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arXiv 2022年

作者： Candela, Eduardo Parada, Leandro Marques, Luis Georgescu, Tiberiu-Andrei Demiris, Yiannis Angeloudis, Panagiotis The Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College London United Kingdom The Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London United Kingdom

Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful method to accomplish this task because it considers the interaction between agents and also allows for decentralized training-which makes it highly scalable. However, transferring policies from simulation to the real world is a big challenge, even for single-agent applications. Multi-agent systems add additional complexities to the Sim-to-Real gap due to agent collaboration and environment synchronization. In this paper, we propose a method to transfer multi-agent autonomous driving policies to the real world. For this, we create a multi-agent environment that imitates the dynamics of the Duckietown multi-robot testbed, and train multi-agent policies using the MAPPO algorithm with different levels of domain randomization. We then transfer the trained policies to the Duckietown testbed and compare the use of the MAPPO algorithm against a traditional rule-based method. We show that the rewards of the transferred policies with MAPPO and domain randomization are, on average, 1.85 times superior to the rule-based method. Moreover, we show that different levels of parameter randomization have a substantial impact on the Sim-to-Real gap. © 2022, CC BY.

关键词： Multi agent systems

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Frequency multiplexed photothermal correlation tomography for non-destructive evaluation of manufactured materials

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International Journal of Extreme Manufacturing 2025年第3期7卷 533-546页

作者： Pengfei Zhu Rongbang Wang Koneswaran Sivagurunathan Stefano Sfarra Fabrizio Sarasini Clemente Ibarra-Castanedo Xavier Maldague Hai Zhang Andreas Mandelis Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory(CVSL)Laval UniversityQuebec G1V 0A6Canada Centre for Composite Materials and Structures(CCMS) Harbin Institute of TechnologyHarbin 150001People’s Republic of China Center for Advanced Diffusion-Wave and Photoacoustic Technologies(CADIPT)and Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies(IANDIT) Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada Department of Industrial and Information Engineering and Economics(DIIIE) University of L’AquilaI-67100 L’Aquila(AQ)Italy Department of Chemical Engineering Materials Environment&UDR INSTM Sapienza University of RomeRome I-00184Italy

Infrared thermography has been widely applied in real industrial inspection of aerospace,energy management systems,engines,and electric ***,two-dimensional imaging modality limits its ***,a technique named frequency multiplexed photothermal correlation tomography(FM-PCT)was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and *** combining advantages of photothermal tomography and pulsed thermography,FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition(stacking)of two-dimensional images from sequential subsurface ***-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are *** features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion(blurring)correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation ***,FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating ***-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

关键词： photothermal coherence tomography nondestructive evaluation infrared thermography impact damage

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Long-Term Water Quality Prediction with Patch Savitsky-Golay Filtering and Transformer

Long-Term Water Quality Prediction with Patch Savitsky-Golay...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yongze Lin Junfei Qiao Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Information Science and Technology Beijing University of Technology Beijing China College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%.

关键词： Filtering Time series analysis Noise Semantics Water quality Telecommunication traffic Predictive models Transformers Market research Hazards

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Collaborative and Autonomous Black Start: Theory and Implementation

Collaborative and Autonomous Black Start: Theory and Impleme...

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Innovative Smart Grid Technologies (ISGT)

作者： Cecilia Klauber Hannah Burroughs Alec Zhou Cyber and Infrastructure Resilience Lawrence Livermore National Laboratory Livermore CA USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA

As the number of and capabilities of distributed and inverter-based energy resources increase, there is also an increasing opportunity to utilize such devices to provide resilience to large-scale power outages. During traditional power system restoration, natural hazard disruption or cyber attack of a central controller could cause the necessary processes to halt, but self-determining, decentralized schemes provide new opportunities for robustness. Therefore, a collaborative, autonomous restoration method is proposed and demonstrated in the field on a test microgrid with distributed energy resources. The methodology and test set-up is described and field demonstration results provided. The novel method is shown to effectively black start the microgrid using grid-forming inverter-based resources in a decentralized fashion, enabling future grid reliability even in the face of natural hazards and cyber disruptions.

关键词： Collaboration Process control Microgrids Reliability theory Hazards Robustness Power system reliability

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Faulty diagnostics model for wind power plant application using AI

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Measurement: Sensors 2023年 25卷

作者： Sudhakar, Puladasu Kamble, Nitin K. K, Geetha Turukmane, Anil V. Perli, Suresh Babu P, Jayaraman Department of Electrical and Electronics Engineering Sree Visvesvaraya Institute of Technology and Science Telangana Mahabubnagar509204 India Department of Robotics and Automation D. Y. Patil College of Engineering Pradhikaran Akurdi Pune411044 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Kattankulathur603203 India Department of Computer Science & Engineering VIT- AP University AmaravatiVijaywada India Department of Electrical Engineering NIT Warangal Telangana WARANGAL506004 India Department of Mechanical Engineering Prathyusha Engineering College Aranvoyalkuppam Tiruvallur Tamil Nadu Chennai602025 India

Researchers desperately ought to build an efficient turbine problem detection & simulation environment given the quick increase in wind energy capacity and the steadily rising operational cost duration of wind generators. The two aspects of fault identification & fault prediction are employed to describe the primary problem features of wind generators. We examine and synthesize the studies on fault detection techniques that rely on vibrating, electrical impulse evaluation, and pattern matching to address the challenging difficulties of defect detection. During the same period, we highlight the computationally efficient, constraints, and potential directions of distinct approaches. We review recent study developments & suggest a defect forecasting model that utilizes a physical breakdown prototype with data analysis theory fused depending on the mechanics and electronic subsystem degradation features of wind energy. In this study, we employ the Internet of Things (IoT) on Deep Learning (DL) architecture to forecast and identify wind energy generation issues. The testing findings demonstrate the method's ability to diagnose faults logically and forecast their types. © 2022 The Authors

