检索结果-内蒙古大学图书馆

2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Li, Xiaoshuang Hu, Shenhua Lv, Chengkan Qin, Haojun Zhu, Fenghua Wang, Feiyue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing China School of Electrical Engineering and Aotumation Wuhan University Wuhan China

ISBN: (纸本)9781728128535

Personal identification has been used more and more widely in information society. Iris recognition, as one of numerous identification methods, has already been applied in many situations, because the iris patterns have stable, unique and non-invasive features. We analyze the general process of iris recognition in this paper, including image preprocessing, feature extraction, classification and recognition. We also study the progress in iris recognition brought by deep learning method. It is our belief that traditional approaches play a very significant role in iris recognition field, while deep learning methods will make a great difference to iris recognition. © 2019 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Graph-FCN for image semantic segmentation

arXiv

引用

arXiv 2020年

作者： Lu, Yi Chen, Yaran Zhao, Dongbin Chen, Jianxin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Beijing University of Chinese Medicine Beijing100029 China

Semantic segmentation with deep learning has achieved great progress in classifying the pixels in the image. However, the local location information is usually ignored in the high-level feature extraction by the deep learning, which is important for image semantic segmentation. To avoid this problem, we propose a graph model initialized by a fully convolutional network (FCN) named Graph-FCN for image semantic segmentation. Firstly, the image grid data is extended to graph structure data by a convolutional network, which transforms the semantic segmentation problem into a graph node classification problem. Then we apply graph convolutional network to solve this graph node classification problem. As far as we know, it is the first time that we apply the graph convolutional network in image semantic segmentation. Our method achieves competitive performance in mean intersection over union (mIOU) on the VOC dataset(about 1.34% improvement), compared to the original FCN model. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-based Framework for Collaborative Production in Distributed and Social Manufacturing

A Blockchain-based Framework for Collaborative Production in...

引用

2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Ouyang, Liwei Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266109 China

ISBN: (纸本)9781728128535

In recent years, novel collaborative production paradigms, such as (re-)distributed manufacturing and social manufacturing, have attracted intensive attention due to their potential of further changing the existing manufacturing modes and industrial structures. Blockchain, as a new distributed computing architecture, is expected to be successfully used in manufacturing, and help solve issues related to interoperability and collaboration, security and supervision, marketization and protocol, democratic organization and global value chain governance, and so on. In this paper, a blockchain-based framework for collaborative production is proposed, aiming at providing novel ideas for the design, implementation and optimization of collaborative production, and also inspiring the upgrading and transformation of manufacturing industries. © 2019 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Attention grasping network: A real-time approach to generating grasp synthesis

Attention grasping network: A real-time approach to generati...

引用

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Gu, Qipeng Su, Jianhua Bi, Xueting Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences China Chinese Academy of Sciences Key Laboratory of Electromagnetic Radiation and Sensing Technology Institute of Electronics Beijing100190 China

ISBN: (纸本)9781728163215

This paper presents a real-time, pixelwise method to generate grasp synthesis based on fully convolutional netural networks (FCN). Our proposed Attention Grasping Network (AGN) applies a novel attention mechanism to robotic grasp detection, which automatically learns to focus on salient features of the input image. The model with attention mechnisms can compensate for the loss of detail information in standard FCN, which increases the sensitivity of the model and accuracy of prediction. In addition, in order to ensure a real-time grasp and save computing resources, the light-weight AGN model predicts the position and angle of grasping point. Our method only takes 22ms to execute the grasp detection pipeline on a GPU-equipped computer, and achieves 97.8% accuracy on Cornell Grasping Dataset. © 2019 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Impacts of export restrictions on the global personal protective equipment trade network during COVID-19

arXiv

引用

arXiv 2021年

作者： Ye, Yang Zhang, Qingpeng Cao, Zhidong Chen, Frank Youhua Yan, Houmin Stanley, H. Eugene Zeng, Daniel Dajun School of Data Science City University of Hong Kong Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen China Department of Management Sciences College of Business City University of Hong Kong Hong Kong Hong Kong Department of Physics Boston University BostonMA02215 United States

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, we develop an integrated network model, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Results show that, the shortage contagion patterns are mainly decided by top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. Besides, export restrictions lead to ineffective and inefficient allocation of PPE around the world, which has no benefits for the world to fight against the pandemic. Copyright © 2021, The Authors. All rights reserved.

