检索结果-内蒙古大学图书馆

3-D Electromagnetic Position Estimation System Using High-Magnetic-Permeability Metal for Continuum Medical Robots

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2581-2588页

作者： Wang, Heng Wang, Shuangyi Liu, Hongbin Rhode, Kawal Hou, Zeng-Guang Rajamani, Rajesh South China Univ Technol Sch Intelligent Engn Guangzhou 511442 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Kings Coll London St Thomas Hosp Sch Biomed Engn & Imaging Sci London SE1 7EH England Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In this letter, a new 3-D electromagnetic position sensing method is proposed for localization of continuum medical robots. An electromagnet and magnetic sensors are placed outside the human body while only a piece of passive mu-metal with high magnetic permeability is attached to the robot moving inside the body, resulting in a wireless non-contacting position estimation system. The mu-metal gets easily magnetized by the electromagnet and thus exerts position-dependent influence on the external magnetic field, which is measured for position estimation using a particle filter. An alternating magnetic field from the electromagnet is used and hence disturbances from nearby ferromagnetic objects can be rejected. The 3-D position estimation system is evaluated on a flexible trans-esophageal robot for ultrasound imaging with motions of insertion and maneuver. Experiments show that the mean position estimation error is about 5 mm and the system is robust in the presence of magnetic disturbances from a ferromagnetic object. This new wireless and robust 3-D position estimation system is demonstrated to have the potential to localize a continuum medical robot, which can enable autonomous navigation of the robot.

关键词： Electromagnetic tracking 3D position estimation high-magnetic-permeability metal continuum medical robot

来源：评论

学校读者我要写书评

暂无评论

Investigating the dynamic memory effect of human drivers via ON-LSTM

引用

Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

来源：评论

学校读者我要写书评

暂无评论

Trip Purposes Mining From Mobile Signaling Data

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第8期23卷 13190-13202页

作者： Li, Zhishuai Xiong, Gang Wei, Zebing Zhang, Yu Zheng, Meng Liu, Xiaoli Tarkoma, Sasu Huang, Min Lv, Yisheng Wu, Chuheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China Beijing Municipal Inst City Planning & Design Beijing 100045 Peoples R China Univ Helsinki Dept Comp Sci Helsinki 00014 Finland

With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users' daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method.

关键词： Cellular networks Trajectory Semantics Unsupervised learning Supervised learning Resource management Public transportation Trip purpose inference cellular network data latent Dirichlet allocation travel behavior big data

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Approximation-Based Task-Space control of Robot Manipulators With Remote Center of Motion Constraint

引用

IEEE TRANSACTIONS ON FUZZY systems 2022年第6期30卷 1564-1573页

作者： Su, Hang Qi, Wen Chen, Jiahao Zhang, Dandan Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2BX England Univ Bristol Dept Engn Math Bristol BS8 1TH Avon England

The presence of unknown physical interaction between the patients' body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach's effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

关键词： Robots Task analysis Minimally invasive surgery Kinematics Force Jacobian matrices End effectors Fuzzy approximation null space physical interaction remote center of motion (RCM) teleoperation

来源：评论

学校读者我要写书评

暂无评论

A Real 3D Embodied Dataset for Robotic Active Visual Learning

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 6646-6652页

作者： Zhao, Qianfan Zhang, Lu Wu, Lingxi Qiao, Hong Liu, Zhiyong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Calif Santa Barbara Dept Math Coll Letters & Sci Santa Barbara CA 93106 USA

Active interaction with environments is one of the striking characteristics of robotic active vision, which allows robots to move to facilitate visual tasks. Recently, several embodied AI platforms have been proposed as the synthetic environments to study robotic active vision, without needing to interact in real world. However, by using synthetic data, model trained on these platforms will suffer performance degradation when applied in reality. In this letter, a real 3D embodied dataset is proposed for robotic active visual learning. The proposed dataset consists of real point cloud data densely collected in 7 real-world indoor scenes. In our embodied dataset, researchers are able to simulate the movements and interactions of robots in indoor environments and obtain real visual data, which will not lead to performance degradation in reality. Furthermore, we proposed a 3D divergency policy that can guide robots to move and collect data to improve visual performance in novel environments. The proposed policy is designed following a simple fact: a good 3D detector should produce consistent 3D detection results for the same object from different viewpoints. Therefore, our policy encourages the robot to explore the area where the detector generates different 3D bounding boxes for the same object and helps the robot improve its visual performance in novel scenes.

