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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Xia, Tianqi Dong, Enzeng Yang, Sen Tong, Jigang Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

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. © 2023 IEEE.

关键词： Dynamic models

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Multilevel Decomposition Time Aggregation Graph Convolution Networks for Skeleton-Based Action Recognition 20

Multilevel Decomposition Time Aggregation Graph Convolution ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

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

ISBN: (纸本)9798350320831

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. © 2023 IEEE.

关键词： Convolution

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Classification of EEG signals based on CNN-Transformer model 20

Classification of EEG signals based on CNN-Transformer model

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Liu, Jianwei Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

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 × 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. © 2023 IEEE.

关键词： Brain computer interface

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A Single-Frame Face Anti-Spoofing Algorithm With Circular CDC and Multi-Scale Spatial Attention 20

A Single-Frame Face Anti-Spoofing Algorithm With Circular CD...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

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

ISBN: (纸本)9798350320831

Face anti-spoofing is to prevent face images with attack properties from entering face recognition and causing confusion or spoofing of the face recognition function. Since most face presentation attacks do not possess true deep information, some recent works use this information as supervisory signal. In this paper, we continue such a pattern by proposing a circular CDC (Central Difference Convolution) with larger sensory field and contextual semantic awareness, and use it in a backbone network for improving the network model's ability to characterize texture features and multi-scale information. Combined with the spatial attention method concat multiple scale feature maps are used to fuse the feature map information from multiple scales. We conduct a series of experiments on OULU-NPU and demonstrate that our network accuracy is better than the original algorithm, especially in terms of generalizability across devices and different environments. © 2023 IEEE.

关键词： Face recognition

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Machine Learning Classification of Riemannian Tangent Spaces Based on MI-BCI 20

Machine Learning Classification of Riemannian Tangent Spaces...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhao, Chunli Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

Motor imagery brain-computer interfaces (BCIs) have been extensively used in stroke rehabilitation, particularly in China where stroke incidence is high. The application of BCI to control prostheses and robotic arms has shown promising results in improving the quality of life for stroke patients. However, a major challenge in this field is improving the accuracy of motor imagery (MI) classification. Several previous studies have reported that stroke patients exhibit atypical patterns of even-trelated desynchronization (ERD) and event-related synchronization (ERS) activation during motor imagery (MI) tasks. It has been suggested that the brain regions affected by stroke may show reduced excitability and relative insensitivity to external interference compared to those of healthy individuals. Therefore, this study proposes that the classification accuracy of stroke patients in the same MI task would be higher compared to that of healthy volunteers. In this study, we employed a feature extraction algorithm that combines the Filter Bank Common Spatial Pattern (FBCSP) and Riemann geometric mean distance methods to compare the classification accuracy of five different classifiers, with the objective of identifying the most suitable classifier for our analysis. The obtained results revealed that the K-Nearest Neighbors (KNN) classifier yielded the highest accuracy, reaching 87.5% for stroke patients and 85% for healthy volunteers, respectively. In addition to accuracy, we also utilized Kappa values as evaluation metrics, which yielded Kappa results of 0.619 and 0.613 for stroke patients and healthy volunteers, respectively. © 2023 IEEE.

关键词： Brain computer interface

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A multi-directional controllable multi-scroll conservative chaos generator:Modelling,analysis,and FPGA implementation

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Chinese Physics B 2021年第2期30卷 232-239页

作者： En-Zeng Dong Rong-Hao Li Sheng-Zhi Du Tianjin Key Laboratory for Control Theory&Applications in Complicated Systems Tianjin University of TechnologyTianjin 300384China Department of Electrical Engineering Tshwane University of TechnologyPretoria 0001South Africa

Combing with the generalized Hamiltonian system theory,by introducing a special form of sinusoidal function,a class of n-dimensional(n=1,2,3)controllable multi-scroll conservative chaos with complicated dynamics is *** dynamics characteristics including bifurcation behavior and coexistence of the system are analyzed in detail,the latter reveals abundant coexisting ***,the proposed system passes the NIST tests and has been implemented physically by *** to the multi-scroll dissipative chaos,the experimental portraits of the proposed system show better ergodicity,which have potential application value in secure communication and image encryption.

