检索结果-内蒙古大学图书馆

IEEE International Conference on Mechatronics and Automation

作者： Tianqi Xia Enzeng Dong Sen Yang Jigang Tong Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

In this paper, facing the aerial transportation task with a large volume cargo, the system of quadrotor with a cable-suspended rigid payload is investigated. The dynamic model of proposed system is built with Lagrange d’Alembert principle. The dynamic model is linearized to design the feedback control method. Finally, two groups of numerical simulation with proposed feedback controller are designed to demonstrate the effectiveness of system model and the results of simulation illustrate satisfactory performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fatigue Driving Detection of EEG Signals by LSTM Deep Neural Network with LPSD and DE

Fatigue Driving Detection of EEG Signals by LSTM Deep Neural...

引用

IEEE International Conference on Mechatronics and Automation

作者： Hao Zhang Enzeng Dong Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

Fatigue driving detection has always been an urgent social problem to be solved, and many complex factors increase the difficulty of detection. Nowadays, more scholars choose to use multi-channel EEG signals for fatigue detection, which is accompanied by the development of deep learning networks. Here, we propose the deep Long Short Term Memory (LSTM) neural network with the Differential Entropy (DE) and Logarithm Power Spectrum Density (LPSD). We first divide the EEG signal into 8-s Hanning windows with 50% overlap, and then use the Short Time Fourier Transform(STFT) on the extracted time window to obtain 50 frequency sub-bands. Finally, LPSD and DE features are extracted from the frequency offspring obtained from the STFT and input into the deep LSTM neural network. The network can choose to learn more task related information by adding soft attention and extract effective time information from EEG signals,thereby improving the model’s performance in detecting driving fatigue. In addition, we also collected EEG signals from 8 subjects in both awake and fatigued states,and used the PERCLOS index to judge the degree of fatigue. Using the collected dataset, we compared the effectiveness of the deep LSTM neural network with five other methods. The results indicate that the deep LSTM neural network has a better classification accuracy of 93.12%. It proves that the network has good performance in fatigue detection of EEG signals.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of EEG signals based on CNN-Transformer model

Classification of EEG signals based on CNN-Transformer model

引用

IEEE International Conference on Mechatronics and Automation

作者： Jianwei Liu Enzeng Dong Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), and P300 event-related potentials are a few of the more commonly used *** Transformer(ViT) is a new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).In this study, we propose a hybrid CNN-Transformer based model that uses CNN to convolve EEG signals in time and space, followed by ViT for global processing, and finally optimizes the model using 10-run $\times 10$-fold cross-validation and validates it on a publicly available dataset of 29 subjects. Final accuracies of 87.23% and 90.79% were achieved on the MI and MA tasks, respectively. Compared to other literature, the model achieved higher classification accuracies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multilevel Decomposition Time Aggregation Graph Convolution Networks for Skeleton-Based Action Recognition

Multilevel Decomposition Time Aggregation Graph Convolution ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Wenhua Li Enzeng Dong Jigang Tong Sen Yang Zufeng Zhang Wenyu Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China College of Artificial Intelligence Nankai University 94 Weijin Road Tianjin China

Skeleton-based human action recognition has become a popular topic among researchers. This is because using skeletal data provides a robust solution to problems encountered in complex environments, such as changes in perspective and background interference. The robustness of skeletal data enables recognition methods to focus on more specific and relevant features. We propose a model called multilevel decomposition time aggregation graph convolution network (MDT-GCN), which utilizes a multilevel graph convolution kernel to capture higher-order spatial dependence relationships between joints. This is achieved by decomposing a human topology graph into smaller graphs, each of which has its own graph convolution kernel. To further enhance the performance of our model, we employ a two-flow framework and channel topology refinement strategy. Our experiments on the NTU-RGB+D60 and NTU-RGB+D120 datasets demonstrate that our MDT-GCN network outperforms the previous algorithm and significantly improves the accuracy of action recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Motor Imagery and Mental Arithmetic Classification Based on Transformer Deep Learning Network

Motor Imagery and Mental Arithmetic Classification Based on ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yuanyuan Ye Jigang Tong Sen Yang Yinghui Chang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Non-invasive electroencephalography (EEG) signals find widespread application in brain-computer interfaces (BCI), with paradigms such as motor imagery (MI), mental arithmetic (MA) and emotion recognition being particularly common. This study aims to explore feature extraction and classification methods for MI and MA tasks. We employed convolutional neural networks (CNN) combined with Transformer networks and Fasternet Block to extract features. Through our reaearch, we obtained features for MI and MA tasks, and used softmax classification for binary classification of these tasks. We conducted on a publicly available dataset consisting of data from 29 subjects. The experimental results demonstrate that our method achieved high classification accuracy in MI and MA tasks. The final accuracy rates for MI and MA tasks were 88.67% and 91.23%, respectively.

关键词： Accuracy Mechatronics Feature extraction Brain modeling Transformers Motors Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

EEG Signal Driving Fatigue Detection based on Differential Entropy

EEG Signal Driving Fatigue Detection based on Differential E...

