates that the proposed method can effectively meet the requirements of fast charging and thermal management when accurate preview information is available. However, failure to predict the charging event can result in performance degradation with longer charging time. A time-varying weighting strategy is proposed to enhance charging performance in the presence of uncertainty. This strategy leverages the battery state-of-charge (SOC) and adjusts the priority of the multi-objective MPC at different phases during charging. Simulated results using a commercial EV use case show improved robustness in charging time using the proposed strategy.
A Discrete-time Networked Competitive Bivirus SIS Model
Authors: Sebin Gracy, Ji Liu, Tamer Basar, Cesar A. Uribe
Date: 2023-10-20 00:00:00

The paper deals with the analysis of a discrete-time networked competitive bivirus susceptible-infected-susceptible (SIS) model. More specifically, we suppose that virus 1 and virus 2 are circulating in the population and are in competition with each other. We show that the model is strongly monotone, and that, under certain assumptions, it does not admit any periodic orbit. We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE). Assuming only virus 1 (resp. virus 2) is alive, we establish a condition for global asymptotic convergence to the single-virus endemic equilibrium of virus 1 (resp. virus 2) -- our proof does not rely on the construction of a Lyapunov function. Assuming both virus 1 and virus 2 are alive, we establish a condition which ensures local exponential convergence to the single-virus equilibrium of virus 1 (resp. virus 2). Finally, we provide a sufficient (resp. necessary) condition for the existence of a coexistence equilibrium.
Deep Learning Based Forecasting-Aided State Estimation in Active Distribution Networks
Authors: Malek Alduhaymi, Ravindra Singh, Firdous Ul Nazir, Bikash C. Pal
Date: 2023-10-20 00:00:00
Tasks: State Estimation

Operating an active distribution network (ADN) in the absence of enough measurements, the presence of distributed energy resources, and poor knowledge of responsive demand behaviour is a huge challenge. This paper introduces systematic modelling of demand response behaviour which is then included in Forecasting Aided State Estimation (FASE) for better control of the network. There are several innovative elements in tuning parameters of FASE-based, demand profiling, and aggregation. The comprehensive case studies for three UK representative demand scenarios in 2023, 2035, and 2050 demonstrated the effectiveness of the proposed approach.
Computationally Efficient Electromagnetic Transient Power System Studies using Bayesian Optimization
Authors: Marius Kuhn, Evelyn Heylen, Willem Leterme
Date: 2023-10-20 00:00:00
Tasks: Bayesian Optimization

The power system of the future will be governed by complex interactions and non-linear phenomena at small time-scales, that should be studied more and more through computationally expensive software simulations. To solve the abovementioned problems, power system engineers face problems with following characteristics: (i) a computationally expensive simulator, (ii) non-linear functions to optimize and (iii) lack of abundance of data. Existing optimization settings involving EMT-type simulations have been developed, but mainly use a deterministic model and optimizer, which may be computationally inefficient and do not guarantee finding a global optimum. Furthermore, the main focus has been on optimization routines, and less attention has been paid to other tasks such as classification. In this paper, an automation framework based on Bayesian Optimization is introduced, and applied to two case studies involving optimization and classification. It is found that the framework has the potential to reduce computational effort, outperform deterministic optimizers and is applicable to a multitude of problems. Nevertheless, it was found that the output of the Bayesian Optimization depends on the number of samples used for initialization, and in addition, careful selection of surrogate models, which should be subject to future investigation.
Data Drift Monitoring for Log Anomaly Detection Pipelines
Authors: Dipak Wani, Samuel Ackerman, Eitan Farchi, Xiaotong Liu, Hau-wen Chang, Sarasi Lalithsena
Date: 2023-10-17 00:00:00
Tasks: Anomaly Detection, Drift Detection

