检索结果-内蒙古大学图书馆

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Li, Haiyang Li, Meng Qi, Xiaozhi Yang, Yuanyuan Hu, Ying Shenyang Aerospace University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Harbin Institute of Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665481090

To optimize the operation ability of the autonomous robot under the background of complex surgery, this paper extracted the state features of milling video sequence images to realize bone layer perception and improve the perception ability of the spinal robot. The bone layer sensing algorithm mainly consists of four parts: improved moving object detection algorithm, moving object tracking algorithm, state feature extraction algorithm and bone layer recognition algorithm. Aiming at the shortcomings of the existing moving target detection methods, this paper proposes an improved moving target detection algorithm based on the existing Background Subtractor MOG algorithm, which is suitable for milling video sequences. The grinding machine state obtained by the improved algorithm is used as the input of the moving target tracking algorithm, and the kernel correlation filter (KCF) is used to realize the target tracking. According to the tracking of the moving target, the state features of the milling area are extracted and the bone layer is identified, so as to realize the bone layer perception in the milling process. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

AGA-RFNN: Adaptive Genetic Algorithm-based Random Forest Neural Network for Pavement Deterioration Prediction

AGA-RFNN: Adaptive Genetic Algorithm-based Random Forest Neu...

引用

2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Xu, Zhenyu Meng, Lenian Zhang, Qieshi Cheng, Jun Philip, Babitha Aljassmi, Hamad Yang, Zhiyong Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China China University of Geosciences School of Economics and Management Wuhan China United Arab Emirates University Department of Civil and Environmental Engineering Al Ain United Arab Emirates Yantai Institute of Technology School of Artificial Intelligence Shandong China

ISBN: (纸本)9798350327182

Pavement deterioration prediction is an important task of road inspection mobile robots. Tree-based deep learning models perform well in this prediction but their accuracy remains unsatisfactory. In this paper, to enhance the prediction accuracy of pavement deterioration, we propose a SELECTOR module and Adaptive Genetic Algorithm-based Random Forest Neural Networks (AGA-RFNNs). The SELECTOR module is first used to refine the input data by exploiting correlation analysis, then the RFNNs are designed to make superior accurate and general predictions by learning predictions from random forests, and finally the AGA is applied to train the RFNNs, which considers both the average and maximum fitness of the population and thereby obtaining the optimal solution. Integrating AGA and RFNN, the proposed AGA-RFNN enhances the accuracy of pavement deterioration prediction systems. Experimental results demonstrate that AGA-RFNN outperforms existing models on four deterioration datasets related to cracking, deflection, international roughness index and rutting, respectively. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

GAN-SLAM: GAN based Monocular Visual-Inertial Simultaneous Localization and Mapping in Dark Environments 5

GAN-SLAM: GAN based Monocular Visual-Inertial Simultaneous L...

引用

5th International Symposium on Autonomous systems, ISAS 2022

作者： Zhang, Qieshi Hao, Luoying Li, Hongjian Ren, Ziliang Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Southern University of Science and Technology Shenzhen China School of Science and Technology Dongguan University of Technology Dongguan China

ISBN: (纸本)9781665487085

In recent years, visual-inertial Simultaneous Localization and Mapping (SLAM) techniques have achieved excellent results due to the complementary nature of visual and inertial sensors, the challenges remain in some environments. When encountering a challenging environment, image features become an important factor affecting the success rate and accuracy of localization of SLAM system. Due to the Convolutional Neural Network (CNN) has the advantage of extracting effective features more accurately, this paper makes a new attempt to replace traditional features with those generated by the network. We combine the state-of-the-art CNN of optical flow estimation and iterative optimization model of traditional SLAM framework, which achieves the accuracy and robustness in challenging environments. To show the performance of our method, some state-of-the-art visual-inertial methods are tested and compared with the challenging sequences of public dataset. Experimental results show that the proposed method can achieve better results in accuracy and robustness and works reliably well for challenging environments. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

SpikingVPR: Spiking Neural Network-Based Feature Aggregation for Visual Place Recognition

SpikingVPR: Spiking Neural Network-Based Feature Aggregation...