引用

IEEE International Conference on Mechatronics and Automation

作者： Danyang Wang Jigang Tong Sen Yang Yinghui Chang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

With the gradual increase of vehicle penetration rate, driving safety has become a problem that people pay attention to, and traffic accidents caused by fatigue are mostly, therefore, it is meaningful to devote ourselves to research on the method of detecting driving fatigue so as to achieve safety warning. Based on this, in this paper, we collect the EEG signals of the experimenter through driving simulation, then extract the features through differential entropy, classify the features with GBDT, and finally determine the fatigue level of the subject with the classification results. We use our method to analyse the difference with sample entropy and fuzzy entropy at the feature level, and with KNN and SVM at the classification level. The comparison shows that our method has significant accuracy in EEG-based fatigue *** can be used accurately in extraction methods through its properties, and differential entropy-based GBDT methods may be useful in detecting driver fatigue.

关键词： Support vector machines Mechatronics Nearest neighbor methods Fatigue Feature extraction Entropy Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Computation and analysis of aero-optic imaging deviation of a blunt nosed aircraft with Mach number 0.5—3

引用

Optoelectronics Letters 2023年第1期19卷 55-59页

作者： XU Liang ZHAO Shiwei XUE Wei WANG Tao Tianjin Key Laboratory for Control Theory&Applications in Complicated Systems School of Electrical Engineering and AutomationTianjin University of TechnologyTianjin300384China China Academy of Aerospace Science and Innovation Beijing100083China School of Intelligent Engineering Sun Yat-sen UniversityGuangzhou510275China

Aero-optic imaging is a kind of optical effect,which describes the imaging deviation on the imaging *** this paper,the effect of the change of Mach number of blunt aircraft on the aero-optic imaging deviation is *** imaging deviations of Mach number 0.5—3 are analyzed *** results show that with the increase of Mach number,imaging deviation increases gradually,and the increase rate is gradually *** deviation slope decreases gradually with the increase of Mach number,and gradually tends to be zero,suggesting that imaging deviation is not sensitive to the change of the larger Mach *** other words,the Mach number of smaller changes can lead to larger imaging *** the Mach number of the aircraft increases,the slope of the imaging offset tends to be closer and closer to *** the Mach number of the aircraft increases to a certain extent,the change of the imaging offset will not have much ***,in order to reduce the impact of flight speed on imaging migration,the aircraft should fly at a higher Mach number.

关键词： deviation closer number

来源：评论

学校读者我要写书评

暂无评论

Differential Enhancement and Commonality Fusion for RGBT Tracking

Differential Enhancement and Commonality Fusion for RGBT Tra...

引用

IEEE International Conference on Mechatronics and Automation

作者： Jianrong Yang Enzeng Dong Jigang Tong Sen Yang Zufeng Zhang Wenyu Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China College of Artificial Intelligence Nankai University 94 Weijin Road Tianjin China

Target tracking is a fundamental task and an important part in the field of computer vision. The image information contained in the visible (RGB) modality and thermal infrared (T) modality has differences. RGBT tracking utilizes this difference to fuse the information of the two modalities, fully combining the uniqueness and complementarity of the information of the two modalities. How to maximize the uniqueness of individual modalities in RGBT tracking and how to effectively combine the complementarity of the two modalities is the key issue in RGBT tracking. For the combination of the uniqueness and complementarity of the two modalities, a novel Differential Enhancement and Commonality Fusion Network (DECFNet) is designed in this paper, in which the main components include a feature enhancement module for the visible modality and a feature enhancement module for the thermal infrared modality, as well as a fusion module that effectively fuses the information of both. The results on three RGB T tracking benchmark datasets demonstrate the effectiveness of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Network Based Object Detection Algorithm With optimized Detection Head for Small Targets

Deep Neural Network Based Object Detection Algorithm With op...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yongchang Zhu Enzeng Dong Jigang Tong Sen Yang Zufeng Zhang Wenyu Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China College of Artificial Intelligence Nankai University 94 Weijin Road Tianjin China

Target detection is a significant research direction in the realm of computer vision, aimed at analyzing and localizing targets in input images to obtain their categories and locations. However, this task is faced with several challenges in complex scenarios, such as large variations in target scales, similarity to the background, dense number, and overlapping with each other, making it difficult for the target detection model to extract different scales and discriminative target features adaptively, leading to low detection accuracy and weak generalization ability. To tackle the aforementioned problems, this study proposes a novel approach by optimizing the detection head of the model, invoking decoupled detection heads, dividing the classification and localization tasks, and optimizing for small targets. This approach effectively reduces the false detection rate and the missed detection rate, enabling the network to better discriminate and recognize distant objects as well as objects of relatively small size. Moreover, the proposed approach outperforms the yolov7 model by 0.8% in the COCO dataset, not only improving the accuracy of detecting distant and small targets but also exhibiting better performance in conventional target detection. Additionally, this approach is capable of handling small targets, overlapping targets, or relatively dense targets, significantly expanding its practical use range.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dual-beam photothermal material trace detection method based on self-mixing interference

引用

Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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：