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arXiv 2025年

作者： Favaro, Pietro Toubeau, Jean-François Vallée, François Dvorkin, Yury Power Systems and Markets Research Group University of Mons Mons Belgium Ralph O’Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and System Engineering Johns Hopkins University BaltimoreMD United States

As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parameters of system dynamics, expressed as constraint of the convex optimization control policy, while the system control signal is being optimized, thus creating an end-to-end learning framework. This is particularly relevant for systems in which behavior changes once the control policy is applied, hence rendering historical data less applicable. The proposed approach can perform system identification — i.e., determine appropriate parameters for the system analytical model — and control simultaneously to ensure that the model’s accuracy is focused on areas most relevant to control. Furthermore, because black-box systems are non-differentiable, we design a loss function that requires solely to measure the system response. We propose pre-training on historical data and constraint relaxation to stabilize the DFL and deal with potential infeasibilities in learning. We demonstrate the usefulness of the method on a building Heating, Ventilation, and Air Conditioning day-ahead management system for a realistic 15-zone building located in Denver, US. The results show that the conventional RC building model, with the parameters obtained from historical data using supervised learning, underestimates HVAC electrical power consumption. For our case study, the ex-post cost is on average six times higher than the expected one. Meanwhile, the same RC model with parameters obtained via DFL underestimates the ex-post cost only by 3%. © 2025, CC BY-NC-ND.

关键词： Convex optimization

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RETRACTED ARTICLE: Distributed energy management for networked microgrids in a three-phase unbalanced distribution network

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Frontiers in Energy 2023年第3期17卷 446-446页

作者： Guodong LIU Thomas BOLLIS Maximiliano F.FERRARIL Aditya SUNDARARAJAN Yang CHEN Mohammed M.OLAMA Kevin TOMSOVIC Grid Components and Controls Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Water Resource Science and Engineering Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Modeling and Simulation Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The Editor-in-Chief has retracted this article because of a substantial overlap with a different article by the same authors that was under submission at the same time[1]*** authors did not explicitly state whether they agree to this ***,'one of the authors'name was spelled wrong in the original article,i.e.,Maximiliano *** should be Maximiliano *** online version of this article contains the full text of the retracted article as electronic supplementary material.

关键词： material. network Ferrari

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A Privacy-Preserving Protocol Level Approach to Prevent Machine Learning Modelling Attacks on PUFs in the Presence of Semi-Honest Verifiers

A Privacy-Preserving Protocol Level Approach to Prevent Mach...

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IEEE International Workshop on Hardware-Oriented Security and Trust (HOST)

作者： Owen Millwood Fei Hongming Prosanta Gope Oğuz Narlı Meltem Kurt Pehlivanoğlu Elif Bilge Kavun Biplab Sikdar Department of Computer Science The University of Sheffield Sheffield UK Department of Electrical and Computer Engineering The National University of Singapore Singapore Department of Computer Engineering Kocaeli University Kocaeli Turkey Secure Intelligent Systems Research Group University of Passau Passau Germany

With the ubiquitous and distributed nature of the Internet-of-Things (IoT), various qualities of traditional communication methods for end devices and their verifiers prove insufficient in solving the challenges this new paradigm faces. Many new hardware and software technologies are proposed in an attempt to provide IoT systems with desired security properties while meeting performance requirements. Physically Unclonable Functions (PUFs) are one such technology receiving particular interest from the wider research community by promising to provide low-cost and highly secure key data to enable lightweight authentication protocols for devices operating over publicly accessible networks. PUFs have been the target of Machine Learning Modelling Attacks (ML-MA), which aim to clone the intrinsic behaviour of the PUF to undermine their security. While many PUF-based schemes have been proposed to defend against adversaries who are guaranteed to be dishonest, an area which has not seen significant consideration is one where a normal communication participant cannot always be assumed to act honestly. To the best of our knowledge, this work is the first to consider the concept of ‘semi-honest verifier’ for PUFbased authentication, taking initial steps to shed light on this prominent issue in IoT by proposing a privacy-preserving mutual authentication protocol which considers security against MLMA in the presence of such verifiers. Furthermore, this work describes hardware-level considerations for PUF obfuscation by utilising a combination of strong PUF, configurable One-Way Function (OWF) and secure DRAM-PUF and is, therefore, one of the first to integrate PUF obfuscation comprehensively at the protocol level.

