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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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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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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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Liquid refractive index measurement method based on orthogonal polarization laser self-mixing interference

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Optoelectronics Letters 2025年

作者： ZHAO Yan WANG Haipeng DU Shengzhi TONG Jigang KANG Jun School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In this paper， a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes， caused by comparing an empty cuvette with a liquid-filled cuvette， is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations， the average relative errors are 0.47%， 0.59%， and 2.17%， respectively， with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.

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Weak feedback self-mixing interference fringe slope discrimination method based on deep learning

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Optoelectronics Letters 2025年

作者： ZHAO Yan LIN Maohua DU Shengzhi TONG Jigang LIU Bin HAN Fangfang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise， a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can discriminate the direction of self-mixing fringes accurately and quickly. For experimental SMI signals， the 1D U-Net can be used for discriminant direction after a one-step normalization. Simulation and experimental results show that the proposed method is suitable for SMI signals with noise within the whole weak feedback regime， and can maintain a high discrimination accuracy for signals interfered by 5dB noise. Combined with fringe counting method， accurate and rapid SMI signal displacement reconstruction can be realized.

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Combined Rigid-Flexible Weight-Bearing Exoskeleton

Combined Rigid-Flexible Weight-Bearing Exoskeleton

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IEEE International Conference on Mechatronics and Automation

作者： Gongsheng Yu Lei Sun School of Electrical Engineering and Automation Tianjin Key Laboratory for Control Theory and Complicated Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Most of the research on rigid exoskeletons has focused on how to further improve the efficiency of the exoskeleton to further reduce the wearer's energy consumption, neglecting the wearer's experience. In this paper, we introduce a rigid-flexible exoskeleton based on active-passive control strategy. By switching the control strategy of the exoskeleton at different stages of the gait cycle, the suppleness of the exoskeleton's interaction with the wearer is improved. In addition, an experiment involving three testers validated the effectiveness of the presented rigid-flexible weight-bearing exoskeleton. The results of the experiment showed that the exoskeleton was able to reduce the wearer's metabolic expenditure by 13.1% when exercising with a weight of 30kg.

关键词： Energy consumption Mechatronics Automation Exoskeletons Switches control systems

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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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MAN: Mix-domain attentional single-stage object detection network with multiple attention mechanisms 20

MAN: Mix-domain attentional single-stage object detection ne...

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

作者： Sun, Lei Han, Yun Gong, Xinxiang Jin, Shuaiyu Tianjin University of Technology School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9798350320831

Deep convolutional neural networks (CNN) have achieved remarkable progress in the image processing field during the past decade. However, most existing CNN-based object detection algorithms mainly delve into network-deepening, overlooking the exploration of the inherent relationship amongst the features. To address this issue, an end-to-end Mix-domain Attention Network (MAN) is proposed. First, the backbone and head of MAN adopt the structure of YOLOv5 (YOLOv5 is a family of object detection architectures and models pre-trained on the COCO dataset by Ultralytics). Then, we embed two key modules in the backbone and head: non-local blocks and stereoscopic attention group (SAG). Non-local blocks are different from CNN's local operations in that they can capture long-distance spatial contextual information (Such as the pixel-to-pixel relationship in images, and the pixel-to-pixel relationship between different frames in video streams) and combined with the residual structure, this allows the feature dimensions of the input and output to be consistent. Subsequently, the features extracted from the non-local blocks will be further passed through a trainable stereoscopic attention group to calculate the correlation of channel-to-channel, pixel-to-pixel, and update the output features. The Non-local operation is the self-attention mechanism and the SAG operation is the channel and spatial attention mechanism. Our innovation is the combination of these two operations to form a Mix-domain attention mechanism. The Non-local increases the connection between the pixel points. The SAG allows the model to focus more on the places that need attention. The combination of these two modules enhances recognition accuracy. The experimental results demonstrate that the two versions of a Mixdomain Attention Network achieves the following results on Microsoft Common Objects in Context (MS-COCO) with an input size of 640∗640: AP(Average Precision)=38.2%, 44.4%,AP05 (IOU >0.5 Average Precisi

关键词： Pixels

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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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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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