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2014 International Conference on Manufacturing technology and Electronics Applications, ICMTEA 2014

作者： Wang, Li Jie Li, Jian Xin Sun, Lian Kui Zhang, Fu Jun Yang, Xiu Kun College of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology China The Higher Educational Key Laboratory on Measuring and Control Technology and Instrumentations of Heilongjiang Province China

ISBN: (纸本)9783038353287

In order to realize the online monitoring for iron grade of the magnetite during the dressing process, an iron grade fast measurement method based on electromagnetic induction is given, critical segment of the method is to make the iron grade information acquisition by adopting an iron ore core inductance sensor. Magnetic field analysis for iron ore core inductors of the sensor is explored in the paper. Taking a magnetite in certain regions of Liaoning as the test typical sample examples, physicochemical properties of its raw ore are analyzed, the iron ore samples are prepared and made, and the dressing process are completed. Ferromagnetics features of the magnetite samples above is analyzed, magnetic field distribution of the iron ore core inductor is analyzed, and analytic calculation formula of the magnetic fields is derived. Theory of solving the inductance of iron ore core inductor adopting finite element method of energy is discussed. Furthermore, total finite element design for the iron ore core inductor is made;distribution of electromagnetic field is analyzed. Influence regulations of self-inductance coefficients of the inductor are obtained, thus, the optimal combination of structural parameters of the inductor are designed. There is important practical significance to realize the iron grade testing automation for magnetite and other iron ores of the thesis research above. © (2014) Trans Tech Publications, Switzerland.

关键词： Finite element method

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A quick detection approach of magnetic iron grade for magnetite

A quick detection approach of magnetic iron grade for magnet...

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2014 International Conference on Energy Research and Power engineering, ERPE 2014

作者： Wang, He Nan Wang, Li Jie Wang, He Bing Men, Guan Hua Liu, Ye Liu, Hong Yan College of measure-control technology and communication engineering Harbin university of science and technology China Higher educational key lab. on measuring and control tech. and instrumentations of Heilongjiang China

ISBN: (纸本)9783038351603

In order to achieve aims of rapid real-time monitoring iron grade during sales process or mineral processing for the iron ores, a quick detection approach to magnetic iron grade of magnetite based on electromagnetic induction is explored in the paper. Information acquisition head is focused on an iron grade sensor, and the iron grade sensor is made up of coil-windings with magnetite core. Information acquisition circuits are given adapting mode of the transformer bridge type rectifier, and construction of the head is designed and machined. Testing mechanism is analyzed from the angle of magnetic field analysis, and the magnetic field model is also built up. Furthermore, magnetic iron grade of some testing samples are measured by the detection device above. It is showed that when the grade range of the magnetic iron of magnetite is varied from 20% to 65.3%, deviation is lower than 1.0%. By research above, it is shown that the detection method of iron grade based on electromagnetic induction is feasible, and it is also significant for improving the total testing level of mineral processing fields. © (2014) Trans Tech Publications, Switzerland.

关键词： Magnetite

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Construction of KAGRA: An underground gravitational wave observatory

arXiv

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arXiv 2017年

作者： Akutsu, T. Ando, M. Araki, S. Araya, A. Arima, T. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Baiotti, L. Barton, M.A. Chen, D. Cho, K. Craig, K. DeSalvo, R. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujibayashi, S. Fujii, Y. Fujimoto, M.-K. Fukushima, M. Furuhata, T. Hagiwara, A. Haino, S. Harita, S. Hasegawa, K. Hasegawa, M. Hashino, K. Hayama, K. Hirata, N. Hirose, E. Ikenoue, B. Inoue, Y. Ioka, K. Ishizaki, H. Itoh, Y. Jia, D. Kagawa, T. Kaji, T. Kajita, T. Kakizaki, M. Kakuhata, H. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kasuya, J. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawamura, S. Kawazoe, F. Kim, C. Kim, J. Kim, J.C. Kim, W. Kimura, N. Kitaoka, Y. Kobayashi, K. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kubo, K. Kumar, R. Kume, T. Kuroda, K. Kuwahara, Y. Lee, H.-K. Lee, H.-W. Lin, C.-Y. Liu, Y. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsumoto, N. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyake, K. Miyamoto, A. Miyamoto, T. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Muraki, Y. Murakoshi, M. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakano, M. Nakao, H. Nakao, K. Narikawa, T. Ni, W.-T. Nonomura, T. Obuchi, Y. Oh, J.J. Oh, S.-H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohmae, N. Okino, K. Okutomi, K. Ono, K. Ono, Y. Oohara, K. Ota, S. Park, J. Peña Arellano, F.E. Pinto, I.M. Principe, M. Sago, N. Saijo, M. Saito, T. Saito, Y. Saitou, S. Sakai, K. Sakakibara, Y. Sasaki, Y. Sato, S. Sato, T. Sato, Y. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shiga, K. Shikano, Y. Shimoda, T. Shinkai, H. Shoda, A. Someya, N. Somiya, K. Son, E.J. Starecki, T. Suemasa, A. Sugimoto, Y. Susa, Y. Suwabe, H. Suzuki, T. Tachibana, Y. Tagoshi, H. Takada, S. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. National Astronomical Observatory of Japan Tokyo181-8588 Japan Graduate School of Science University of Tokyo Tokyo113-0033 Japan Department of Physics Graduate School of Science University of Tokyo Tokyo113-0033 Japan Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Tokyo113-0032 Japan Department of Physics Graduate School of Science Osaka City University Osaka558-8585 Japan Graduate School of Science and Technology Hirosaki University Aomori036-8561 Japan Department of Physics Graduate School of Science and Engineering Tokyo Institute of Technology Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Chiba277-8582 Japan Institute for Cosmic Ray Research University of Tokyo Gifu506-1205 Japan Graduate School of Science Osaka University Osaka560-0043 Japan Department of Physics Sogang University Seoul121-742 Korea Republic of University of Sannio at Benevento BeneventoI-82100 Italy Sezione di Napoli NapoliI-80126 Italy University of Toyama Toyama930-8555 Japan Department of Physics Graduate School of Science Kyoto University Kyoto606-8502 Japan Institute of Physics Academia Sinica Taipei11529 Taiwan University of Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik HannoverD-30167 Germany Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of Daejeon34055 Korea Republic of Department of Physics Myongji University Yongin449-728 Korea Republic of Department of Computer Simulation Inje University Gimhae-shi50834 Korea Republic of Daejeon34047 Korea Republic of Department of Physical Science Graduate School of Science Hiroshima University Hiroshima903-0213 Japan Department of Applied Physics Graduate School of Science Fukuoka University Fukuoka814-0180 Japan Graduate School of Science and Engineer

