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arXiv 2018年

作者： Novoa, José Escudero, Juan Pablo Wuth, Jorge Poblete, Victor King, Simon Stern, Richard Yoma, Néstor Becerra Electrical Engineering Department Speech Processing and Transmission Laboratory Universidad de Chile Santiago Chile Institute of Acoustics Universidad Austral de Chile Valdivia Chile Centre for Speech Technology Research University of Edinburgh Edinburgh United Kingdom Department of Electrical and Computer Engineering and Language Technologies Institute Carnegie Mellon University Pittsburgh United States

This paper evaluates the robustness of a DNN-HMM-based speech recognition system in highly-reverberant real environments using the HRRE database. The performance of locally-normalized filter bank (LNFB) and Mel filter bank (MelFB) features in combination with Non-negative Matrix Factorization (NMF), Suppression of Slowly-varying components and the Falling edge (SSF) and Weighted Prediction Error (WPE) enhancement methods are discussed and evaluated. Two training conditions were considered: Clean and reverberated (Reverb). With Reverb training the use of WPE and LNFB provides WERs that are 3% and 20% lower in average than SSF and NMF, respectively. WPE and MelFB provides WERs that are 11% and 24% lower in average than SSF and NMF, respectively. With clean training, which represents a significant mismatch between testing and training conditions, LNFB features clearly outperform MelFB features. The results show that different types of training, parametrization, and enhancement techniques may work better for a specific combination of speaker-microphone distance and reverberation time. This suggests that there could be some degree of complementarity between systems trained with different enhancement and parametrization methods. Index Terms: Speech recognition, enhancement techniques, reverberation, real environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Reverberation

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A Geometric Approach to Second-Order Consensus of Heterogeneous Networked Systems

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IEEE Transactions on Cybernetics 2018年 2018 Jan 9页

作者： Su, Housheng Ye, Yanyan Chen, Xia He, Haibo School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China. China China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA. United States

This paper investigates second-order consensus of networked systems with heterogeneous intrinsic nonlinear dynamics via a geometrical method, in which the nonlinear dynamics are governed by both velocity and position. First, two necessary conditions are deduced for the existence of consensus solutions by analyzing the inherent nonlinear dynamics of isolated nodes. Then a closed invariant set is constructed via geometrical methods. The nonempty of the set implies the existence of consensus solution. Assuming that the primary and the second dimension about the above invariant set are the same linear subspace, the system can be divided into two simple subsystems through a linear transformation. In addition, some sufficient conditions are proposed for reaching the global consensus based on matrix theory and Lyapunov method. Finally, numerical simulation results are provided to illustrate the validity of theoretical analysis. IEEE

关键词： Multi agent systems

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Compressively Sensed Image Recognition

arXiv

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arXiv 2018年

作者： Deǧerli, Ayşen Aslan, Sinem Yamaç, Mehmet Sankur, Bülent Gabbouj, Moncef Tampere University of Technology Laboratory of Signal Processing Tampere Finland Ca' Foscari University of Venice European Centre for Living Technology Venice Italy Ege University International Computer Institute Izmir Turkey Boǧaziçi University Electrical and Electronics Engineering Department Istanbul Turkey

—Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

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Dense matter with eXTP

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Science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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Preventive Soot Blowing Strategy Based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy Based on State of Health Pr...

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Chinese Control Conference (CCC)

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering Taiyuan China North University of China School of Computer Science and Technology Taiyuan China Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal- fired power plant boiler. A preventive soot blowing strategy based on state of health prediction for heat transfer surface was proposed. The average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing strategy. According to the theory of renewal process, an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was established. By using Particle Swarm Optimization (PSO), the optimal preventive cycle and soot-blowing threshold were obtained, and the long-run average cost rate was the lowest. The model is validated with experiment data of a 300MW coal-fired power plant boiler and the parameters of prediction model are obtained. The results can verify the feasibility of proposed strategy. It can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Thermal engineering Radio frequency Coal Combustion Heat engines Heat transfer

