pwc-archive/papers-with-abstracts huggingface dataset:
Neuromorphic Control of a Pendulum
Authors: Raphael Schmetterling, Fulvio Forni, Alessio Franci, Rodolphe Sepulchre
Date: 2024-04-08 00:00:00

We illustrate the potential of neuromorphic control on the simple mechanical model of a pendulum, with both event-based actuation and sensing. The controller and the pendulum are regarded as event-based systems that occasionally interact to coordinate their respective rhythms. Control occurs through a proper timing of the interacting events. We illustrate the mixed nature of the control design: the design of a rhythmic automaton, able to generate the right sequence of events, and the design of a feedback regulator, able to tune the timing of events.
A Power Management and Control System for Portable Ecosystem Monitoring Devices
Authors: Marcel Balle, Wenxiu Xu, Kevin FA Darras, Thomas Cherico Wanger
Date: 2024-04-08 00:00:00
Tasks: Management

Recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies help ecosystem monitoring to shift towards automated monitoring with low power sensors and embedded vision on powerful processing units. Vision-based monitoring devices need an effective power management and control system (PMCS) with system-adapted power input and output capabilities to achieve power-efficient and self-sustainable operation. Here, we present a universal power management solution for automated ecosystem monitoring devices, compatible with commonly used off-the-shelf edge processing units (EPUs). The proposed design is specifically adapted for battery-powered EPU systems by incorporating power-matched energy harvesting (EH), a power switch with low-power sleep mode, and simple system integration in an MCU-less architecture with automated operation. We use a 4-month environmental case study to monitor plant growth under 4mg microplastic (MP) exposure, demonstrating that the setup achieved continuous and sustainable operation. In this plant phenology case study, our power management module is deployed in an embedded vision camera equipped with a 5W solar panel and five various environmental sensors. This work shows the usability of the power management board in environmentally relevant use cases and for tasks in agricultural applications.
Network-Constrained Unit Commitment with Flexible Temporal Resolution
Authors: Zekuan Yu, Haiwang Zhong, Guangchun Ruan, Xinfei Yan
Date: 2024-04-08 00:00:00

Modern network-constrained unit commitment (NCUC) bears a heavy computational burden due to the ever-growing model scale. This situation becomes more challenging when detailed operational characteristics, complicated constraints, and multiple objectives are considered. We propose a novel simplification method to determine the flexible temporal resolution for acceleration and near-optimal solutions. The flexible temporal resolution is determined by analyzing the impact on generators in each adaptive time period with awareness of congestion effects. Additionally, multiple improvements are employed on the existing NCUC model compatible with flexible temporal resolution to reduce the number of integer variables while preserving the original features. A case study using the IEEE 118-bus and the Polish 2736-bus systems verifies that the proposed method achieves substantial acceleration with low cost variation and high accuracy.
Optimizing Parameters of the LinDistFlow Power Flow Approximation for Distribution Systems
Authors: Babak Taheri, Rahul K. Gupta, Daniel K. Molzahn
Date: 2024-04-08 00:00:00

The DistFlow model accurately represents power flows in distribution systems, but the model's nonlinearities result in computational challenges for many applications. Accordingly, a linear approximation known as \mbox{LinDistFlow} (and its three-phase extension LinDist3Flow) is commonly employed. This paper introduces a parameter optimization algorithm for enhancing the accuracy of this approximation for both balanced single-phase equivalent and unbalanced three-phase distribution network models, with the goal of aligning the outputs more closely with those from the nonlinear DistFlow model. Using sensitivity information, our algorithm optimizes the LinDistFlow approximation's coefficient and bias parameters to minimize discrepancies in predictions of voltage magnitudes relative to the nonlinear DistFlow model. The parameter optimization algorithm employs the Truncated Newton Conjugate-Gradient (TNC) method to fine-tune coefficients and bias parameters during an offline training phase to improve the LinDistFlow approximation's accuracy. % in optimization applications. Numerical results underscore the algorithm's efficacy, showcasing accuracy improvements in $L_{1}$-norm and $L_{\infty}$-norm losses of up to $92\%$ and $88\%$, respectively, relative to the traditional LinDistFlow model. We also assess how the optimized parameters perform under changes in the network topology and demonstrate the optimized LinDistFlow approximation's efficacy in a hosting capacity optimization problem.
Minimax Least-Square Policy Iteration for Cost-Aware Defense of Traffic Routing against Unknown Threats
Authors: Yuzhen Zhan, Li Jin
Date: 2024-04-07 00:00:00