关键词： Performance evaluation Protocols Authentication Cloning Machine learning Physical unclonable function Hardware

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Evolutionary Computing based Analysis of Diversity in Grammatical Evolution

Evolutionary Computing based Analysis of Diversity in Gramma...

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2021 International Conference on Artificial Intelligence and Smart systems, ICAIS 2021

作者： Youssef, Ayman Gupt, Krishn Kumar Raja, Muhammad Adil Murphy, Aidan Ryan, Conor Research Group Department of Computer Science and Information Systems Limerick Ireland Limerick Institute of Technology Bds and Department of Electrical and Electronic Engineering Limerick Ireland

ISBN: (纸本)9781728195377

Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple *** this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population. © 2021 IEEE.

关键词： Computational grammars

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on electrical engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Drone-assisted Remote Gas Sensing in Volcanic Scenarios

Drone-assisted Remote Gas Sensing in Volcanic Scenarios

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Dario Calogero Guastella Giuseppe Sutera Gaetano Giudice Domenico Longo Giovanni Muscato Department of Electrical Electronic and Computer Engineering Robotic Systems Group University of Catania Italy Istituto Nazionale di Geofisica e Vulcanologia Catania Italy Department of Agriculture Food and Environment University of Catania Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The study of volcanoes is of primary importance due to the great impact that eruptions can have on the economy, safety, air traffic, and several other human activities. A main indicator of volcanic activities, crucial to predicting events, is based on the analysis of gases emitted from a volcano's vents. Acquisition of gas samples is, however, challenging due to extreme working conditions and safety precautions. A further challenge is also locating and relocating volcanic gas measurement devices, as plume density and direction can quickly change. In this paper, we propose to use an open-path gas analyzer mounted on a Pan-Tilt Unit that tracks a multirotor equipped with suitable mirrors. This allows to autonomously perform gas measurement missions in volcanic environments via Tunable Diode Laser Absorption Spectroscopy. The great advantage brought by such a multirotor-based approach is the rapid and accurate measurement of gases, which makes monitoring and prediction activities safer, timely, and cost-effective. The effectiveness and accuracy of the proposed approach has been tested in a real application.

关键词： Gases Spectroscopy Accuracy Volcanic activity Vents Urban areas Pollution measurement

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On constraints in first-order optimization: a view from non-smooth dynamical systems

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 11681-11727页

作者： Michael Muehlebach Michael I. Jordan Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen Germany Department of Electrical Engineering and Computer Sciences Department of Statistics University of California Berkeley CA

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning.

关键词： convex optimization nonconvex optimization constrained optimization non-smooth dynamical systems gradient-based optimization convergence rate analysis

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Long-Range Low-Power Wireless Sensor to Monitor Temperature and Humidity of Dairy Calves

Long-Range Low-Power Wireless Sensor to Monitor Temperature ...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Jia Xue Kow Johan Osorio Estevez Ana Flavia Souza Lima Yuezhong Xu Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Virginia Tech Blacksburg Virginia USA School of Animal Sciences Virginia Tech Blacksburg Virginia USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents a low-power wireless sensor for monitoring the temperature and humidity of dairy calves over extended distances. The wireless sensor consists of a microcontroller with a LoRa ® radio, temperature and humidity sensors, and a power controller. The sensor samples data once every minute and remains in sleep mode most of the time. To save power during sleep, the power controller shuts off power to the sensor. The sensor was prototyped, and its operation was verified in a field test with three calves. The sensor is active for data sensing, processing, and transmission for about 458 ms in each cycle and dissipates an average of 16.13 mW during the active period. It is estimated that the sensor would operate for approximately 33 months using a CR2477 coin cell battery with a sampling rate of one data point per minute.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Temperature distribution Humidity Sensors Batteries Biomedical monitoring Monitoring

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Fast Swarming of UAVs in GNSS-denied Feature-poor Environments without Explicit Communication

arXiv

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arXiv 2024年

作者： Horyna, Jiří Krátký, Vít Pritzl, Václav Báča, Tomáš Ferrante, Eliseo Saska, Martin Multi-Robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague Czech Republic Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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