检索结果-内蒙古大学图书馆

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jia Guo Zengguang Hou Xiaoliang Xie Shuncai Yao Qiaoli Wang Xuechen Jin School of Electrical and Control Engineering North University of China Taiyuan China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Firefighters are primarily tasked to handle fire incidents, but they are often exposed to high risks when extinguishing fire. Firefighting robots are actively being researched to reduce fire fighters injuries and deaths as well as increase their effectiveness on performing tasks. However, the major concern is how to make the flame detection methods to satisfy the high precision requirement of firefighting robot. Therefore, the flame detection of firefighting robot has become a hot topic in this area. In this paper, a Faster R-CNN model is proposed to detect flame in noisy images of fire ground. Firstly, the region generation network is used to extract the candidate flame regions. Secondly, the candidate flame regions are convoluted and pooled to extract the flame characteristics. Thirdly, the output features of Region Proposal Network (RPN) are fed into two fully connected layers: a box-regression layer which recognizes the locations of objects and a box-classification layer which classifies the objects. The dataset used in the experiment was obtained by video capture. The network is pre-trained based on Google platform Tensorflow, and the obtained precision and frame rate of the proposed method are up to 99.8% and 1.4 FPS, respectively. The experimental results demonstrate that the method equipped merits such as automatically extract the flame characteristics, effectively improve the precision of flame detection, and has excellent generalization ability and robustness.

关键词： Service robots Feature extraction Convolutional neural networks Image classification Regression analysis Object detection Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Object Reconstruction with Deep Learning: A Survey

Object Reconstruction with Deep Learning: A Survey

引用

IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Zishu Gao En Li Guodong Yang Zhe Wang Yunong Tian Zize Liang Rui Guo Shengchuan Li The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences and The University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Grid Shandong Electric Power Company Jinan China State Grid Liaoning Electric Power Company Limited Shenyang China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Object reconstruction is one of the most crucial branches of computer vision. With the development of deep learning, many tasks have achieved remarkable improvements in computer vision. 3D reconstruction with deep learning also has attracted much attention in recent years. Deep learning methods based on CNN-based and GAN-based architectures have been adopted for 3D object prediction. In addition, researchers utilize different inputs such as RGB and depth images to achieve prediction based on different problem. In this paper, we provide a detailed overview of recent advances in 3D object reconstruction. The reviewed approaches are categorized into three groups depending on the input modality: RGB-based, depth-based and other-input-based. Particularly, we introduce the various methods and indirectly classify the shape representation. As a survey, we discuss the strong and weak points of exciting approaches.

关键词： Three-dimensional displays Image reconstruction Shape Machine learning Generators Two dimensional displays Automation

来源：评论

学校读者我要写书评

暂无评论

A Novel Blockchain Oracle Implementation Scheme Based on Application Specific Knowledge Engines

A Novel Blockchain Oracle Implementation Scheme Based on App...

引用

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

作者： Wang, Shuai Lu, Hao Sun, Xingkai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728128535

In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme. © 2019 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Ordinal Optimization for Optimal Orientation Problems in 3D Printing

引用

IFAC-PapersOnLine 2020年第5期53卷 97-102页

作者： Can Luo Gang Xiong Zhishuai Li Zhen Shen Li Wan MengChu Zhou Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan China Macau University of Science and Technology Taipa Macau

Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.

关键词： Digital Manufacturing Genetic Algorithm Intelligent Optimization Machine Learning Ordinal Optimization Orientation Optimization 3D Printing

来源：评论

学校读者我要写书评

暂无评论

A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

引用

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

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Robust Trajectory Tracking of Delta Parallel Robot Using Sliding Mode control

Robust Trajectory Tracking of Delta Parallel Robot Using Sli...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tingting Su Xu Liang Guangping He Quanliang Zhao Lei Zhao North China University of Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

For purpose of realizing robust trajectory tracking of parallel Delta robot with uncertain model parameters, a sliding mode control approach based on Delta robot dynamics is proposed. Once the reference trajectory is given, we can obtain the sliding mode controller, so that Delta parallel robot can move along the reference trajectory. Meanwhile, the disturbance and uncertainty of robot system are considered in the proposed method, which is beneficial to improve the reliability of controller and practicability of robot. The system stability is proved by Lyapunov method. Simulation experiment results demonstrate that the robust trajectory tracking of parallel Delta robot can be realized by the proposed method, and the designed controller is simple, robust and reliable.

关键词： Parallel robots Trajectory Trajectory tracking Robot kinematics Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Finite-time Passive control of Networked Semi-Markov Jump systems with Time-varying Delays

Finite-time Passive Control of Networked Semi-Markov Jump Sy...

引用

第三十八届中国控制会议

作者： Yidao Ji Wei Wu School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the finite-time control issue of semi-Markov jump systems with time-varying delays based under passivity framework. Firstly, sufficient criterion is derived to ensure that finite-time boundedness condition can be satisfied and the desired passivity performance can be achieved simultaneously. Then, state-feedback controller procedure is presented with help of linear matrix inequalities(LMIs). Finally, a simulation example is provided for showing the applicability of the developed method.

关键词： finite-time boundedness semi-Markov jump systems time-varying delays passivity

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Image Aesthetic Quality Assessment

A Comprehensive Survey on Image Aesthetic Quality Assessment

引用

International Conference on Computer and Information Science (ACIS)

作者： Hongtao Yang Ping Shi Saike He Da Pan Zefeng Ying Ling Lei College of Information Engineering Communication University of China Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

Image aesthetic quality assessment has demonstrated tremendous success in variety of application domains in recent years. This field has been growing so rapidly that various approaches have been proposed trying to solve this challenging problem. This report presents a comprehensive survey on image aesthetic quality assessment, mainly focus on the contributions and novelties of the existing approaches recently. In this work, we firstly illustrate datasets related to image aesthetics and investigate feature extractions. Then five different aesthetic tasks are reviewed, including aesthetic classification, aesthetic regression, aesthetic distribution, aesthetic factors and aesthetic description. In addition, we reviewed recent applications concerning image aesthetics. Finally, different evaluation criterions in different literatures are summarized. We hope the survey could serve as a comprehensive reference and be useful for those who are interested in exploiting image aesthetic for their research.

关键词： Feature extraction Task analysis Deep learning Image color analysis Quality assessment Support vector machines Visualization

来源：评论

学校读者我要写书评

暂无评论

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

引用

Physical Review E 2020年第4期102卷 042314-042314页

作者： Yang Ye Qingpeng Zhang Zhongyuan Ruan Zhidong Cao Qi Xuan Daniel Dajun Zeng School of Data Science City University of Hong Kong Hong Kong SAR China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou Zhejiang China 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 Artificial Intelligence and Data Science Institute Shenzhen Guangdong China

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

关键词： Epidemic Spreading Multilayer & multiplex networks Social networks

来源：评论

学校读者我要写书评

暂无评论

Lane change decision-making through deep reinforcement learning with rule-based constraints

arXiv

引用

arXiv 2019年

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin Chen, Yaran State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level lateral decision-making and lowlevel rule-based trajectory modification, a safe and efficient lane change behavior can be achieved. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator. The generated policy is evaluated on the simulator for 10 times, and the results demonstrate that the proposed rule-based DQN method outperforms the rule-based approach and the DQN method. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making

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：