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Author Correction: Speed-dependent and mode-dependent modulations of spatiotemporal modules in human locomotion extracted via tensor decomposition

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Scientific reports 2020年第1期10卷 4514页

作者： Ken Takiyama Hikaru Yokoyama Naotsugu Kaneko Kimitaka Nakazawa Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Nakacho Koganei Tokyo Japan. t.j.ken.takiyama@***. Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Nakacho Koganei Tokyo Japan. Rehabilitation Engineering Laboratory Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada. The University of Tokyo Department of Life Sciences Graduate School of Arts and Sciences Tokyo Japan.

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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3D deep learning enables fast imaging of spines through scattering media by temporal focusing microscopy

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arXiv 2019年

作者： Wei, Zhun Boivin, Josiah R. Xue, Yi Chen, Xudong So, Peter T.C. Nedivi, Elly Wadduwage, Dushan N. Center for Advanced Imaging Faculty of Arts and Sciences Harvard University CambridgeMA02138 United States Picower Institute for Learning and Memory Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Dept. of Biological Engineering Laser Biomedical Research Center Dept. of Mechanical Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Biology Massachusetts Institute of Technology CambridgeMA02139 United States

Today the gold standard for in vivo imaging through scattering tissue is the point-scanning two-photon microscope (PSTPM). Especially in neuroscience, PSTPM is widely used for deep-tissue imaging in the brain. However, due to sequential scanning, PSTPM is slow. Temporal focusing microscopy (TFM), on the other hand, focuses femtosecond pulsed laser light temporally, while keeping wide-field illumination, and is consequently much faster. However, due to the use of a camera detector, TFM suffers from the scattering of emission photons. As a result, TFM produces images of poor spatial resolution and signal-to-noise ratio (SNR), burying fluorescent signals from small structures such as dendritic spines. In this work, we present a data-driven deep learning approach to improve resolution and SNR of TFM images. Using a 3D convolutional neural network (CNN) we build a map from TFM to PSTPM modalities, to enable fast TFM imaging while maintaining high-resolution through scattering media. We demonstrate this approach for in vivo imaging of dendritic spines on pyramidal neurons in the mouse visual cortex. We show that our trained network rapidly outputs high-resolution images that recover biologically relevant features previously buried in the scattered fluorescence in the TFM images. In vivo imaging that combines TFM and the proposed 3D convolution neural network is one to two orders of magnitude faster than PSTPM but retains the high resolution and SNR necessary to analyze small fluorescent structures. The proposed 3D convolution deep network could also be potentially beneficial for improving the performance of many speed-demanding deep-tissue imaging applications such as in vivo voltage imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

Modeling and power controls of wind energy conversion systems based on doubly fed induction generator 6

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Modeling and power controls of wind energy conversion system...

6th International Renewable and Sustainable Energy Conference, IRSEC 2018

作者： Bouderbala, Manale Bossoufi, Badre Aroussi, Hala Alami Lagrioui, Ahmed Taoussi, Mohmmed Ihedrane, Yasmine Ghamrasni, Madiha El Laboratory of Electrical Engineering Maintenance Higher School of Technology Oujda Morocco Department of Electrical and Computer Engineering 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 School of Engineers Elecrtrical 'epartment 5DEDW 0RURFFR Morocco

ISBN: (纸本)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. © 2018 IEEE.

关键词： Wind power

Improving positioning accuracy of the mobile laser scanning in GPS-denied environments: An experimental case study

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arXiv 2019年

作者： Liu, Wi Li, Zhixiong Sun, Shuaishuai Malekian, Reza Ma, Zhenjun Li, Weihua School of Mechanical and Electrical Engineering China University of Mining and Technology Xuzhou221116 China Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment China University of Mining and Technology Xuzhou210008 China School of Engineering Ocean University of China Tsingdao266100 China School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong WollongongNSW2522 Australia New Industry Creation Hatchery Center Tohoku University Sendai980-8577 Japan Department of Computer Science and Media Technology Malmö University Malmö20506 Sweden

The positioning accuracy of the mobile laser scanning (MLS) system can reach the level of centimeter under the conditions where GPS works normally. However, in GPS-denied environments this accuracy can be reduced to the decimeter or even the meter level because the observation mode errors and the boresight alignment errors of MLS cannot be calibrated or corrected by the GPS signal. To bridge this research gap, this paper proposes a novel technique that appropriately incorporates the robust weight total least squares (RWTLS) and the full information maximum likelihood optimal estimation (FIMLOE) to improve the positioning accuracy of the MLS system under GPS-denied environment. First of all, the coordinate transformation relationship and the observation parameters vector of MLS system are established. Secondly, the RWTLS algorithm is used to correct the 3D point observation model;then the uncertainty propagation parameter vector and the boresight alignment errors between the laser scanner frame and the IMU frame are calibrated by FIMLOE. Lastly, experimental investigation in indoor scenarios was performed to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method is able to significantly improve the positioning accuracy of an MLS system in GPS-denied environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Global positioning system

