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16th IEEE International Conference on Mechatronics and Automation, ICMA 2019

作者： Zhou, Jinlai Song, Yang Shi, Xue Xu, Chenguang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Periodontitis Department Tianjin Stomatological Hospital Tianjin300041 China

ISBN: (纸本)9781728116983

As a connective tissue connecting teeth and alveolar bone, periodontal ligament (PDL) has the mechanical properties of nonlinearity, viscoelasticity, anisotropy. In this paper, the pig mandibular central incisors were taken as subjects, a loading machine(CARE-MI00) was used to perform compression tests and the PDL stress-relaxation characteristics under different strain conditions were obtained. The stress-time curves and the relaxation modulus curves were obtained. The three-parameters Zener model and five-parameter generalized Maxwell model proposed by the author were used to fit the data. The experimental results show that the elastic modulus of PDL decreases gradually in the apex(ranges from 2.64 to 3.06 MPa), cervical margin(ranges from 1.44 to 1.76 MPa) and midroot (ranges from 1.03 to 1. 06MPa). Zener model has a relatively high fitting degree, and the squared correction coefficient is about 0.98, however, the adjusted R square of the five-parameter model was above 0.99. Compared with the Zener model, the five-parameter model can better reflect the sudden drop of relaxation modulus at the initial stage, which is more suitable for describing stress-relaxation in viscoelastic behavior of PDL. The advantage of this paper is to compare the descriptive ability of three-parameter and five-parameter generalized Maxwell equation to the mechanical properties of PDL and to provide an effective procedure for the preparation of PDL specimens. © 2019 IEEE.

关键词： Elastic moduli

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Augmented Reality and Quick Response Code Technology in engineering Drawing Course

Augmented Reality and Quick Response Code Technology in Engi...

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IEEE International Conference on Mechatronics and Automation

作者： Xiaofei Wang Chenfei Chen Zhaotong Li Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Augmented reality (AR) technology is a cutting technology. It can enhance the perception of reality and provide more information of the real world. AR technology is used in many fields, like medical, industry, and education. In this paper, AR is introduced into engineering drawing course. engineering drawing course is difficult to learn as it is a knowledge that students rarely contact with. To have a close contact with the models and the views, a convenient AR application (AR app) is proposed in the paper. With the help of the AR app, virtual models and real views are combined well. The models can be moved and rotated by touching the screen. Some videos of model assembly and disassembly can be obtained when scanning the quick response code (QR code). Thus, it is convenient for students to use mobile phone to study engineering drawing effectively. The interactive teaching environment enhances students' spatial thinking and cultivates their sense of innovation.

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Modulating motor cortical oscillation with coordinated reset multifocal transcranial magnetic stimulation

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Journal of neurophysiology 2023年第5期129卷 1061-1071页

作者： Kai-Hsiang Stanley Chen Tzu-Kang Hung Rupesh Kumar Chikara Yu-Ting Kuo Yi-Ping Liu Yan-Siou Dong Yih-Ru Wu Li-Wei Ko Ying-Zu Huang Robert Chen Department of Neurology National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan. Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan. Jane and John Justin Neurosciences Center Cook Children's Health Care System Fort Worth Texas United States. Department of Bioengineering University of Texas at Arlington Arlington Texas United States. Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan. Neuroscience Research Center and Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Cognitive Neuroscience College of Health Science and Technology Taoyuan Taiwan. Krembil Research Institute University Health Network Toronto Ontario Canada. Division of Neurology Department of Medicine University of Toronto Toronto Ontario Canada. Edmond J. Safra Program in Parkinson's Disease University Health Network Toronto Ontario Canada.

According to the theory of coordinated reset (CR) stimulation, multifocal bursts of stimuli delivered in a random order with a specific interval may reduce the resonance power of the oscillatory generator in the epicenter. We develop a noninvasive coordinated multifocal burst stimulation (COMBS) with three repetitive transcranial stimulation machines based on CR theory to modulate the target frequency in the primary motor cortex and to assess its effect on motor cortical excitability in separate experiments. Electroencephalography and electromyography were recorded in 16 healthy participants during a finger-tapping task, both before and after the intervention. The resting oscillatory power at the targeted frequency was not changed by COMBS. α-Band power was increased in both preparation and movement stages and the low β-band power was increased in the movement stage of the finger tapping task. The extent of low β-band event-related desynchronization was reduced by COMBS. There were no changes in reaction time, but there was a trend for a reduced error rate after COMBS. In another 14 healthy participants, there were no significant changes in cortical excitability before and after COMBS measured by rest motor threshold, short interval intracortical inhibition, short interval intracortical facilitation, and cortical silent period. The result indicates that COMBS may modify the cortical oscillatory power and its perturbation within specific movement stage. This is the first study, to our knowledge, to apply coordinated reset (CR) neuromodulation to the motor cortex with three repetitive transcranial magnetic stimulation (rTMS) stimulators to assess its effect on cortical oscillation. The results revealed enhancement of α-band power specifically in preparation and movement stages and low β-band power in the movement stage of a motor task. It postulated that CR stimulation may modify the motor cortical oscillation in the specific movement stages.

关键词： coordinated reset event-related desynchronization low beta oscillation motor cortical excitability repetitive transcranial magnetic stimulation (rTMS)

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Support Vector Regression Modeling based Data-Driven Iterative Learning control for Czochralski Crystal Growth Process

Support Vector Regression Modeling based Data-Driven Iterati...

