检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Automation (ICRA)

作者： Hyeon-Seok Seong Do-Hyeong Kim Igor Gaponov Jee-Hwan Ryu Department of Civil and Environmental Engineering KAIST Daejeon South Korea Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

This paper presents a study on a compliant cable-driven exoskeleton for hip assistance in lifting tasks that is aimed at preventing low-back pain and injuries in the vocational setting. In the proposed concept, we used twisted string actuator (TSA) to design a light-weight and powerful exoskeleton that benefits from inherent TSA advantages. We have noted that nonlinear nature of twisted strings' transmission ratio (decreasing with twisting) closely matched typical torque-speed requirements for hip assistance during lifting tasks and tried to use this fact in the exoskeleton design and motor selection. Hip-joint torque and speed required to lift a 10-kg load from stoop to stand were calculated, which gave us a baseline that we used to design and manufacture a practical exoskeleton prototype. Preliminary experimental trials demonstrated that the proposed device was capable of generating required torque and speed at the hip joint while weighing under 6 kg, including battery.

关键词： Hip Exoskeletons Torque Pulleys Actuators Kinematics Task analysis

来源：评论

学校读者我要写书评

暂无评论

Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization

Online Refinement of Low-level Feature Based Activation Map ...

引用

International Conference on computer vision (ICCV)

作者： Jinheng Xie Cheng Luo Xiangping Zhu Ziqi Jin Weizeng Lu Linlin Shen Shenzhen Institute of Artificial Intelligence of Robotics of Society Guangdong Key Laboratory of Intelligent Information Processing Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University

ISBN: (纸本)9781665428132

We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objects using the low-level feature based activation maps. In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner. In the second stage, we employ an evaluator to evaluate the activation maps predicted by the activation map generator. Based on this, we further propose a weighted entropy loss, an attentive erasing, and an area loss to drive the activation map generator to substantially reduce the uncertainty of activations between object and background, and explore less discriminative regions. Based on the low-level object information preserved in the first stage, the second stage model gradually generates a well-separated, complete, and compact activation map of object in the image, which can be easily thresholded for accurate localization. Extensive experiments on CUB-200-2011 and ImageNet-1K datasets show that our framework surpasses previous methods by a large margin, which sets a new state-of-the-art for WSOL. Code will be available soon.

关键词： Location awareness computer vision Uncertainty Codes Drives Generators Entropy

来源：评论

学校读者我要写书评

暂无评论

Modeling Overlapping Execution/Observation Brain Pathways

Modeling Overlapping Execution/Observation Brain Pathways

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Michail Maniadakis Manolis Hourdakis Panos Trahanias Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece Department of Computer Science University of Crete Greece

Recent brain imaging studies on primates revealed that a network of brain areas is activated both during observation and during execution of movements. The present work aims at modeling this group of areas, implementing a distributed computational system. The modeling process follows the agent-based coevolutionary framework that is very effective in terms of designing complex distributed systems addressing successfully the multi-modality of the interacting regions. The implemented model is successfully embedded in a simulated humanoid robot, replicating existing biological findings.

关键词： Brain modeling Biological system modeling computer science Computational modeling computer architecture computer vision Robot vision systems Laboratories Humanoid robots Humans

来源：评论

学校读者我要写书评

暂无评论

Shape-Aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains 23rd

Shape-Aware Meta-learning for Generalizing Prostate MRI Segm...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Quande Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong T Stone Robotics Institute The Chinese University of Hong Kong Shatin Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597122

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains (Code and dataset are available at https://***/liuquande/SAML). © 2020, Springer Nature Switzerland AG.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer 24

Frequency Guidance Matters: Skeletal Action Recognition by F...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Wenhan Zheng, Ce Yang, Zihao Chen, Chen Das, Srijan Lu, Aidong Department of Computer Science University of North Carolina at Charlotte CharlotteNC United States Robotics Institute Carnegie Mellon University PittsburghPA United States One Inventory Organization Microsoft Corporation RedmondWA United States Center for Research in Computer Vision University of Central Florida OrlandoFL United States

ISBN: (纸本)9798400706868

Recently, transformers have demonstrated great potential for modeling long-term dependencies from skeleton sequences and thereby gained ever-increasing attention in skeleton action recognition. However, the existing transformer-based approaches heavily rely on the naive attention mechanism for capturing the spatiotemporal features, which falls short in learning discriminative representations that exhibit similar motion patterns. To address this challenge, we introduce the Frequency-aware Mixed Transformer (FreqMixFormer), specifically designed for recognizing similar skeletal actions with subtle discriminative motions. First, we introduce a frequency-aware attention module to unweave skeleton frequency representations by embedding joint features into frequency attention maps, aiming to distinguish the discriminative movements based on their frequency coefficients. Subsequently, we develop a mixed transformer architecture to incorporate spatial features with frequency features to model the comprehensive frequency-spatial patterns. Additionally, a temporal transformer is proposed to extract the global correlations across frames. Extensive experiments show that FreqMiXFormer outperforms SOTA on 3 popular skeleton action recognition datasets, including NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our project is publicly available at: https://***/wenhanwu95/FreqMixFormer. © 2024 ACM.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction

arXiv

引用

arXiv 2019年

作者： Zhang, Pu Ouyang, Wanli Zhang, Pengfei Xue, Jianru Zheng, Nanning Institute of Artificial Intelligence and Robotics Xian Jiaotong University China University of Sydney SenseTime Computer Vision Research Group Australia