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First-year ion-acoustic wave observations in the solar wind by the RPW/TDS instrument on board Solar Orbiter

arXiv

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arXiv 2021年

作者： Píša, David Souček, J. Santolík, O. Hanzelka, M. Nicolaou, G. Maksimovic, M. Bale, S.D. Chust, T. Khotyaintsev, Y. Krasnoselskikh, V. Kretzschmar, M. Lorfèvre, E. Plettemeier, D. Steller, M. Štverák, Š. Trávníček, P. Vaivads, A. Vecchio, A. Horbury, T. O'Brien, H. Evans, V. Angelini, V. Owen, C.J. Louarn, P. Institute of Atmospheric Physics Czech Academy of Sciences Bocni II 1401 Prague141 00 Czech Republic Faculty of Mathematics and Physics Charles University V Holesovickach 2 Prague 818000 Czech Republic Southwest Research Institute San AntonioTX78238 United States Mullard Space Science Laboratory University College London Holmbury St. Mary Surrey DorkingRH5 6NT United Kingdom LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen Meudon92195 France Space Sciences Laboratory University of California BerkeleyCA United States Physics Department University of California BerkeleyCA United States LPP CNRS Ecole Polytechnique Sorbonne Université Observatoire de Paris Université Paris-Saclay Palaiseau Paris France Uppsala Sweden LPC2E CNRS 3A avenue de la Recherche Scientifique Orléans France Université d'Orléans Orléans France CNES 18 Avenue Edouard Belin Toulouse31400 France Technische Universität Dresden Würzburger Str. 35 DresdenD-01187 Germany Space Research Institute Austrian Academy of Sciences Graz Austria Astronomical Institute The Czech Academy of Sciences Prague Czech Republic Department of Space and Plasma Physics School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm Sweden Radboud Radio Lab Department of Astrophysics Radboud University Nijmegen Netherlands Department of Physics Imperial College LondonSW7 2AZ United Kingdom Institut de Recherche en Astrophysique et Planétologie 9 Avenue du Colonel ROCHE BP 4346 Toulouse Cedex 431028 France

Context. Electric field measurements of the Time Domain Sampler (TDS) receiver, part of the Radio and plasma Waves (RPW) instrument on board Solar Orbiter, often exhibit very intense broadband wave emissions at frequencies below 20 kHz in the spacecraft frame. During the first year of the mission, the RPW/TDS instrument was operating from the first perihelion in mid-June 2020 and through the first flyby of Venus in late December 2020. Aims. In this paper, we present a year-long study of electrostatic fluctuations observed in the solar wind at an interval of heliocentric distances from 0.5 to 1 AU. The RPW/TDS observations provide a nearly continuous data set for a statistical study of intense waves below the local plasma frequency. Methods. The on-board and continuously collected and processed properties of waveform snapshots allow for the mapping plasma waves at frequencies between 200 Hz and 20 kHz. We used the triggered waveform snapshots and a Doppler-shifted solution of the dispersion relation for wave mode identification in order to carry out a detailed spectral and polarization analysis. Results. Electrostatic ion-acoustic waves are the most common wave emissions observed between the local electron and proton plasma frequency by the TDS receiver during the first year of the mission. The occurrence rate of ion-acoustic waves peaks around perihelion at distances of 0.5 AU and decreases with increasing distances, with only a few waves detected per day at 0.9 AU. Waves are more likely to be observed when the local proton moments and magnetic field are highly variable. A more detailed analysis of more than 10000 triggered waveform snapshots shows the mean wave frequency at about 3 kHz and wave amplitude about 2.5 mV/m. The wave amplitude varies as R-1.38 with the heliocentric distance. The relative phase distribution between two components of the E-field projected in the Y-Z Spacecraft Reference Frame (SRF) plane shows a mostly linear wave polarization. Electric f

关键词： Polarization

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Morphological geodesic active contour based automatic aorta segmentation in thoracic CT images

Morphological geodesic active contour based automatic aorta ...

