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10th International Conference on Industrial Engineering and Applications, ICIEA 2023

作者： Zhang, Yafei Wang, Youming School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781643684086

The covariance-driven stochastic subspace modal parameter identification method has been widely used in the field of engineering structures. Effective determination of the model order of the structural system is the key to applying this method to identify the modal parameters. It is particularly difficult to determine the model order for unstable systems affected by noise disturbances and computational errors. In order to effectively determine the model order, an exponential eigenvalue entropy incremental covariance-driven stochastic subspace identification (EE-COV-SSI) algorithm is proposed. The condition number of the state matrix is used to determine the degree of perturbation of the response signal to the system stability. Meanwhile, the identification accuracy of the modal parameters is reflected by calculating the modal frequency coefficient of variation. Finally, the method is applied to the modal analysis of a four-story frame structure. The results show that the method can accurately identify the model order and improve the identification accuracy of the modal parameters. © 2023 The authors and IOS Press.

关键词： Modal analysis

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

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IET Cyber-Systems and Robotics 2022年第1期4卷 38-50页

作者： Junsuo Qu Chenxue Tang Yuan Zhang Kai Zhou Abolfazl Razi School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent ProcessXi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Communication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Informatics Computing and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

关键词： machine learning pedestrian identification robustness visual surveillance visual tracking

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LBM modeling of three-dimensional mixed convection in CZ crystal growth on curvilinear coordinates system

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Results in Physics 2024年 61卷

作者： Zhang, Ni Liu, Ding The Department of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an 710121 China The Shaanxi Key Laboratory of Complex System Control and Intelligent of Information Processing Xi'an University of Technology Xi'an 710048 China

A three-dimensional (3D) thermal lattice Boltzmann model of crystal growth is presented to study the magneto-hydrodynamics (MHD) mixed convection in the melt of Czochralski silicon single crystal growth in this paper. In order to solve the problem of lattice Boltzmann method's difficulty in solving the flow of 3D cylindrical cavity, the 3D physical model of crystal growth is transformed from the Cartesian coordinate system to the Curvilinear coordinate system. And the irregular grids in the Cartesian coordinate system are transformed into regular grids in the Curvilinear coordinate system to facilitate the establishment of the Lattice Boltzmann model. Under the above operations, two coupled distribution functions are established to solve the governing equations. Two D3Q19 models are adopted to solve the melt velocity and temperature. And the 3D lattice Boltzmann modeling of crystal growth with cylinder boundary is realized by interpolation in Curvilinear coordinate system. The comparison with the experiment and other references proves that the proposed model and boundary treatment scheme is correct and it proposes a new way for studying the 3D crystal growth or other similar 3D cylindrical cavity simulations. Furthermore, the unstable melt flow for two cases are also simulated in this paper and the critical Reynolds number are also obtained. Results show that the introduction of the transverse magnetic field enhances the stability of the crystal growth system and increases the adjustable range of crystal rotation parameter. The obtained conclusion has an important reference for adjusting the process parameters during the CZ crystal growth. © 2024 The Author(s)

关键词： 3D mixed convection Computer simulation Heat transfer Lattice Boltzmann Method Silicon single crystal growth

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Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism 5

Small Object Detection Algorithm Based on RPANet and Positio...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tang, Zongbing Yang, Dan Qu, Junsuo School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an Robertic Intelligent Systems International Science and Technology Cooperation Base Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection. © 2022 ACM.

关键词： Convolution

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A Gender Recognition Method for Chinese Mitten Crab in Low Light Environment Based on Machine Vision

A Gender Recognition Method for Chinese Mitten Crab in Low L...

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2024 China Automation Congress, CAC 2024

作者： Tian, Junlong Guo, Ya Liu, Yingfei Zhang, Jianguo Jiang, Yongnian International Joint Research Center for Intelligent Optical Sensing and Applications Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Aquatic and Livestock Station of Yixing Agriculture and Rural Bureau Yixing China Wuxi Agricultural Equipment Technology Extension Center Wuxi China Jiangsu Zhongnong IoT Technology Co. LTD Yixing China

ISBN: (纸本)9798350368604

The acquisition of gender information of Chinese mitten crabs is crucial for survival rate assessment and biomass estimation. Machine vision is commonly used for the automatic gender recognition of Chinese mitten crabs, but the low light conditions caused by deeper containers reduce the accuracy of existing algorithms. To improve the accuracy of gender recognition of Chinese mitten crabs in low light environments, this study incorporates the GAM into the backbone of YOLOv7, improving the capability to distinguish gender-specific features. A lightweight upsampling operator, CARAFE, replaces the original upsampling module, expanding the model’s receptive field and better aggregating global information. Using Focal-CIoU to mitigate the impact of low-quality prediction boxes, enabling the model to better optimize high-quality ones. By constructing an image dataset of Chinese mitten crabs in low-light environments to validate the model’s performance, experimental results demonstrate that the average precision of the model established in this study is 0.870, 10.6% higher than the most update algorithm GMNet-YOLOv4, effectively performing the classification task of Chinese mitten crabs in low-light environments. © 2024 IEEE.

