检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Sun, bangshan Morozko, Fyodor Salter, Patrick S. Moser, Simon Pong, Zhikai Patel, Raj b. Walmsley, Ian A. Wang, Mohan Hazan, Adir barré, Nicolas Jesacher, Alexander Fells, Julian He, Chao Katiyi, Aviad Tian, Zhen Nan Karabchevsky, Alina booth, Martin J. Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom School of Electrical and Computer Engineering Ben‐Gurion University of the Negev P.O.B. 653 Be'er Sheva8410501 Israel Institute of Biomedical Physics Medical University of Innsbruck Müllerstraße 44 Innsbruck6020 Austria Ultrafast Quantum Optics group Department of Physics Imperial College London London United Kingdom Department of Physics University of Oxford Oxford United Kingdom Friedrich‐Alexander‐University Erlangen‐Nürnberg Paul‐Gordan‐Straße 6 Erlangen91052 Germany State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun130012 China

Photonics integrated circuitry would benefit considerably from the ability to arbitrarily control waveguide cross-sections with high precision and low loss, in order to provide more degrees of freedom in manipulating propagating light. Here, we report a new method for femtosecond laser writing of optical‐fib.e‐compatib.e glass waveguides, namely spherical phase induced multi‐core waveguide (SPIM‐WG), which addresses this challenging task with three dimensional on‐chip light control. Fabricating in the heating regime with high scanning speed, precise deformation of cross‐sections is still achievab.e along the waveguide, with shapes and sizes finely controllab.e of high resolution in both horizontal and vertical transversal directions. We ob.erved that these waveguides have high refractive index contrast of 0.017, low propagation loss of 0.14 db/cm, and very low coupling loss of 0.19 db coupled from a single mode fib.e. SPIM‐WG devices were easily fabricated that were ab.e to perform on‐chip beam rotation through varying angles, or manipulate polarization state of propagating light for target wavelengths. We also demonstrated SPIM‐WG mode converters that provide arbitrary adiabatic mode conversion with high efficiency between symmetric and asymmetric non‐uniform modes;examples include circular, elliptical modes and asymmetric modes from ppKTP (periodically‐poled potassium titanyl phosphate) waveguides which are generally applied in frequency conversion and quantum light sources. Created inside optical glass, these waveguides and devices have the capability to operate across ultra‐broad bands from visib.e to infrared wavelengths. The compatibility with optical fib.e also paves the way toward packaged photonic integrated circuitry, which usually needs input and output fib.e connections. © 2021, CC bY-NC-ND.

关键词： Single mode fibers

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Shared Metadata for Data-Centric Materials science

arXiv

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arXiv 2022年

作者： Ghiringhelli, Luca M. baldauf, Carsten bereau, Tristan brockhauser, Sandor Carbogno, Christian Chamanara, Javad Cozzini, Stefano Curtarolo, Stefano Draxl, Claudia Dwaraknath, Shyam Fekete, Ádám Kermode, James Koch, Christoph T. Kühbach, Markus Ladines, Alvin Noe Lambrix, Patrick Lenz-Himmer, Maja-Olivia Levchenko, Sergey Oliveira, Micael Michalchuk, Adam Miller, Ron Onat, berk Pavone, Pasquale Pizzi, Giovanni Regler, benjamin Rignanese, Gian-Marco Schaarschmidt, Jörg Scheidgen, Markus Schneidewind, Astrid Sheveleva, Tatyana Su, Chuanxun Usvyat, Denis Valsson, Omar Wöll, Christof Scheffler, Matthias Physics Department IRIS Adlershof Humboldt-Universität zu Berlin Germany The NOMAD Lab The Fritz Haber Institute The Max Planck Society Humboldt University Berlin Germany Fritz Haber Institute of the Max Planck Society Berlin Germany Van’t Hoff Institute for Molecular Sciences Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands TIB Leibniz Information Centre for Science and Technology University Library Welfengarten 1B Hanover30167 Germany AREA Science Park località Padriciano Trieste34149 Italy Center for Autonomous Materials Design Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Lawrence Berkeley National Laboratory BerkeleyCA United States Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Computer and Information Science Linköping University Sweden The Swedish e-Science Research Centre Linköping University Sweden Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany Berlin12489 Germany Department of Mechanical and Aerospace Engineering Carleton University OttawaONK1S 5B6 Canada École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland UCLouvain Chemin des Étoiles 8 Louvain-la-NeuveB-1348 Belgium Eggenstein-Leopoldshafen76344 Germany Jülich Center for Neutron Science MLZ Forschungszentrum Jülich GmbH Lichtenbergstraße 1 Garching85748 Germany CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Chemistry Department Humboldt-Universität zu Berlin Germany Department of Chemistry University of North Texas DentonTX76201 United States Eggenstein-Leopoldshafen76344 Germany

