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

作者： Zhang, Hao 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

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. © 2023 IEEE.

关键词： Long short-term memory

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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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Short-term Load Prediction Based on Whale Optimized WTCN-BiGRU Hybrid Neural Network 18th

Short-term Load Prediction Based on Whale Optimized WTCN-Bi...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Gao, Qiang Chen, Jiaqi Ji, Yuehui Liu, Junjie Xu, Liang Zhao, Xuewen The School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems Tianjin300384 China

ISBN: (纸本)9789811962028

The accuracy of short-term power load prediction provides according to power department companies to make reasonable production scheduling plans and avoid resource waste. In this paper, a Temporal Convolutional Network (TCN) -BiGRU-Attention short period power load forecasting means on the strength of the Whale Optimization Algorithm (WOA) is proposed. TCN is improved by the Morlet mother wavelet basis function. Train network hyperparameters by WOA. The WOA-WTCN- BiGRU-Attention hybrid neural network is formed. Firstly, the high-dimensional features of power load are extracted by the WTCN network. Secondly, the BiGRU network is used to learn the dynamic change rules of high-dimensional characteristics. Ultimately, the Attention mechanism is important to endow homologous proportion to characteristics through mapping weighting and learning parameter matrices to reduce information loss. The proposed method is compared with the prediction results of WTCN-LSTM, WTCN-BiGRU, and TCN-BiGRU-Attention models by taking an open data set from an Australian region as an example. The results show that the proposed method has a more accurate prediction effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Open Data

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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 availab.e 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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Method and system for testing the working characteristics of vehicle fuel injector

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Recent Patents on Mechanical Engineering 2021年第1期14卷 63-74页

作者： Liu, Bin Li, Yanhao Han, Fangfang Qiao, Qian Li, Jianwen Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin300384 China

Background: The vehicle fuel injector directly affects the power performance, emission characteristics and economic efficiency of the engine. At present, several testing standards are stipu-lated on accurate measurement of the working characteristics of fuel injectors. Objective: The purpose of this study is to provide an efficient patent about the method and system for testing the working characteristics of a vehicle fuel injector. Methods: Two key techniques are significant to establish a standard test environment. The patent about the pressure control approach and the injector driving method was proposed. The adaptive fuzzy PID approach was applied to control pressure. An adaptive multi-functional driver was designed and developed for both the voltage-driven and current-driven port fuel injectors. Results: The experiments on the two key technologies were conducted. During the dynamic flow test, the settling time using the system was 1ms. For the static flow test, the settling time was 1 s. The maximum changing rate of pressure fluctuation was 1%. Then, the system was verified by test on the flow characteristics and dynamic response of the voltage-driven and current-driven injectors. The linear deviation of each dynamic flow curve was between ± 3% to ± 6%. Furthermore, the deviation of the dynamic flow curves from the repeated test was small. The maximum deviation of the static flow results was less than 4% and the dynamic response of the injector varied with the change of the pressure. Conclusion: The system meets the requirements of the related standards. The experimental results prove that the system is stable and reliable. © 2021 Bentham Science Publishers.

关键词： Dynamic response

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Research on Optical Power Attenuation Compensation Algorithm of φ-OTDR System 20

Research on Optical Power Attenuation Compensation Algorithm...

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

作者： Liu, Yinhao Zhang, Baofeng Zhang, Zhili TianJin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems No. 391 Binshuixi Road Tianjin300384 China Intelligent Manufacturing Institute TianJin Sino-German University of Applied Sciences No. 2 Yashen Road Tianjin300350 China

ISBN: (纸本)9798350320831

The φ-OTDR technology is an optical fibre sensing method that enables real-time monitoring of fibre vibrations by using optical time domain reflection technology. However, during the process of transmitting signals through optical fibres, the issue of optical power attenuation arises, which ultimately results in a reduction of the long-distance signal recognition ability of the φ-OTDR system. This paper presents an innovative optical power attenuation compensation technique that utilizes the φ-OTDR system to address the challenge of signal attenuation in optical fibre. The proposed solution enhances the long-distance recognition ability of weak signals. To solve the attenuation issue, the polynomial fitting method is used. A comparison between the system's performance with and without a Raman amplifier is presented. To locate signals, mean square error and deviation from the average are utilized, while signal strength is determined by calculating the signal-to-noise ratio after fitting. This approach emphasizes precision in detecting relevant signals and optimizing system performance. The results show that the signal recognition ability of the φ-OTDR system improves significantly with the implementation of this compensation algorithm. © 2023 IEEE.

关键词： Optical fibers

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Numerical study on aero-optical imaging deviations of vehicles at different altitudes

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Optoelectronics Letters 2022年第2期18卷 97-102页

作者： XU Liang ZHANG Ziye WANG Tao WU Jianhui CHEN Xi Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and AutomationTianjin University of TechnologyTianjin 300384China School of Intelligent Systems Engineering Sun Yat-sen UniversityGuangzhou 510006China China Academy of Launch Vehicle Technology Beijing 100076China

When the vehicle is flying in the atmosphere at high speed, the optical head and the atmosphere will have severe friction, thus forming a complex flow field, which makes the target image shift in the optical imaging system. The influence of altitude on aero-optical imaging deviation is studied in this paper. The geometric modeling and mesh generation of a typical blunt nosed high-speed vehicle were carried out, and the three-dimensional(3 D) flow field density was obtained by a large amount of computational fluid dynamic calculation. In order to complete the optical calculation, the backward ray tracing method and the backward ray tracing stop criterion were used. The results show that as the height increases, the imaging deviation decreases gradually, and the imaging deviation slope increases and tends to be flat and close to zero.

关键词： calculation deviation backward

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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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