检索结果-内蒙古大学图书馆

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Nan Zhou Hong Cheng Ya-Li Zheng Liang-Tian He Witold Pedrycz School of Mathematical Science University of Electronic Science and Technology of China Chengdu Sichuan China Center for Robotics University of Electronic Science and Technology of China Chengdu Sichuan China Department of Electrical and Computer Engineering University of Alberta AB Canada

ISBN: (纸本)9781509029181

Given the high-dimensionality of the original data, dimensionality reduction becomes a necessary step in data processing. In this study, a novel unsupervised feature selection model is proposed, which regards the unsupervised feature selection process as nonnegative subspace learning. Considering the efficiency of the learned subspace which can better indicate the selected features, a nonnegative sparsity adaptive subspace learning framework is proposed. It adapts the sparsity by weighted ℓ 2, 1 model. Specifically, the weights are defined by multi-stage support detection. Then we provide an approach to solve this weighted ℓ 2, 1 constraint non-convex problem leading to the Non-negative Sparsity Adaptive Subspace Learning (NSASL) algorithm. By the experiments which are conducted on real-word datasets, the superiority of proposed method over seven state-of-the-art unsupervised feature selection algorithms is verified.

关键词： Indexes Pattern recognition Face Wavelet analysis Feature extraction STEM Adaptation models

来源：评论

学校读者我要写书评

暂无评论

A Novel Extended Potential Field Controller for Use on Aerial Robots

A Novel Extended Potential Field Controller for Use on Aeria...

引用

IEEE International Conference on Automation science and Engineering

作者： Alexander C. Woods Hung M. La Quang P. Ha Advanced Robotics and Automation Laboratory Department of Computer Science and Engineering University of Nevada Reno 89557 USA School of Electrical Mechanical and Mechatronic Systems University of Technology Sydney Australia

ISBN: (纸本)9781509024100

Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

关键词： Drones Navigation Robot sensing systems Target tracking Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Optofluidic Systems: Miniaturized, Battery-Free Optofluidic Systems with Potential for Wireless Pharmacology and Optogenetics (Small 4/2018)

引用

Small 2018年第4期14卷

作者： Kyung Nim Noh Sung Il Park Raza Qazi Zhanan Zou Aaron D. Mickle Jose G. Grajales-Reyes Kyung-In Jang Robert W. Gereau IV Jianliang Xiao John A. Rogers Jae-Woong Jeong Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Center for Remote Health Science Technologies Texas A&M University College Station TX 77843 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Mechanical Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Anesthesiology Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea Department of Neuroscience Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Materials Science and Engineering Biomedical Engineering Neurological Surgery Chemistry Mechanical Engineering Electrical and Computer Science Northwestern University Evanston IL 60208 USA Center for Bio-Integrated Electronics Simpson Querrey Institute for BioNanotechnology Northwestern University Evanston IL 60208 USA School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea

来源：评论

学校读者我要写书评

暂无评论

PREFACE

引用

JOURNAL OF CONTEMPORARY CHINESE ART 2017年第1期4卷 3-4页

作者： Jiang Jiehong Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Baden-Württemberg Karlsruhe Germany Department of Information Technology and Human Factors National Institute of Advanced Industrial Science and Technology Tsukuba Japan Seoul National University Seoul South Korea Jacobs School of EngineeringInstitute for Contextual Robotics San Diego CA United States Department of Computer Science Stanford University Stanford CA United States

来源：评论

学校读者我要写书评

暂无评论

Dual-mode video stabilization based on adaptive motion clustering 15

Dual-mode video stabilization based on adaptive motion clust...

引用

7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Wang, Dong Wang, Bin Zhao, Sicheng Sun, Xiaoshuai Yao, Hongxun Liu, Hong State Key Laboratory of Robotics and System Harbin Institute of Technology China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781450335287

Many target tracking videos suffer from significant shake due to the undesirable camera motion or target vibration. These degraded video records have a negative impact on viewing experience as well as the follow-up analysis and application. Most existing digital video stabilization methods aim at removing camera shake using the techniques of global motion estimation and compensation between adjacent frames, while paying little attention to motion pattern and the relationship between the foreground and background contents. In this paper, we present a novel approach to stabilize the videos containing large dynamic object, which can realize the normal stabilization task and describe the target motion path at the meantime. We combine K-means and Gaussian mixture model to distinguish the motion modes of background and foreground without applying any prior knowledge about the moving object. The proposed approach gives a flexible choice in stabilizing background motion as traditional algorithms or foreground motion for salient target surveillance. © 2015 ACM.