Major construction and initial-phase operation of a second-generation gravitational-wave detector KAGRA has been completed. The entire 3-km detector is installed underground in a mine in order to be isolated from background seismic vibrations on the surface. This allows us to achieve a good sensitivity at low frequencies and high stability of the detector. Bare-bones equipment for the interferometer operation has been installed and the first test run was accomplished in March and April of 2016 with a rather simple configuration. The initial configuration of KAGRA is named iKAGRA. In this paper, we summarize the construction of KAGRA, including the study of the advantages and challenges of building an underground detector and the operation of the iKAGRA interferometer together with the geophysics interferometer that has been constructed in the same tunnel. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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Light Scattering Technique to Detect the Temperature of the Milk Proteins control Design

Light Scattering Technique to Detect the Temperature of the ...

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International Conference on Instrumentation, measurement, Computer, communication and control (IMCCC)

作者： Lifeng Liu Zhen Zhou Zehong Lv Dixing Li College of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781479965762

The use of light scattering techniques can be achieved for the detection of the protein ontent of milk, but there are many factors that affect the test results in the detection process. One of the important factors effect of detection result is temperature. This article will explore the detection process temperature affect the test results and the fuzzy PID technology to achieve a milk protein detection process of constant temperature, in order to improve detection accuracy.

关键词： Proteins Dairy products Temperature control Light scattering Temperature Temperature measurement PD control

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Design of a wireless monitoring network for granary temperature and humidity based on Zigbee

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International Journal of u- and e- Service, Science and technology 2014年第2期7卷 77-82页

作者： Dong, Jingwei Li, Huile Liu, Yuan Guo, Yanwen Tang, Guanhua School of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

The granary monitoring systems in used in China are mostly based on wired networks such as RS-485 serial bus, CAN fieldbus, etc. Inevitably these monitoring systems have drawbacks of high installation cost, high failure rate and high maintenance cost. Aiming at these problems, this paper designed a granary temperature and humidity monitoring system based on Zigbee wireless network. The monitoring system consists of sensor modules as Zigbee end devices, a host PC connected to a Zigbee coordinator and some Zigbee routers if necessary. The sensor module is a temperature and humidity testing device using a CC2430 as the controller and a SHT11 as the sensor. All testing results are displayed as figures and curves in real-time on the host PC, and history data are saved in a database file for further analysis. © 2014 SERSC.

关键词： Zigbee

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Research on the Prediction Model of Coal and Gas Outburst Based on Principal Component Analysis and Radial Basis Function Neural Network

Research on the Prediction Model of Coal and Gas Outburst Ba...

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International Conference on Instrumentation, measurement, Computer, communication and control (IMCCC)

作者： Yongchao Guan Shuai Wang Zhen Zhou College of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781479965762

To improve the accuracy and efficiency of coal and gas outburst prediction, the principal component analysis was combined with radial basis function neural network for the prediction of the situation of coal and gas outburst in this paper. The impact factors of coal and gas outburst in a coal mine are the object of the study. Principal component analysis method was used to extract the principal component factors, and then the large contribution of three principal components was selected to replace the original nine factors, with the main ingredient as an input parameter radial basis function neural network. Coal and gas outburst is divided into four levels to build predictive models of coal and gas outburst. 16 outstanding groups of typical samples of the neural network prediction model were selected for training, and three groups of testing samples were tested with trained neural network prediction model, with results showing that projections are consistent with the actual situation.