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findab.e, Accessib.e, Interoperab.e, and Reusab.e) must not be too narrow. besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for big-Data Driven Materials science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. © 2022, CC bY.

关键词： Metadata

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Developments and applications of the OPTIMADe API for materials discovery, design, and data exchange

arXiv

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arXiv 2024年

作者： evans, Matthew L. bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar b. beltrán, Daniel blokhin, evgeny boland, Tara M. balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez eckert, Hagen eimre, Kristjan Montero, María elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu bonella, Sara botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open Databases Integration for Materials Design (OPTIMADe) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADe specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADe in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADe API in materials research that continue to drive its ongoing development. © 2024, CC bY.

关键词： electronic data interchange

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Inverse design of figure eight fiber laser by artificial neural network

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Optical Fiber Technology 2025年 94卷

作者： Mahdi Kasmi Abdullah S. Karar Ahmad Atieh Kaboko Jean-Jacques Monga ehsan Adibnia Hafedh Mahmoud Zayani Mohamed Salhi Alexander Perepelov Faouzi bahloul University of Carthage SERCOM Laboratory Tunisia Polytechnic School EPT B.P. 743 2078 Tunisia Optiwave Systems Inc 7 Capella Court Suite 900 Ottawa K2E 8A7 Ontario Canada Department of Electronics Carleton University 1125 Colonel By Drive Ottawa K1S 5B6 Ontario Canada University of Johannesburg Electrical and Electronic Engineering Science Department Auckland Park 2006 South Africa Faculty of Electrical and Computer Engineering University of Sistan and Baluchestan (USB) P.O. Box *** Zahedan Iran Department of Electrical Engineering College of Engineering Northern Border University Arar Saudi Arabia Univ Angers LPHIA SFR MATRIX F-49000 Angers France Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 Building 1 Moscow 121205 Russia University of Tunis El Manar National Engineering School of Tunis B.P. 37 1002 Tunisia

Fiber lasers have become indispensab.e tools in modern photonics, offering unparalleled efficiency, stability, and versatility. Among them, the figure-eight fiber laser (F8FL) has gained prominence for its ability to generate ultra-short pulses with high peak power, making it highly suitab.e for applications in ultrafast spectroscopy, nonlinear microscopy, and optical frequency comb generation. However, designing and optimizing F8FLs remains a significant challenge due to the intricate interplay of nonlinear effects, dispersion management, and gain dynamics. Traditional design approaches rely on numerical simulations and iterative experimental tuning, which are computationally expensive and often yield suboptimal results. To address these challenges, we introduce a machine learning-based inverse design framework for optimizing F8FL parameters. Using a dataset generated from numerical simulations, an artificial neural network (ANN) is trained to establish a direct mapping between pulse characteristics and the key amplifier parameters, including small-signal gain and saturation energy. This approach enab.es rapid and accurate prediction of laser settings required to achieve a target pulse profile, significantly reducing the computational burden compared to conventional numerical methods. Our results demonstrate that the trained ANN model achieves excellent agreement with numerical simulations, effectively predicting the optimal parameters for producing high-energy rectangular pulses in the dissipative soliton resonance (DSR) regime. To validate the effectiveness of the predicted parameters, the ANN outputs were independently verified using OptiSystem simulations, confirming strong agreement with the desired pulse profiles. This study highlights the potential of machine learning in photonics, paving the way for the development of self-optimizing, adaptive laser systems with enhanced precision and efficiency. The proposed methodology can be extended to other nonlinear o

