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Surface Charging Phenomena on HVDC Spacers for Compressed SF_(6) Insulation and Charge Tailoring Strategies

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CSEE Journal of Power and Energy systems 2020年第1期6卷 83-99页

作者： Chuanyang Li Yuan Xu Chuanjie Lin Geng Chen Youping Tu Yao Zhou Zhipeng Lei Tao Han Simone Vincenzo Suraci Jian Wang Weidong Liu M.Tariq Nazir Shun He Andrea Cavallini Giovanni Mazzanti Davide Fabiani Jinliang He Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA. China Electric Power Research Institute Beijing 100192China Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”University of BolognaViale Risorgimento 2Bologna40136Italy State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206China Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control National and Provincial Joint EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Smart Grid of Education Ministry School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Mechanical and Manufacturing Engineering University of New South Wales SydneyNSW 2052Australia Electric Power Research Institute Yunnan Electric Power Grid Co.ltd.KunmingYunnan 650217China

Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover *** paper discusses the laws regarding distribution patterns of surface charges and the related *** field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous *** addition,the main surface charge control methods for epoxy resin are summarized and *** potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.

关键词： GIL HVDC insulator SF6 surface charge accumulation surface flashover

Experimental researches and testing of silica twisted six-GeO2-doped core microstructured optical fiber. Part I: fusion splicing with telecommunication optical fibers and differential mode delay map measurements

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Experimental researches and testing of silica twisted six-Ge...

Optical Technologies for Telecommunications 2022

作者： Bourdine, Anton V. Dashkov, Michael V. Evtushenko, Alexander S. Pchelkin, Grigori A. Sachuk, Konstantin P. Khadjaev, Mukhammadzoir S. Vasilets, Alexander A. Zaitseva, Elena S. Barashkin, Alexey Yu. Demidov, Vladimir V. Ter-Nersesyants, Egishe V. Dmitriev, Eugeniy V. Shishova, Nadezhda A. Semenov, Andrey B. Tiwari, Manish Bylina, Maria S. Glagolev, Sergey F. Polyakova, Elena V. Dukelskii, Konstantin V. Dept. of Communications Lines Povolzhskiy State University of Telecommunications and Informatics 77 Moscow av. Samara443090 Russia JSC "Scientific Production Association State Optical Institute Named after Vavilov S.I." 36/1 Babushkin str. St. Petersburg192171 Russia OptoFiber Lab" LLC Skolkovo Innovation Center 7 Nobel str. Moscow143026 Russia Dept. of Photonics and Communication Lines Saint Petersburg State University of Telecommunications named after M.A. Bonch-Bruevich 22 Bolshevikov av. St. Petersburg193232 Russia Dept. of Telecommunication Engineering Samarkand branch of Tashkent University of Information Technology named after Mukhammad al-Khwarizmi 2A Ibn Sino str. Samarkand140100 Uzbekistan Department of Physics and Mathematics Branches of Science and Information Technologies Volga Region State University of Physical Culture Sport and Tourism 35 Universiade Village Kazan420010 Russia Samara Branch Radio Research & Development Institute 217 Sovetskaya Armiya str. Samara443011 Russia Dept. of Multichannel Telecommunication Systems Moscow Technical University of Communications and Informatics 8a Aviamotornaya str. Moscow111024 Russia Dept. of Mechanization and Automation of Civil Engineering National Research University Moscow State University of Civil Engineering" 26 Yaroslavskoye av. Moscow129337 Russia Optoelectronics and Photonics Research Lab Department of Electronics and Communication Engineering Manipal University Jaipur303007 India Faculty of Photonics and Optical Information School of Photonics ITMO University bldg. A 49 Kronverksky alley St. Petersburg197101 Russia

ISBN: (纸本)9781510667150

This work presents results of test series, performed for earlier on designed and successfully fabricated silica few-mode microstructured optical fibers (MOF) with six GeO2-doped cores, induced twisting 100 revolutions per meter, typical "telecommunication" outer diameter 125 µm, core diameter 8.7 µm, air hole diameter 4.6 µm, pitch 7.2 µm, and core quasi-step / graded refractive index profiles with height 0.0360 / 0.0275, respectively. Part I introduces attempts for splicing of typical telecommunication optical fibers and fabricated samples of MOFs by commercially available field arc fusion splicer kits and results of differential mode delay map measurements, performed for laser excited large core (multimode) optical fibers with core diameters 50 and 100 µm, jointed via free space to described above 2 m long pilot samples of 6-GeO2-core MOFs at both receiving and transmitting ends under laser-excited gaussian optical pulse launching with precision offset conditions, while Part II is concerned with researches of spectral responses, measured for fiber Bragg gratings, recorded in these MOFs. © 2023 SPIE.