关键词： Data sets for robotic vision deep learning for visual perception reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

SRGCN: Graph-based multi-hop reasoning on knowledge graphs

引用

NEUROCOMPUTING 2021年 454卷 280-290页

作者： Wang, Zikang Li, Linjing Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen Peoples R China

Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reasoning based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first construct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the existing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outperforms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly efficient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Knowledge graph Multi-hop reasoning Graph convolutional network

来源：评论

学校读者我要写书评

暂无评论

Target Tracking control of a Biomimetic Underwater Vehicle Through Deep Reinforcement Learning

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第8期33卷 3741-3752页

作者： Wang, Yu Tang, Chong Wang, Shuo Cheng, Long Wang, Rui Tan, Min Hou, Zengguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China NUCTECH Co Ltd Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor-critic reinforcement learning framework, the deep deterministic policy gradient actor-critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor-critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method.

关键词： Reinforcement learning Target tracking Robots Sports Aerospace electronics Mobile robots Underwater vehicles Biomimetic underwater vehicle (BUV) reinforcement learning target tracking control

来源：评论

学校读者我要写书评

暂无评论

Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss

引用

NEUROCOMPUTING 2021年 449卷 117-135页

作者： Li, Qianzhong Zhang, Yujia Sun, Shiying Zhao, Xiaoguang Li, Kang Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligences Beijing Peoples R China Information Sci Acad China Elect Technol Grp Corp Beijing Peoples R China

Zero-shot object detection (ZSD) aims to locate and recognize novel objects without additional training samples. Most existing methods usually map visual features to semantic space, resulting in a hubness problem, and learning an effective feature mapping between the two modalities remains a considerable challenge. In this work, we propose a novel end-to-end framework, Semantic-Visual Auto-Encoder (SVAE) network, to tackle the above issues. Distinct from previous works that utilize fully-connected layers to learn the feature mapping, we implement a 1-dimensional convolution with various shared filters to con-struct the auto-encoder, which maps semantic features to visual space to alleviate the hubness problem. Specifically, we design a novel loss function, Softplus Margin Focal Loss (SMFL), for object classification channel to align the projected semantic features in visual space and address the class imbalance problem. The SMFL improves the discrimination of projections on positive and negative categories and maintains the property of focal loss. Besides, to promote the localization performance for novel objects, we also pro -vide semantic information for object localization channel and utilize a trainable matrix to align the semantic-visual mapping, considering noises in semantic representations. We conduct extensive exper-iments on four challenging benchmarks. The experimental results show the competitive performances compared with state-of-the-art approaches. Especially, we achieve 8.39%/6.58% mean average precision (mAP) improvements for ZSD/general-ZSD on Microsoft COCO benchmark. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Zero-shot object detection Softplus margin focal loss Semantic-visual alignment Auto-encoder architecture

来源：评论

学校读者我要写书评

暂无评论

Parallel crop planning based on price forecast

引用

INTERNATIONAL JOURNAL OF INTELLIGENT systems 2022年第8期37卷 4772-4793页

作者： Fan, Menghan Kang, Mengzhen Wang, Xiujuan Hua, Jing He, Chaoxing Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Qingdao AgriTech Co Ltd Qingdao Peoples R China Chinese Acad Agr Sci Inst Vegetables & Flowers Beijing Peoples R China

Agrifood system actors operate within diverse sociocultural, economic, and biophysical settings. For growers, crop planning, usually a yearly business plan, is a key decision to make on when, what, and how many to plant. It is a challenging task as it deals with multiple constraints in volatile economic and/or climate environment. Most crop planning models have difficulty in adapting to changing situation. In this study, a parallel system of crop planning composed of the artificial system, computational experiment, and parallel execution is proposed. The farmers are described as agents, and the decision is made based on the heuristic searching of optimal plan;the adaption of plan is triggered autonomously given strong environment changes. Focus is given to economic environment, which is indicated as product price. In a case study, the economic environment of the artificial system is built based on the monthly and weekly price information for 13 products during 7 years. The computational experiment provides the initial cropping plan and harvest time, with social and ecological constraints. Result shows that the cropping plan can further adapt to price variation. This flexible cropping plan system can strengthen the capability of cooperatives serving small-scale farmers.

关键词： agent-based modeling crop planning parallel agricultural management price prediction

来源：评论

学校读者我要写书评

暂无评论

Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

引用

IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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：