In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to learn social behaviors. However, many of these methods rely on previous neighboring hidden states but ignore the important current intention of the neighbors. In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism. To effectively extract the social effect of neighbors, we further introduce a social-aware information selection mechanism consisting of an element-wise motion gate and a pedestrian-wise attention to select useful message from neighboring pedestrians. Experimental results on two public datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed SR-LSTM and we achieves state-of-the-art results. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Navigation Command Matching for vision-based Autonomous Driving

Navigation Command Matching for Vision-based Autonomous Driv...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Yuxin Pan Jianru Xue Pengfei Zhang Wanli Ouyang Jianwu Fang Xingyu Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China SenseTime Computer Vision Research Group University of Sydney Australia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Learning an optimal policy for autonomous driving task to confront with complex environment is a long- studied challenge. Imitative reinforcement learning is accepted as a promising approach to learn a robust driving policy through expert demonstrations and interactions with environments. However, this model utilizes non-smooth rewards, which have a negative impact on matching between navigation commands and trajectory (state-action pairs), and degrade the generalizability of an agent. Smooth rewards are crucial to discriminate actions generated from sub-optimal policy. In this paper, we propose a navigation command matching (NCM) model to address this issue. There are two key components in NCM, 1) a matching measurer produces smooth navigation rewards that measure matching between navigation commands and trajectory; 2) attention-guided agent performs actions given states where salient regions in RGB images (i.e. roadsides, lane markings and dynamic obstacles) are highlighted to amplify their influence on the final model. We obtain navigation rewards and store transitions to replay buffer after an episode, so NCM is able to discriminate actions generated from suboptimal policy. Experiments on CARLA driving benchmark show our proposed NCM outperforms previous state-of-the- art models on various tasks in terms of the percentage of successfully completed episodes. Moreover, our model improves generalizability of the agent and obtains good performance even in unseen scenarios.

关键词： Navigation Task analysis Trajectory Learning (artificial intelligence) Autonomous vehicles Smoothing methods Current measurement

来源：评论

学校读者我要写书评

暂无评论

A Multi-stage Single-Point Incremental Sheet Forming for Enhanced Homogeneous Thickness Distribution

A Multi-stage Single-Point Incremental Sheet Forming for Enh...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ghadeer Issa Karam Almaghout Alexander Maloletov Alexandr Klimchik Intelligent Robotics Systems Laboratory Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Lincoln Centre for Autonomous Systems (L-CAS) School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a multi-stage approach to enhance homogeneous thickness distribution in single-point incremental sheet forming. Traditional methods often struggle with achieving uniform material thickness, compromising the final part’s structural integrity and performance. Our proposed methodology leverages a V-shape starting stage, systematically designed to address these challenges by improving material flow and distribution throughout the forming process. We employ finite element analysis to validate the efficiency of the proposed approach and compare it with conventional single-stage forming processes. The simulations demonstrate significant improvements in achieving a more uniform thickness distribution across the formed parts. The comparative analysis highlights the potential of the proposed multi-stage approach for industrial applications and enhances material homogeneity. This methodology advances the precision and reliability of formed parts and opens new avenues for research and development in sheet metal forming technologies.

关键词： Geometry Accuracy Shape Simulation Metals Production Manufacturing Reliability Information technology Research and development

来源：评论

学校读者我要写书评

暂无评论

Real-time Fish Detection in Trawl Nets

Real-time Fish Detection in Trawl Nets

引用

Oceans (Conference)

作者： R. Prados R. Garcia N. Gracias L. Neumann Havard Vagstol Computer Vision and Robotics Institute University of Girona Girona Spain ICREA Pg. Lluís Companys 23 Barcelona Spain Scantrol Deep Vision AS Bergen Norway

This work presents advances towards the development of the Deep vision system, consisting of a stereo camera setup attached to a trawl, which will make it possible to identify and measure the fish passing through without bringing them aboard the boat. This identification and measuring task requires a preprocessing in which the specimens should be segmented from the image background. Several lighting artifacts such as non-uniform illumination, shadows and reflections are some of the challenges to be addressed during the segmentation procedure. This paper describes the segmentation pipeline used to segment the fish from the background in an efficient and robust way.

关键词： Fish segmentation Trawl camera system HSV

来源：评论

学校读者我要写书评

暂无评论

Reward Planning for Underactuated Robotic Systems: A Study on Pendubot with Parameters Uncertainty

Reward Planning for Underactuated Robotic Systems: A Study o...

引用

School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Sinan Ibrahim Ramil Khusainov Ali Jnadi S. M. Ahsan Kazmi Institute of Robotics and Computer Vision Innopolis University Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia Computer Science and Creative Technologies University of the West of England (UWE) Bristol United Kingdom

There is a burgeoning interest in the field of Reinforcement Learning (RL) across various domains, including robotics. Traditional control approaches for robotic systems, such as semi-definite programming (SDP) or mixed integer programming (MIP), typically rely on the assumption of a known and deterministic model of the environment. In contrast, this study aims to introduce a novel RL method for planning the reward function specifically tailored for underactuated robotic systems with parameter uncertainty. We propose a method with a single RL agent to address the challenge of swing up and balance of a Pendubot system with uncertain parameters. The ultimate objective is to enhance the system’s ability to adapt and perform reliably in the face of varying uncertainties.

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：