检索结果-内蒙古大学图书馆

Restoring After Central Nervous System Injuries:Neural Mechanisms and Translational Applications of Motor Recovery

Neuroscience Bulletin 2022年第12期38卷 1569-1587页

作者： Zhengrun Gao Zhen Pang Yiming Chen Gaowei Lei Shuai Zhu Guotao Li Yundong Shen Wendong Xu Department of Hand Surgery Huashan HospitalFudan UniversityShanghai 200040China The National Clinical Research Center for Aging and Medicine Fudan UniversityShanghai 200040China Department of Hand and Upper Extremity Surgery Jing’an District Central HospitalFudan UniversityShanghai 200040China Institutes of Brain Science Fudan UniversityShanghai 200040China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200040China Institute of Automation State Key Laboratory of Management and Control for Complex SystemsChinese Academy of SciencesBeijing 100190China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China Research Unit of Synergistic Reconstruction of Upper and Lower Limbs after Brain Injury Chinese Academy of Medical SciencesShanghai 200400China

Central nervous system(CNS)injuries,including stroke,traumatic brain injury,and spinal cord injury,are leading causes of long-term *** is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated *** studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote ***,the ability to increase plasticity in the injured brain is restricted and difficult to ***,over several decades,researchers have been prompted to enhance the compensation by the unaffected *** experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor *** addition,several clinical treatments have been designed to restore ipsilateral motor control,including brain stimulation,nerve transfer surgery,and brain–computer interface ***,we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

关键词： Stroke Traumatic brain injury Spinal cord injury Brain-computer interface system Neuroplasticity Ipsilateral motor control Axon regrowth

来源：评论

学校读者我要写书评

暂无评论

Kinematics Analysis of a Manipulator for the Power Transmission Line Maintenance Robot

Kinematics Analysis of a Manipulator for the Power Transmiss...

引用

Chinese control Conference (CCC)

作者： Minghao Chen En Li Feng Zhang Rui Guo Zize Liang Yuwei Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shandong Luneng Intelligence Technology Co. Ltd. Jinan China The State Grid Shandong Electric Power Company Jinan China

With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down risks and ensure inspectors' safety. Based on the power transmission line maintenance platform, a 5-DOF manipulator is designed to perform the maintenance tasks for the quad bundle conductors. Then the kinematics analysis of the manipulator is analyzed. The manipulator doesn't meet the Pieper criterion. So an improved adaptive particle swarm optimization (APSO) is proposed for figuring out the inverse kinematics. The weights of the pose and the constraint of the prismatic joint are introduced to the algorithm. What's more, half of the particles are initialized around the incipient value of joints and half of the particles are randomly initialized with the constraint of joints in this algorithm. Results show that the manipulator is able to work in the environment of quad bundle conductors and the proposed algorithm can improve the precision of inverse kinematics and prevent the leap of joints.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Acp-Net: Asymmetric Center Positioning Network for Real-Time Text Detection

SSRN

引用

SSRN 2024年

作者： Zhu, Boyuan Liu, Fagui Chen, Xi Tang, Quan Chen, C.L. Philip South China University of Technology No.342 Outer Ring East Road Panyu Guangzhou510006 China Peng Cheng Laboratory No. 2 Xingke 1st Street Nanshan Shenzhen518055 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No.95 Zhongguancun East Road Haidian Beijing100190 China

Scene text detection plays a crucial role in numerous application fields. However, despite the varying focus on real-time performance, almost all existing detection models employ the Feature Pyramid Network (FPN) structure for feature extraction, overlooking its inherent limitations. The integration of high-resolution multi-channel features within FPN demands significant computational resources. Although FPN which treats local and global features as equivalent, has been proven to be stable in various fields, it remains to be reconsidered whether this stability strategy is the most suitable for text features. To this end, we propose an Asymmetric Center Positioning Network (ACP-Net), which replaces the FPN structure and achieves precise and real-time text detection in complex scenarios. Specifically, we explore an asymmetric feature structure that includes two independent branches to acquire both global and local information, as well as an adaptive weighted fusion module to effectively capture the long-range dependencies from these two branches. Furthermore, we present a text center positioning module to enhance the understanding of text features by learning the feature centers. Comprehensive assessments conducted across various terminals have demonstrated the superiority of ACP-Net in terms of both accuracy and speed. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Hydrodynamic Modeling and Parameter Identification of a Bionic Underwater Vehicle:RobDact

引用

Cyborg and Bionic systems 2022年第1期2022卷 164-176页

作者： Qiyuan Cao Rui Wang Tiandong Zhang Yu Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of A utomationChinese Academy of SciencesBeijingChina School of Artific ial Itelligence University of Chinese Academy of Sciences BeijingChina Center for Excellence in Brain Science and Itelligence Technology Chinese Academy of SciencesShanghaiChina

In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.

