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2nd IEEE Advanced Information Management, Communicates, Electronic and Automation control Conference, IMCEC 2018

作者： Fang, Mingju Xu, Zhengguo Zhejiang University College of Control Science and Engineering Hangzhou China State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9781538618035

I220 this work, automatic fault diagnosis of a steam turbine-generator system based on the vibration data is studied. In practice, the sampling rate of the data might be different. In this work, when the sampling rate is high, we show that the renowned empirical mode decomposition (EMD) works well to extract the features, which further are used to train a neural network. However, in our case only vibration data with a relatively low sampling rate is available for the two most common faults, i.e., rotor unbalance and rubbing between the rotor and cylinder. To copy with this situation, we propose a novel data preprocessing method and a novel statistic feature. The testing results show that the neural network with features extracted by our method outperforms significantly the ones with the EMD and the classical statistic features. Our method is simple to implement and has low computational complexity. It can be utilized to mitigate the disturbance caused by the different values of signal when the system is of the same state and to capture the temporal relation between different inputs. © 2018 IEEE.

关键词： Empirical mode decomposition

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A Green Paradigm for Internet of Things: Ambient Backscatter Communications

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China Communications 2019年第7期16卷 109-119页

作者： Wei Zhang Yao Qin Wenjing Zhao Minzheng Jia Qiang Liu Ruisi He Bo Ai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology and also with Institute of Telecommunication Satellites China Academy of Space Technology (CAST) Beijing Key Lab of Transportation Data Analysis and Mining Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Beijing Polytechnic College Beijing 100042 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.

关键词： ambient backscatter (AmBC) RF-powered device Internet of Things (IoT) battery-free tag wire-less sensor

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Wide-angle giant photonic spin Hall effect

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Physical Review B 2022年第7期106卷 075409-075409页

作者： Zhihao Chen Yu Chen Yaodong Wu Xinxing Zhou Handong Sun Tony Low Hongsheng Chen Xiao Lin Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications School of Physics and Electronics Hunan Normal University Changsha 410081 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies (CDPT) School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China

The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e., δ/λ<10−1, where λ is the wavelength), which impedes its precise measurement. To date, proposals to generate a giant spin Hall effect (namely, with δ/λ>102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of Δθ<1∘). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with Δθ>70∘ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.

关键词： Light-matter interaction Photonics Spin Hall effect Spin-orbit coupling

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Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data

arXiv

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arXiv 2020年

作者： Xu, Hao Zhang, Dongxiao Wang, Nanzhe BIC-ESAT ERE and SKLTCS College of Engineering Peking University Beijing100871 China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen518055 China Intelligent Energy Lab Peng Cheng Laboratory Shenzhen518000 China

Data-driven discovery of partial differential equations (PDEs) has attracted increasing attention in recent years. Although significant progress has been made, certain unresolved issues remain. For example, for PDEs with high-order derivatives, the performance of existing methods is unsatisfactory, especially when the data are sparse and noisy. It is also difficult to discover heterogeneous parametric PDEs where heterogeneous parameters are embedded in the partial differential operators. In this work, a new framework combining deep-learning and integral form is proposed to handle the above-mentioned problems simultaneously, and improve the accuracy and stability of PDE discovery. In the framework, a deep neural network is firstly trained with observation data to generate meta-data and calculate derivatives. Then, a unified integral form is defined, and the genetic algorithm is employed to discover the best structure. Finally, the value of parameters is calculated, and whether the parameters are constants or variables is identified. Numerical experiments proved that our proposed algorithm is more robust to noise and more accurate compared with existing methods due to the utilization of integral form. Our proposed algorithm is also able to discover PDEs with high-order derivatives or heterogeneous parameters accurately with sparse and noisy data. © 2020, CC-BY-NC-ND.