关键词： Protective clothing

来源：评论

学校读者我要写书评

暂无评论

Generalized Visual-Tactile Transformer Network for Slip Detection

引用

IFAC-PapersOnLine 2020年第2期53卷 9529-9534页

作者： Shaowei Cui Junhang Wei Xiaocan Li Rui Wang Yu Wang Shuo Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Future Technology University of Chinese Academy of Sciences Beijing 100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 200031 China

Slip detection plays a vital role in robotic dexterous grasping and manipulation, and it has long been a challenging problem in the robotic community. Different from traditional tactile perception-based methods, we propose a Generalized Visual-Tactile Transformer (GVT-Transformer) network to detect slip based on visual and tactile spatiotemporal sequences. The main novelty of GVT-Transformer is its ability to address unaligned vision and tactile data in various formats captured by various tactile sensors. Furthermore, we train and test our proposed network on a public and our visual-tactile grasping datasets. The experimental results show that our method is more suitable for sliding detection tasks than previous visual-tactile learning methods and more versatile.

关键词： Information sensor fusion Perception sensing Intelligent robotics Deep neural networks Visual-tactile fusion perception

来源：评论

学校读者我要写书评

暂无评论

A graph-based semi-supervised fraud detection framework 4

A graph-based semi-supervised fraud detection framework

引用

4th IEEE International Conference on Cybernetics, Cybconf 2019

作者： Jing, Rongrong Zheng, Xiaolong Tian, Hu Zhang, Xingwei Chen, Weiyun Wu, Dash Desheng Zeng, Daniel Dajun The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen518129 China School of Management Huazhong University of Science Technology Wuhan430074 China

ISBN: (纸本)9781728100302

Credit card payment has become one of the most commonly used consumption methods in modern society, yet risks of fraud transactions using credit cards also increased. Numerous methods have been proposed for credit card fraud detection during past decades. However, most of the existing frameworks mainly focus on directly processing structured data. While they lack inner relations between features of raw descriptions for credit owners, this could lead to information deficiency. Therefore, we proposed a graph-based semi-supervised fraud detection framework. In this work, the structured dataset is translated to graph format through the sample similarity in order to improve the effect of label propagation on the graph. We further adopt the GraphSAGE algorithm which has been demonstrated to show excellent performance on node classification tasks. Experimental results on the real-world dataset show that our graph-based model can outperform state-of-the-art baselines. We argue that our model could be extended to other classification tasks using structured data. © 2019 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

The Prediction of the Epidemic Trend of COVID-19 Using Neural Networks

引用

IFAC-PapersOnLine 2020年第5期53卷 857-862页

作者： Jing Yang Zhen Shen Xisong Dong Xiuqin Shang Wei Li Gang Xiong J. Yang is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with College of Information Sciences & Technology Beijing University of Chemical Technology Beijing 100029 China. Z. Shen is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao Shandong 266113 China. X. Dong X. Shang and W. Li are with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China. G. Xiong is with Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan Guangdong 523808 China.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blowin’ in the wind: Smog and suicidal ideation among school-age children

引用

China Economic Review 2025年 93卷

作者： Xin Zhang Xi Chen Hong Sun Yuanjian Yang School of Statistics Beijing Normal University Beijing 100875 China Department of Health Policy and Management Yale School of Public Health New Haven CT 06520 USA Department of Economics Yale University New Haven CT 06511 USA Jiangsu Provincial Center for Disease Control and Prevention Nanjing 210009 China State Key Laboratory of Climate System Prediction and Risk Management School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing 210044 China

This paper attempts to provide one of the first population-based causal estimates of the effect of air pollution on suicidal ideation—a key precursor to suicide attempt and completion—among school-age children. We use daily variations in the local wind direction as instruments to address endogeneity in pollution exposure. Matching a unique risk behavior survey of 55,000 students from 273 schools with comprehensive data on air pollutants and weather conditions according to the exact date and location of schooling, our findings indicate that a 1 % decline in daily PM2.5 is associated with a 0.36 % reduction in the probability of suicidal ideation. Moreover, the dose-response relationship reveals that the marginal effects increase significantly and non-linearly with elevated concentration of PM2.5. The effect is particularly pronounced among younger, male, students from low-educated families, and students with lower grades.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A simulator with an elastic guidewire and vascular system for minimally invasive vascular surgery

引用

Science China(Information Sciences) 2018年第10期61卷 250-252页

作者： Xiaoran CHENG Xiaoliang XIE Guibin BIAN Zengguang HOU Shiqi LIU Zhanjie GAO State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Simulators play an important role in minimally invasive vascular surgery training. A challenging task for the simulator is to model an elastic guidewire and an elastic vascular system, both of which are important aspects for vascular ***, many simulators treat the virtual vascular system as a rigid object, which reduces the immersion of operators. In this study, a virtual guidewire is first modeled based on Cosserat rod

关键词： Pi Figure A simulator with an elastic guidewire and vascular system for minimally invasive vascular surgery

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：