检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

24,618 篇 会议
154 篇 期刊文献
57 册 图书
1 篇 学位论文

馆藏范围

24,830 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

10,141 篇 工学
- 6,339 篇 控制科学与工程
- 4,109 篇 计算机科学与技术...
- 2,664 篇 软件工程
- 2,151 篇 电气工程
- 1,865 篇 机械工程
- 1,146 篇 仪器科学与技术
- 735 篇 信息与通信工程
- 585 篇 生物工程
- 474 篇 生物医学工程（可授...
- 431 篇 光学工程
- 403 篇 力学（可授工学、理...
- 386 篇 电子科学与技术（可...
- 263 篇 化学工程与技术
- 252 篇 交通运输工程
- 251 篇 材料科学与工程（可...
- 244 篇 动力工程及工程热...
- 217 篇 土木工程
- 206 篇 建筑学
- 183 篇 安全科学与工程
3,073 篇 理学
- 1,766 篇 数学
- 905 篇 物理学
- 642 篇 生物学
- 578 篇 统计学（可授理学、...
- 482 篇 系统科学
- 229 篇 化学
1,224 篇 管理学
- 959 篇 管理科学与工程(可...
- 312 篇 工商管理
- 306 篇 图书情报与档案管...
328 篇 医学
- 287 篇 临床医学
- 205 篇 基础医学(可授医学...
124 篇 经济学
106 篇 法学
87 篇 农学
53 篇 教育学
27 篇 文学
16 篇 军事学
13 篇 艺术学
1 篇 哲学

主题

2,481 篇 predictive model...
1,995 篇 robots
1,827 篇 robotics and aut...
1,741 篇 computational mo...
1,715 篇 automation
1,656 篇 data models
1,585 篇 robot sensing sy...
1,491 篇 analytical model...
1,410 篇 adaptation model...
1,390 篇 training
1,187 篇 accuracy
1,141 篇 mathematical mod...
1,100 篇 trajectory
1,048 篇 mobile robots
922 篇 feature extracti...
845 篇 robustness
840 篇 robot kinematics
793 篇 control systems
718 篇 solid modeling
698 篇 uncertainty

机构

179 篇 university of ch...
144 篇 school of artifi...
111 篇 robotics institu...
89 篇 school of automa...
84 篇 institute of aut...
72 篇 shenyang institu...
68 篇 the robotics ins...
68 篇 school of electr...
68 篇 institutes for r...
64 篇 state key labora...
62 篇 school of mechat...
50 篇 department of au...
49 篇 department of au...
48 篇 state key labora...
46 篇 school of automa...
44 篇 school of mechan...
42 篇 robotics institu...
39 篇 school of automa...
32 篇 school of automa...
30 篇 college of autom...

作者

37 篇 secchi cristian
34 篇 yuechao wang
32 篇 yun-hui liu
31 篇 cristian secchi
29 篇 shugen ma
28 篇 lining sun
24 篇 xiang li
22 篇 jian wang
22 篇 jianda han
22 篇 fabio ramos
20 篇 xinyu wu
20 篇 wu xinyu
20 篇 daniela rus
19 篇 tomizuka masayos...
19 篇 wei li
19 篇 levine sergey
18 篇 c. laugier
18 篇 jie zhao
18 篇 chen chen
18 篇 dinesh manocha

语言

23,753 篇 英文
993 篇 其他
102 篇 中文
2 篇 西班牙文
1 篇 俄文

检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24830 条记录，以下是3841-3850 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Fabric defect detection using tactile information

Fabric defect detection using tactile information

引用

IEEE international conference on robotics and automation (ICRA)

作者： Long, Xingming Fang, Bin Zhang, Yifan Luo, GuoYi Sun, Fuchun Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781728190778

This paper proposes a method of fabric structure defect detection based on tactile information. Different from traditional visual-based detection methods, the proposed method uses a tactile sensor to attain the information of fabric. The advantage of using tactile information is to avoid different irregular dyeing patterns and reduce the influence of ambient light. Therefore, the proposed method can be more concise and universal, which makes the defect detection system more robust. Experiments are conducted to verify the performance of the developed tactile method. The results demonstrate that the proposed method is much better than the visual method in the detection of structural defects. In addition, we propose a design of a tactile sensing device that can greatly improve the actual detection efficiency of the tactile method. It is showed that the proposed method is efficient enough to meet our requirements and it provides a possible solution for the application of the tactile method in actual fabric production.

