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Machine learning in polymer informatics

InfoMat 2021年第4期3卷 353-361页

作者： Wuxin Sha Yan Li Shun Tang Jie Tian Yuming Zhao Yaqing Guo Weixin Zhang Xinfang Zhang Songfeng Lu Yuan-Cheng Cao Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina Shenzhen Power Supply Col.Ltd. ShenzhenChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina

Polymers have been widely used in energy storage,construction,medicine,aerospace,and so ***,the complexity of chemical composition and morphology of polymers has brought challenges to their *** to the integration of machine leaming algorithms and large data resources,the data-driven methods have opened up a new road for the development of poly-mer science and *** emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new poly-mers by using machine learning models based on reliable *** the grad-ual supplement of currently available databases,the emergence of new databases and the continuous improvement of machine learning algorithms,the research paradigm of polymer informatics will be more efficient and widely *** on these points,this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduc-tion for researchers in materials,artificial intelligence,and other fields.

关键词： polymer learning artificial

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Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties

arXiv

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arXiv 2023年

作者： Pei, Jianhua Wang, Jingyu Shi, Dongyuan Wang, Ping State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto Canada

Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex dynamics, reduce the likelihood of enhancing the quality of measurements. Fortunately, denoising diffusion models exhibit powerful learning and generation abilities for the complex underlying physics of the real world. To this end, this paper proposes an improved detection and imputation based two-stage denoising diffusion model (TSDM) to identify and reconstruct the measurements with various cyber-physical uncertainties. The first stage of the model comprises a classifier-guided conditional anomaly detection component, while the second stage involves diffusion-based measurement imputation component. Moreover, the proposed TSDM adopts optimal variance to accelerate the diffusion generation process with subsequence sampling. Extensive numerical case studies demonstrate that the proposed TSDM can accurately recover power system measurements despite renewables-induced strong randomness and highly nonlinear dynamics. Additionally, the proposed TSDM has stronger robustness compared to existing reconstruction networks and exhibits lower computational complexity than general denoising diffusion models. Copyright © 2023, The Authors. All rights reserved.

关键词： Electric power system measurement

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Short-term solar eruptive activity prediction models based on machine learning approaches:A review

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science China Earth sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

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Long-term Water Quality Prediction based on Intelligent Optimization and Seasonal-trend Decomposition

Long-term Water Quality Prediction based on Intelligent Opti...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Ziqi Wang Xiangxi Wu Jing Bi Haitao Yuan Jia Zhang MengChu Zhou Faculty of Information Technology Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the applications of water quality prediction in the field of regional water environment management are increasing. It refers to predicting the elemental values of the water environment in the future based on past monitoring data, which is essential to realize the real-time evaluation of water quality and dynamic control of pollution sources. However, the water environment indicators are affected by various elements, which have a large volatility and non-linear characteristics. In addition, most of the existing water quality predictions focus on single-step predictive modeling of single elements of the water environment and lack multi-step predictive analysis of multifactor data of the water environment. In this paper, a novel long-term prediction model based on genetic simulated annealing-based particle swarm optimization (GSPSO) with seasonal-trend decomposition using LOESS (STL) is proposed and named GSPSO-STL-Autoformer (GS-Autoformer). It realizes the multi-factor and long-term prediction of water quality time series data. Firstly, the Autoformer’s hyperparameters are optimized by the GSPSO to improve its convergence speed. Secondly, the multi-factor features are decomposed by the STL to make the model more focused on learning feature information of each component. Finally, the long-term prediction is realized by the Autoformer. Comparative experiments with state-of-the-art peers show that the GS-Autoformer can effectively improve the accuracy of multi-factor and long-term predictions.

