检索结果-内蒙古大学图书馆

IEEE International Conference on Robotics and Biomimetics

作者： Jun Li Bo Yang Wankou Yang Changyin Sun Hong Zhang School of Automation Southeast University Nanjing China School of Instrument Science and Engineering Southeast University Nanjinz China Department of Computing Science University of Alberta Edmonton AB Canada

ISBN: (纸本)9781728103785;9781728103778

Instance-level image retrieval, which aims to find all the images correctly matching the query instance, has attracted extensive attention in the community of computer vision and multimedia. The success of recent retrieval systems largely depends on an image signature with sufficient discriminative power for visual representation. For the past few years, massive feature learning algorithms have sprung up, and they have significantly advanced the development of instance retrieval. In particular, CNN features derived from a deep neural network allows encoding high-level image contents and demonstrates unrivaled superiority to the hand-crafted shallow image signatures. However, the shallow features still play a beneficial role in visual matching particularly when dramatic variances in viewpoint and scale are present, since they inherit certain invariance from the local robust descriptor, e.g. SIFT. Thus, it is important to leverage the complementarity between these two heterogeneous signatures for multi-view fusion in instance retrieval. Since it is still an open problem, in this paper, we propose a subspace-based multi-view fusion strategy where a shared subspace is uncovered from the original high-dimensional features yielding a compact latent representation. Since our method works in an unsupervised fashion without any training process involved, it lends itself to various real-time robotic vision tasks, e.g. place recognition and scene description. Experiments on public benchmark datasets reveal the proposed method works better than other classic fusion approaches and achieves the state-of-the-art performance.

关键词： Visualization Task analysis Robots Image retrieval Real-time systems Image representation Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Software Realization of Network Off-line Scheduling with Time-Triggered Ethernet on the Basis of Response Constraint

Software Realization of Network Off-line Scheduling with Tim...

引用

International Conference on Computer Science & Education (ICCSE)

作者： Qing Wang Liping Teng Yaotong Yang Huimin Wu Wenxing Hong Wenjian Li School of Electrical and Information Engineering Tianjin University Tianjin China Automation Department Xiamen University Xiamen China Tianjin Jinhang Computing Technology Research Institute Tianjin China

Time-Triggered Ethernet (TTE) is a new network communicational technic with Ethernet, which combines time service and event service. Reasonable time planning and schedule design enable three different types of traffic with various time-critical characteristics including Time-Triggered (TT) traffic, Rate-Constrained (RC) traffic and Best-Effort (BE) traffic to be transmitted regulated by their own rules and priority efficiently. This paper raises a method suited for generating TTE network off-line time schedule with response constraint. Also, this research introduces response constraint as an important consideration on the basis of existed hybrid traffic partitioning scheduling strategy. At the same time, the network schedule is used to form a composite schedule table strategy to simplify the schedule generation process. Furthermore, a software is designed to generate off-line schedule, and its rationality about generating schedule periodically is verified by practical examples.

关键词： Schedules Transmitters Task analysis Synchronization Software Receivers Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Stabilization with a specified external gain for linear MIMO systems and its applications to control of networked systems

arXiv

引用

arXiv 2018年

作者： Zhu, Lijun Chen, Zhiyong Chen, Xi Hill, David J. Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia Engineering Research Center of Metallurgical Automation and Measurement Technology Ministry of Education Wuhan

This paper studies a stabilization problem for linear MIMO systems subject to external perturbation that further requires the closed-loop system render a specified gain from the external perturbation to the output. The problem arises from control of networked systems, in particular, robust output synchronization of heterogeneous linear MIMO multi-agent systems via output feedback/communication. We propose a new approach that converts a class of MIMO systems into a normal form via repeated singular value decomposition and prove that a stabilization controller with a specified external gain can be explicitly constructed for the normal form. Two scenarios with static state feedback and dynamic output feedback are investigated. By integrating the reference model and internal model techniques, the robust output synchronization problem for MIMO multi-agent systems is converted into a stabilization problem with a specified external gain and solved by the developed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

A Parametric Time Frequency-Conditional Granger Causality Method Using Ultra-regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs

arXiv

引用

arXiv 2018年

作者： Li, Yang Lei, Mengying Cui, Weigang Guo, Yuzhu Wei, Hua-Liang Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3DJ United Kingdom