Dynamic routing is one of the representative control scheme in transportation, production lines, and data transmission. In the modern context of connectivity and autonomy, routing decisions are potentially vulnerable to malicious attacks. In this paper, we consider the dynamic routing problem over parallel traffic links in the face of such threats. An attacker is capable of increasing or destabilizing traffic queues by strategic manipulating the nominally optimal routing decisions. A defender is capable of securing the correct routing decision. Attacking and defensive actions induce technological costs. The defender has no prior information about the attacker's strategy. We develop an least-square policy iteration algorithm for the defender to compute a cost-aware and threat-adaptive defensive strategy. The policy evaluation step computes a weight vector that minimizes the sampled temporal-difference error. We derive a concrete theoretical upper bound on the evaluation error based on the theory of value function approximation. The policy improvement step solves a minimax problem and thus iteratively computes the Markov perfect equilibrium of the security game. We also discuss the training error of the entire policy iteration process.
Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking
Authors: Matheus Lohse, Rafael S. Castro, Aurelio T. Salton, Minyue Fu
Date: 2024-04-07 00:00:00
Tasks: Quantization

This paper addresses the task of tracking Lissajous trajectories in the presence of quantized positioning sensors. To do so, theoretical results on tracking of continuous time periodic signals in the presence of output quantization are provided. With these results in hand, the application to Lissajous tracking is explored. The method proposed relies on the internal model principle and dispenses perfect knowledge of the system equations. Numerical results show that an arbitrary small scanning resolution is achievable despite large sensor quantization intervals.
SoPhAr: Solar Phased-Arrays to boost the range of electric, hydrogen and SAF airliners in a solar world
Authors: Christian Claudel
Date: 2024-04-07 00:00:00

In late 2022, ICAO member states adopted a long-term global aspirational goal (LTAG) to achieve net zero carbon emissions from international aviation by 2050. To date however, no economically scalable solution to the aviation decarbonization problem has been proposed. Despite considerable research on potential alternative fuels including e-fuels, Sustainable Aviation Fuel (SAF), Hydrogen or Ammonia, and extensive research on purely electric propulsion, low-carbon propulsion methods are unable to replace fossil-fuels with identical or better economics. A possible alternative to current propulsion technologies is to directly beam the required propulsive power to aircraft. Several techniques have been considered to date, in particular laser energy beaming and microwave energy beaming. This paper proposes a possible concept where future airliners are mostly powered with ground-generated power. With expected improvements and scaling in solar panel manufacturing, the proposed concept would be economically competitive even with current jet fuel prices, while considerably reducing CO2 emissions.
A 97% Peak Efficiency Single-Inductor-Multiple-Output DC-DC Converter with a Shared Bootstrap Gate Driver
Authors: Mohammadreza Zeinali
Date: 2024-04-06 00:00:00

This paper describes a SIMO DC-DC converter capable of generating all the required voltages (buck and boost) utilizing n-type transistors for the output switches. The design incorporates only one shared bootstrap capacitor and a single pad to connect the top plate of the off-chip bootstrap capacitor to the on-chip drivers. By utilizing one off-chip bootstrap capacitor and sharing it between the multiple outputs, this work presents a low cost and low area solution for implementing a SIMO DC-DC converter using n-type transistor switches. A prototype of the proposed SIMO DC-DC converter is fabricated in 180nm SILTERRA BCD Technology.
A code-driven tutorial on encrypted control: From pioneering realizations to modern implementations
Authors: Nils Schlüter, Junsoo Kim, Moritz Schulze Darup
Date: 2024-04-06 00:00:00
Tasks: Decision Making

