检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Li, Qi Zhao, Xiaoxu Deng, Longjiang Shi, Zhongtai Liu, Sheng Wei, Qilin Zhang, Linbo Cheng, Yingchun Zhang, Li Lu, Haipeng Gao, Weibo Huang, Wei Qiu, Cheng-Wei Xiang, Gang Pennycook, Stephen John Xiong, Qihua Loh, Kian Ping Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Chemistry Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore 117549 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Nanjing Tech University Nanjing211816 China College of Physical Science and Technology Sichuan University Chengdu610064 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The "Zeeman effect" offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (geff = -6.4) and 10 K (geff = -11) in a CVD-grown Fe-doped MoS2 monolayer;the effective Landé geff factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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Wrist tremor suppression based on repetitive control with multi-muscle electrical stimulation ⁎

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IFAC-PapersOnLine 2019年第29期52卷 31-36页

作者： Zan Zhang Bing Chu Yanhong Liu Haichuan Ren D.H. Owens School of Electrical Engineering Zhengzhou University Zhengzhou China Department of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street Sheffield S1 3JD UK

Tremor is a rhythmic, alternating swing motion caused by involuntary repetition of muscle contraction and relaxation. Although it does not endanger life, it will make the work and daily life of patients difficult. Functional electrical stimulation (FES) has been shown as a promising technique for tremor suppression. Wrist motion is produced by a group of muscles in a collective and coordinate way. However, existing FES-based design methods mostly aim at one pair of muscles associated with the wrist motion, thus limiting the performance of tremor suppression. Furthermore, the possible high level of stimulation required for a single muscle pair can also accelerate muscle fatigue of the patients. To address these problems, this paper uses multiple muscles FES to suppress tremor by fully considering the properties of wrist motion. This paper develops a wrist musculoskeletal model with Hammerstein structure, identifies its parameters, and proposes repetitive controllers based on frequency modified inverse algorithm to suppress tremor. Experimental results are presented to demonstrate its advantages over single muscle stimulation based tremor suppression.

关键词： Tremor suppression functional electrical stimulation (FES) repetitive control multi-muscle electrical stimulation experimental verification

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Lei An Jiajia Zhao Huijun Di School of Computer Science & Technology Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset.

关键词： Image translation Infrared image Generative adversarial networks Infrared imaging Visible Imagery images Infrared radiation Information fusion

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A synchronization method for local applications of EtherCAT master-slave in open CNC system

A synchronization method for local applications of EtherCAT ...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Li, Beibei Lin, Hu Sun, Shujie Zheng, Liaomo School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang Liaoning Province China School of Electromechanical and Automotive Engineering Yantai University Yantai Shandong Province China

ISBN: (纸本)9781538680698

A synchronization method for local applications of EtherCAT master-slave is proposed in this paper. Firstly, initial shift time is defined according to the communication structure of EtherCAT master-slave in open CNC system. Then, the initial shift time and clock rate ratio between master clock and reference clock is calculated for initialization of SYNC0 signal. After that, master's cycle remaining time is defined for adjusting the master cycle time. Finally, reference shift time and measuring shift time is defined and calculated. The time difference of them is used as the basis for compensating the cycle remaining time. The master's cycle time is adjusted by compensating the cycle remaining time and the synchronization of local applications of EtherCAT master-slave is realized by adjusting the master's cycle. Experimental results showed that good synchronization effect of local applications of EtherCAT master-slave can be achieved in open CNC system. © 2018 IEEE.

关键词： Synchronization

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Privacy of real-time pricing in smart grid

arXiv

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arXiv 2019年

作者： GhoddousiBoroujeni, Mahrokh Fay, Dominik Dimitrakakis, Christos Kamgarpour, Maryam Department of Electrical Engineering Sharif University of Technology Iran EECS school KTH Royal Institute of Technology Sweden Computer Science and Engineering faculty Chalmers University of Technology Sweden Automatic Control Laboratory D-ITET ETH Zurich Switzerland

Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates before publishing them to ensure users' privacy. Last, the proposed algorithm is tested on data inspired by household occupancy models and its performance is compared to an alternative solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric power utilization

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Optimal resource allocation for delay minimization in NOMA-MEC networks

arXiv

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arXiv 2020年

作者： Fang, Fang Xu, Yanqing Ding, Zhiguo Shen, Chao Peng, Mugen Karagiannidis, George K. Department of Engineering Durham University DurhamDH1 3LE United Kingdom School of Electrical and Electronic Engineering University of Manchester M13 9PL United Kingdom State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Group Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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SmartPV-AIoT: an AIoT-integrated framework for fault diagnosis and remote monitoring in photovoltaic systems

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Energy Conversion and Management: X 2025年