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International Conference on computer Vision and Image processing, CVIP 2016

作者： Dasgupta, Avijit Mukhopadhyay, Sudipta Mehre, Shrikant A. Bhattacharyya, Parthasarathi Computer Vision and Image Processing Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Institute of Pulmocare & Research KolkataWest Bengal700156 India

ISBN: (纸本)9789811021060

Automatic aorta segmentation and quantification in thoracic computed tomography (CT) images is important for detection and prevention of aortic diseases. This paper proposes an automatic aorta segmentation algorithm in both contrast and non-contrast CT images of thorax. The proposed algorithm first detects the slice containing the carina region. Circular Hough Transform (CHT) is applied on the detected slice to localize ascending and descending aorta (circles with lowest variances) followed by a morphological geodesic active contour to segment the aorta from CT stack. The dice similarity coefficients (DSC) between the ground truth and the segmented output were found to be 0.8845 ± 0.0584 on LIDC-IDRI dataset. © Springer Science+Business Media Singapore 2017.

关键词： Image segmentation

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Combing context and commonsense knowledge through neural networks for solving winograd schema problems

Combing context and commonsense knowledge through neural net...

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2017 AAAI Spring Symposium

作者： Liu, Quan Jiang, Hui Ling, Zhen-Hua Zhu, Xiaodan Wei, Si Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York University Canada National Research Council Canada Ottawa Canada IFLYTEK Research Hefei China

ISBN: (纸本)9781577357797

This paper proposes a general framework to combine context and commonsense knowledge for solving the Winograd Schema (WS) and Pronoun Disambiguation Problems (PDP). In the proposed framework, commonsense knowledge bases (e.g. cause-effect word pairs) are quantized as knowledge constraints. The constraints guide us to learn knowledge enhanced embeddings (KEE) from large text corpus. Based on the pre-trained KEE models, this paper proposes two methods to solve the WS and PDP problems. The first method is an unsupervised method, which represents all the pronouns and candidate mentions in continuous vector spaces based on their contexts and calculates the semantic similarities between all the possible word pairs. The pronoun disambiguation procedure could then be implemented by comparing the semantic similarities between the pronoun (to be resolved) and all the candidate mentions. The second method is a supervised method, which extracts features for all the pronouns and candidate mentions and solves the WS problems by training a typical mention pair classification model. Similar to the first method, the features used in the second method are also extracted based on the KEE models. Experiments conducted on the available PDP and WS test sets show that, these two methods both achieve consistent improvements over the baseline systems. The best performance reaches 62% in accuracy on the PDP test set of the first Winograd Schema Challenge. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vector spaces

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A computationally efficient detector: Using both the test and training data for disturbance correlation estimation

A computationally efficient detector: Using both the test an...

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2017 International Conference on Radar Systems, Radar 2017

作者： Liu, Jun Zhao, Hong-Yan Liu, Weijian Zhu, Shengqi Liu, Hongwei Li, Hongbin National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Wuhan Electronic Information Institute Wuhan430019 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9781785615153

This paper examines a target detection problem in colored Gaussian disturbance with an unknown covariance matrix. In many classic adaptive detectors, the covariance estimator is formed by using only the training data. This necessitates calculating a new covariance estimator for each cell under test (CUT) during the cell-by-cell target search process. We consider herein an alternative approach that forms the covariance matrix estimate by using both test and training data for detection in homogeneous environments. This approach is computationally much more efficient since the covariance matrix estimator is computed only once and can be applied for target detection at each CUT. Using this estimator, we propose a new detector with two tunable parameters, which includes several existing detectors as special cases. Closed-form expressions for the probabilities of false alarm and detection are derived in the matched and mismatched cases. Simulation results reveal that the rejection capability of mismatched signals of the proposed detector can be flexibly controlled by adjusting its tunable parameters. © 2017 Institution of engineering and technology. All rights reserved.

关键词： Covariance matrix

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