引用

第三十八届中国控制会议

作者： Junchao Ren Ding Liu Yin Wan Ni Zhang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In view of the previous Czochralski(Cz) monocrystalline silicon growth control method based on mechanism model, it is difficult to accurately reflect the nonlinear and real-time variation characteristics of crystal growth process. This paper proposes a data-driven iterative learning control method based on support vector regression, which combines crystal diameter prediction model by support vector regression(SVR) off-line training with crystal growth mechanism model, and then control the crystal diameter and output key variable information in the framework of iterative learning algorithm. Simulation results for diameter control of crystal growth process and the analysis of key variable information show that the proposed method is effective and has a fast crystal diameter learning speed, and good performances of setpoint tracking and disturbance rejection, high crystal growth speed.

关键词： Data-Driven Czochralski Monocrystalline Silicon Support Vector Regression Iterative Learning control

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Stable Self-Charged Perovskite Quantum Rods for Liquid Laser with Near-Zero Threshold

arXiv

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arXiv 2025年

作者： Li, Jialu Han, Xue Wang, Wenjie Wang, Jinhui Zhang, Tingting Wu, Yuting Zhang, Guofeng Li, Bin Yang, Changgang Guo, Wenli Zhang, Mi Chen, Ruiyun Qin, Chengbing Hu, Jianyong Yang, Zhichun Liu, Shaoding Wang, Yue Gao, Yunan Ma, Jie Xiao, Liantuan Jia, Suotang State Key Laboratory of Quantum Optics Technologies and Devices Institute of Laser Spectroscopy Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan030006 China Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology 79 Yingze Street Taiyuan030024 China School of Microelectronics College of Materials Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Beijing100871 China

Colloidal quantum dots (QDs) are promising optical gain materials that require further threshold reduction to realize their full potential. While QD charging theoretically reduces the threshold to zero, its effectiveness has been limited by strong Auger recombination and unstable charging. Here we theoretically reveal the optimal combination of charging number and Auger recombination to minimize the lasing threshold. Experimentally, we develop stable self-charged perovskite quantum rods (QRs) as an alternative to QDs via state engineering and Mn-doping strategy. An unprecedented two-order-of-magnitude reduction in nonradiative Auger recombination enables QRs to support a sufficient charging number of up to 6. The QR liquid lasing is then achieved with a near-zero threshold of 0.098 using quasi-continuous pumping of nanosecond pulses, which is the lowest threshold among all reported QD lasers. These achievements demonstrate the potential of the specially engineered QRs as an excellent gain media and pave the way for their prospective applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical pumping

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Robust Tensor Principal Component Analysis Based on F-norm

Robust Tensor Principal Component Analysis Based on F-norm

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IEEE International Conference on Mechatronics and Automation

作者： Weimin Ge Jinxiang Li Xiaofeng Wang National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Binshui Xidao 391 Xiqing District Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Tensor principal component analysis (TPCA) has been widely used to extract appearance features of colour objects or colour scenes presented in tensor form. Using L 1 -norm as the distance metric, the existing TPCA-L 1 with its greedy (TPCA-L 1 -G) and non-greedy (TPCA-L 1 -NG) algorithms were proposed to reduce the sensitivity to outliers. The two algorithms, however, only derived TPCA solution in a form of two-dimensional matrix without actually extending them to the tensor subspaces and without performing experimental verification for tensor data. Simultaneously, the spatial correlations between pixels of the raw tensor data cannot be effectively preserved. To solve the problems, a robust tensor principal component analysis algorithm based on F-norm distance metric is proposed in this paper, which not only preserves the spatial structure of the raw data, but also reduces the sensitivity to outliers in tensor data. Experiments on reconstruction error and classification rate show that the proposed algorithm is effective and superior to other tensor algorithms in performances.

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An Incremental Feature Extraction Method for Weld Surface Defect Recognition

An Incremental Feature Extraction Method for Weld Surface De...

引用

IEEE International Conference on Mechatronics and Automation

作者： Jun Liu Keguang Ren Xiaofeng Wang Weimin Ge National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Binshui Xidao 391 Xiqing District Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

To meet the requirements of online feature extraction for weld defect recognition, an Incremental 2DPCA algorithm (I2DPCA) without estimating the covariance matrix of weld surface images is firstly proposed. A set of weld features with multiple orthogonal principal components can be obtained by a recursive estimation method, instead of the eigenvalue decomposition method or the singular value decomposition method. The computational complexity will be greatly reduced and the online feature extraction speed will be improved. To extract more structure features and reduce the dimension of weld images, an Incremental row-column Bidirectional PCA (IBDPCA) algorithm is presented based on I2DPCA, which preserves the weld features in the horizontal and vertical directions of weld images, and realizes the incremental feature extraction and data dimensionality reduction in the row and column directions. Finally, a series of comparative experiments are performed on the collected weld defect dataset. The results show that the proposed algorithm is superior to other PCA algorithms in convergence rate, classification rate, and complexity. The convergence rate is more than 99%, the classification rate can reach 96%, which can meet the online feature extraction requirements for weld defect recognition.

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

引用

arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Diameter Detection of Silicon Single Crystal Based on Brain Storm Optimization Algorithm Hough Transform

Diameter Detection of Silicon Single Crystal Based on Brain ...

引用

第三十八届中国控制会议

作者： Lu Kang Ding Liu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In order to improve the speed and accuracy of silicon single crystal diameter detection, this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection Brain storm optimization algorithm Hough transform

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