检索结果-内蒙古大学图书馆

37th Chinese Control Conference, CCC 2018

作者： Zheng, Guoxian Zhang, Lei Zhang, Huaxi Yulin Ding, Bo State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China MIS INSSET Universite de Picardie Jules Verne Amiens80039 France National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410008 China

ISBN: (纸本)9789881563941

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

引用

International Joint Conference on Neural Networks

作者： Mingyang Geng Yiying Li Bo Ding Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot vision-based trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

来源：评论

学校读者我要写书评

暂无评论

VISUAL CONFUSION LABEL TREE FOR IMAGE CLASSIFICATION

VISUAL CONFUSION LABEL TREE FOR IMAGE CLASSIFICATION

引用

IEEE International Conference on Multimedia and Expo

作者： Yuntao Liu Yong Dou Ruochun Jin Rongchun Li National University of Defense Technology National Laboratory for Parallel and Distributed Processing Changsha China

Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124× and 115× speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline.

关键词： Image classification Convolution neural network Confusion graph Label tree

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

Cloud-Based Framework for Scalable and Real-Time Multi-Robot...

引用

IEEE International Conference on Web Services (ICWS)

作者： Pengfei Zhang Huaimin Wang Bo Ding Suning Shang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.

关键词： Simultaneous localization and mapping Cloud computing Robot kinematics Task analysis Instruction sets Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Software Ranker: A New Comprehensive Software Ranking Approach

Software Ranker: A New Comprehensive Software Ranking Approa...

引用

IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Yarong Zeng Xunhui Zhang Yaozong Li Tao Wang Gang Yin Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defence Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

With the popularity of the open source, the open source community has accumulated a large number of open source project. While these massive projects provide developers with rich reusable resource, they also bring difficulties for users to choose the appropriate software. Therefore, it is very meaningful to rank the software and tell users which is better. In this paper, we propose a novel approach that ranks software based on a global perspective different from traditional software evaluation and ranking methods. We evaluate the software from four dimensions, namely community popularity, development activity, software health and team health. Each dimension contains some metrics. We demonstrate the effectiveness of our method through comparative experiments. This method has been integrated into to the OSSEAN platform to form a software leaderboard.

关键词： Mathematical model Open source software Tools Software quality Software measurement

来源：评论

学校读者我要写书评

暂无评论

A Research on Area Coverage Algorithm for Robotics

A Research on Area Coverage Algorithm for Robotics

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Yaru Kang Dianxi Shi National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781538674178

Area coverage path planning is a special path planning method, which requires the robot to go through every point except obstacles in workspace. It has been used in many fields, such as lawn mowing, snow removal, search and rescue task, pesticide spraying, demining robots, cleaning robots and so on. This paper presents the achievements of the last two decades on area coverage. We propose a new Classification based on method of Choset. We analyze and summarize the recently research results by the new classification method and list the advantages and disadvantages. Finally, we give a summary of the classification methods and propose future research directions.

关键词： Path planning Task analysis Robot sensing systems Classification algorithms Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

High Performance Graph Analytics with Productivity on Hybrid CPU-GPU Platforms

High Performance Graph Analytics with Productivity on Hybrid...

引用

2018年第二届高性能编译、计算和通信国际会议

作者： Haoduo Yang Huayou Su Qiang Lan Mei Wen Chunyuan Zhang Department of Computer National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives(PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0 X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-theart GPU-based framework.

关键词： parallel Computing Graph Analytics Hybrid CPU-GPU

来源：评论

学校读者我要写书评

暂无评论

Simulation analysis of heavy-ion-induced single-event response for nanoscale bulk-Si FinFETs and conventional planar devices

引用

Science China(Technological Sciences) 2017年第3期60卷 459-466页

作者： YU JunTing CHEN ShuMing CHEN JianJun HUANG PengCheng College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

FinFET technologies are becoming the mainstream process as technology scales *** on 28-nm bulk-Si FinFETs and

关键词： charge collection bipolar amplification reversed bipolar effect single-event effect(SEE) single-event transient(SET) FinFET planar device

来源：评论

学校读者我要写书评

暂无评论

Instance-Specific Algorithm Selection via Multi-Output Learning

引用

Tsinghua Science and Technology 2017年第2期22卷 210-217页

作者： Kai Chen Yong Dou Qi Lv Zhengfa Liang the National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410037China the College of Computer National University of Defense TechnologyChangsha 410037China

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more *** kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.

关键词： algorithm selection multi-output learning extremely randomized trees performance prediction constraint satisfaction

来源：评论

学校读者我要写书评

暂无评论

Denoising convolutional autoencoder based B-mode ultrasound tongue image feature extraction

arXiv

引用

arXiv 2019年

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing University of Posts and Telecommunications Automation Dept. Beijing100876 China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China Department of Linguistics University of Michigan Ann Arbor United States

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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：