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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Dong, Enzeng Jing, Cunlei Zhang, Zufeng Tianjin University of Technology Tianjin Key Laboratory for Control Theory Applications in Complicated Systems Tianjin China

ISBN: (纸本)9781728164151

This paper proposes an improved pedestrian detection algorithm, the Haar-like feature and Adaboost cascade classifier Histogram of Oriented Gradient (HOG) feature are utilizing to construct hybrid features library, then the weak classifiers in Adaboost are trained by using the combined hybrid features. And the strong classifiers are built based on the weak classifiers. In the proposed algorithm, For improving the detection speed, the HOG feature is rapidly calculated by integral histogram instead of gradient histogram features, and the experimental results showed that the method proposed improved the detection performance and detection accuracy of pedestrians. © 2020 IEEE.

关键词： Adaptive boosting

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

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

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IEEE International Conference on Mechatronics and Automation

作者： Fanhang Yang 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 Xiqing District Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

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.

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Fault Location Based on Energy Relative Entropy 17

Fault Location Based on Energy Relative Entropy

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Zhao, Hui Wang, Lei Wang, Hongjun Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9781728164151

When wavelet transform and empirical mode decomposition(EMD) transform decompose the transient signal of line fault, the problem of missing some fault information will occur, which will affect the accuracy of fault location of distribution line. A new fault location method based on the relative entropy of variational mode decomposition (VMD) transformation energy is ***, the fault zero sequence current signal collected by the detection device is decomposed by VMD transformation, and the transient energy of different components of the signal is calculated. Then, according to the characteristics of energy relative entropy difference between the sections, the fault section of the line is determined. The experimental results show that the method is not affected by the distance of the fault, the initial phase angle of the fault and the transition resistance, and it can accurately locate the fault section of the line. © 2020 IEEE.

关键词： Variational mode decomposition

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Study on 6D Pose Estimation System of Occlusion Targets for the Spherical Amphibious Robot based on Neural Network 20

Study on 6D Pose Estimation System of Occlusion Targets for ...

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

作者： Du, Chaofeng Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory BinshuiXidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China School of Life Science Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

The amphibious robot needs to accurately estimate the 6D pose of the target in tasks such as target tracking, docking with the recovery module, and target grasping. The current research on target 6D pose estimation is mainly applied to unoccluded targets, but when the target is occluded, the target's pose cannot be accurately identified. Compared with other algorithms, the PVNet algorithm shows better robustness when target is occluded, but the accuracy is still low. To improve the accuracy of the PVNet algorithm, this paper adds the confidence score prediction of the prediction vector at the last layer of the PVNet network, and designs a vector confidence score loss function to train the network. Before generating the hypothetical keypoints, the pixels whose confidence score is lower than the set threshold are screened out, so that the generated hypothetical 2D keypoints are closer to the true 2D keypoints. Finally, the method in this paper is compared with the Tekin, PoseCNN, Oberweger and Pvnet algorithm, and demonstrate the superiority of the proposed method. © 2023 IEEE.

关键词： Semantics

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Discrete sliding mode repetitive control based on parabola approach law 17

Discrete sliding mode repetitive control based on parabola a...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Jia, Chao Gao, Xuewei Wei, Jiaqi He, Haocheng Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjiin300384 China

ISBN: (纸本)9781728164151

According to the characteristics of parabola function, a new discrete-time sliding mode repetitive control method based on parabola approach law is proposed for the periodic trajectory tracking of uncertain discrete-time systems in modern industrial production. The discrete sliding mode controller based on parabola approach law can effectively suppress chattering, and the discrete sliding mode repetitive controller based on ideal switching dynamics can solve the problem of periodic disturbance. The convergence characteristics of parabola approach law are analyzed, including absolute convergence layer, monotone convergence layer and steady-state error band, which are used to describe the dynamic performance of system error under different parameters. The simulation results verify the effectiveness of the proposed control method. © 2020 IEEE.

关键词： controllers

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Rotation forest based on multimodal genetic algorithm

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Journal of Central South University 2021年第6期28卷 1747-1764页

作者： XU Zhe NI Wei-chen JI Yue-hui School of Electrical and Electronic Engineering Tianjin University of TechnologyTianjin 300384China Academic Affairs Office Tianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory for Control Theory&Applications in Complicated Industry Systems Tianjin 300384China

In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature ***,it is a common practice to rotate the feature space ***,a large number of trees are required to ensure the performance of the ensemble *** random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this *** is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature ***,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization *** classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification *** results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.

关键词： ensemble learning decision tree multimodal optimization genetic algorithm

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Design of control System for Lower Limb Rehabilitation Robot on the Healthy Side sEMG Signal 20

Design of Control System for Lower Limb Rehabilitation Robot...

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

作者： Yu, Shiqin Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

With the number of stroke patients increasing year by year, rehabilitation exoskeleton robot has been paid more and more attention. For the rehabilitation exoskeleton robot, human-computer interaction ability is an important index, which affects the effect of rehabilitation therapy to a great extent. Surface Electromyography (sEMG) signals are the combined effect of sEMG signals and electrical activities on nerve stem on the skin surface, which can reflect neuromuscular activities in advance and can be used to predict movement intention by sEMG signals. Therefore, this article proposes to use sEMG signals to monitor the motion information of the healthy leg in real-time, extract the characteristics of the electromyography signals, use the sEMG of the healthy leg as the control signal, reflect the motion patterns reflected by the sEMG collected from the healthy side, and then use the motion intention recognition method of Long Short Term Memory(LSTM) neural network to identify the motion intention of the prosthetic limb by identifying the motion patterns of the swing phase of the healthy side. The results indicate that the predicted maximum Root Mean Squared Error(RMSE) is 5.3729, which proves the feasibility of using LSTM model for motion intention recognition and contributes to the real-time and accuracy of lower limb exoskeleton rehabilitation. © 2023 IEEE.

关键词： Long short-term memory

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment 20

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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

作者： Tian, Wei Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process. © 2023 IEEE.

关键词： Reinforcement learning

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Garlic Scale Teeth Image Recognition Based on Sobel algorithm 17

Garlic Scale Teeth Image Recognition Based on Sobel algorith...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Wang, Yunliang Wang, Liutao Wu, Yanjuan Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications I-n Complicated Systems Tianjin300384 China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164151

In order to solve the problem of intelligent seeding garlic which needs single seed and scale bud upward, this paper proposes a new method for garlic seed shape recognition based on Sobel edge recognition and sliding window algorithm to mark the position coordinate of the garlic seed bud. The method uses TMS320F28335 DSP as the main control chip, and compiles and debug the algorithm program on the CCS software simulation platform. The system uses OV7670 camera with FIFO to complete the data acquisition of garlic seed image. The main chip of DSP carries out the edge recognition of the garlic seed and the position coordinate recognition of the garlic seed bud, moreover, the position coordinate of the garlic bud is recorded also. Through the establishment of Matlab.software interface experimental platform, 500 different images of garlic seeds are used as the experimental verification examples and the correct rate reached 96%. The proposed method provides an efficient method for the automatic and intelligent planting of garlic to identify the position of garlic seed bud, which is convenient for the vertical planting of garlic seed. © 2020 IEEE.

关键词： MATlab

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks control systems Iterative algorithms Intelligent systems

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