关键词： Partial differential equations

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An Overview of Digital Twin technology for Power Electronics: State-of-the-Art and Future Trends

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IEEE Transactions on Power Electronics 2025年

作者： Wu, Chenhao Cui, Zhexin Xia, Qian Yue, Jiguang Lyu, Feng Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Tongji University Center for Marine Science and Technology Shanghai200092 China Tongji University College of Electronic and Information Engineering Shanghai201804 China University of Nottingham Power Electronics Machines and Control Research Group NottinghamNG10 1NX United Kingdom Tongji University State Key Laboratory of Marine Geology School of Ocean and Earth Science Center for Marine Science and Technology Shanghai200092 China

Power electronic systems (PESs) are pivotal in power generation, transmission, and diverse industrial applications. With the increasing shift toward digitalization, digital twin (DT) technology has emerged as a transformative enabler, enhancing the informatization and intelligence of PESs. This paper offers a comprehensive overview of DT applications for PESs, highlighting core features of real-time synchronization, accurate mapping, seamless data interaction, and high-fidelity modeling. Four prevalent DT modeling approaches are reviewed, with a detailed discussion of their respective strengths and challenges in the context of PESs. The paper further examines the role of DT across three critical lifecycle phases—design, control, and maintenance—illustrating how DT optimizes and drives innovation at each phase. By reviewing over 170 publications, this study identifies key trends, implementation challenges, and research gaps, offering valuable insights into the state-of-the-art and future directions for DT in PESs. This work aims to serve as a foundational reference for researchers and practitioners, fostering a deep understanding of the potential of DT and providing a comprehensive grasp of the application in academics and the industry. © 1986-2012 IEEE.

关键词： Predictive maintenance

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Batch process monitoring based on WGNPE–GSVDD related and independent variables

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Chinese Journal of Chemical engineering 2018年第12期26卷 2549-2561页

作者： Yongyong Hui Xiaoqiang Zhao College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou 730050China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of TechnologyLanzhou 730050China National Experimental Teaching Center of Electrical and Control Engineering Lanzhou University of TechnologyLanzhou 730050China

In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.

关键词： Batch process Monitoring Related and independent variables Global-local Support vector data description

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control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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Living Near More Greenness Associated with Increased Bone Strength: A Large Epidemiological Study in China

SSRN

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SSRN 2022年

作者： Jiang, Ye Kang Zhuo, Bai Ma Guo, Bing Zeng, Pei-Bin Guo, Yu-Ming Chen, Gong-Bo Wei, Jing He, Rui-Feng Li, Zhi-Feng Zhang, Xue-Hui Wang, Zi-Yun Li, Xuan Wang, Lei Zeng, Chun-Mei Chen, Lin Xiao, Xiong Zhao, Xing West China School of Public Health and West China Fourth Hospital Sichuan University Sichuan Chengdu China Division of Pulmonary Diseases State Key Laboratory of Biotherapy West China Hospital of Sichuan University Sichuan Chengdu China School of Medicine Tibet University Tibet Lhasa China Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University MelbourneVIC3004 Australia Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment Department of Occupational and Environmental Health School of Public Health Sun Yat-sen University Guangdong Guangzhou China Department of Atmospheric and Oceanic Science Earth System Science Interdisciplinary Center University of Maryland College ParkMD United States Tibet Center for Disease Control and Prevention Tibet Lhasa China Chongqing Municipal Center for Disease Control and Prevention Chongqing China School of public Health Kunming Medical University Yunnan Kunming China School of Public Health The key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China Jianyang Center for Disease Control and Prevention Sichuan Chengdu China

Background Numerous studies show that living near more greenness may benefit various health outcomes. Nevertheless, no studies have investigated the greenness-bone linkage in the general population with large sample sizes. Moreover, to what extent ambient air pollution (AAP) and physical activity (PA) mediate this relationship is unclear. We aimed to explore the association between greenness and bone strength in Chinese adults and the potential mediating role of AAP and PA behind it. Methods A total of 66,102 adults from the China Multi-Ethnic Cohort (CMEC) in 2018-2019 were enrolled. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were employed as the residential greenness definition. Calcaneus quantitative ultrasound index (QUI) was regarded as an indicator of bone strength. Multiple linear regression models and causal mediation analyses were used to estimate the residential greenness-bone strength association and the potential pathways operating through PA and AAP. Stratification analyses were also adopted to identify susceptible populations. Results Increased residential exposure to greenness were significantly associated with higher QUI, with changes (95% confidence interval) of 3.04 (2.81, 3.26), 3.35 (3.12, 3.58), 2.44 (2.22, 2.66), and 2.71 (2.49, 2.93) for every interquartile range (IQR) increased in NDVI 500m , NDVI 1000m , EVI 500m , EVI 1000m , respectively. Gender, urbanicity, and annual family income significantly modified the links of greenness and bone strength, with more remarkable associations in males, urban residents, and subjects with higher family income. In the NDVI 500m /EVI 500m -QUI relationship, the mediating role of AAP was high reached 39.19% and 52.53%, while that of PA was only 1.64% and 1.68%. Conclusion Living in a greener place may improve bone strength, especially among males, urban residents, and the wealthier. These protective effects may be mainly operated by reducing the hazard effects of A