Logs enable the monitoring of infrastructure status and the performance of associated applications. Logs are also invaluable for diagnosing the root causes of any problems that may arise. Log Anomaly Detection (LAD) pipelines automate the detection of anomalies in logs, providing assistance to site reliability engineers (SREs) in system diagnosis. Log patterns change over time, necessitating updates to the LAD model defining the `normal' log activity profile. In this paper, we introduce a Bayes Factor-based drift detection method that identifies when intervention, retraining, and updating of the LAD model are required with human involvement. We illustrate our method using sequences of log activity, both from unaltered data, and simulated activity with controlled levels of anomaly contamination, based on real collected log data.
Effects of phylogeny on coexistence in model communities
Authors: Carlos A. Servan, Jose A. Capitan, Zachary R. Miller, Stefano Allesina
Date: 2023-10-22 00:00:00

Species' interactions are shaped by their traits. Thus, we expect traits -- in particular, trait (dis)similarity -- to play a central role in determining whether a particular set of species coexists. Traits are, in turn, the outcome of an eco-evolutionary process summarized by a phylogenetic tree. Therefore, the phylogenetic tree associated with a set of species should carry information about the dynamics and assembly properties of the community. Many studies have highlighted the potentially complex ways in which this phylogenetic information is translated into species' ecological properties. However, much less emphasis has been placed on developing clear, quantitative expectations for community properties under a particular hypothesis. To address this gap, we couple a simple model of trait evolution on a phylogenetic tree with Lotka-Volterra community dynamics. This allows us to derive properties of a community of coexisting species as a function of the number of traits, tree topology and the size of the species pool. Our analysis highlights how phylogenies, through traits, affect the coexistence of a set of species. Together, these results provide much-needed baseline expectations for the ways in which evolutionary history, summarized by phylogeny, is reflected in the size and structure of ecological communities.
Topological Portfolio Selection and Optimization
Authors: Yuanrong Wang, Antonio Briola, Tomaso Aste
Date: 2023-10-23 00:00:00
Tasks: Portfolio Optimization

Modern portfolio optimization is centered around creating a low-risk portfolio with extensive asset diversification. Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance. However, covariance is typically estimated from historical lookback observations, and it is prone to noise and may inadequately represent future market behavior. As a remedy, information filtering networks from network science can be used to mitigate the noise in empirical covariance estimation, and therefore, can bring added value to the portfolio construction process. In this paper, we propose the use of the Statistically Robust Information Filtering Network (SR-IFN) which leverages the bootstrapping techniques to eliminate unnecessary edges during the network formation and enhances the network's noise reduction capability further. We apply SR-IFN to index component stock pools in the US, UK, and China to assess its effectiveness. The SR-IFN network is partially disconnected with isolated nodes representing lesser-correlated assets, facilitating the selection of peripheral, diversified and higher-performing portfolios. Further optimization of performance can be achieved by inversely proportioning asset weights to their centrality based on the resultant network.
Noise robustness and metabolic load determine the principles of central dogma regulation
Authors: Teresa W. Lo, Han James Choi, DeAn Huang, Paul A. Wiggins
Date: 2023-10-20 00:00:00

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network
Authors: Xiao-Peng Jin, An-Dong Xiong, Wei zhang, Xiao-Qing Wang, Fan Liu, Chang-Heng Li, Xu-Ri Yao, Xue-Feng Liu, Qing Zhao
Date: 2023-10-20 00:00:00
Tasks: Super-Resolution

Achieving both high-performance and wide field-of-view (FOV) super-resolution imaging has been attracting increasing attention in recent years. However, such goal suffers from long reconstruction time and huge storage space. Parallel compressive imaging (PCI) provides an efficient solution, but the super-resolution quality and imaging speed are strongly dependent on precise optical transfer function (OTF), modulation masks and reconstruction algorithm. In this work, we propose a wide FOV parallel compressive super-resolution imaging approach based on physics enhanced network. By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV. Numerical simulations and practical experiments demonstrate the effectiveness and superiority of the proposed approach. We achieve high-quality reconstruction with 4x4 times super-resolution enhancement using only three designed masks to reach real-time imaging speed. The proposed approach promotes the technology of rapid imaging for super-resolution and wide FOV, ranging from infrared to Terahertz.