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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Latent Matrices for Tensor Network Decomposition and to Tensor Completion

arXiv

引用

arXiv 2022年

作者： Yang, Peilin Sun, Weijun Zhao, Qinbin Zhou, Guoxu School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China RIKEN Tokyo103-0027 Japan Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China

The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when facing higher-order and large-scale data. Naturally, there arises an interesting question: can a new model be proposed that decomposes the tensor into smaller ones and speeds up the computation of the algorithm? This work gives a positive answer by formulating a novel higher-order tensor decomposition model that utilizes latent matrices based on the tensor network structure, which can decompose a tensor into smaller-scale data than the FCTN decomposition, hence we named it Latent Matrices for Tensor Network Decomposition (LMTN). Furthermore, three optimization algorithms, LMTN-PAM, LMTN-SVD and LMTN-AR, have been developed and applied to the tensor-completion task. In addition, we provide proofs of theoretical convergence and complexity analysis for these algorithms. Experimental results show that our algorithm has the effectiveness in both deep learning dataset compression and higher-order tensor completion, and that our LMTN-SVD algorithm is 3-6 times faster than the FCTN-PAM algorithm and only a 1.8 points accuracy drop. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

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Sensitivity Analysis of Petrophysical Parameters for Estimating Hydrate Saturation in the Shenhu Area

引用

Applied Geophysics 2020年第5期17卷 649-659,899,900页

作者： Liu Xue-Qin Liu Huai-Shan Xing Lei Qin Zhi-Liang Ma Ben-Jun Wang Min Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China Key Lab of Submarine Geosciences and Prospecting Techniques MOE and College of Marine GeosciencesOcean University of ChinaQingdao 266100P.R China Evaluation and Detection Technology Laboratory of Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266061China Tianjin Sino-German University of Applied Sciences School of Intelligent ManufacturingTianjin300350

Acoustic and electrical methods are commonly used to evaluate hydrate saturation based on P-wave velocity(Vp)and resistivity,*** evaluate hydrate saturation using petrophysical parameters directly related to the presence of *** petrophysical parameters sensitive to hydrate saturation were first analyzed using the equivalent medium rock physical model,logging intersection plots,and petrophysical parameter *** simulated annealing global optimization method was then used to estimate the hydrate saturation profile in the Shenhu Area,*** petrophysical parameters Vp,λρ,andλμ,which are associated with the rock elastic and shear moduli,are highly sensitive to hydrate saturation for an estimated saturation range of *** range is consistent with that obtained from the original well diameter ***,the parameters Vs andμρ,which are only related to the rock shear modulus,yield high hydrate saturation estimates of 0.22-0.43 and exhibit some deviations from the real-time *** to its sensitivity,the Poisson’s ratio is least desired for hydrate evaluation among the studied *** sensitivity of hydrate saturation depends on the petrophysical model used for studying hydrate physical properties and storage analysis.

关键词： natural gas hydrate saturation sensitivity parameters petrophysical properties

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Optimal Tracking Control of Injection Speed in Injection Molding Machine with Gaussian Process Learning and Model Predictive Control

Optimal Tracking Control of Injection Speed in Injection Mol...

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第43届中国控制会议

作者： Jianpu Cai Zhigang Ren Bo Zhang Zongze Wu School of Automation Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Injection molding, as a common plastic processing technology, has significant importance in modern manufacturing industry. However, due to the nonlinear dynamic characteristics of injection molding machines(IMMs) and the influence of various factors such as raw materials, equipment, and environment during the injection molding process(IMP), uncertainty residuals are generated, which makes it difficult to achieve optimal tracking control of injection speed, thereby affecting product quality and production efficiency. To address this issue, we propose a learning-based model predictive control(LMPC) *** scheme uses Gaussian process regression(GPR) to predict and model uncertainty residuals in the IMP. The residual model of IMM is learned through historical injection molding data, and the model predictive control(MPC) is used to achieve optimal tracking control of injection speed based on the predicted model, thereby automatically adjusting the injection speed according to requirements. In addition, we use differential dynamic programming with control constraints to solve the optimization ***, we conduct comprehensive numerical simulation experiments to prove the effectiveness and efficiency of the proposed LMPC scheme in injection speed control of IMM.

关键词： Injection molding model predictive control optimal control gaussian process learning control

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An Accurate Error Compensation Method for Multi-Axis Parallel Machine Through Singularized Jacobi Geometric Parameter Correction and Coupling Error Evaluation

SSRN

引用

SSRN 2023年

作者： Luo, Yuheng Gao, Jian Chen, Disai Zhang, Lanyu State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Inspection and Manufacturing IoT of Ministry of Education Guangdong University of Technology Guangzhou510006 China

Jacobian model is widely used for error compensation of multi-axis parallel mechanisms. However, the deviation between the nominal geometrical parameters and actual parameters in the Jacobi model and the equivalent replacement and high-order rounding operations in Jacobi modeling process will result in the indefinite equation solving problem and modeling error problem, which inevitably induces certain residual errors of the Jacobi model compensation method. To tackle these problems, this paper proposes an optimal Jacobian correction method through a geometrical parameter singularized Jacobian correction model and a coupling error evaluation modular for multi-axis parallel mechanism. In this method, a new singularized geometrical error solution method (SESM) is constructed to obtain the Jacobian correction parameters accurately instead of iteration. The method can solve the indefinite equation problem and partial posture non-solution problem. The SESM can also compensate the modeling error induced by the equivalent infinitesimal replacement and the high-order small amount ignorance operation. Considering that the Jacobian model with different singularized geometrical parameter will have certain coupling effect and result in different compensation effect, a sensitivity-based error predictive evaluation method (EPEM) is proposed to determine the optimal correction parameter of the Jacobian model for the pose of the entire workspace and thus realize an accurate pose error compensation. The proposed method is tested on a three-axis parallel mechanism. Experimental results demonstrated the effectiveness and advancement of the proposed method, which can further reduce the position and orientation error by 64.93% and 55.29%, respectively, by comparison with the uncorrected Jacobian compensation method. This method provides us a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment deve

关键词： Error compensation

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Design of an Automatic Locking Nut Tightening Control System for a Vision Guided Robot

Design of an Automatic Locking Nut Tightening Control System...