关键词： Performance evaluation Visualization automation conferences Tactile sensors Production Color

来源：评论

学校读者我要写书评

暂无评论

MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

MMA-Net: Multiple Morphology-Aware Network for Automated Cob...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhengxuan Qiu Jie Yang Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18°, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement.

关键词： Training Image segmentation Sensitivity Scoliosis Noise Morphology Reliability

来源：评论

学校读者我要写书评

暂无评论

Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

引用

international conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

来源：评论

学校读者我要写书评

暂无评论

Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving

Pay Attention to How You Drive: Safe and Adaptive Model-Base...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sean J. Wang Honghao Zhu Aaron M. Johnson Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Autonomous off-road driving is challenging as unsafe actions may lead to catastrophic damage. As such, developing controllers in simulation is often desirable. However, robot dynamics in unstructured off-road environments can be highly complex and difficult to simulate accurately. Domain randomization addresses this problem by randomizing simulation dynamics to train policies that are robust towards modeling errors. While these policies are robust across a range of dynamics, they are sub-optimal for any particular system dynamics. We introduce a novel model-based reinforcement learning approach that aims to balance robustness with adaptability. We train a System Identification Transformer (SIT) and an Adaptive Dynamics Model (ADM) under a variety of simulated dynamics. The SIT uses attention mechanisms to distill target system state-transition observations into a context vector, which provides an abstraction for the target dynamics. Conditioned on this, the ADM probabilistically models the system’s dynamics. Online, we use a Risk-Aware Model Predictive Path Integral controller to safely control the robot under its current understanding of dynamics. We demonstrate in simulation and in the real world that this approach enables safer behaviors upon initialization and becomes less conservative (i.e. faster) as its understanding of the target system dynamics improves with more observations. In particular, our approach results in an approximately 41% improvement in lap-time over the non-adaptive baseline while remaining safe across different environments.

关键词： Adaptation models System dynamics Reinforcement learning Predictive models Transformers Probabilistic logic Vectors

来源：评论

学校读者我要写书评

暂无评论

DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies

DisCo RL: Distribution-Conditioned Reinforcement Learning fo...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Nasiriany, Soroush Pong, Vitchyr H. Nair, Ashvin Khazatsky, Alexander Berseth, Glen Levine, Sergey Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728190778

Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity. Categorical contexts preclude generalization to entirely new tasks. Goal-conditioned policies may enable some generalization, but cannot capture all tasks that might be desired. In this paper, we propose goal distributions as a general and broadly applicable task representation suitable for contextual policies. Goal distributions are general in the sense that they can represent any state-based reward function when equipped with an appropriate distribution class, while the particular choice of distribution class allows us to trade off expressivity and learnability. We develop an off-policy algorithm called distribution-conditioned reinforcement learning (DisCo RL) to efficiently learn these policies. We evaluate DisCo RL on a variety of robot manipulation tasks and find that it significantly outperforms prior methods on tasks that require generalization to new goal distributions.