关键词： Silica

Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields

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arXiv 2023年

作者： van der Ploeg, Chris Nyberg, Truls Sánchez, José Manuel Gaspar Silvas, Emilia van de Wouw, Nathan Netherlands Organisation for Applied Scientific Research Integrated Vehicle Safety Group Helmond5700 AT Netherlands Eindhoven University of Technology Dynamics and Control Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands Eindhoven University of Technology Control Systems Technology Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands KTH Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Scania CV AB Södertälje151 87 Sweden

As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

TumorCP: A simple but effective object-level data augmentation for tumor segmentation

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arXiv 2021年

作者： Yang, Jiawei Zhang, Yao Liang, Yuan Zhang, Yang He, Lei He, Zhiqiang Electrical and Computer Engineering University of California Los Angeles United States Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Lenovo Corporate Research & Development Lenovo Ltd. Beijing China

Deep learning models are notoriously data-hungry. Thus, there is an urging need for data-efficient techniques in medical image analysis, where well-annotated data are costly and time consuming to collect. Motivated by the recently revived "Copy-Paste" augmentation, we propose TumorCP, a simple but effective object-level data augmentation method tailored for tumor segmentation. TumorCP is online and stochastic, providing unlimited augmentation possibilities for tumors’ subjects, locations, appearances, as well as morphologies. Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7.12% on tumor Dice. Moreover, together with image-level data augmentation, it beats the current state-of-the-art by 2.32% on tumor Dice. Comprehensive ablation studies are performed to validate the effectiveness of TumorCP. Meanwhile, we show that TumorCP can lead to striking improvements in extremely low-data regimes. Evaluated with only 10% labeled data, TumorCP significantly boosts tumor Dice by 21.87%. To the best of our knowledge, this is the very first work exploring and extending the "Copy-Paste" design in medical imaging domain. Code is available at: https://***/YaoZhang93/TumorCP. © 2021, CC BY-NC-ND.

关键词： Tumors

Rectification and bolometric terahertz radiation detectors based on perforated graphene structures exhibiting plasmonic resonant response

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arXiv 2025年

作者： Ryzhii, V. Tang, C. Ryzhii, M. Otsuji, T. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Center of Excellence ENSEMBLE3 Warsaw01-919 Poland International Research Institute of Disaster Science Tohoku University Sendai980-0845 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Electronics of the Future Inc. ViennaVA22181-6117 United States

We propose and evaluate the characteristics of the terahertz (THz) detectors based on perforated graphene layers (PGLs). The PGL structures constitute the interdigital in-plane arrays of the graphene microribbons (GMRs) connected by the sets of narrow constrictions, which form the graphene nanoribbon (GNR) bridges. The PGL detector operation is associated with the rectification and hot-carrier bolometric mechanisms. The excitation of plasmonic oscillations in the GMRs can reinforce these mechanisms. . The room temperature PGL detector responsivity and detectivity are calculated as function of the radiation frequency and device structure parameters. The effects of the rectification and hot-carrier mechanisms are compared. The PGL THz detectors under consideration can exhibit highly competitive values of responsivity and detectivity. © 2025, CC BY.

关键词： Terahertz wave detectors

Rechargeable Aqueous Zinc-Ion Batteries in MgSO4/ZnSO4 Hybrid Electrolytes

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Nano-Micro Letters 2020年第5期12卷 33-48页

作者： Yingmeng Zhang Henan Li Shaozhuan Huang Shuang Fan Lingna Sun Bingbing Tian Fuming Chen Ye Wang Yumeng Shi Hui Ying Yang College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060GuangdongPeople’s Republic of China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale OptoelectronicsShenzhen UniversityShenzhen 518060People’s Republic of China Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province ShenzhenPeople’s Republic of China Institute of Microscale Optoelectronics Shenzhen UniversityShenzhen 518060People’s Republic of China Pillar of Engineering Product Development Singapore University of Technology and Design8 Somapah RoadSingapore 487372Singapore School of Physics and Telecommunication Engineering South China Normal UniversityGuangzhou 510006People’s Republic of China Key Laboratory of Material Physics of Ministry of Education School of Physics and EngineeringZhengzhou UniversityZhengzhou 450052People’s Republic of China

MgSO4 is chosen as an additive to address the capacity fading issue in the rechargeable zinc-ion battery system of MgxV2O5·nH2O//ZnSO4//*** with different concentration ratios of ZnSO4 and MgSO4 are *** batteries measured in the 1 M ZnSO4^-1 M MgSO4 electrolyte outplay other competitors,which deliver a high specific capacity of 374 mAh g^-1 at a current density of 100 mA g^-1 and exhibit a competitive rate performance with the reversible capacity of 175 mAh g^-1 at 5 A g^-*** study provides a promising route to improve the performance of vanadium-based cathodes for aqueous zinc-ion batteries with electrolyte optimization in cost-effective electrolytes.