引用

Sensing, Measurement & Data Analytics in the era of Artificial intelligence (ICSMD), International Conference on

作者： Gulin Wang Ziliang Ren Qieshi Zhang Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Visual Place Recognition (VPR) is a crucial task in robotics and autonomous driving, where it identifies the geographic location of a query image by matching it with a reference database. VPR faces significant challenges, including appearance variations due to lighting, occlusion, weather, and seasonal changes, alongside the need for real-time processing under low computational cost and latency. To address these challenges, we introduce Spiking Neural Networks (SNN) into the VPR task, specifically focusing on the feature aggregation component. SNN, with their event-driven nature, offer advantages in computational efficiency and energy consumption, making them well-suited for energy-constrained environments such as drones. Our approach transforms the feature aggregation stage of VPR into a spiking, event-driven mechanism, which maintains high recall performance while integrating seamlessly with traditional ANN-based methods. Experimental results demonstrate that our SNN-based method effectively maintains high recall performance, validating its potential in autonomous driving and other related applications.

关键词： Energy consumption Spiking neural networks Transforms Real-time systems Computational efficiency Visual databases Autonomous vehicles Robots Visual place recognition Meteorology

来源：评论

学校读者我要写书评

暂无评论

DFPC-SLAM:A Dynamic Feature Point Constraints-Based SLAM Using Stereo Vision for Dynamic Environment

引用

Guidance, Navigation and Control 2023年第1期3卷 46-60页

作者： Bo Zeng Chengqun Song Cheng Jun Yuhang Kang CAS Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P.R.China University of Chinese Academy of Sciences Beijing P.R.China The Chinese University of Hong Kong Hong Kong P.R.China

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm formobile robots in dynamic scenes often results in a signicant decrease in accuracy due to thein°uence of dynamic objects. In this paper, feature points are divided into dynamic and staticfrom semantic information and multi-view geometry information, and then static region featurepoints are added to the pose-optimization, and static scene maps are established for dynamicscenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and theresults show that the proposed algorithm has higher accuracy in highly dynamic scenes comparedto the visual SLAM baseline.

关键词： SLAM stereo vision semantic segmentation multi-view geometry dynamic scenes.

来源：评论

学校读者我要写书评

暂无评论

Brain Rhythmic Activity and Hemodynamic Response During Wrist Extension Induced by Functional Electrical Stimulation 17

Brain Rhythmic Activity and Hemodynamic Response During Wris...

引用

17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Mao, He Peng, Yinghu Zheng, Yue Li, Guanglin Li, Hai Xu, Yongan Fang, Peng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Southern Medical University Neurorehabilitation Laboratory Department of Rehabilitation Medicine Shenzhen Hospital Shenzhen518101 China Second Affiliated Hospital Institute of Emergency Medicine Zhejiang University School of Medicine Department of Emergency Medicine Hangzhou310009 China

ISBN: (纸本)9798350355154

Functional electrical stimulation (FES) is an effective therapy for motor function restoration after stroke. To investigate brain activation during FES-induced wrist extension, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) were simultaneously measured for 6 healthy subjects. We found an increase in oxygenated hemoglobin (HbO) concentration, which was accompanied by a decrease in deoxygenated hemoglobin (HbR) concentration in bilateral sensorimotor cortex (SMC) during movement. We also found event-related desynchronization (ERD) of alpha and beta EEG rhythms during movement and event-related synchronization (ERS) after the cessation of the movement. Neurovascular coupling (NC) was evaluated by the correlation coefficient (cc) between alpha ERD and HbO, and a higher cc value was found on the contralateral hemisphere. These findings demonstrate the potential application of EEG-fNIRS for quantifying NC during FES therapy. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Novel Gesture Recognition Technique based on sEMG Armband and Leap Motion Controller

A Novel Gesture Recognition Technique based on sEMG Armband ...

引用

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Huanqing Xie, Jun Xiao, Yi Yang, Yuzhe Ren, Zhiyuan Zhang, Ruiwen Dong, Yuanzhe Tang, Xi Li, Guanglin Fang, Peng School of Mechanical Engineering Xi'An Jiaotong University Xi'an710049 China China Astronauts Research and Training Center National Key Laboratory of Human Factors Engineering Beijing100094 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