关键词： Accuracy Time series analysis Water quality Simulated annealing Predictive models Water pollution Data models Predictive analytics Particle swarm optimization Water resources

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

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IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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Ultrafast Negative Diffusion in gold films

Ultrafast Negative Diffusion in gold films

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Block, A. Yu, R. Un, I.W. Varghese, S. Liebel, M. van Hulst, N.F. Fan, S. Tielrooij, K.J. Sivan, Y. BIST and CSIC Bellaterra Spain Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be'er Sheva8410501 Israel ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Department of Applied Physics TU Eindhoven Den Dolech 2 Eindhoven5612 AZ Netherlands

Using ultrafast thermo-modulation microscopy, we show that the spatio-temporal heat diffusion in gold films has an initial ps-scale, electron-dominated diffusion, followed by an unexpected negative diffusion stage, an... 详细信息

ISBN: (纸本)9781957171395

关键词： Heat flux

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Spatial and Contextual Path Network for Image Inpainting

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Intelligent Automation & Soft Computing 2024年第2期39卷 115-133页

作者： Dengyong Zhang Yuting Zhao Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinTaiwan Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the *** feature extraction is the core of image *** enough space for information and a larger receptive field is very important to realize high-precision image ***,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same *** order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path *** the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial *** the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept *** order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two *** fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight *** of images in order to extract context information,we add attention to the context path refinement *** modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective *** show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

关键词： Image inpainting attention deep learning convolutional network

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Dual-loop joint torque control based on lower limb exoskeleton robots 18

Dual-loop joint torque control based on lower limb exoskelet...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Meitong Zhang, Chi Yi, Feng Shu, Xindong Yuan, Ting Li, Zheng Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science and Technology School of Electrical Engineering Shijiazhuang050000 China

ISBN: (纸本)9798350312201

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control. © 2023 IEEE.

关键词： Torque control

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A Liquid-Based Reconfigurable Fabry-Perot Cavity Antenna for Beam Steering

A Liquid-Based Reconfigurable Fabry-Perot Cavity Antenna for...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Mubashir Ashfaq Muhammad U. Khan Tayyaba Farooqi Ravi Kumar Arya Junwei Dong Raj Mittra School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Xiangshan Laboratory Zhongshan Institute of Changchun University of Science and Technology China Department of Electrical and Computer Engineering University of Central Florida FL USA

In this work, a liquid-based reconfigurable beam steerable antenna is presented. The proposed antenna is a Fabry-Pérot cavity antenna (FPCA) wherein the metasurface forming the cavity has four-quadrant 3D-printed liquid chambers. The chambers are sandwiched between two 16×16 conductive cross grid and square patch resonant element array layers making the metasurface. The source antenna is a planar coaxial-fed patch antenna radiating at 10 GHz. The proposed metasurface along with the source antenna forms a FPCA having a gain of 7 dBi. Beam steering is achieved and controlled by filling water chambers in different combinations. Beam steering of ±30° is achieved in two orthogonal planes i.e., xz-plane and yz-plane.

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Reinforcement Learning-Based Distributed Event-Triggered Leaderless Consensus for Unknown Linear Multi-Agent Systems

Reinforcement Learning-Based Distributed Event-Triggered Lea...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiangkai Wu Jiang Long Wei Wang Zhen Han School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Automation Science and Electrical Engineering Beihang University. Zhongguancun Laboratory Beijing China China North Vehicle Research Institute Beijing China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

In this paper, the leaderless consensus control problem for general linear multi-agent systems with completely unknown dynamics is investigated. Different from the existing results, both event-triggered communication and controller updates are considered under a directed graph condition. The primary challenges addressed in this paper are twofold: the completely unknown system dynamics for each agent and the inherent asymmetry of the Laplacian matrix that characterizes the communication topology. To tackle these challenges, the hybrid iteration reinforcement learning algorithm is utilized to derive the unique solution to the algebraic Riccati equation (ARE). Furthermore, the distributed consensus control schemes and the triggering conditions are proposed without using the precise knowledge of the system dynamics of all the agents. It can be proved that all the closed-loop signals are globally uniformly bounded. Besides, the practical leaderless consensus can be reached with discretized communication and controller updates, effectively avoiding Zeno behavior in all the agents. Finally, we present illustrative simulation results which underscore the effectiveness of the proposed control scheme.

关键词： Industrial electronics Laplace equations System dynamics Simulation Riccati equations Directed graphs Reinforcement learning

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