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Deep learning insights into spatial patterns of stab.e isotopes in Iran’s precipitation: a novel approach to climatological mapping

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Isotopenpraxis Isotopes in environmental and Health Studies 2024年第4期60卷

作者： Mojtaba Heydarizad Rogert Sori Masoud Minaei Hamid Ghalibaf Mohammadabadi elham Mahdipour a State Key Laboratory of Marine Geology Tongji University Shanghai People’s Republic of China b Environmental Physics Laboratory (EPhysLab) Centro de Investigación Mariña Universidade de Vigo Ourense Spain c Department of Geography Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Irand Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab) Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran e Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran f Computer Engineering Department Khavaran Institute of Higher Education Mashhad Iran

AbSTRACTStab.e isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stab.e isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stab.e isotopes. Additionally, deep learning models were employed to simulate the stab.e isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Sub.equently, the recursive feature elimination (RFe) technique was applied to identify influential parameters impacting Iran’s precipitation stab.e isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stab.e isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (Re) map was generated to quantify deviations between ob.erved and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, Re maps confirmed acceptab.e accuracy in simulating the stab.e isotope content, albeit with minor weaknesses ob.erved in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

关键词： Deep learning DNN isotope hydrology LSTM Iran precipitation stab.e isotopes simulation

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The ZTF Source Classification Project: III. A Catalog of Variab.e Sources

arXiv

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arXiv 2023年

作者： Healy, brian F. Coughlin, Michael W. Mahabal, Ashish A. Laz, Theophile Jegou du Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics Pacific Lutheran University TacomaWA98447 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical Engineering Indian Institute of Technology Gandhinagar Gujarat 382055 India Department of Astronomy University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gujarat Gandhinagar382055 India School of Statistics University of Minnesota MinneapolisMN55455 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States

The classification of variab.e ob.ects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series ob.ervations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGboost machine-learning (ML) algorithms to perform dichotomous classification of variab.e ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGb classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGb classifiers. The resulting classification catalog is availab.e to the public, and the software developed for SCoPe is open-source and adaptab.e to future time-domain surveys. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Advancing wetland aboveground biomass mapping: Insights from high-resolution PlanetScope data in the Mississippi River Delta

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Remote Sensing Applications: Society and environment 2025年 38卷

作者： Mohammadali Hemati Masoud Mahdianpari Hodjat Shiri Fariba Mohammadimanesh bahram Salehi Jianghua Wu Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's NL A1C 5S7 Canada C-Core St. John's NL A1B 3X5 Canada Civil Engineering Department Faculty of Engineering and Applied Sciences Memorial University of Newfoundland St. John's NL A1B 3X7 Canada Canada Centre for Remote Sensing Natural Resources Canada 580 Booth Street Ottawa ON K1A 0E4 Canada Department of Environmental Resources Engineering State University of New York College of Environmental Science and Forestry (ESF) NY 13210 USA Environment and Sustainability School of Science and the Environment Memorial University of Newfoundland Corner Brook Newfoundland Canada

Wetland ecosystems are vital for global carbon sequestration and environmental stability, highlighting the importance of accurately estimating aboveground biomass (AGb). This study leverages high-resolution PlanetScope satellite data to map AGb in the dynamic tidal marshes of the Mississippi River Delta (MRD). PlanetScope SuperDove surface reflectance data with 3-m spatial resolution was analyzed using machine learning regression models—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to estimate AGb during the spring and fall seasons of 2021. Field data collected during these seasons provided the reference samples for creating three datasets: one for spring, one for fall, and a combined multi-temporal dataset. For each machine learning method, three models were developed: two for individual seasons and one multi-temporal model. The study incorporated vegetation-sensitive spectral indices to enhance AGb prediction, and their importance was analyzed across the models. The multi-temporal ANN model achieved the highest accuracy, with an RMSe of 1.96 Mg/ha and an R-squared value of 0.84. Using this model, AGb maps for all seasons were generated, revealing seasonal variations in biomass across the wetlands. These results demonstrate the potential of high-resolution remote sensing data and machine learning models to provide accurate and detailed AGb maps, filling spatial and temporal gaps in biomass data. Using spaceborne high-resolution data to produce wetland AGb maps is essential for informing wetland management and conservation policies, as these maps enab.e monitoring biomass distribution, prioritizing conservation areas, and assessing the effectiveness of conservation strategies to support biodiversity and climate change mitigation.

