检索结果-内蒙古大学图书馆

Jerk-Level Cyclic Motion Planning and Control for Constrained Redundant Robot Manipulators Using Zhang Dynamics:Theoretics

学校读者我要写书评

暂无评论

Jerk-Level Cyclic Motion Planning and Control for Constraine...

第30届中国控制与决策会议

作者： Min Yang Yunong Zhang Huanchang Huang Dechao Chen Jian Li School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

In this paper,based on Zhang dynamics method（ZDM）,a cyclic motion planning（CMP） scheme is developed to remedy the joint drift phenomenon of redundant robot manipulators constrained by joint physical *** existing works resolved at velocity level or acceleration level,the scheme in this paper is resolved at jerk *** achieve superior performance,the jerk-level CMP scheme is improved by the feedback control,which further leads to a jerk-level cyclic motion planning and control（CMPC） *** cyclic motion criterion and the avoidance of joint physical limits（e.g.,joint-angle,joint-velocity,joint-acceleration and joint-jerk limits） are both considered into the jerklevel CMP and CMPC *** importantly,the correctness of the presented jerk-level schemes is guaranteed by the corresponding engineering-type theoretical analyses and ***,the jerk-level CMPC scheme is reformulated as a dynamical quadratic program（DQP） which can be solved by a piecewise-linear projection equation neural network（PLPENN）,and also can be solved by a suitable numerical algorithm.

关键词： Constrained robot manipulators Joint drift phenomenon Jerk level Feedback control Theoretics

Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

学校读者我要写书评

暂无评论

Hand Gesture Recognition with Generalized Hough Transform an...

International Conference on information Science and Technology (ICIST)

作者： Bo Liao Jing Li Zhaojie Ju Gaoxiang Ouyang School of Information Engineering Nanchang University Nanchang Jiangxi China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth Portsmouth U.K. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Hand gesture recognition plays an important role in human-computer interaction. With the development of depth cameras, color images combined with depth images can provide richer information for hand gesture recognition. In this paper, we propose a hand gesture recognition system based on the data captured by Intel RealSense Front-Facing Camera SR300. Considering that the pixels in depth images collected by RealSense are not one-to-one to those in color images, the recognition system maps depth images to color images based on generalized Hough transform in order to segment hand from a complex background in color images using the depth information. Then, it recognizes different hand gestures by a novel double-channel convolutional neural network containing two input channels which are color images and depth images. Moreover, we built a hand gesture database of 24 different kinds of hand gestures representing 24 letters in the English alphabet. It contains a total of 168,000 images which are 84,000 RGB images and 84,000 depth images. Experimental results on our newly collected hand gesture database demonstrate the effectiveness of the proposed approach, and the recognition accuracy is 99.4%.

关键词： information science Transforms Convolutional neural networks Databases

Corrections to “Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction”

学校读者我要写书评

暂无评论

IEEE Access 2020年 8卷 76595-76595页

作者： Lihong Dai Jinguo Liu Zhaojie Ju Yang Gao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China School of Computing University of Portsmouth Portsmouth U.K. Space Technology for Autonomous and Robotic Systems Laboratory (STAR LAB) Surrey Space Centre University of Surrey Guildford U.K.

In the above article [1] , the affiliations of authors Lihong Dai and Yang Gao need to be corrected. Moreover, the biography of Yang Gao needs to be corrected. The specific corrections are as follows.

关键词： Robots Biographies Aerospace electronics Automation Manufacturing Research and development Object recognition

Adaptive morphological reconstruction for seeded image segmentation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Lei, Tao Jia, Xiaohong Liu, Tongliang Liu, Shigang Meng, Hongying Nandi, Asoke K. School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science University of Sydney Sydney Australia School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Col- lege of Electronic and Information Engineering Tongji University Shanghai200092 China

—Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Source code is available at https://***/SUST-reynole/AMR. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

A taxonomy and future directions for sustainable cloud computing: 360 degree view

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Gill, Sukhpal Singh Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Parkville3010 Australia

The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions. Copyright © 2017, The Authors. All rights reserved.

关键词： Energy efficiency

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

Syntax aware lstm model for semantic role labeling 2

学校读者我要写书评

暂无评论

Syntax aware lstm model for semantic role labeling

2nd Workshop on Structured Prediction for Natural Language Processing, SPNLP 2017, held in conjunction with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Qian, Feng Sha, Lei Chang, Baobao Liu, Lu-Chen Zhang, Ming Key Laboratory of Computational Linguistics Ministry of Education School of Electronics Engineering and Computer Science Peking University China Institute of Network Computing and Information Systems School of Electronics Engineering and Computer Science Peking University China

ISBN: (纸本)9781945626937

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92%. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1%) to bi-LSTM model and also brings slight improvement (0.3%) when added to the stateof- the-art model. © EMNLP *** right reserved.

关键词： Forestry

Discrete-Time Control and Simulation of Ship Course Tracking Using ZD Method and ZFD Formula 4NgSFD 3

学校读者我要写书评

暂无评论

Discrete-Time Control and Simulation of Ship Course Tracking...

2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Huinan Xiao Jinjin Wang Jian Li Pei Chen School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

ISBN: (纸本)9781509053643;9781509053636

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

关键词： Marine vehicles Trajectory Electronic mail Process control Convergence Mathematical model