关键词： Machine vision

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The Mode Order Determination of Covariance-Driven Stochastic Subspace Identification Based on Exponential Functions for Engineering Structures

SSRN

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SSRN 2022年

作者： Wang, Youming Zhang, Yafei School of Automation Xi’an Key Laboratory of Advanced Control and Intelligent Process Xi’an University of Posts and Telecommunications Xi’an710121 China

As the noise interference in the raw input data causes the error of singular value decomposition of Hankel matrices in stochastic subspace identification (SSI) algorithm, it is difficult to accurately determine the mode order and identify the engineering structural *** address the problem, a mode order determination method is proposed for enhance covariance driven stochastic subspace identification (ECOV-SSI) algorithm in the modal analysis of engineering structures, where exponential increment entropy theory is employed to construct the relationship between the eigenvalue entropy and the mode order. The mode order is determined from the minimum eigenvalue entropy increment. For the selection of Toeplitz matrix row number of ECOV-SSI algorithm influences the identification accuracy, the comparative analysis between power spectral density (PSD) estimation and stabilization diagram is described to determine the row number of the Toeplitz matrix and avoid mode absence. The modal analysis experiments on the frame and bridge verifies the accuracy and effectiveness of the proposed method compared to traditional SSI and Data-SSI methods. © 2022, The Authors. All rights reserved.

关键词： Entropy

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A Method for Constructing Optical Detection Model of Wheat Seed Purity Based on Sample Generation and Contrast Learning Strategy

SSRN

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SSRN 2024年

作者： Yan, Shengqi Zhao, Xin Zhu, Qibing Huang, Min Guo, Xinnian Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi214122 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Suqian University Suqian223800 China

Seed purity is an essential indicator of seed quality, and use of optical techniques for seed purity testing has been extensively reported. Currently, most optical detection models for seed purity rely on the assumptions that the training set used to develop detection model contains all possible impurity varieties, and that the number of positive(the variety of interest) and negative samples(all possible impurities contained in that variety) in the training set is balanced. This assumption is often difficult to fulfil in practice due to the complexity of the seed production process. In order to solve the problem of unbalanced samples in the construction of the optical detection model and the problem of detection of unknown impurities (categories of impurities not contained in the training set) faced by the model in practical applications, an optical detection model construction method for wheat seed purity based on sample generation and contrast learning strategy was proposed in this study. Firstly, a linear data generation strategy is used to expand the dataset of impurity samples, enhance the diversity of impurity samples and improve the imbalance between the positive and negative samples, so as to improve the model's ability to identify impurity seeds. At the same time, a contrast learning strategy is introduced to strengthen the feature differences between the positive and negative samples and improve the model's recognition accuracy for unknown impurity samples. Hyperspectral images of six varieties with a total of 4200 wheat seeds were acquired, and a purity detection model for each variety was built based on the average spectra of the seeds. The purity detection model for each variety was constructed through four varieties (containing the variety itself as the positive sample, and three of the remaining five varieties as a small number of negative samples), while the test sample consisted of all six variety samples. The results showed that the recognition acc

关键词： Learning systems

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Fixed-time 6-DOF coordinated control of spacecraft formation flying

Fixed-time 6-DOF coordinated control of spacecraft formation...

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第33届中国控制与决策会议

作者： Ruixia Liu Jing Chu Huanchao Du School of Automation Xi'an University of Posts and Telecommunications Xian Key Laboratory of Advanced Control and Intelligent Process Xian University of Posts and Telecommunications

This paper investigates the fixed-time coordinated control problem of six-degrees-of-freedom(6-DOF) dynamic model for multiple spacecraft formation flying(SFF) with external disturbances.A new multi-spacecraft nonsingular fixed-time terminal sliding mode vector is ***,a 6-DOF fixed-time coordinated control strategy with adaptive tuning laws is proposed,such that the practical fixed-time stability of the controlled system is ensured in the presence of upper bounds unknown external *** theoretically proves that the relative tracking errors of attitude and position can converge into the regions in fixed ***,a numerical example is exploited to show the usefulness of the theoretical results.

关键词： Fixed-time control Spacecraft Formation Flying Coordinated control

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