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International Renewable and Sustainable Energy Conference (IRSEC)

作者： Manale Bouderbala Badre Bossoufi Hala Alami Aroussi Ahmed Lagrioui Mohmmed Taoussi Yasmine Ihedrane Madiha El Ghamrasni Laboratory of Electrical Engineering and Maintenance Higher School of Technology Oujda Morocco Department of Electrical and Computer Engineering The Higher National School of Arts and Trades Moulay Ismail University Meknes Morocco LISTA Laboratory Faculty of Sciences Dhar El Mahraz University Sidi Mohammed Ben Abdellah Fez Morocco Elecrtrical Department Mohammadia School of Engineers Rabat Morocco

ISBN: (纸本)9781728111834;9781728111827

This study focuses on the wind energy conversion systems which consist of a turbine, multiplier, generator and power electronic devices. In our case we opted for the doubly fed induction generator (DFIG); this choice is due to its advantages as well as its ability to adapt to changing wind. On the other hand, this machine is characterized with its non-linearity. Moreover, it is necessary to apply a control in order to have a maximum of power and to maintain the reactive power at zero. Therefore, we will start with the modeling of the system. Then, the maximization of the power using the strategy of maximum power point tracking (MPPT) will be detailed, finally, we will apply the two control techniques namely: direct field oriented control (DFOC) and indirect field oriented control (IFOC) in order to compare them. The results are presented in the Matlab/ Simulink environment to ensure the performance of the control.

关键词： Stators Rotors Doubly fed induction generators Wind turbines Wind energy Mathematical model Aerodynamics

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The status of KAGRA underground cryogenic gravitational wave telescope 15

The status of KAGRA underground cryogenic gravitational wave...

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15th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2017

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, A.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Quynh, L.Nguyen Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Alleano, F.E.P. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. Van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Aomori Hirosaki036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Osaka Toyonaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon 34141 Korea Republic of Yuseong Daejeon34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Chiba Kashiwa277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy Department o

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. © Published under licence by IOP Publishing Ltd.

关键词： Interferometers

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Author Correction: A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features

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Nature medicine 2025年 2025 Mar 28页

作者： Johanna Klughammer Daniel L Abravanel Åsa Segerstolpe Timothy R Blosser Yury Goltsev Yi Cui Daniel R Goodwin Anubhav Sinha Orr Ashenberg Michal Slyper Sébastien Vigneau Judit Jané-Valbuena Shahar Alon Chiara Caraccio Judy Chen Ofir Cohen Nicole Cullen Laura K DelloStritto Danielle Dionne Janet Files Allison Frangieh Karla Helvie Melissa E Hughes Stephanie Inga Abhay Kanodia Ana Lako Colin MacKichan Simon Mages Noa Moriel Evan Murray Sara Napolitano Kyleen Nguyen Mor Nitzan Rebecca Ortiz Miraj Patel Kathleen L Pfaff Caroline B M Porter Asaf Rotem Sarah Strauss Robert Strasser Aaron R Thorner Madison Turner Isaac Wakiro Julia Waldman Jingyi Wu Jorge Gómez Tejeda Zañudo Diane Zhang Nancy U Lin Sara M Tolaney Eric P Winer Edward S Boyden Fei Chen Garry P Nolan Scott J Rodig Xiaowei Zhuang Orit Rozenblatt-Rosen Bruce E Johnson Aviv Regev Nikhil Wagle Klarman Cell Observatory Broad Institute of Harvard and MIT Cambridge MA USA. klughammer@genzentrum.lmu.de. Gene Center and Department of Biochemistry Ludwig Maximilians Universität München Munich Germany. klughammer@genzentrum.lmu.de. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. daniel_abravanel@dfci.harvard.edu. Harvard Medical School Boston MA USA. daniel_abravanel@dfci.harvard.edu. Klarman Cell Observatory Broad Institute of Harvard and MIT Cambridge MA USA. Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA. Baxter Laboratory in Stem Cell Biology Department of Microbiology and Immunology Stanford University School of Medicine Stanford CA USA. Department of Media Arts and Sciences McGovern Institute Massachusetts Institute of Technology Cambridge MA USA. Center for Cancer Genomics Dana-Farber Cancer Institute Boston MA USA. Faculty of Engineering Gonda Brain Research Center and Institute of Nanotechnology Bar-Ilan University Ramat Gan Israel. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Department of Microbiology Immunology and Genetics Faculty of Health Sciences Ben-Guiron University Beersheba Israel. Center for Immuno-Oncology Dana-Farber Cancer Institute Boston MA USA. Gene Center and Department of Biochemistry Ludwig Maximilians Universität München Munich Germany. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. AstraZeneca R&D Boston MA USA. Department of Microbiology Immunology and Cancer Biology University of Virginia Charlottesville VA USA. Department of Media Arts and Sciences Massachusetts Institute of Technology Cambridge MA USA. Department of Biological Engineering Massachusetts I