关键词： Coal Predictive models Principal component analysis Radial basis function networks Mathematical model Data models

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Scalable load balancing for large-scale web server clusters

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Journal of Computational Information Systems 2014年第13期10卷 5763-5771页

作者： Huang, Chuibi Wang, Jinlin Wu, Gang Chen, Jun Department of Automation University of Science and Technology of China Key Laboratory of Network Communication System and Control Hefei China Institute of Acoustics Chinese Academy of Sciences National Network New Media Engineering Research Center Beijing China

Load balancing is a fundamental technology to provide scalability for web server clusters. But in the scenarios where the web server clusters have massive amount of user accesses, a single load balancing server is unable to carry the user visits. Therefore load balancing tasks should be undertaken among a set of load balancing servers. Correspondingly the loads among these servers also need to be balanced. In this paper, we proposed a load balancing scheme that could provide scalability, availability and reliability for large-scale web server clusters. The scheme combines existing routing techniques and splits the function of load balancing into three levels that could be implemented by different devices, thereby enabling the web server cluster scales with user accesses. The experiment results have demonstrated the effectiveness of the program. © 2014 by Binary Information Press

关键词： Scalability

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Space QUEST mission proposal: Experimentally testing decoherence due to gravity

arXiv

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arXiv 2017年

作者： Joshi, Siddarth Koduru Pienaar, Jacques Ralph, Timothy C. Cacciapuoti, Luigi McCutcheon, Will Rarity, John Giggenbach, Dirk Lim, Jin Gyu Makarov, Vadim Fuentes, Ivette Scheidl, Thomas Beckert, Erik Bourennane, Mohamed Bruschi, David Edward Cabello, Adan Capmany, Jose Carrasco-Casado, Alberto Diamanti, Eleni Dusek, Miloslav Elser, Dominique Gulinatti, Angelo Hadfield, Robert H. Jennewein, Thomas Kaltenbaek, Rainer Krainak, Michael A. Lo, Hoi-Kwong Marquardt, Christoph Milburn, Gerard Peev, Momtchil Poppe, Andreas Pruneri, Valerio Renner, Renato Salomon, Christophe Skaar, Johannes Solomos, Nikolaos Stipčević, Mario Torres, Juan P. Toyoshima, Morio Villoresi, Paolo Walmsley, Ian Weihs, Gregor Weinfurter, Harald Zeilinger, Anton Zukowski, Marek Ursin, Rupert Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Boltzmanngasse 3 ViennaA-1090 Austria Centre for Quantum Computation & Communication Technology Department of Physics University of Queensland St LuciaQLD4072 Australia European Space Agency Keplerlaan 1 - P.O. Box 299 AG Noordwijk ZH2200 Netherlands Department of Electrical and Electronic Engineering University of Bristol Merchant Venturers Building Woodland Road BristolBS8 1UB United Kingdom German Aerospace Center Institute of Communications and Navigation Muenchner Strasse 20 Wessling82234 Germany Institute for Quantum Computing Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Precision Engineering Department Fraunhofer IOF Albert-Einstein-Straße 7 Jena07745 Germany Physics department Stockholm University Albanova universitetscentrum Universitetsvägen 10 Stockholm114 18 Sweden York Centre for Quantum Technologies Department of Physics University of York HeslingtonYO10 5DD United Kingdom University of Seville SevillaE-41012 Spain iTEAM Research Institute Universitat Politcnica de Valencia Valencia46022 Spain 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan CNRS Universite Pierre et Marie Curie Paris75005 France Department of Optics Faculty of Science Palacky University 17. Iistopadu 12 Olomouc772 00 Czech Republic Max Planck Institute for the Science of Light Bldg. 24 Günther-Scharowsky Str. 1 ErlangenD-91058 Germany Piazza Leonardo da Vinci 32 Milano20133 Italy School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom Vienna Center of Quantum Science and Technology Faculty of Physics University of Vienna Boltzmanngasse 5 ViennaA-1090 Austria NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Quantum Information and Quantum Con

Models of quantum systems on curved space-times lack sufficient experimental verification. Some speculative theories suggest that quantum properties, such as entanglement, may exhibit entirely different behavior to purely classical systems. By measuring this effect or lack thereof, we can test the hypotheses behind several such models. For instance, as predicted by Ralph and coworkers [T C Ralph, G J Milburn, and T Downes, Phys. Rev. A, 79(2):22121, 2009;T C Ralph and J Pienaar, New Journal of Physics, 16(8):85008, 2014], a bipartite entangled system could decohere if each particle traversed through a different gravitational field gradient. We propose to study this effect in a ground to space uplink scenario. We extend the above theoretical predictions of Ralph and coworkers and discuss the scientific consequences of detecting/failing to detect the predicted gravitational decoherence. We present a detailed mission design of the European Space Agency's (ESA) Space QUEST (Space - Quantum Entanglement Space Test) mission, and study the feasibility of the mission scheme. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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