The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and service-based decision-making. At the same time, connected systems are susceptible to cyberattacks and data leakages. Against this background, encrypted control aims to increase the security and safety of cyber-physical systems. A central goal is to ensure confidentiality of process data during networked controller evaluations, which is enabled by, e.g., homomorphic encryption. However, the integration of advanced cryptographic systems renders the design of encrypted controllers an interdisciplinary challenge. This code-driven tutorial paper aims to facilitate the access to encrypted control by providing exemplary realizations based on popular homomorphic cryptosystems. In particular, we discuss the encrypted implementation of state feedback and PI controllers using the Paillier, GSW, and CKKS cryptosystem.
Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
Authors: Vincent W. Hill
Date: 2024-04-06 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-critic variant of deep Q-learning known as Deep Deterministic Policy Gradient, while the disturbance magnitudes evolve via independent stochastic processes. Simulation results are then compared with those from a classical control system.
Stability Assessment of Low-Inertia Power Systems: A System Operator Perspective
Authors: Manuel Hurtado, Mohammad Jafarian, Taulant Kerci, Simon Tweed, Marta Val Escudero, Eoin Kennedy, Federico Milano
Date: 2024-04-06 00:00:00

This paper discusses the stability assessment of low-inertia power systems through a real-world large-scale low-inertia system, namely, the All-Island power system (AIPS) of Ireland and Northern Ireland. This system currently accommodates world-record levels of system non-synchronous penetration namely 75% (planning to increase to 80% next year). The paper discusses one-month results obtained with the state-of-the-art stability tool called look-ahead security assessment (LSAT). This tool carries out rotor-angle, frequency and voltage stability analyses and is implemented in the control centres of the transmission system operators (TSOs). The paper shows that, at the time of writing, the main binding stability constraint of the AIPS is related to the limits on the rate of change of frequency (RoCoF).
Emerging Challenges of Integrating Solar PV in the Ireland and Northern Ireland Power Systems
Authors: Taulant Kerci, Manuel Hurtado, Simon Tweed, Marta Val Escudero, Eoin Kennedy, Federico Milano
Date: 2024-04-06 00:00:00

This paper discusses emerging operational challenges associated with the integration of solar photovoltaic (PV) in the All-Island power system (AIPS) of Ireland and Northern Ireland. These include the impact of solar PV on: (i) dispatch down levels; (ii) long-term frequency deviations; (iii) voltage magnitude variations; and (iv) operational demand variations. A case study based on actual data from the AIPS is used to analyze the above challenges. It is shown that despite its (still) relatively low penetration compared to wind power penetration, solar PV is challenging the real-time operation of the AIPS, e.g., maintaining frequency within operational limits. EirGrid and SONI, the transmission system operators (TSOs) of the AIPS, are working toward addressing all the above challenges.
A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Authors: Zemin Sun, Geng Sun, Long He, Fang Mei, Shuang Liang, Yanheng Liu
Date: 2024-04-06 00:00:00
Tasks: Edge-computing

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear programming (MINLP), we propose a two time-scale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the utility of the system, the QoE of MDs, and the revenue of MEC servers.
Evaluation and Optimization of Positional Accuracy for Maritime Positioning Systems
Authors: Atilla Alpay Nalcaci, Fidan Mehmeti, Wolfgang Kellerer, Florian Schiegg
Date: 2024-04-06 00:00:00
Tasks: Vessel Detection

Navigation and trajectorial estimation of maritime vessels are contingent upon the context of positional accuracy. Even the smallest deviations in the estimation of a given vessel may result in detrimental consequences in terms of economic and ecologic quotients. To ensure an agile and precise environment for maritime vessel positional estimation, preexisting marine radar technologies can be utilized in a way that ensures a higher level of precision compared to GNSS-based identification and positioning. In this paper, we present a positional optimization for radarbased vessel navigation systems that utilize the installment of vessel detection sensors. The main objective of this research is to employ as fewer sensors as possible while preserving the attainable error threshold for positioning that is defined by International Maritime Organization (IMO). Our approach leads most of the time to a positioning error of up to 5 m along shorelines and rivers and up to 50 m along open coastal regions.
Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Works
Authors: Pengyuan Lu, Lin Zhang, Mengyu Liu, Kaustubh Sridhar, Fanxin Kong, Oleg Sokolsky, Insup Lee
Date: 2024-04-06 00:00:00

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS's physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.
Integrated Optimal Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures
Authors: Zehui Lu, Hao Tu, Huazhen Fang, Yebin Wang, Shaoshuai Mou
Date: 2024-04-05 00:00:00
Tasks: Management, Model Predictive Control