关键词： Aqueous zinc-ion batteries Hybrid electrolytes Electrolyte additives Magnesium ions Vanadates

Prioritizing Strategies for Building the Resilience of Public Health systems to Disasters Across Multiple Communities and Countries

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International Journal of Disaster Risk Science 2024年第1期15卷 1-17页

作者： Benjamin J.Ryan Mayumi Kako Shelby Garner Rok Fink Ismail Tayfur Jonathan Abrahams Sanjaya Bhatia Adriana Campelo Matthew Fendt Alicia Fontenot Nahuel Arenas Garcia Tim Hatch Ryoma Kayano LaShonda Malrey‑Horne Makiko MacDermot Md Moshiur Rahman Chaverle Noel Shuhei Nomura Jeremy P.Novak Maria Opazo Kendell Oliver Luciana Peters Sohel Rahman PerihanŞimşek Andrew Stricklin Raymond Swienton Bryan W.Brooks Frist College of Medicine Belmont UniversityNashvilleTN 37212USA Department of Environmental Science Environmental Health Science ProgramCollege of Arts and SciencesBaylor UniversityWacoTX 76798USA Division of Nursing Science School of Biomedical and Health SciencesHiroshima UniversityHiroshima 734-8553Japan Gordon E.Inman College of Health Sciences and Nursing Belmont UniversityNashvilleTN 37212USA Faculty of Health Sciences University of Ljubljana1000 LjubljanaSlovenia Department of Emergency Medicine University of Health Sciences34668 IstanbulTurkey Monash University Accident Research Center MelbourneVIC 3800Australia Global Education and Training Institute United Nations Ofce for Disaster Risk ReductionIncheon 22004Republic of Korea Regional Ofce for the Americas and Caribbean United Nations Ofce for Disaster Risk ReductionPanama City 0843-03441Panama Engineering and Computer Science Baylor UniversityWacoTX 76798USA Department of Public Health Robbins College of Health and Human SciencesBaylor UniversityWacoTX 76798USA Alabama Department of Public Health MontgomeryAL 36092USA World Health Organization Centre for Health Development(WHO Kobe Centre) Kobe 651-0073Japan Waco-McLennan County Public Health District WacoTX 76707USA Department of Health Policy and Management School of MedicineKeio UniversityTokyo 160-8582Japan Department of Global Health Policy Graduate School of MedicineThe University of TokyoTokyo 113-0033Japan Tokyo Foundation for Policy Research Tokyo 106-6234Japan Global Development College BrisbaneQLD 4000Australia Municipality of Pudahuel 13124 PudahuelChile City Hall of Porto Alegre Porto Alegre 90010-170Brazil Ministry of Health and Family Welfare Government of the People’s Republic of BangladeshDhaka 1000Bangladesh Department of Emergency Aid and Disaster Management Trabzon University61335 TrabzonTurkey Department of Emergency Medicine UT SouthwesternDallasTX 75390USA

The COVID-19 pandemic highlighted the urgent need to strengthen public health *** response,the United Nations Disaster Risk Reduction(UNDRR) Public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple *** aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system *** conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience *** were drawn from 70 strategies identified from 13 distinct workshops in eight *** two surveys,23 strategies were *** received ratings of "High" or Very High" from89% of *** related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of *** Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system *** leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

关键词： Health system resilience Public policy Resilience scorecard Workshops

An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

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arXiv 2023年

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Zeng, Fang Liu, Zhengliang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang The School of Automation Northwestern Polytechnical University Xi'An710072 China The School of Computer Science Northwestern Polytechnical University Xi'An710072 China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China The school of computing University of Georgia AthensGA30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States The Department of Radiology Massachusetts General Hospital Boston02114 United States

The "Impression" section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the "Findings" section, which can be laborious and error-prone. Although deep-learning based models, such as BERT, have achieved promising results in Automatic Impression Generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, Large Language Models (LLMs) like ChatGPT have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both MIMIC-CXR and OpenI datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various *** Codes 68T50, 68T37, 68T20 Copyright © 2023, The Authors. All rights reserved.

关键词： Radiology

Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning 37

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Telescope Array Surface Detector Energy and Arrival Directio...

37th International Cosmic Ray Conference, ICRC 2021

作者： Kalashev, O.E. Ivanov, D. Kuznetsov, M.Yu. Rubtsov, G.I. Sako, T. Tsunesada, Y. Zhezher, Y.V. Abbasi, R.U. Abe, M. Abu-Zayyad, T. Allen, M. Arai, Y. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Suzawa, T. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Institute for Nuclear Research of the Russian Academy of Sciences Moscow117312 Russia Moscow Institute for Physics and Technology 9 Institutskiy per. Moscow Region Dolgoprudny141701 Russia Novosibirsk State University Pirogova 2 Novosibirsk630090 Russia High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT84112-0830 United States Service de Physique Théorique Université Libre de Bruxelles Boulevard du Triomphe CP225 Brussels1050 Belgium Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Graduate School of Science Osaka City University Osaka Osaka558-0022 Japan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka City University Osaka Osaka558-8585 Japan Department of Physics Loyola University Chicago ChicagoIL United States The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Todai Institutes for Advanced Study University of Tokyo Chiba Kashiwa Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University

A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure. © Copyright owned by the author(s).

关键词： Deep neural networks