关键词： Pattern matching

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Enhancing Outage-Constrained Secure EE with RIS Under a Multi-Antenna Eavesdropper

Enhancing Outage-Constrained Secure EE with RIS Under a Mult...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zongze Li Qingfeng Lin Yik-Chung Wu Derrick Wing Kwan Ng Arumugam Nallanathan Peng Cheng Laboratory Shenzhen China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Electrical Engineering and Telecommunications the University of New South Wales Australia School of Electronic Engineering and Computer Science at Queen Mary University of London UK

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the physical layer security by reconfiguring the wireless propagation environment. However, due to the hostile nature of potential eavesdroppers and the cascaded channel brought by the RIS, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas, the design of the optimal phase-shift, power allocation, and secure transmission data rate are intractable due to the couplings of random channel matrices in the outage probability. To overcome these challenges, this paper for the first time reveals an analytical transformation for handling the outage probabilistic constraint in the secure energy efficiency maximization problem due to multi-antenna eavesdropper. The resultant problem is readily handled under the alternating maximization framework. Simulation results unveil that the proposed probabilistic constraint transformation and the associated optimization algorithm provide superior secure energy efficiency over the baseline schemes of random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS.

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Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

Advancing Personalized Prostate Cancer Therapy Through Hormo...

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IEEE International Symposium on Biomedical Imaging

作者： I. Abdelhalim A. Alksas H. M. Balaha M. Badawy M. Abou El-Ghar N. S. Alghamdi M. Ghazal S. Contractor E. V. Bogaert D. Gondim S. R. Silva F. Khalifa A. El-Baz Department of Bioengineering University of Louisville Louisville KY USA Radiology Department Urology and Nephrology Center Mansoura Egypt Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology University of Louisville Louisville KY USA Department of Pathology and Laboratory Medicine University of Louisville Louisville KY USA Department of Radiation Oncology University of Louisville Louisville KY USA Electrical and Computer Engineering Department Morgan State University Baltimore MD USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.

关键词： Sensitivity Magnetic resonance imaging Soft sensors Medical treatment Feature extraction Prediction algorithms Data mining

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Phase-resolving spin-wave microscopy using infrared strobe light

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Physical Review Applied 2024年第6期22卷 064081-064081页

作者： Yuzan Xiong Andrew Christy Muntasir Mahdi Rui Sun Yi Li Robert D. Geil James F. Cahoon Frank Tsui Binbin Yang Department of Physics and Astronomy University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 USA Department of Chemistry University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 USA Department of Electrical and Computer Engineering Auburn University Auburn Alabama 36849 USA Department of Physics and Organic and Carbon Electronics Lab (ORaCEL) North Carolina State University Raleigh North Carolina 27695 USA Materials Science Division Argonne National Laboratory Argonne Illinois 60439 USA Department of Electrical and Computer Engineering North Carolina A&T State University Greensboro North Carolina 27411 USA

The need for sensitively and reliably probing magnetization dynamics has been increasing in various contexts such as studying novel hybrid magnonic systems, in which the spin dynamics strongly and coherently couple to other excitations, including microwave photons, light photons, or phonons. Recent advances in quantum magnonics also highlight the need for employing the magnon phase as quantum state variable, which is to be detected and mapped out with high precision in on-chip micro- and nanoscale magnonic devices. Here, we demonstrate a facile optical technique that can directly perform concurrent spectroscopic and imaging functionalities with spatial and phase resolutions, using infrared strobe light operating at 1550-nm wavelength. To showcase the methodology, we spectroscopically studied the phase-resolved spin dynamics in a bilayer of Permalloy and yttrium iron garnet Y3Fe5O12 (YIG), and spatially imaged the backward-volume spin-wave modes of YIG in the dipolar spin-wave regime. Using the strobe light probe, the detected precessional phase contrast can be directly used to construct the map of the spin wave’s wave front, in the continuous-wave regime of spin-wave propagation and in the stationary state, without needing any optical reference path. By selecting the applied field, frequency, and detection phase, the spin-wave images can be made sensitive to the precession amplitude and phase. Our results demonstrate that infrared optical strobe light can serve as a versatile platform for magneto-optical probing of magnetization dynamics, with potential implications in investigating hybrid magnonic systems.

关键词： Yttrium iron garnet

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A 23 – 32 GHz LNA with Near 5 W Power Handling Capability Using 180 nm GaN HEMT Technology

A 23 – 32 GHz LNA with Near 5 W Power Handling Capability U...

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European (Formerly European Gallium Arsenide and Other Semiconductors Application Symposium - EGASS) Microwave Integrated Circuit Conference (EuMIC)

作者： Nicholas C. Miller Matthew Hodek Christopher Barasich Edward Gebara John D. Albrecht John Papapolymerou Air Force Research Laboratory Sensors Directorate USA Department of Electrical and Computer Engineering Michigan State University USA Electromagnetic Sensor Technologies Inc. USA

This paper presents a robust 23-32 GHz low noise amplifier (LNA) based on 180 nm gate length gallium nitride (GaN) high electron mobility transistor (HEMT) technology. This two stage LNA exhibits the highest reported power handling capability of at least 4. S9W(36.9dBm). The LNA also provides competitive noise figure (NF), gain, and an output third order intercept point (OIP3) of 1.62-2.03 dB, 9-16dB, and 23.4-30.6 dBm, respectively, across the 23-32 GHz frequency band. These results provide an excellent example of a robust GaN-based LNA which could enable next generation receiver topologies without the need for radio frequency input limiters.

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