作者： Ehtisham Lodhi Xiaoli Liu Gang Xiong M. Adil Khan Zeeshan Lodhi Tahir Nawaz Adil Dilawar Sasu Tarkoma Feiyue Wang Zhejiang University-University of Illinois Urbana-Champaign Institute International Campus Haining 314400 China The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science University of Helsinki Helsinki 00560 Finland School of Transportation Fujian University of Technology Fuzhou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science COMSATS University Islamabad Abbottabad Campus 22060 Pakistan Department of Electronics Technology University of Technology Nowshera 24100 Pakistan Department of Mechatronics Engineering National University of Sciences and Technology H-12 Islamabad 44000 Pakistan The SKL of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper presents SmartPV-AIoT, a cost-effective and edge-computing-based framework for real-time fault detection and environmental monitoring in photovoltaic (PV) systems. Addressing the challenges faced in remote or intermittently connected environments, the proposed system integrates electrical and environmental sensor data using a Raspberry Pi 4 and NodeMCU microcontroller. A lightweight Res-BiLSTM model, combining residual learning and bidirectional long short-term memory (BiLSTM) layers, is deployed at the edge to perform local fault classification by analyzing seven key parameters derived from I to V curve measurements and ambient conditions. Additionally, the framework employs multi-sensor data fusion by integrating voltage, current, irradiance, and temperature measurements to support comprehensive fault detection and predictive maintenance. The framework was validated on a 4.56  kW PV installation using a dataset of 2400 labeled samples representing normal operation, open-circuit, short-circuit, and partial shading conditions. The classifier achieved a fault detection accuracy of 96.67 %, with a maximum inference latency of 290  ms and low power overhead, making it suitable for real-time, on-site diagnostics. The system supports offline operation with automatic data synchronization upon connectivity restoration and includes mobile-based monitoring for remote accessibility. The proposed framework is designed for modular deployment and scales linearly with array size. Overall, SmartPV-AIoT offers a scalable, resilient, and practical solution for intelligent PV monitoring, particularly in resource-constrained or off-grid environments.

关键词： Photovoltaic (PV) Artificial Intelligence of Things (AIoT) Green energy resources Deep learning Fault detection Fault classification Monitoring system Edge intelligence

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

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Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Scoring the terabit/s goal: Broadband connectivity in 6G

arXiv

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Bicaïs, Simon Björnson, Emil Bourdoux, André Buzzi, Stefano D'Andrea, Carmen Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Makki, Behrooz Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, Mohammadjavad Sarieddeen, Hadi Shahabuddin, Shahriar Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland CEA-Leti France KTH Royal Institute of Technology Sweden Linköping University Sweden IMEC Belgium University of Cassino and Southern Latium Italy Italy Department of Electrical and Electronics Engineering Kadir Has University Turkey Fivecomm Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University United States Ericsson Research Gothenburg Sweden Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Nokia Finland Department of Electrical Engineering Chalmers University of Technology Sweden State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Huawei Technologies Germany

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. © 2020, CC BY.

关键词： MIMO systems

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A 3D-Optimized AI Imaging Model for the Skin Tumor Burden Assessment of Mycosis Fungoides

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Journal of Investigative Dermatology 2025年

作者： Huizhong Wang Zihao Liu Haihao Pan Kecen Liu Yujie Wen Yao Qin Jingyang Dang Mingjia Li Ziyang Cui Tingting Jiang Yang Wang Department of Dermatology and Venereology Peking University First Hospital Beijing 100034 China National Clinical Research Center for Skin and Immune Diseases Beijing 100034 China NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics Beijing 100034 China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing 100871 China Intel Labs China Department of Dermatology Nanfang Hospital Southern Medical University Guangzhou 510515 Peking-Tsinghua Center for Life Sciences Peking University Beijing 100871 China

Mycosis fungoides (MF) is characterized by widespread skin patches that may progress to plaques and tumors, necessitating precise tumor burden assessment for staging and treatment guidance. However, existing methods, including the widely accepted modified Severity Weighted Assessment Tool (mSWAT), present significant challenges in routine practice due to their time-consuming nature and inter-observer variability. This study developed an AI model, mSWAT-Net, to estimate mSWAT scores using clinical images of MF patients. Notably, the overlap area segmentation sub-module of mSWAT-Net addressed double-counting errors in multi-angle photos through training on 3,904 annotated images generated from 61 three-dimensional (3D) human images. Across 2,463 standardized full-body photographs from 134 imaging series, mSWAT-Net demonstrated comparable performance to experienced cutaneous lymphoma specialists, achieving intraclass correlation coefficients (ICCs) of 0.917 (internal validation) and 0.846 (temporal validation) for mSWAT score. Moreover, mSWAT-Net outperformed three junior dermatologists in image-based scoring (ICC: 0.917 vs. 0.777), and demonstrated robust performance when compared to ground truth derived from 3D patient imaging (ICC: 0.812). Finally, mSWAT-Net was deployed as a free web application to support MF management in clinical settings. These findings highlight the potential of mSWAT-Net as an accurate, automated clinical tool for facilitating patient follow-up, treatment monitoring, and remote consultations.

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