— Objective: This study proposes a new parametric TF (time-frequency)-CGC (conditional Granger causality) method for high-precision connectivity analysis over time and frequency in multivariate coupling nonstationary systems, and applies it to scalp and source EEG signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke’s spectral measure is combined with the TVARX (time-varying autoregressive with exogenous input) modelling approach, which uses multiwavelets and ultra-regularized orthogonal least squares (UROLS) algorithm aided by APRESS (adjustable prediction error sum of squares), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal data themselves but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. Results: The proposed method is validated on two simulations and then applied to multichannel motor imagery (MI)-EEG signals at scalp- and source-level, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEG data are physiologically and anatomically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. Conclusion: Experimental results confirm the effectiveness of the proposed TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. Significance: The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

引用

arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Composite Learning for Trajectory Tracking Control of Robot Manipulators with Output Constraints

Composite Learning for Trajectory Tracking Control of Robot ...

引用

International Conference on Information Science and Technology (ICIST)

作者： Dianye Huang Chenguang Yang Yongping Pan Shilu Dai Zhaojie Ju Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Biomedical Engineering National University of Singapore Singapore Singapore School of Computing University of Portsmouth Portsmouth UK

In this paper, a trajectory tracking scheme for robot manipulators with unknown dynamics is investigated, with the consideration of output constraints as well as small bounded external disturbances. Firstly, a modified backstepping control scheme is employed to control the robot manipulators where in the first step of the design a tan-type barrier Lyapunov candidate is chosen in order to tackle the constraint problem. Secondly, the philosophy of dynamic surface control is incorporated to implement the calculation of prediction errors, which can also reduce “explosion of complexity” of the backstepping scheme. In addition, composite learning is introduced for a better estimation of unknown parameters, and for canceling out the uncertainties of the robot manipulators. Stability analysis shows that the proposed control scheme guarantees a small bounded tracking error with parameter convergence in the absence of the stringent persistent excitation condition. Finally, a simulation is conducted and the results demonstrate the superiority of the proposed controller in the aspects of tracking capability and parameter estimation.

关键词： Manipulator dynamics Backstepping Uncertainty Control systems Convergence

来源：评论

学校读者我要写书评

暂无评论

An input-delay method for event-triggered control of nonlinear systems

arXiv

引用

arXiv 2018年

作者： Zhu, Lijun Chen, Zhiyong Hill, David J. Du, Shengli Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia College of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China

The paper proposes a novel event-triggered control scheme for nonlinear systems based on the input-delay method. Specifically, the closed-loop system is associated with a pair of auxiliary input and output. The auxiliary output is defined as the derivative of the continuous-time input function, while the auxiliary input is defined as the input disturbance caused by the sampling or equivalently the integral of the auxiliary output over the sampling period. As a result, it forms a cyclic mapping from the input to the output via the system dynamics and back from the output to the input via the integral. The event-triggering law is constructed to make the mapping contractive such that the stabilization is achieved and an easy-to-check Zeno-free condition is provided. Within this framework, we develop a theorem for the event-triggered control of interconnected nonlinear systems which is employed to solve the event-triggered control for lower-triangular systems with dynamic uncertainties. Copyright © 2018, The Authors. All rights reserved.

关键词： Nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

A novel SVM parameter tuning method based on advanced whale optimization algorithm

引用

Journal of Physics: Conference Series 2019年第2期1237卷 022140-022140页

作者： Yin, Xuehao Yin, Jiabao Li, Chao Hou, YanDong Department of Computing Jiangsu Automation Research Institute Lian Yungang Jiangsu 222000 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin Heilongjiang 150000 China

The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Detecting incongruity between news headline and body text via a deep hierarchical encoder

arXiv

引用

arXiv 2018年

作者： Yoon, Seunghyun Park, Kunwoo Shin, Joongbo Lim, Hongjun Won, Seungpil Cha, Meeyoung Jung, Kyomin Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Automation and Systems Research Institute Seoul National University Seoul Korea Republic of Qatar Computing Research Institute Doha Qatar School of Computing KAIST Daejeon Korea Republic of Daejeon Korea Republic of

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Thermal Conductivity: Thermal Conductivity of Amorphous Materials (Adv. Funct. Mater. 8/2020)

引用

Advanced Functional Materials 2020年第8期30卷

作者： Wu‐Xing Zhou Yuan Cheng Ke‐Qiu Chen Guofeng Xie Tian Wang Gang Zhang School of Materials Science and Engineering & Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Xiangtan 411201 China Institute of High Performance Computing A*STAR Singapore 138632 Singapore Department of Applied Physics School of Physics and Electronics Hunan University Changsha 410082 China School of Automation Science and Electrical Engineering Beihang University Beijing 100083 China

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：