关键词： automation conferences Reinforcement learning Task analysis Robots

来源：评论

学校读者我要写书评

暂无评论

DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-S...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Jingyu Li Zhe Liu Jinghua Hou Dingkang Liang School of Electronic Information and Communication Huazhong University of Science and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://***/hust-jy/DDS3D

关键词：

来源：评论

学校读者我要写书评

暂无评论

ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

ZS6D: Zero-shot 6D Object Pose Estimation using Vision Trans...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Philipp Ausserlechner David Haberger Stefan Thalhammer Jean-Baptiste Weibel Markus Vincze Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department University of Applied Sciences Technikum Vienna Austria

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC- based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to MegaPose, we improve the Average Recall on all three datasets and compared to OSOP we improve on two datasets. The code is available at https://***/PhilippAuss/ZS6D.

关键词： Training Computer vision Visualization Codes Computational modeling Pose estimation Transformers

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

Reinforcement Learning for Robust Parameterized Locomotion C...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Li, Zhongyu Cheng, Xuxin Peng, Xue Bin Abbeel, Pieter Levine, Sergey Berseth, Glen Sreenath, Koushil Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728190778

Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling;any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-toreal transfer, domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. The learned policies enable Cassie to perform a set of diverse and dynamic behaviors, while also being more robust than traditional controllers and prior learning-based methods that use residual control. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw. (Video(1))

关键词： Legged locomotion Training Learning systems Target tracking automation System dynamics conferences

来源：评论

学校读者我要写书评

暂无评论

Omnidirectional Dense SLAM for Back-to-back Fisheye Cameras

Omnidirectional Dense SLAM for Back-to-back Fisheye Cameras

引用

IEEE international conference on robotics and automation (ICRA)

作者： Weijian Xie Guanyi Chu Quanhao Qian Yihao Yu Shangjin Zhai Danpeng Chen Nan Wang Hujun Bao Guofeng Zhangv State Key Lab of CAD&CG Zhejiang University SenseTime Research Tetras.AI

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose a real-time visual-inertial dense SLAM system that utilizes the online data streams from back-to-back dual fisheye cameras setup, providing 360 ◦ coverage of the environment. Firstly, we employ a sliding-window-based front-end to estimate real-time poses from the binocular fisheye images and IMU data. Then, we implement a lightweight panoramic depth completion network based on multi-basis depth representation. The network takes panoramic images (obtained by stitching dual-fisheye images with extrinsics and intrinsic parameters) and sparse depths (generated by the front-end local tracking) as input and predicts multiple depth bases along with corresponding confidence as output. The final dense depth is the linear combination of the multiple depth bases. Thanks to the multi-basis depth representation, we can continuously optimize the 360° depth with the traditional optimizer to achieve higher global consistency in depth. We conducted experiments on both simulated and real-world datasets to evaluate our method. The results demonstrate that the proposed method outperforms SoTA methods in terms of depth prediction and 3D reconstruction. In addition, we develop a demo that can run on a mobile to demonstrate the real-time capabilities of our method.

关键词： Simultaneous localization and mapping Three-dimensional displays Cameras Real-time systems robotics and automation Streams

来源：评论

学校读者我要写书评

暂无评论

Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion

Mapless-Planner: A Robust and Fast Planning Framework for Ag...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Ji, Jialin Wang, Zhepei Wang, Yingjian Xu, Chao Gao, Fei Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313000 Peoples R China Natl Engn Res Ctr Ind Automat Ningbo Inst Ningbo 315000 Peoples R China

ISBN: (纸本)9781728190778

Maintaining a map online is resource-consuming while a robust navigation system usually needs environment abstraction via a well-fused map. In this paper, we propose a mapless local planner which directly conducts such abstraction on the unfused sensor data. A limited-memory data structure with a reliable proximity query algorithm is proposed for maintaining raw historical information. A sampling-based scheme is designed to extract the free-space skeleton. A smart waypoint selection strategy enables to generate high-quality trajectories within the resultant flight corridors. Our planner differs from other mapless ones in that it can abstract and exploit the environment information more efficiently. The online replan consistency and success rate are both significantly improved against conventional mapless methods.

关键词： automation Navigation conferences Robot sensing systems Data structures Skeleton Trajectory

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 381 382 383 384 385 386 387 388 389 390 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：