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arXiv 2025年

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based high-frequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2025, CC BY-NC-ND.

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Feedback Design for Devising Optimal Epidemic Control Policies

arXiv

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arXiv 2022年

作者： Niazi, Muhammad Umar B. Paré, Philip E. Johansson, Karl H. Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Elmore Family School of Electrical and Computer Engineering Purdue University IN United States

This paper proposes a feedback design that effectively copes with uncertainties for reliable epidemic monitoring and control. There are several optimization-based methods to estimate the parameters of an epidemic model by utilizing past reported data. However, due to the possibility of noise in the data, the estimated parameters may not be accurate, thereby exacerbating the model uncertainty. To address this issue, we provide an observer design that enables robust state estimation of epidemic processes, even in the presence of uncertain models and noisy measurements. Using the estimated model and state, we then devise optimal control policies by minimizing a predicted cost functional. To demonstrate the effectiveness of our approach, we implement it on a modified SIR epidemic model. The results show that our proposed method is efficient in mitigating the uncertainties that may arise in epidemic monitoring and control. © 2022, CC BY.

关键词： Robust control

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Transient Analysis of Space-Time Frequency-Dispersive Metal Slabs

Transient Analysis of Space-Time Frequency-Dispersive Metal ...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： Pablo H. Zapata-Cano Salvador Moreno-Rodríguez Stamatios Amanatiadis Antonio Alex-Amor Zaharias D. Zaharis Carlos Molero School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) Universidad de Granada Granada Spain Department of Electrical and Systems Engineering Philadelphia University of Pennsylvania Pennsylvania United States

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

In this paper, we analyze the scattering of an incident plane wave on a space-time metal slab in the presence of dispersion. Frequency dispersion is taken into account here by means of the Drude model. Analytical results, obtained via first-principle calculations, are in good agreement with in-house finite-difference time-domain (FDTD) computations.

关键词： Transient response Time-frequency analysis Metals Scattering Time-domain analysis Slabs Transient analysis Dispersion Physics Finite difference methods

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Deep Learning for Fault Detection and Diagnosis: Application to Photovoltaic System 19

Deep Learning for Fault Detection and Diagnosis: Application...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Marweni, Manel Fezai, Radhia Hajji, Mansour Mansouri, Majdi National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Im-age 5019 Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

With expanding requests for effectiveness and product quality and advancing integration of au-tomatic control systems in high-cost and safety-critical processes, Fault Detection and Diagnosis (FDD) in photo-voltaic (PV) have gained a very important role. However, PV systems can be subject to failures. These faults may include the distinctive system components such as PV modules, converters/inverters, connection lines, which can lead to a reduce in the operation performance. Thus, FDD is one of the key technologies to improve the efficiency of grid-connected PV performances. The most well-known data-driven based FDD methods are Deep Learning (DL)-basd approaches. The main advantages of DL algorithms, are that they can learn automatically high- level features from PV data in a high-order. This paper therefore presents the appli-cation of DL-based tools for fault diagnosis purposes and provides a comparative study using PV systems. These techniques include Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and BIdirectional Long-Short Term Memory (BILSTM). The implemented FDD based frameworks are validated using an emulated Grid-Connected PV (GCPV) system. The results show that the DL-based diagnosis methods are able to detect and diagnose faults under different operating modes. © 2022 IEEE.

关键词： Long short-term memory

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Transmission Loss of a Millimeter Wave Pulse Through a Waveguide Window 23

Transmission Loss of a Millimeter Wave Pulse Through a Waveg...

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23rd International Vacuum Electronics Conference, IVEC 2022

作者： Jao, Ruei-Fu Aranganadin, Kaviya Hsu, Hua-Yi Verboncoeur, John P. Lin, Ming-Chieh School of Information Technology Guangdong Industry Polytechnic Guangdong510300 China Hanyang University Multidisciplinary Computational Laboratory Department of Electrical and Biomedical Engineering Seoul04763 Korea Republic of National Taipei University of Technology Department of Mechanical Engineering Taipei10608 Taiwan Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9781665443258

Millimeter waves have become increasingly important in wireless communication today. It is well known that a waveguide window exhibits almost perfect transmission for the center frequency of millimeter waves. However, this might not be true for a pulsed wave. In this work, we study the transmission loss of a millimeter wave pulse centered at 35 GHz traveling through a rectangular waveguide window. The transmission function of a continuous wave within the bandwidth from 0 to 100 GHz has been obtained using the transfer matrix method. The millimeter wave of 35 GHz pulsed from 100 ps to 1 us in time domain has been Fourier transformed to the frequency domain so that the transmittance of the pulsed wave in the frequency domain can be calculated. The transmission loss has been analyzed for various pulse durations. After inverse Fourier transform of the transmittance in the frequency domain back to the time domain, one can compare the result with the incident pulse to see the pulse distortion and transmission loss. A finite difference time domain simulation has been performed to verify the calculated results by the transfer matrix method combined with Fourier transform. Detailed methodology and analysis of the study will be presented. © 2022 IEEE.

关键词： Transfer matrix method

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Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning

Service Function Chaining in LEO Satellite Networks via Mult...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Khai Doan Marios Avgeris Aris Leivadeas Ioannis Lambadaris Wonjae Shin Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Software and IT Engineering École de Technologie Supérieure Montreal QC Canada Korea University School of Electrical Engineering Seoul South Korea

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

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Radar-Communications Waveform Co-Design Over-the-Air Using the WISCANet SDR Network

Radar-Communications Waveform Co-Design Over-the-Air Using t...

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IEEE National Conference on Radar

作者： Shammi A. Doly Alex R. Chiriyath Andrew Herschfelt Jacob Holtom Shankarachary Ragi Daniel W. Bliss School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA Department of Electrical Engineering South Dakota School of Mines & Technology Rapid SD

Many waveform co-design studies demonstrate theoretical performance enhancements but rarely provide a clear path toward implementing a tractable solution. To address this limitation, we developed a waveform co-design technique to maximize the joint radar-communications network's joint per-formance and a computationally tractable method for optimizing it. This waveform co-design technique is based on the theory of partially-observable Markov decision processes (POMDPs), which we solve using an approximate dynamic programming approach called nominal belief-state optimization (NBO). The POMDP framework's natural look-ahead feature allows us to trade between the short-term and long-term performance of both radar and communications tasks, which allows it to adapt to changes in system requirements and environmental conditions. Using the WISCANet over-the-air experimental radio testbed, we implement a simple joint radar-communications system and demonstrate this waveform design and optimization technique in a hardware-in-the-loop over-the-air demonstration. We further extend the problem by proposing a real-time waveform optimization solution using a Kalman Filter in a dynamic environment.

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Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret

arXiv

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arXiv 2024年

作者： Hua, Youqing Liu, Shuai Hong, Yiguang Johansson, Karl Henrik Wang, Guangchen School of Control Science and Engineering Shandong University Jinan China Nanyang Technological University Singapore Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining O(1) sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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