This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a balance between charging speed and battery health. A control-oriented thermal-NDC (nonlinear double-capacitor) battery model is proposed to describe the electrical and thermal dynamics, incorporating the effects of both an active thermal source and ambient temperature. A state-feedback model predictive control algorithm is then developed for optimal fast charging and active thermal management. Numerical experiments validate the algorithm under extreme temperatures, showing that the proposed algorithm can energy-efficiently adjust the battery temperature, thereby balancing charging speed and battery health. Additionally, an output-feedback model predictive control algorithm with an extended Kalman filter is proposed for battery charging when states are partially measurable. Numerical experiments validate the effectiveness under extreme temperatures.
Optimal Policy Synthesis from A Sequence of Goal Sets with An Application to Electric Distribution System Restoration
Authors: İlker Işık, Onur Yigit Arpali, Ebru Aydin Gol
Date: 2024-04-05 00:00:00

Motivated by the post-disaster distribution system restoration problem, in this paper, we study the problem of synthesizing the optimal policy for a Markov Decision Process (MDP) from a sequence of goal sets. For each goal set, our aim is to both maximize the probability to reach and minimize the expected time to reach the goal set. The order of the goal sets represents their priority. In particular, our aim is to generate a policy that is optimal with respect to the first goal set, and it is optimal with respect to the second goal set among the policies that are optimal with respect to the first goal set and so on. To synthesize such a policy, we iteratively filter the applicable actions according to the goal sets. We illustrate the developed method over sample distribution systems and disaster scenarios.
Does nematic order allow groups of elongated cells to sense electric fields better?
Authors: Kurmanbek Kaiyrbekov, Brian A. Camley
Date: 2024-04-06 00:00:00

Collective response to external directional cues like electric fields plays a pivotal role in processes such as tissue development, regeneration, and wound healing. In this study we focus on the impact of anisotropy in cell shape and local cell alignment on the collective response to electric fields. We model elongated cells that have a different accuracy sensing the field depending on their orientation with respect to the field. Elongated cells often line up with their long axes in the same direction - "nematic order" - does this help the group of cells sense the field more accurately? We use simulations of a simple model to show that if cells orient themselves perpendicular to their average velocity, alignment of a cell's long axis to its nearest neighbors' orientation can enhance the directional response to electric fields. However, for cells to benefit from aligning, their accuracy of sensing must be strongly dependent on cell orientation. We also show that cell-cell adhesion modulates the accuracy of cells in the group.
Physics Event Classification Using Large Language Models
Authors: Cristiano Fanelli, James Giroux, Patrick Moran, Hemalata Nayak, Karthik Suresh, Eric Walter
Date: 2024-04-05 00:00:00
Tasks: Chatbot, Classification, Language Modeling, Language Modelling, Large Language Model

The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applications for detectors, accelerators, and experimental control. The hackathon, held on the final day of the workshop, involved using a chatbot powered by a Large Language Model, ChatGPT-3.5, to train a binary classifier neutrons and photons in simulated data from the \textsc{GlueX} Barrel Calorimeter. In total, six teams of up to four participants from all over the world took part in this intense educational and research event. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
Authors: Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque, Akila Sampath, Yiyi Huang, Md Osman Gani, Jianwu Wang
Date: 2024-04-03 00:00:00
Tasks: Causal Discovery, Causal Inference, Survey, Time Series

This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science. More specifically, the paper presents an overview of causal discovery and causal inference, explains the underlying causal assumptions, and enlists evaluation techniques and key terminologies of the domain area. The paper elicits the various state-of-the-art methods introduced for time-series and spatiotemporal causal analysis along with their strengths and limitations. The paper further describes the existing applications of several methods for answering specific Earth Science questions such as extreme weather events, sea level rise, teleconnections etc. This survey paper can serve as a primer for Data Science researchers interested in data-driven causal study as we share a list of resources, such as Earth Science datasets (synthetic, simulated and observational data) and open source tools for causal analysis. It will equally benefit the Earth Science community interested in taking an AI-driven approach to study the causality of different dynamic and thermodynamic processes as we present the open challenges and opportunities in performing causality-based Earth Science study.