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A Characterization of Guesswork on Swiftly Tilting Curves

arXiv

引用

arXiv 2018年

作者： Beirami, Ahmad Calderbank, Robert Christiansen, Mark Duffy, Ken Medard, Muriel Department of Electrical and Computer Engineering Duke University DurhamNC United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Ea Digital Platform Data & AI Electronic Arts Redwood CityCA United States Hamilton Institute Maynooth University Ireland

Given a collection of strings, each with an associated probability of occurrence, the guesswork of each of them is their position in a list ordered from most likely to least likely, breaking ties arbitrarily. Guesswork is central to several applications in information theory: Average guesswork provides a lower bound on the expected computational cost of a sequential decoder to decode successfully the transmitted message;the complementary cumulative distribution function of guesswork gives the error probability in list decoding;the logarithm of guesswork is the number of bits needed in optimal lossless one-to-one source coding;and guesswork is the number of trials required of an adversary to breach a password protected system in a brute-force attack. In this paper, we consider memoryless string-sources that generate strings consisting of i.i.d. characters drawn from a finite alphabet, and characterize their corresponding guesswork. Our main tool is the tilt operation on a memoryless string-source. We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family of the string-source. We provide an operational meaning to the tilted families by proving that two memoryless string-sources result in the same guesswork on all strings of all lengths if and only if their respective categorical distributions belong to the same tilted family. Establishing some general properties of the tilt operation, we generalize the notions of weakly typical set and asymptotic equipartition property to tilted weakly typical sets of different orders. We use this new definition to characterize the large deviations for all atypical strings and characterize the volume of weakly typical sets of different orders. We subsequently build on this characterization to prove large deviation bounds on guesswork and provide an accurate approximation of its probability mass function. Copyright © 2018, The Au

关键词： Decoding

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A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA Department of Electrical Engineering and Computer Science MIT Cambridge MA USA Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA Simons Center Data Analysis Simons Foundation New York NY USA Department of Mechanical Engineering MIT Cambridge MA USA Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA Department of Biological Physics Eotvos University Budapest Hungary Department of Biological Engineering MIT Cambridge MA USA MIT Center for Neurobiological Engineering MIT Cambridge MA USA Department of Brain and Cognitive Sciences MIT Cambridge MA USA MIT McGovern Institute for Brain Research MIT Cambridge MA USA

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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Convolutional neural networks for non-iterative reconstruction of compressively sensed images

arXiv

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arXiv 2017年

作者： Lohit, Suhas Kulkarni, Kuldeep Kerviche, Ronan Turaga, Pavan Ashok, Amit Department of Electrical Computer and Electrical Engineering and the Department of Arts Media and Engineering Arizona State University TempeAZ United States College of Optical Sciences University of Arizona TucsonAZ United States

Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this work, we propose a data driven non-iterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution, ReconNet, is a deep neural network, whose parameters are learned end-to-end to map block-wise compressive measurements of the scene to the desired image blocks. Reconstruction of an image becomes a simple forward pass through the network and can be done in real-time. We show empirically that our algorithm yields reconstructions with higher PSNRs compared to iterative algorithms at low measurement rates and in presence of measurement noise. We also propose a variant of ReconNet which uses adversarial loss in order to further improve reconstruction quality. We discuss how adding a fully connected layer to the existing ReconNet architecture allows for jointly learning the measurement matrix and the reconstruction algorithm in a single network. Experiments on real data obtained from a block compressive imager show that our networks are robust to unseen sensor noise. Finally, through an experiment in object tracking, we show that even at very low measurement rates, reconstructions using our algorithm possess rich semantic content that can be used for high level inference. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Mutual coupling reduction using metamaterial supersubstrate for high performance & densely packed planar phased arrays

Mutual coupling reduction using metamaterial supersubstrate ...