关键词： Stabilization

来源：评论

学校读者我要写书评

暂无评论

On the Potential Cosmogenic Origin of the Ultra-high-energy Event KM3-230213A

引用

Astrophysical Journal Letters 2025年第2期984卷 L41-L41页

作者： Adriani, O. Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Alves Garre, S. Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Argüelles, C. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardacová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Benfenati Gualandi, F. Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Berti, E. Bertin, V. Betti, P. Biagi, S. Boettcher, M. Bonanno, D. Bottai, S. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzas, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipovic, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geisselbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Idrissi Ibnsalih, W. Illuminati, G. Janik, O. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczynski, P. Kamp, N. Keegans, J. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Lemaître, V. Leonora, E. Le INFN Sezione di Firenze via Sansone 1 Sesto Fiorentino 50019 Italy Università di Firenze Dipartimento di Fisica e Astronomia via Sansone 1 Sesto Fiorentino 50019 Italy INFN Sezione di Catania (INFN-CT) Via Santa Sofia 64 Catania 95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 Strasbourg F-67000 France Université de Haute Alsace rue des Frères Lumière Mulhouse 68093 Cedex France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France IFIC-Instituto de Física Corpuscular CSIC Universitat de València c/Catedrático José Beltrán 2 Paterna Valencia 46980 Spain Università di Napoli 'Federico II' Dip. Scienze Fisiche 'E. Pancini' Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli 80126 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli 80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma 00185 Italy Universitat Politècnica de Catalunya Laboratori d'Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú 08800 Spain Subatech IMT Atlantique IN2P3 CNRS Nantes Université 4rue Alfred Kastler-La Chantrerie BP *** Nantes France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia 46730 Spain Harvard University Department of Physics Laboratory for Particle Physics and Cosmology Lyman Laboratory 17Oxford St. Cambridge 02138 MA United States Université Paris Cité CNRS Astroparticule et Cosmologie Paris F-75013 France INFN Sezione di Genova Via Dodecaneso 33 Genova 16146 Italy Università di Genova Via Dodecaneso 33 Genova 16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6boulevard Maréchal Juin Caen 14050 France Comenius University in Bratislava Department of Nuclear Physics and Biophysics

On 2023 February 13, the KM3NeT/ARCA telescope observed a track-like event compatible with a ultra-highenergy muon with an estimated energy of 120 PeV, produced by a neutrino with an even higher energy, making it the most energetic neutrino event ever detected. A diffuse cosmogenic component is expected to originate from the interactions of ultra-high-energy cosmic rays with ambient photon and matter fields. The flux level required by the KM3NeT/ARCA event is, however, in tension with the standard cosmogenic neutrino predictions based on the observations collected by the Pierre Auger Observatory and Telescope Array over the last decade of the ultra-highenergy cosmic rays above the ankle (hence from the local Universe, z ≲ 1). We show here that both observations can be reconciled by extending the integration of the equivalent cosmogenic neutrino flux up to a redshift of z_max = 6 and considering either source evolution effects or the presence of a subdominant independent proton component in the ultra-high-energy cosmic-ray flux, thus placing constraints on known cosmic accelerators. © 2025. The Author(s). Published by the American Astronomical Society.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Knowledge Engineering with Big Data

引用

IEEE Intelligent Systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Surgical data science: Enabling next-generation surgery

arXiv

引用

arXiv 2017年

作者： Maier-Hein, Lena Vedula, Swaroop Speidel, Stefanie Navab, Nassir Kikinis, Ron Park, Adrian Eisenmann, Matthias Feussner, Hubertus Forestier, Germain Giannarou, Stamatia Hashizume, Makoto Katic, Darko Kenngott, Hannes Kranzfelder, Michael Malpani, Anand März, Keno Neumuth, Thomas Padoy, Nicolas Pugh, Carla Schoch, Nicolai Stoyanov, Danail Taylor, Russell Wagner, Martin Hager, Gregory D. Jannin, Pierre Heidelberg Germany Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Chair for Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Computer Science University of Bremen Bremen Germany Fraunhofer MEVIS Bremen Germany Department of Surgery Center of Anne Arundel Health System AnnapolisMD United States Department of Surgery Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Computer Science University of Haute-Alsace Mulhouse France Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Department of Advanced Medical Initiatives Graduate School of Medical Sciences Kyushu University Fukuoka Japan Department for General Visceral and Transplant Surgery Heidelberg University Hospital Heidelberg Germany Department of Computer Science Johns Hopkins University BaltimoreMD United States University of Leipzig Leipzig Germany ICube University of Strasbourg CNRS IHU Strasbourg France Department of Surgery University of Wisconsin MadisonWI United States IWR Heidelberg University Heidelberg Germany Department of Computer Science University College London London United Kingdom Université de Rennes 1 Rennes France INSERM Rennes France

Future advances in surgical care require, to a greater and greater extent, a close partnership between caregivers, patients, technology, and information systems. As part of the move towards personalized medicine, interventional care will increasingly transform from an artisanal craft based on physicians’ individual experiences, preferences and traditions into a discipline that relies on objective decision-making based on large-scale data from heterogeneous sources. Copyright © 2017, The Authors. All rights reserved.

关键词： Surgery

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Model Predictive Formation Control for Quadcopters

引用

IFAC-PapersOnLine 2015年第19期48卷 39-44页

作者： Ribeiro, Tiago T. Conceição, André G.S. Sa, Inkyu Corke, Peter LaR - Robotics Laboratory Department of Electrical Engineering Federal University of Bahia Salvador Brazil Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Australia

This paper presents a Nonlinear Model Predictive Formation Control for a group of Quadcopters. In this approach the controllers are distributed to the quadcopters and the coupling is done through its objective function. The path following problem is expressed through a dynamic model of the quadcopter's state errors relative to a Serret-Frenet frame, and the coordination problem is solved through adjustments in the robots' speeds combined with the path update rate for each robot. A strategy based on constraints relaxing is proposed to solve the problem of high state and formation errors, factors that directly affect feasibility and stability. Simulation results considering a realistic scenario are provided to demonstrate the performance of the proposed control strategy. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Problem solving Model predictive control Nonlinear systems Predictive control systems Unmanned aerial vehicles (UAV) Control strategies Coordination problems Formation control Nonlinear model predictive control Objective functions Path following problems Realistic scenario Serret Frenet frame

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Zebrafish as an animal model in epilepsy studies with multichannel EEG recordings

引用

Scientific reports 2017年第1期7卷 18112页

作者： Sung-Joon Cho Donghak Byun Tai-Seung Nam Seok-Yong Choi Byung-Geun Lee Myeong-Kyu Kim Sohee Kim School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea. School of Mechanical Engineering Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea. Department of Neurology Chonnam National University Medical School Gwangju 61469 Republic of Korea. Department of Biomedical Sciences Chonnam National University Medical School Gwangju 61469 Republic of Korea. Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea. soheekim@dgist.ac.kr.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：