With the rapid development of human-computer interaction technology, it has become very common to use various sensors for gesture recognition to control machines or to complete specified tasks. This study proposes a fusion method that uses Leap Motion for gesture recognition and gForcePro+ to evaluate actual force of hands. A three-dimensional (3-D) virtual gesture recognition platform was designed in Unity. Leap Motion was used to control gestures in the virtual environment, and surface electromyography (sEMG) data was used to distinguish different forces of the same gesture. The success of grasping objects was judged by detecting and analyzing the sEMG signals and Leap Motion signals, combined with the size of the grasping objects. Experimental results showed that Leap Motion and gForcePro+ can be used together to control hand motion. In addition, the proposed system can be used to distinguish the same gesture with different force sizes, which can reflect different forces when the hand grasp objects with the same action. It was proved that the fusion technique of gesture recognition system based on Leap Motion and sEMG is effective for online gesture recognition. © 2022 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Head-Mounted Display based Brain-Computer Interface for Virtual Robotic Arm Operation

Performance Evaluation of Head-Mounted Display based Brain-C...

引用

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Yang, Yuzhe Xie, Jun Xiao, Yi Ren, Zhiyuan Zhang, Huanqing Xiao, Xianzi Dong, Yuanzhe Tang, Xi Li, Guanglin Fang, Peng Xi'An Jiaotong University School of Mechanical Engineering Xi'an710049 China China Astronauts Research and Training Center National Key Laboratory of Human Factors Engineering Beijing100094 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

The steady-state visual evoked potential based brain-computer interface (SSVEP BCI) mostly uses desktop display to present the visual stimulation, and it cannot meet the needs of immersive brain-computer interaction. This study proposed the head-mounted display (HMD) implementation of the SSVEP BCI. And based on the head-mounted virtual reality (VR) glasses device, this study established the BCI system in the VR environment for the operation of the virtual robotic arm. In offline experiments, the system accuracy was 86.66% and the information transfer rate (ITR) was 18.33 bits/min. And in the online experiment, the recognition accuracy was 88.08% and the ITR was 19.26 bits/min. Experimental results verified the effectiveness of the proposed HMD based BCI system in the operation of the virtual robotic arm. © 2022 IEEE.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

EEG Feature in Amputee with Five Projected Fingers: A Comparison Study

EEG Feature in Amputee with Five Projected Fingers: A Compar...

引用

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liang, Wenyuan Qin, Changyu Dong, Yuanzhe Tang, Xi Li, Guanglin Fang, Peng Bi, Sheng National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing100876 China Beijing Machine and Equipment Institute Beijing100039 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

The somatosensory ERP provides a tool to understand the tactile sensation mechanism for the projected finger. A total of four subjects (1 amputee & 3 able-bodied) were recruited. The tactile sensation is evoked via TENS. Through applying the stimulation electrodes on the target fingers, simultaneously the EEG data was recorded. Two major findings are concluded as follows. First, the somatosensory ERP of the early-stage in three groups are similar, and then it could infer that the projected finger may have a similar delivery pathway as the natural finger. Second, since three groups show similar cortical processing characteristics, it could infer that the projected finger evoke similar somatosensory neuro information as the natural finger. This tactile sensation mechanism study shows the uniformity between the natural finger and the natural finger. © 2022 IEEE.

关键词： Artificial limbs

来源：评论

学校读者我要写书评

暂无评论

Finger joint angle estimation based on sEMG signals and deep learning method

Finger joint angle estimation based on sEMG signals and deep...

引用

2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Ma, Chenfei Guo, Weiyu Xu, Lisheng Li, Guanglin College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang110819 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Conventional movement recognition methods are normally based on classification algorithms, which could only provide discrete movement classification rather than natural human body continuous movements. In this paper, we utilized the deep learning methods to estimate eight complicated movements of fingers by extracting the kinematic information based on surface electromyographic (sEMG) signals. Aiming at realizing continuous estimation, we adopted four representative models, AlexNet, Residual neural network (ResNet), Long Short-term Memory network (LSTM) and Gate Recurrent Unit (GRU) in this study. Convolutional kind models (AlexNet and ResNet) are chosen because of their irreplaceable feature extraction ability. And recurrent kind models (LSTM and GRU) are chosen because they are suitable for time-series signal processing. We took 10 degrees of freedom (DoFs) of joint angles from one hand as the target, 12 channels of sEMG as input and trained the models with the stochastic gradient descent and backpropagation. The models were tested on 8 abled subjects. The results indicated that the employed AlexNet turned out to show the best estimation performance and stability than other models. We realized the AlexNet is more suitable for sEMG based continuous movement estimation. © 2021 IEEE.

关键词： Long short-term memory

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：