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arXiv 2024年

作者： Chen, Nan Wang, Xiucheng Li, Zan Yin, Zhisheng Luan, Tom Shen, Xuemin The State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China School of Cyber Engineering Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultrareliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Cai, Yi Zhang, Yunjing Lee Swindlehurst, A. Han, Zhu Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Center for Pervasive Communications and Computing University of California Irvine United States

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a "disco" IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are ineffective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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AI-Based Load Forecasting for Demand Response

AI-Based Load Forecasting for Demand Response

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Chintureena Thingom Mohan. S E. Afreen Banu E. Saranya M. Jasmin K. Anuradha Department of Computer Science & Engineering Alliance School of Advanced Computing Alliance University Bengaluru Karnataka India Department of Electrical And Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Engineering Shah & Anchor Kutchhi Engineering College Mumbai Maharashtra India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mallareddy Engineering College (Autonomous) Hyderabad Telangana India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Accurate load forecasting is essential for energy system demand response, energy distribution, and energy waste reduction. This work investigates artificial intelligence-based energy load estimation methods, focussing on Gradient Boosting Machines (GBM) to improve forecast accuracy. The study will focus on GBM use. The research project begins with extensive data preprocessing. This solution includes imputation for missing data and data inclusion to fix dataset imbalances. To improve model performance, autoencoder-based feature selection reduces data dimensionality while keeping crucial information. We also use a correlation matrix to find and remove duplicate characteristics. After that, the Gradient Boosting Machine classifier is used for regression jobs to manage nonlinear relationships and reduce prediction mistakes. This improves forecast accuracy. The studies show that the suggested strategy enhances load forecasting accuracy compared to standard models. This study shows that AI-driven methods can improve demand responsiveness. This can help smart grids and telecoms manage energy more efficiently and sustainably.

关键词： Accuracy Load forecasting Predictive models Feature extraction Boosting Demand response Data models Imputation Smart grids Load modeling

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Effects of Salt Spray on c-Si Photovoltaic Modules in the Brazilian Region

Effects of Salt Spray on c-Si Photovoltaic Modules in the Br...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Mendelsson R.M. Neves Allan Silveira Hugo S. Alvarez Rodrigo M. Garcia Francisco C. Marques Marcelo G. Villalva Dept. of Systems and Energy - DSE School of Electrical and Computer Engineering Campinas Brazil Communications Department - DECOM School of Electrical and Computer Engineering Campinas Brazil Dept. Research and Development - R&D Build Your Dreams Company - BYD Campinas Brazil Dept. of Applied Physics - DFA Institute of Physics Gleb Wataghin Campinas Brazil

Brazil has great potential for this type of energy generation due to its geographic location, allowing the development of viable photovoltaic (PV) projects in several regions. its use in places close to the sea has increased, with its use on boats and even resorts and hotels. This proximity to the sea requires attention to the local salinity, more precisely to the saline mist. This article will describe the methodology used to carry out the salinity resistance test of PV modules, choosing a specific classification of corrosive atmosphere according to the brazilian environment on the coast where the module will be placed in real conditions.

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Nonreciprocal field transformation with active acoustic metasurfaces

arXiv

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arXiv 2023年

作者： Wen, Xinhua Cho, Choonlae Zhu, Xinghong Park, Namkyoo Li, Jensen Department of Physics The Hong Kong University of Science and Technology Kowloon Hong Kong Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Field transformation, as an extension of the transformation optics, provides a unique means for nonreciprocal wave manipulation, while the experimental realization remains a significant challenge as it requires stringent material parameters of the metamaterials, e.g., purely nonreciprocal bianisotropic parameters. Here, we develop and demonstrate a nonreciprocal field transformation in a 2D acoustic system, using an active metasurface that can independently control all constitutive parameters and achieve purely nonreciprocal Willis coupling. The field-transforming metasurface enables tailor-made field distribution manipulation, achieving localized field amplification by a predetermined ratio. Interestingly, the metasurface demonstrates the self-adaptive capability to various excitation conditions and can extend to other geometric shapes. The metasurface also achieves nonreciprocal wave propagation for internal and external excitations, demonstrating a one-way acoustic device. Such a field-transforming metasurface not only extends the framework of the transformation theory for nonreciprocal wave manipulation, but also holds significant potential in applications such as ultra-sensitive sensors and nonreciprocal communication. Copyright © 2023, The Authors. All rights reserved.

关键词： Acoustic fields

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Integrated Sensing and Communication System Based on Radio Frequency Resonance Beam

arXiv

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arXiv 2025年

作者： Guo, Yixuan Xia, Shuaifan Xiong, Mingliang Liu, Qingwen Zhou, Shengli Fang, Wen Jiang, Qingwei Yan, Gang Mu, Jiangchuan The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States The School of Physics Science and Engineering Tongji University Shanghai200092 China

To address the challenge of complex beam control in traditional multiple-input multiple-output (MIMO) systems, research has proposed establishing adaptive beam alignment by utilizing retro-directive antenna (RDA) arrays to create echo resonance between the base station (BS) and user equipment (UE), thereby reducing system computational load. However, in conventional resonant beam systems (RBS), the use of the same frequency for uplink and downlink inevitably leads to echo interference issues. Therefore, this paper proposes an innovative design for an resonance beam-based integrated sensing and communication (RB-ISAC) system to achieve efficient passive sensing and bidirectional communication. In this system, the UE does not actively transmit signals but instead relies on a passive phase conjugation and frequency conversion structure to separate the uplink and downlink carrier frequencies. Additionally, through effective compensation for signal propagation loss, resonance is achieved after multiple iterations, at which point the beam’s field distribution becomes a low-diffraction-loss, high-focusing pattern, automatically aligning the transmitter with the receiver. This enables high-precision passive positioning while facilitating uplink and downlink communication. Simulation results demonstrate that the proposed system can achieve resonance after multiple iterations, and can support uplink and downlink communication within a range of 5 meters while achieving passive direction of arrival (DOA) estimation with an error of less than 2 degrees. Copyright © 2025, The Authors. All rights reserved.

关键词： Directive antennas

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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Controlling Ultrafast Photoemission via Simultaneous Laser Mixing and Shaping

Controlling Ultrafast Photoemission via Simultaneous Laser M...

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2023 Ultrafast Optics, UFO 2023

作者： Lemons, Randy Hirschman, Jack Neveu, Nicole Duris, Joseph Marinelli, Agostino Durfee, Charles Carbajo, Sergio SLAC National Accelerator Laboratory Stanford University 2575 Sand Hill Rd Menlo ParkCA94025 United States Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Electrical and Computer Engineering Department of Physics and Astronomy University of California Los AngelesCA90095 United States

We present a novel, versatile framework to generate W-level temporally shaped, near transform-limited, UV picosecond pulses via non-colinear sum frequency generation and demonstrate it producing temporally flattop, high-power UV pulses capable of enhancing femtosecond- and attosecond-level electron and X-ray free electron lasers brightness. © 2023 The Author(s).

关键词： Electromagnetic pulse

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