检索结果-内蒙古大学图书馆

IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Chenting Gu Xiaofeng Liu Yongsheng Ou Chao Liu Hao Tao Tianyun Shi HoHai University Nanjing China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Key Laboratory of Special Robots Changzhou China Changzhou Key Laboratory of Robots and Intelligent Technology Changzhou China College of IoT Engineering Hohai University Changzhou China Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China China Ship Development and Design Center Wuhan Tian-yun Shi China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

LED based positioning technology has recently become a popular choice due to the low cost, convenience for maintenance and strong adaptability to the environment. In this paper, we introduce an illuminance-distance analysis method with a rolling shutter camera, for a low-cost indoor positioning system to improve robustness in the case of insufficient LEDs. Firstly, the binary threshold method is used to decode the light signals. Then the gray value information of image is extracted to analyze the depth of LEDs, using the illuminance attenuation theorem. It provides potential solution to the position of devices with the camera by combining depth information, image and camera intrinsic. Our proposed method reduced the minimum number of LEDs observed by the camera from three to two, using only the camera. We evaluated the proposed method with real scene data and demonstrated the effectiveness of illumination-distance model based localization.

关键词： Light emitting diodes Cameras Receivers Light sources Robots Mathematical model Image sensors

来源：评论

学校读者我要写书评

暂无评论

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

引用

The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Visual SLAM based on geometric cluster matching

Visual SLAM based on geometric cluster matching

引用

2019 IEEE International Conference on Real-Time computing and Robotics, RCAR 2019

作者： Tian, Chaoran Ou, Yongsheng Qu, Yangyang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University OfChinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes OfAdvanced Technology Chinese Academy OfSciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728137261

The approach of feature-based visual SLAM is very popular in Micro Aerial Vehicle (MAV) navigation and wheeled mobile robot (WMR) navigation. Most modern feature-based visual SLAM frameworks extract a fixed number of feature points and then match the feature points by brute force or adopt Fast Library for Approximate Nearest Neighbors (FLANN). The features' extracting and matching processes will determine the localization accuracy and mapping quality significantly. In this paper, we propose a feature matching algorithm for the robot equipped with a binocular camera, which provides accurate real-time localization performance and robustness. In our feature matching method, both the geometric relationship of image feature points and the similarity of descriptor are considered. This paper presents a visual SLAM framework based on geometric cluster matching and the effectiveness of the proposed method is verified by practical experiments. © 2019 IEEE.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Binocular depth estimation using convolutional neural network with siamese branches

Binocular depth estimation using convolutional neural networ...

引用

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Parameters optimization of PID controller based on improved fruit fly optimization algorithm 9th

Parameters optimization of PID controller based on improved ...

引用

9th International Conference on Swarm intelligence, ICSI 2018

作者： Zhang, Xiangyin Chen, Guang Jia, Songmin Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China

ISBN: (纸本)9783319938141

Fruit fly optimization algorithm (FOA) is a novel bio-inspired technique, which has attracted a lot of researchers’ attention. In order to improve the performance of FOA, a modified FOA is proposed which adopts the phase angle vector to encoded the fruit fly location and brings in the double sub-swarms mechanism. This new strategies can enhance the search ability of the fruit fly and helps find the better solution. Simulation experiments have been conducted on fifteen benchmark functions and the comparisons with the basic FOA show that θ-DFOA performs better in terms of solution accuracy and convergence speed. In addition, the proposed algorithm is used to optimization the PID controller, and the promising performance is achieved. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Joint object contour points and semantics for instance segmentation

arXiv

引用

arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Zhu, Mai Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science University of Auckland New Zealand

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network’s attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8% on Cityscapes dataset and 0.8% on COCO dataset. Copyright © 2020, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Accurate 2D Localization for Mobile Robot by Multi-sensor Fusion

Accurate 2D Localization for Mobile Robot by Multi-sensor Fu...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Xiaogang Ruan Shaoda Liu Dingqi Ren Xiaoqing Zhu Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing

ISBN: (纸本)9781538653746

To achieve fully autonomous operation for indoor Mobile Robot. Localization solution has become an active research topic in the past decade. Normally a localization system is required to be reliable, fast, and have high accuracy. But single UWB measurement is very discrete and you can not get the exact orientation of the mobile robot. To overcome these shortcomings, we propose a novel UWB/Gyroscope/Odometer-integrated system, in which UWB measures the distance between the tag and the base station through Time of Flight and then calculating the tag position through the triangulation algorithm. Gyroscope and odometer can get the angular and linear velocity of mobile robot respectively. Which can provide accurate short-term tracking performance. The results of multi-sensor are fused using an Extended Kalman Filter (EKF). The experiment results also show that the algorithm significantly improved accuracy and get reliable yaw angle.

关键词： Mobile robots Robot kinematics Mathematical model Gyroscopes Robot sensing systems Base stations

来源：评论

学校读者我要写书评

暂无评论

Global context aware RCNN for object detection

arXiv

引用

arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Xie, Haoyu Zhu, Mai Tie, Ming Chen, Junxin School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Science and Technology on Space Physics Laboratory Beijing100076 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

A local search strategy based on density for NSGA2 algorithm

引用

Kongzhi yu Juece/Control and Decision 2018年第1期33卷 60-66页

作者： Li, San-Yi Li, Wen-Jing Qiao, Jun-Fei College of Automation Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

In order to reduce the amount of calculation and keep the advantage of local search strategy simultaneously, this paper proposes a kind of local search method based on density for the NSGA2(NSGA2-DLS) algorithm. Firstly, the sparse degree of each solution is evaluated with the density of each solution in the solution space. Then the non-dominated solution with the smallest sparse degree is defined as the sparse solution, and the sparse solutionig is searched around locally during every genetic process. The NSGA2-DLS algorithm adopts extreme optimization strategy and random search strategy simultaneously to improve the quality of solutions and convergence rate. The performance of the NSGA2-DLS algorithm is compared with the performance of the baseline NSGA2 algorithm and two other reported local search NSGA2 algorithms for multi-objective test problems, including ZDT and DTLZ functions. The simulation results show that the solution quality and the calculation amount of the NSGA2-DLS algorithm are much better than that of other methods. © 2018, Editorial Office of Control and Decision. All right reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Feature Subset Discernibility Evaluation Method for Upper Limb Rehabilitation Training Based on the Discernibility of Relative Distance

Feature Subset Discernibility Evaluation Method for Upper Li...

引用

IEEE International Conference on Mechatronics and Automation

作者： Guoyu Zuo Zhaokun Xu Jiahao Lu Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

In this paper, a feature subset discernibility hybrid evaluation method based on the discernibility of relative distance and support vector machine (DRD-SVM) is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4-5 stage patients, in which a relative distance is introduced into evaluating the discernibility between classes considering the joint effect of both candidate and selected features. First, a feature subset search strategy is used to search a set of candidate feature subsets. Then the DRD is used to evaluate the candidate feature subsets, the best subset is selected as a new selected feature subset, and the feature subset with the best performance of SVM classification is selected as the optimal feature subset. Finally, feature selection experiment was carried out on upper limb routine rehabilitation training samples of the Brunnstrom 4-5 stage. The experimental results shows that, compared with the F-score method and the DFS one, the proposed method can obtain the feature subsets with higher accuracy and smaller feature dimension, which improves its effectiveness and feasibility.

关键词： Training Feature extraction Support vector machines Filtering theory Filtering algorithms Sensors Search problems

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：