引用

Automation, Control and Robotics Engineering (CACRE), International Conference on

作者： Kai Chang Bolin Lu Jian Yin Guangchao Tan Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Shenyang China

A set of automatic locking nut tightening control system for a vision guided robot is designed and implemented. Through hand-eye calibration, the coordinate system is unified. By the designed tool plate and tapered tooling, the calibrated reference location for the tightening tool is realized. According to the position and orientation of the reference screw, the vision system detects the offset of the current screw position, and guides the robot to adjust the pose of the tightening tool. By setting up the network topology of the control system, the data interaction of each subsystem is completed, and the automatic workflow is realized. The application of the system can avoid the relative movement between the tool and screw during the tightening process, and improve the tightening quality.

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detection of wrong and missing installation of threaded steel wire based on pointnet++

Detection of wrong and missing installation of threaded stee...

引用

2022 International Conference on Advanced manufacturing Technology and manufacturing Systems, ICAMTMS 2022

作者： Cheng, Qilong Gou, Rui Su, Rui Gou, Wei Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Liaoning Shenyang110179 China Xi'an Aerospace Engine Co. Ltd Xi'an710199 China

ISBN: (纸本)9781510656840

In the mechanical manufacturing industry, the detection of wrong and missing installation of threaded steel wire is of great significance and ensures the reliability and quality of products. Especially in aerospace engineering, the traditional detection of wrong and missing installation mostly adopts two-dimensional detection technology, which has the problems of poor detection effect and affected by light source. Aiming at this problem, a detection method of wrong and missing installation of threaded steel wire based on pointnet++ was proposed, which constructs a network framework based on pointnet++, by training the network,it realizes the recognition and direction judgment of threaded steel wire, and the accuracy is more than 95%. © 2022 SPIE.

关键词： Light sources

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Application of Lenze 9400 servo in dynamic splicing of bobbin paper

Application of Lenze 9400 servo in dynamic splicing of bobbi...

引用

2021 IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2021

作者： Xu, Hongqing Tian, Junmin Li, Chengpeng Zhao, Qian Yang, Xu Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Tobacco Shaanxi Industrial Company Limited Xi'an710065 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Shenyang110179 China SIASUN Robot & Automation Co. Ltd. Shenyang110169 China

ISBN: (纸本)9781665432511

In the process of cigarette, the dynamic splicing of running bobbin paper and spare bobbin paper plays a very important role in the continuous operation of Cigarette Making and Filter Assembling Group. However, at present, the dynamic splicing system of some domestic Cigarette Making and Filter Assembling Group mostly adopts magnetic particle clutch. After using the magnetic particle clutch for a period of time, it will not rotate or rotate too slowly due to wear and tear. It is easy to cause the rotation speed of spare bobbin paper and running bobbin paper to be inconsistent when splicing, resulting in relative speed, resulting in failure of splicing. Therefore, a dynamic splicing system based on Lenze 9400 servo controller is proposed. The system can guarantee the smooth splicing of the running and spare bobbin paper, reduce The Times of maintenance and improve the production efficiency. ©2021 IEEE.

关键词： Tobacco

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Improving Prediction of GNSS Satellite Visibility in Urban Canyon Based on Graph Transformer 36

Improving Prediction of GNSS Satellite Visibility in Urban C...

引用

36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zheng, Shaolong Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Rsearch and Innovation Center of Excellence Nicosia Cyprus

ISBN: (纸本)9780936406350

Signals from global navigation satellite systems (GNSS) suffer from serious multipath errors in urban areas caused by building blockages and reflections. The use of deep neural networks offer great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first is the inability to exploit effectively the irregular GNSS dataset because of inconsistent numbers of visible satellites in different epochs. The second is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network for predicting satellite visibility that effectively learns environment representations from the irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from the irregular GNSS measurements, a sky satellite graph is constructed as the input to a graph neural network by using the satellites captured in the same epoch, which can represent the spatial relationships between the satellites and enhance the model to enable learning of satellite-related features sufficiently well. To improve generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites for extracting the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, we develop a novel graph transformer neural network (GTNN) for predicting satellite visibility, which can not only handle irregular GNSS measurements but also learn an environment representation via graph attention. Comparative experiments were carried out on real-world GNSS meas

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Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration

arXiv

引用

arXiv 2022年

作者： Qiu, Yichun Sun, Weijun Zhou, Guoxu Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China RIKEN Tokyo103-0027 Japan

Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools to meet this need, but most existing methods perform poorly in the presence of noise interference. Inspired by the remarkable performance of randomized block Krylov iteration (rBKI) in reducing the effect of tail singular values, this work designs an rBKI-based Tucker decomposition (rBKI-TK) for accurate approximation, together with a hierarchical tensor ring decomposition based on rBKI-TK for efficient compression of large-scale data. Besides, the error bound between the deterministic LRA and the randomized LRA is studied. Numerical experiences demonstrate the efficiency, accuracy and scalability of the proposed methods in both data compression and denoising. © 2022, CC BY.

关键词： Tensors

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