关键词： multi-directional controllable multi-scroll conservative chaos coexisting flows field programmable gate array(FPGA)

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A Method for Allocating Hybrid Teaching Credit Hours Using Hierarchical Analysis

A Method for Allocating Hybrid Teaching Credit Hours Using H...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Xiangang Meng Xiaojing Li Liang Xu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Hybrid teaching combines traditional teaching and flipped classroom, which is a new teaching mode with the advantages of both modes. In hybrid teaching mode, reasonable allocation of credit hours between traditional teaching and flipped classroom is an important issue for the teaching work. This paper first proposes a method of using hierarchical analysis to allocate credit hours for hybrid teaching of Digital Electronic Technology courses. Firstly, we divided hybrid teaching into stage and content layers, and determined the various elements of stage and content layers. Secondly, the requirements of the Analytic Hierarchy Process were applied to organize these two levels, and pairwise importance comparisons were made for each element within the level. Index judgment matrices were established for the stage level and content level, respectively. Then, the calculation steps of the Analytic Hierarchy Process were designed, and matrix operations were used to obtain the normalized weights of each element in the stage and content layers of traditional teaching and flipped classrooms. This weight was used to determine the class hours to be allocated for each stage and content. Finally, the feasibility and effectiveness of the method proposed in this paper were verified using the example of hybrid teaching hour allocation in Chapter 4 of the Digital Electronic Technology course at Tianjin University of Technology.

关键词： Electronic learning Logic circuits Education Analytic hierarchy process systems simulation Resource management Online services Indexes

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EBD-YOLO：a lightweight auxiliary driving detection algorithm based on YOLOv5

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Optoelectronics Letters 2025年

作者： SUN Lianshuai SHI Tao DING Yao LI Song Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems， School of Electrical Engineer-ing and Automation， Tianjin University of Technology College of Electrical Engineering， North China University of Science and Technology

A lightweight road-assistant detection algorithm， EBD-YOLO， based on YOLOv5s is proposed to address the problems of high model complexity， computation cost， and difficulty in deployment on resource-limited embedded terminals in existing assisted driving detection algorithms. First， lightweight Transformer model EfficientViT was used as the backbone feature extraction network of YOLOv5s model to reduce network parameters and calculation costs. Secondly， a Focal-GIoU Loss function is proposed for bounding box regression to accelerate model convergence and reduce loss. Thirdly， the feature pyramid structure is improved to a weighted bi-directional feature pyramid network (BiFPN) to enhance localization and semantic features. Then， a dynamic head framework is added to unify the attention mechanism with the object detection head to improve its performance. Finally， a Soft-CIoU＿NMS algorithm is proposed in the post-processing stage to enhance occluded targets' localization and detection ability and reduce the missed detection rate. We conducted experiments on the KITTI and BDD100K datasets for autonomous driving， and the results showed that the EBD-YOLO model reduced in size by 38.4% and 37.2%， respectively. In comparison， the computational cost was reduced by 48.1%. As measured by mAP@0.5， the detection accuracy improved by 0.5% and 5.8%， respectively， and mAP@0.5:0.95 improved by 2.8% and 7%， respectively. These improvements satisfied the requirements for deployment on embedded terminals in cars.

关键词：

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Point Density-aware Channel-wise Transformer for LiDAR 3D Object Detection 20

Point Density-aware Channel-wise Transformer for LiDAR 3D Ob...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Fanhang Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Xiqing District Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

We present a two-stage 3D object detection framework from point clouds, named Point Density-aware Channel-wise Transformer (PD-CT3D), which investigate the property of point density. This architecture uses 3D sparse CNN to effectively generate high-quality proposals at the first stage, then integrates the inherent property of point density and 3D CNN-based voxel features by density-aware proposal grid pooling. Specifically, each generated proposal from the first stage is divided into grids to aggregate corresponding voxel-wise features and raw point-based features for encoding the representative features from the whole scene. Subsequently, a channel-wise encoder-decoder transformer is adopted to extract the encoded density-aware features and decode them into a global representation for final refinement. Experiments on the widely used KITTI dataset show that the PD-CT3D achieves competitive performance among state-of-the-art methods. © 2023 IEEE.

关键词： Object detection

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Backstepping Sliding Mode control of Underactuated Unmanned Surface Vehicle Based on Finite Time Observer

Backstepping Sliding Mode Control of Underactuated Unmanned ...

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IEEE International Conference on Mechatronics and Automation

作者： Jianniang Qiu Yanjuan Wu Qing Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Addressing the challenge of the unmanned ship's susceptibility to external environmental disturbances and the impact of model parameter uncertainty on its control system is paramount. Herein, leveraging the motion characteristics of the unmanned vessel, we establish both its kinematic and dynamic models for a three-degree-of-freedom underactuated system. A finite-time observer-based backstepping sliding mode control approach is crafted to anticipate model uncertainties and environmental perturbations. Incorporating the LOS (line-of-sight) guidance law into the controller design aims to mitigate controller input chatter and enhance convergence speed and tracking precision. Furthermore, a first-order filter signal is employed as a substitute for the virtual control law, thereby circumventing differential explosions triggered by its derivative. Validation of the control algorithm's feasibility and practicality is achieved through rigorous simulation experiments.

关键词： Uncertain systems Backstepping Uncertainty Accuracy Tracking Explosions Stability analysis

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