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International Conference on Microwaves, Radar and Wireless Communications

作者： Mohammad Alibakhshikenari Alessandro Salvucci Giorgio Polli Bal S. Virdee Chan H. See Raed Abd-Alhameed Francisco Falcone Aurora Andújar Jaume Anguera Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome ITALY London Metropolitan University School of Computing & Digital Media London UK School of Engineering University of Bolton Bolton UK School of Electrical Engineering & Computer Science University of Bradford UK Electric and Electronic Engineering Department Universidad Pública de Navarra SPAIN Technology Department Barcelona SPAIN Department of Electronics and Telecommunication Universitat Ramon Llull Barcelona SPAIN

ISBN: (纸本)9781538637234

This paper proposes on an effective mutual coupling suppression technique for planar phased array. This is achieved locating a metamaterial superstrate patch between radiation elements of phased array. The superstrate patch is realised by incorporating slots inside the patch, where the slots are arranged in 2×3 array column. The proposed mutual radiation decoupling technique is implemented on FR-4 substrate. Average coupling suppression of 5 dB is achieved on a low permittivity substrate over its operational frequency band. The proposed technique is (i) simple to implement; (ii) planar design; (iii) easily realised in practice; (iv) overcomes the shortcomings of poor front-to-back ratio previously reported in other radiation suppression techniques; and (v) applicable for densely packed microstrip. Furthermore, the proposed planar technique is highly versatile for various applications having stringent performance requirements.

关键词： Mutual coupling Antenna arrays Metamaterials Microwave antennas Broadband antennas Wireless communication

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Author Correction: High-Gain Metasurface in Polyimide On-Chip Antenna Based on CRLH-TL for Sub-Terahertz Integrated Circuits

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Scientific reports 2020年第1期10卷 13650页

作者： Mohammad Alibakhshikenari Bal S Virdee Chan H See Raed A Abd-Alhameed Francisco Falcone Ernesto Limiti Electronic Engineering Department University of Rome "Tor Vergata" Via del Politecnico 1 00133 Rome Italy. alibakhshikenari@ing.uniroma2.it. London Metropolitan University Center for Communications Technology & Mathematics School of Computing & Digital Media London N7 8DB UK. School of Engineering & the Built Environment Edinburgh Napier University 10 Colinton Road Edinburgh EH10 5DT UK. School of Engineering University of Bolton Deane Road Bolton BL3 5AB UK. School of Electrical Engineering & Computer Science University of Bradford Bradford BD7 1DP UK. Electrical and Electronic Engineering Department Public University of Navarre 31006 Pamplona Spain. Electronic Engineering Department University of Rome "Tor Vergata" Via del Politecnico 1 00133 Rome Italy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Indic handwritten script identification using offline-online multi-modal deep network

arXiv

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arXiv 2018年

作者： Bhunia, Ayan Kumar Mukherjee, Subham Sain, Aneeshan Bhunia, Ankan Kumar Roy, Partha Pratim Pal, Umapada Institute for Media Innovation Nanyang Technological University Singapore Centre for Vision Speech and Signal Processing University of Surrey England United Kingdom Department of ECE Institute of Engineering & Management Kolkata India Department of EE Institute of Engineering & Management Kolkata India Department of Electrical Engineering Jadavpur University Department of CSE Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. Our method uses a multi-modal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, the proposed framework can work for both offline and online script identification which alleviates the need for designing two separate script identification modules for individual modality. We also propose a novel conditional multi-modal fusion scheme to combine the information from offline and online modality which takes into account the original modality of the data being fed to our network and thus it combines adaptively. An exhaustive experimental study has been done on a data set including English(Roman) and 6 other official Indic scripts. Our proposed scheme outperforms traditional classifiers along with handcrafted features and deep learning based methods. Experiment results show that using only character level training data can achieve competitive performance against traditional training using word level data. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Compact Supervisory Control of Discrete Event Systems by Petri Nets With Data Inhibitor Arcs

Compact Supervisory Control of Discrete Event Systems by Pet...

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作者： Chen, YuFeng Li, ZhiWu Barkaoui, Kamel Wu, NaiQi Zhou, MengChu School of Electro-Mechanical Engineering Xidian University Xi'an710071 China Cedric Laboratory and Computer Science Department Conservatoire National des Arts et Métiers Paris75141 France Institute of Systems Engineering Macau University of Science and Technology 999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Renewable Energy Research Group King Abdulaziz University Jeddah Saudi Arabia

This work proposes a novel structure in Petri nets, namely data inhibitor arcs, and their application to the optimal supervisory control of Petri nets. A data inhibitor arc is an arc from a place to a transition labeled with a set of integers. A transition is disabled by a data inhibitor arc if the number of tokens in the place is in the set of integers labeled on it. Its formal definitions and properties are given. Then, we propose a method to design an optimal Petri net supervisor with data inhibitor arcs to prevent a system from reaching illegal markings with respect to control specifications. Two techniques are developed to reduce the supervisor structure by compressing the number of control places. Finally, a number of examples are used to illustrate the proposed approaches and experimental results show that they can obtain optimal Petri net supervisors for the net models that cannot be optimally controlled by pure net supervisors. A significant result is that the proposed approach can always lead to an optimal supervisor with only one control place for bounded Petri nets on the premise that such a supervisor exists. © 2016 IEEE.

关键词： Petri nets

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