关键词： Aboveground biomass Machine learning Mississippi River Delta PlanetScope Tidal marsh Wetland

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Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO

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Chinese Physics C 2025年第3期49卷 37-63页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram Abid Aleem Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Weidong bai Nikita balashov Wander baldini Andrea barresi Davide basilico eric baussan Marco bellato Marco beretta Antonio bergnoli Daniel bick Lukas b.eger Svetlana biktemerova Thilo birkenfeld Iwan Morton-blake David blum Simon blyth Anastasia bolshakova Mathieu bongrand Clément bordereau Dominique b.eton Augusto brigatti Riccardo brugnera Riccardo bruno Antonio budano Jose busto Anatael Cab.era barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Stéphane Callier Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Chi Chan Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen Shaomin Chen Yixue Chen Yu Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng Yu Chin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-Ting Chin Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi barbara Clerbaux Marta Colomer Molla Selma Conforti DiLorenzo Alberto Coppi Daniele Corti Simon Csakli Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La Taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding bayu Dirgantara Carsten Dittrich Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Katherine Dugas Stefano Dusini Hongyue Duyang Jessica eck Timo enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Marco Fargetta Dmitry Fedoseev Zhengyong Fei Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Alexandre Göttela Marco Grassi Maxim Gromov Vasily Gromov Minghao Gu X Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-Sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi’an Jiaotong University Xi’anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

The Jiangmen Underground Neutrino Ob.ervatory(JUNO)is a multi-purpose neutrino experiment under construction in South *** paper presents an updated estimate of JUNO’s sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power *** measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angleθ_(23).The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Ob.ervatory(TAO)detectors utilizing the best availab.e knowledge to date about the location and overburden of the JUNO experimental site,local and global nuclear reactors,JUNO and TAO detector responses,expected event rates and spectra of signals and backgrounds,and systematic uncertainties of analysis *** find that a 3σmedian sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years×26.6 GW thermal power.

关键词： JUNO neutrino neutrino mass ordering

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GMSH'S APPROACH TO RObUST MeSH GeNeRATION OF SURFACeS WITH IRReGULAR PARAMeTRIZATIONS 28

GMSH'S APPROACH TO ROBUST MESH GENERATION OF SURFACES WITH I...

引用

28th International Meshing Roundtab.e, IMR 2019

作者： Remacle, Jean-Francois Geuzaine, Christophe Place du Levant 1 Louvain-la-Neuve1348 Belgium Université de Liége Department of Electrical Engineering and Computer Science Montefiore Institute B28 Allée de la Découverte 10 Liège4000 Belgium

ISBN: (纸本)9781733489003

This paper proposes a robust and effective approach to overcome a major difficulty associated to surface finite element mesh generation: the handling surfaces with irregular (singular) parametrizations such as spheres, cones or other surfaces of revolution produced by common computer Aided Design tools. The main idea is to represent triangles incident to irregular points as trapezoids with one degenerated edge. This new approach has b.en implemented in Gmsh and examples containing thousands of surfaces with irregular points are presented at the end of the paper. © 2019 Proceedings of the 28th International Meshing Roundtab.e, IMR 2019. All rights reserved.

关键词： Mesh generation

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Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

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Procedia CIRP 2021年 104卷 641-646页

作者： Peter burggräf Johannes Wagner benjamin Heinbach Fabian Steinberg Alejandro R. Pérez M. Lennart Schmallenbach Jochen Garcke Daniela Steffes-lai Moritz Wolter Chair of International Production Engineering and Management (IPEM) Universität Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 1 53757 Sankt Augustin Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven 1 53757 Sankt Augustin Germany Institute for Numerical Simulation Universität Bonn Endenicher Allee 19b 53115 Bonn Germany Institute for Computer Science Universität Bonn Endenicher Allee 19a 53115 Bonn Germany

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was ab.e to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

关键词： Machine-Learning Predictive Quality Production Quality Assurance Classification

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