关键词： dynamics(CFD) equations hydrodynamic

来源：评论

学校读者我要写书评

暂无评论

PROTOTYPICAL CONTEXT-AWARE DYNAMICS GENERALIZATION FOR HIGH-DIMENSIONAL MODEL-BASED REINFORCEMENT LEARNING

arXiv

引用

arXiv 2022年

作者： Wang, Junjie Mu, Yao Li, Dong Zhang, Qichao Zhao, Dongbin Zhuang, Yuzheng Luo, Ping Wang, Bin Hao, Jianye The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Hong Kong Hong Kong Huawei Noah's Ark Lab

The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging. Although the recurrent structure utilized in current advances helps to capture local dynamics, modeling only state transitions without an explicit understanding of environmental context limits the generalization ability of the dynamics model. To address this issue, we propose a Prototypical Context-Aware Dynamics (ProtoCAD) model, which captures the local dynamics by time consistent latent context and enables dynamics generalization in high-dimensional control tasks. ProtoCAD extracts useful contextual information with the help of the prototypes clustered over batch and benefits model-based RL in two folds: 1) It utilizes a temporally consistent prototypical regularizer that encourages the prototype assignments produced for different time parts of the same latent trajectory to be temporally consistent instead of comparing the features;2) A context representation is designed which combines both the projection embedding of latent states and aggregated prototypes and can significantly improve the dynamics generalization ability. Extensive experiments show that ProtoCAD surpasses existing methods in terms of dynamics generalization. Compared with the recurrent-based model RSSM, ProtoCAD delivers 13.2% and 26.7% better mean and median performance across all dynamics generalization tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification 5th

OSTER: An Orientation Sensitive Scene Text Recognizer with C...

引用

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Feng, Zipeng Du, Chen Wang, Yanna Xiao, Baihua The 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

ISBN: (纸本)9783030414030

Scene texts in China are always arbitrarily arranged in two forms: horizontally and vertically. These two forms of texts exhibit distinctive features, making it difficult to recognize them simultaneously. Besides, recognizing irregular scene texts is still a challenging task due to their various shapes and distorted patterns. In this paper, we propose an orientation sensitive network aiming at distinguishing between Chinese horizontal and vertical texts. The learned orientation is then passed into an attention selective network to adjust the attention maps of the sequence recognition model, leading it working for each type of texts respectively. In addition, a lightweight centerline rectification network is adopted, which enables the irregular texts more readable while no redundant labels are needed. A synthetic dataset named SCTD is released to support our training and evaluate the proposed model. Extensive experiments show that the proposed method is capable of recognizing arbitrarily-aligned scene texts accurately and efficiently, achieving state-of-the-art performance over a number of public datasets. © Springer Nature Switzerland AG 2020.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification 27th

Learning from the Negativity: Deep Negative Correlation Meta...

引用

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030678319

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

EiHi Net: Out-of-Distribution Generalization Paradigm

arXiv

引用

arXiv 2022年

作者： Wei, Qinglai Yuan, Beiming Chen, Diancheng State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Systems Engineering Macau University of Science and Technology 999078 China

This paper develops EiHi to solve the out of distribution (O.o.D.) problem in recognition, especially the distribution shift of background. EiHi net is a model learning paradigm that can be blessed on any visual backbone. Different from the traditional deep model, namely find out correlated relationship between sample features and corresponding categories, EiHi net suffers less from spurious correlations. Fusing SimCLR and VIC-Reg via explicit and dynamical establishment of original - positive - negative sample pair as a minimal learning element, EiHi net iteratively establishes a relationship close to the causal one between features and labels, while suppresses the spurious correlations. To further reinforce the ability of the proposed model on causal reasoning, we develop a human-in-the-loop strategy, with few guidance samples, to prune the representation space directly. Finally, it is shown that the developed EiHi net makes significant improvements in the most difficult and typical O.o.D. dataset NICO, compared with the current SOTA results, without any domain (e.g. background) information. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

In-Situ 3D Printing Monitoring in Dynamic Environments via Self-Supervised Deep Neural Network Adaptation

In-Situ 3D Printing Monitoring in Dynamic Environments via S...

引用

IEEE International Conference on automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology 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 Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in gastric cancer:applications and challenges

引用

Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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：