检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

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Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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Fragment Distance-Guided Dual-Stream Learning for Automatic Pelvic Fracture Segmentation

SSRN

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SSRN 2024年

作者： Zeng, Bolun Wang, Huixiang Joskowicz, Leo Chen, Xiaojun Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Department of Orthopedics National Center for Orthopedics Shanghai Sixth People’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai China School of Computer Science and Engineering Edmond and Lily Safra Center for Brain Sciences Hebrew University of Jerusalem Jerusalem Israel Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, a novel dual-stream learning framework is proposed for the automatic segmentation and category labeling of pelvic fractures. First, we develop a dual-branch architecture leveraging distance learning from bone fragments for enhanced spatial information, aiding in fracture segmentation and improving the identification of bone fragments with variable positioning. Second, we implement a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on two pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean F1-score of 0.935[[EQUATION]]0.068 and 0.930[[EQUATION]]0.057 in the dataset FracCLINIC, and 0.955[[EQUATION]]0.072 and 0.912[[EQUATION]]0.129 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Surrogate-Model-Based Sequential Algorithm for Weather-Dependent Probabilistic Power Flow With High Calculation Efficiency

TechRxiv

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TechRxiv 2023年

作者： Zhang, Haochen Lin, Xingyu Peng, Sui Tang, Junjie Ponci, Ferdinanda Monti, Antonello Electrical and Computer Engineering Department University of California Los Angeles90095 United States State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China Grid Planning and Research Center Guangdong Power Grid Corporation CSG Guangzhou510080 China Institute for Automation of Complex Power Systems E.ON Energy Research Center RWTH Aachen University Aachen52074 Germany Digital Energy Fraunhofer FIT Aachen52072 Germany

Recently, a novel power flow model called weather dependent power flow (WDPF) has been proposed to incorporate conductor temperatures in power flow calculation. It is promising to adopt WDPF model in probabilistic analysis since it provides more accurate electrical and thermal states of a power system. However, calculation of the WDPF model is more computationally expensive than that of conventional power flow model, and hence repetitive calculation of the WDPF in probabilistic analysis is quite time-consuming. To overcome this challenge, a surrogate-model-based sequential algorithm is proposed for calculation of the WDPF model with high computational efficiency and good accuracy. Compared to the method that directly learns the whole WDPF model, the proposed method only learns the power flow sub-model whereas applies a linearization method to solve the thermal resistance sub-model. The construction of the proposed method considers the learning difficulty of the sub-model and the whole WDPF model, thus ensuring the high calculation accuracy. This study is the first to conduct a probabilistic power flow analysis based on the WDPF model. Numerical results in the IEEE 30-bus, 57-bus, and 118-bus systems demonstrate the importance of adopting the WDPF model in probabilistic analysis and the efficacy of the proposed method. © 2023, CC BY.

关键词： Linearization

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

引用

arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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One-to-Many Semantic Communication systems: Design, Implementation, Performance Evaluation

arXiv

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arXiv 2022年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics 210000 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications 210003 China The Department of Electrical and Computer Engineering Utah State University LoganUT United States

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Influence of copper on the by-products of different oil-paper insulations

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Journal of Physics: Conference Series 2011年第1期310卷

作者： Jian Hao Ruijin Liao George Chen Chao Ma The State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University China School of Electronics and Computer Science University of Southampton UK Gansu Electric Power Research Institute China

Transformer failure caused by the corrosion of copper material in transformer attracts great attention of researchers and engineers. In this paper, Karamay No. 25 naphthenic mineral oil, Karamay No. 25 paraffinic mineral oil, Kraft paper and copper were used to compose four combinations of oil-paper insulation samples. The ageing by-products and dielectric properties of the four combinations of oil-paper insulation samples were compared after they were thermally aged at 130°C. The influence of copper on the by-products and dielectric properties of different oil-paper insulations was obtained. The results show that copper can accelerate the ageing rate of insulation oils and reduce their AC breakdown voltage. The content of copper substance dissolved in insulating oil increases with ageing time at first and then decreases. The paper aged in the oil-paper insulation sample with copper has higher moisture content than the one without copper. Results of energy dispersive spectroscopy (EDS) in the scanning electron microscope (SEM) show that there is copper product deposited on the surface of insulation paper. The insulation oil and paper aged in the oil-paper insulation sample with copper have higher dielectric loss and conductivity than that without copper.

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Ultra-efficient frequency comb generation in AlGaAs-on-insulator microresonators

arXiv

引用

arXiv 2019年

作者： Chang, Lin Xie, Weiqiang Shu, Haowen Yang, Qifan Shen, Boqiang Boes, Andreas Peters, Jon D. Jin, Warren Liu, Songtao Moille, Gregory Yu, Su-Peng Wang, Xingjun Srinivasan, Kartik Papp, Scott B. Vahala, Kerry Bowers, John E. Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing100871 China T. J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States School of Engineering RMIT University MelbourneVIC3000 Australia Microsystems and Nanotechnology Division National Institute of Standards and Technology GaithersburgMD20899 United States Time and Frequency Division National Institute of Standards and Technology BoulderCO80305 United States

Recent advances in nonlinear optics have revolutionized the area of integrated photonics, providing on-chip solutions to a wide range of new applications. Currently, the state of the art integrated nonlinear photonic devices are mainly based on dielectric material platforms, such as Si3N4 and SiO2. While semiconductor materials hold much higher nonlinear coefficients and convenience in active integration, they suffered in the past from high waveguide losses that prevented the realization of highly efficient nonlinear processes on-chip. Here we challenge this status quo and demonstrate an ultra-low loss AlGaAs-on-insulator (AlGaAsOI) platform with anomalous dispersion and quality (Q) factors beyond 1.5 × 106. Such a high quality factor, combined with the high nonlinear coefficient and the small mode volume, enabled us to demonstrate a record low Kerr frequency comb generation threshold of ~36 µW for a resonator with a 1 THz free spectral range (FSR), ~100 times lower compared to that in previous semiconductor platform. Combs with >250 nm broad span have been generated under a pump power lower than the threshold power of state of the art dielectric micro combs. A soliton-step transition has also been observed for the first time from an AlGaAs resonator. This work is an important step towards ultra-efficient semiconductor-based nonlinear photonics and will lead to fully integrated nonlinear photonic integrated circuits (PICs) in near future. Copyright © 2019, The Authors. All rights reserved.

关键词： Silica

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Collaborative filtering recommendation algorithm based on cloud model

引用

Ruan Jian Xue Bao/Journal of Software 2007年第10期18卷 2403-2411页

作者： Zhang, Guang-Wei Li, De-Yi Li, Peng Kang, Jian-Chu Chen, Gui-Sheng State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China Institute of Electronic System Engineering Beijing 100840 China Information Security Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen 518055 China School of Computer Science and Technology Shandong Jianzhu University Ji'nan 250101 China

Recommendation system is one of the most important technologies applied in e-commerce. Similarity measuring method is fundamental to collaborative filtering algorithm, and traditional methods are inefficient especially when the user rating data are extremely sparse. Based on the outstanding characteristics of Cloud Model on the process of transforming a qualitative concept to a set of quantitative numerical values, a novel similarity measuring method, namely the likeness comparing method based on cloud model (LICM) is proposed in this paper. LICM compares the similarity of two users on knowledge level, which can overcome the drawback of attributes' strictly matching. This work analysis traditional methods throughly and puts forward a novel collaborative filtering algorithm, which is based on the LICM method. Experiments on typical data set show the excellent performance of the present collaborative filtering algorithm based on LICM, even with extremely sparsity of data.

关键词： Electronic commerce

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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