Collaborative learning for extremely low bit asymmetric hashing

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arXiv 2018年

作者： Luo, Yadan Li, Yang Shen, Fumin Yang, Yang Cui, Peng Huang, Zi School of Information Technology and Electrical Engineering University of Queensland Brisbane4072 Australia Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Extremely low bit (e.g., 4-bit) hashing is in high demand for retrieval and network compression, yet it could hardly guarantee a manageable convergence or performance due to its severe information loss and shrink of discrete solution space. In this paper, we propose a novel Collaborative Learning strategy for high-quality low-bit deep hashing. The core idea is to distill bit-specific representations for low-bit codes with a group of hashing learners, where hash codes of various length actively interact by sharing and accumulating knowledge. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Multiple views from different embedding heads provide supplementary guidance as well as regularization for extremely low bit hashing, hence making convergence faster and more stable. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and show that 8-bit hash codes generated by MAH achieve 94:4% of MAP score, which significantly surpasses the performance of 48-bit codes by the state-of-the-arts for image retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Hash functions

High Frequency Functional Ultrasound in Mice

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High Frequency Functional Ultrasound in Mice

2018 IEEE International Ultrasonics Symposium, IUS 2018

作者： Koekkoek, S.K.E. Soloukeytbalvandany, S. Generowicz, B.S. Vanhoogstraten, W.S. Deoude, N.L. Boele, H.J. Strydis, C. Leus, G. Bosch, J.G. Vander Steen, A.F.W. De Zeeuw, C.I. Kruizing, P. Department of Neuroscience Erasmus Medical Center Rotterdam Netherlands Department of Neurosurgery Erasmus Medical Center Rotterdam Netherlands Department of Biomedical Engineering Erasmus Medical Center Rotterdam Netherlands Netherlands Institute of Neuroscience Royal Dutch Academy for Arts and Sciences Amsterdam Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9781538634257

Functional ultrasound (fUS) is a relatively new imaging modality to study the brain with a high spatiotemporal resolution and a wide field-of-view. In fUS detailed images of cerebral blood flow and volume are used to derive functional information, as changes in local flow and/or volume may reflect neuronal activation through neurovascular coupling. Most fUS studies so far have been performed in rats. Translating fUS to mice, which is a favorable animal model for neuroscience, pleads for a higher spatial resolution than what has been reported so far. As a consequence the temporal sampling of the blood flow should also be increased in order to adequately capture the wide range in blood velocities, as the Doppler shifts are inversely proportional to the spatial resolution. Here we present our first detailed images of the mouse brain vasculature at high spatiotemporal resolution. In addition we show some early experimental work on tracking brain activity upon local electrical stimulation. © 2018 IEEE.

关键词： Ultrasonics

Pre-training of deep bidirectional protein sequence representations with structural information

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arXiv 2019年

作者： Min, Seonwoo Park, Seunghyun Kim, Siwon Choi, Hyun-Soo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon24341 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://***/mswzeus/PLUS. © 2019, CC BY-NC-ND.

关键词： Proteins

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Concept for an Energy Data Aggregation Layer for Production Sites - A combination of AutomationML and OPC UA

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Concept for an Energy Data Aggregation Layer for Production ...

IEEE International Conference on Emerging Technologies and Factory Automation

作者： Andreas Wurger Karl-Heinz Niemann Alexander Fay Faculty I - Electrical and Computer Engineering University of Applied Sciences and Arts Hannover Germany Institute of Automation Technology Helmut-Schmidt-University Hamburg Germany

In industrial production facilities, technical Energy Management Systems are used to measure, monitor and display energy consumption related information. The measurements take place at the field device level of the automation pyramid. The measured values are recorded and processed at the control level. The functionalities to monitor and display energy data are located at the MES level of the automation pyramid. So the energy data from all PLCs has to be aggregated, structured and provided for higher level systems. This contribution introduces a concept for an Energy Data Aggregation Layer, which provides the functionality described above. For the implementation of this Energy Data Aggregation Layer, a combination of AutomationML and OPC UA is used.

关键词： Technical energy management Energy efficiency AutomationML OPC UA Aggregation server