检索结果-内蒙古大学图书馆

作者： Ai Le Zhizhong Zhang Institute of Criminal Science and Technology People's Public Security University of China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

In today's society, the growing instability has made identification technology a hot issue. Most traditional methods are based on a series of hand-defined preprocesses such as binarization. However, these preprocesses require strong prior knowledge. And that will lead to dropped or false extractions of minutiae. In this paper, a fingerprint triangle region recognition approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks(including JudgeNet and LocateNet). Results show that our approach performs better in accuracy and robustness.

关键词： Neural network minutiae fingerprint image convolutional layer

来源：评论

学校读者我要写书评

暂无评论

Object Recognition, Localization and Grasp Detection Using a Unified Deep Convolutional Neural Network with Multi-task Loss

Object Recognition, Localization and Grasp Detection Using a...

引用

2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018

作者： Jia, Qun Cao, Zhiqiang Zhao, Xionglei Pang, Lei Yu, Yingying Yu, Unzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9781728103761

Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with an RGB-D camera. A deep Convolutional Neural Network is designed to predict good grasps in real-time on GTX1080, with using region proposal techniques. A contribution of this work is our proposed network framework can perform classification, location and grasp detection simultaneously so that in a single step, it not only recognizes the category and bounding-box of the object, but also finds a good grasp line. Besides, in training process, we minimize a multi-task loss objective function of object classification, location and grasp detection in order to train the network end-to-end. Our experimental evaluation on a real robotic manipulator demonstrates that the robotic manipulator can fulfill the grasping task effectively. © 2018 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

A Vision-Based Path Planning and Following System for a Miniature Robotic Fish

A Vision-Based Path Planning and Following System for a Mini...

引用

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

作者： Yue Lu Xingyu Chen Zhengxing Wu Yan Yan Junzhi Yu University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Most existing path following algorithms for middle-or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation.

关键词： Fish Robot kinematics Path planning Robot sensing systems Solenoids Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

引用

IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1104-1112页

作者： Yuling Hu Xiwei Liu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044China State Key Laboratory fo Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing 100190China Qingdao Academy of Intelligent Industries Qingdao 266109China

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

关键词： Computational experiments grouping evacuation graph theory high-rise building fire management strategies

来源：评论

学校读者我要写书评

暂无评论

Multi-Target Tracking with Trajectory Prediction and Re-Identification

Multi-Target Tracking with Trajectory Prediction and Re-Iden...

引用

Chinese Automation Congress (CAC)

作者： Xuesong Li Yating Liu Kunfeng Wang Yong Yan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China State Grid Zhejiang Electric Power Company LTD Hangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Due to the complexity and clutter of real-world scenes, occlusion becomes a long-lasting difficulty in object tracking. Most existing tracking methods cannot effectively handle occlusion. In this paper, we propose a novel tracking framework that combines trajectory prediction and multi-cue appearance modeling to deal with the occlusion difficulty. When a target is completely occluded by background or other targets, it is unable to observe the target position. Therefore, we propose a Long Short-Term Memory (LSTM) model that merges attention mechanism and interaction module to predict the locations of all targets in the next frame. Considering that partial occlusion and inaccuracy of object bounding boxes often take place, we propose a multi-branch deep network architecture combining global and local features to realize accurate tracking and person re-identification (ReID). According to the experimental results on multiple benchmark datasets, our method achieves state-of the-art performance and outperforms many existing approaches.

关键词： Feature extraction Trajectory Target tracking Decoding Machine learning Task analysis

来源：评论

学校读者我要写书评

暂无评论

Collaborative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge Automation

引用

IFAC-PapersOnLine 2020年第5期53卷 572-577页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical-Social systems for Smart City: An Implementation Based on Intelligent Loop

引用

IFAC-PapersOnLine 2020年第5期53卷 501-506页

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS Intelligent Loop Human-Cyber-Physical Ternary Space

来源：评论

学校读者我要写书评

暂无评论

Human Motion Prediction Based on Visual Tracking 4

Human Motion Prediction Based on Visual Tracking

引用

4th International Conference on Robotics and Automation Engineering, ICRAE 2019

作者： Zou, Juncheng Yin, Weihua Wang, Everett X. Wang, Jiancheng Lu, Yan-Feng School of Mechanical Engineering Beijing University of Science and Technology China Institute of Science and Technology Innovation Dongguan University of Technology China School of Information Engineering Guangdong University of Technology Guangzhou China Cloud Computing Center Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728147406

ISBN: (纸本)9781728147406

Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision. © 2019 IEEE.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

AI education system for primary and secondary schools 126

AI education system for primary and secondary schools

引用

126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Gong, Xiaoyan Zhao, Liang Tang, RenHao Guo, YaNa Liu, Xiwei He, Jian Wang, Fei-Yue Tang, Ying Shi, Wanruo Niu, Xiaojie Wang, Xinzhu Institute of Smart Education Qingdao Academy of Intelligent Industries China Faculty of Information Technology Beijing University of Technology China Qingdao Academy of Intelligent Industries Institute of Automation Chinese Academy of Sciences China National Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences China Rowan University United States Beijing Normal University China

This paper presents an AI education system and related AI curricula specifically designed for primary and secondary students at different cognitive levels. In this system, complicated AI algorithms are encapsulated and modularized with friendly and easy-to-use interfaces. AI curricula are developed based on constructivism, project-based learning and multidisciplinary integration. Typical teaching cases, such as speech recognition, text recognition, image recognition, intelligent transportation, smart home, intelligent robots, etc., which are used to enhance comprehension of AI concepts and applications, are also discussed. © American Society for Engineering Education, 2019

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Target Tracking control Based on Dual Model Fusion

Target Tracking Control Based on Dual Model Fusion

引用

IEEE International Conference on Robotics and Biomimetics

作者： Kang Li Wenzhong Zha Xiangrui Meng Xiaoguang Zhao Key Laboratory of Cognition and Intelligence Technology China Electronics Technology Group Corporation Information Science Academy CETC Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Recent years have witnessed rapid progress in target tracking. To track a moving target for mobile robots, however, both performance and speed of the algorithm are indispensable. This paper proposes a dual model fusion strategy to improve target tracking drift. Among them, the spatiotemporal context model in middle-level feature space (MFSTC) is utilized to ameliorate target tracking effect when illumination or appearance changes, the mean shift based on 3D back projection (MS3D) is fused to allow the algorithm to tackle occlusion and deformation. Tracking controller based on visual servo is also designed for mobile robots. We validate the efficiency of the proposed fusion model on the university of Birmingham RGB-D Tracking Benchmark (BTB) and show that our approach compares favorably with the state-of-the-arts, mobile robots using our approach can track targets robustly under various challenging scenes.

关键词：

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：