关键词： Housing

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Revealing the Host of Antibiotic Resistance Genes in Tap Water by an Integrated Metagenomic Assembly and Binning Approach

SSRN

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SSRN 2022年

作者： Yang, Juan Wang, Hong Yang, Ying Yu, Li-Ping Shen, Zhong-Zeng Meng, Xiang-Zhou Key Laboratory of Yangtze River Water Environment Ministry of Education College of Environmental Science and Engineering Tongji University Shanghai200092 China Jiaxing-Tongji Environmental Research Institute 1994 Linggongtang Road Zhejiang Province Jiaxing314051 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China School of Marine Sciences Sun Yat-sen University Zhuhai519082 China Suzhou Jingtian Lover Environmental Technology Co. Ltd. Jiangsu Province Suzhou215228 China

The presence of antibiotic resistance genes (ARGs) in tap water pose direct threat to human health, however, the antibiotic resistome of ARGs and their hosts are not well understood. By using an integrated metagenomic assembly and binning approach, this study characterized the co-occurrence of ARGs and mobile genetic elements (MGEs), and their potential hosts in retrieved metagenome-assembled genomes (MAGs) in tap water collected from Jiaxing City, East China. Among the 130 ARG subtypes detected, omp R and bac A were the most dominant, with average abundances of 100.7 and 66.4 ×/Gb, respectively. Multiple ARGs were detected and dominated by mdt C -Mux B cluster, which co-existed with MGEs like relaxase and transposase. Metagenomic binning retrieved 173 high-quality MAGs and Acidovorax was one of the most predominant genera carrying multiple ARGs. The family Burkholderiaceae were potential hosts of ARGs conferring resistance to aminoglycoside, bacitracin and multidrug. Genera containing opportunistic pathogens such as Legionella and Sphingomonas demonstrated multiple resistances to multidrug, both acted as potential pathogenic hosts of mex F. The occurrence of ARGs and their hosts along water supply pipeline may provide new insights on how to reduce risk of ARG to human health in drinking water. © 2022, The Authors. All rights reserved.

关键词： Potable water

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technology for monitoring wetting range of true-triaxial coal seam water injection based on acoustic emission array 53

Technology for monitoring wetting range of true-triaxial coa...

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53rd U.S. Rock Mechanics/Geomechanics Symposium

作者： Cheng, Weimin Chen, Jian Wang, Gang Fan, Jiuyuan Hu, Yanwei Shandong Universityof Scienceand Technology Mine DisasterPrevention and Control-Ministry of State Key Laboratory Breeding Base Qingdao266590 China Shandong University of Science and Technology College of Mining and Safety Engineering Qingdao266590 China

Coal seam water injection is an important technological means for coal mines to control disasters such as mine gas, coal dust, and ground stress. At present, advanced true triaxial fracturing and wetting equipment is generally used for experiments on coal seam water injection mechanism. Monitoring the wetting range of water injection is key to study the anisotropic coal seam wetting law, water–gas displacement mechanism, pore fracture structure wetting characteristics, and other important theories. However, only a few studies are conducted on high-precision monitoring of sample wetting range. This work put forwards direct measurement method and envelope localization method, which are two independently designed true triaxial fracturing wettability range monitoring methods based on the acoustic emission array technology. The two methods exhibit high detection and recognition accuracy. A verification test was carried out in the non-confined state, and the selected data were analyzed using *** use these two methods to locate the wetting contour surface. The feasibility of the two schemes is determined through analysis, and the methods, which are supported by the acoustic emission array method, can monitor the wetting law of coal seamwater injection. Copyright 2019 ARMA, American Rock Mechanics Association.

关键词： Wetting

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