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IEEE Instrumentation and Measurement Technology Conference

作者： Yan Li Shangjie Ren Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

With the development of engineering technology, the application of computational fluid dynamics (CFD) based on the Navier-Stokes equations is becoming increasingly widespread in fields such as climate and architecture. However, for some applications such as hemodynamics, CFD cannot be widely used due to computational costs and equipment limitations. In order to achieve the simple and fast acquisition of hemodynamic parameters, a neural network based parameter acquisition method is proposed. The proposed method uses the U-Net combined with an attention mechanism to learn the complete solution of wall shear stress calculated by the Navier-Stokes equations on a traditional CFD solver. The numerical results proved that compared with traditional CFD methods, the speed of the proposed model has been increased by three orders of magnitude, with low errors.

关键词： Computational fluid dynamics Tomography Predictive models Optical fiber networks Optical imaging Hemodynamics Numerical models

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Spatial-Temporal Evolutionary Graph Attention Network for Bearing Remaining Useful Life Prediction

Spatial-Temporal Evolutionary Graph Attention Network for Be...

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Asian control Conference

作者： Sirui Du Feng Dong Shumei Zhang Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Currently., most deep learning-based rolling bearing remaining useful life (RUL) prediction methods lack interpretability and fail to fully exploit feature structural information and temporal sequence information. To address these issues., this paper proposes a spatial-temporal evolutionary graph attention network for precise RUL prediction of bearings. Firstly., canonical variable analysis (CVA) is utilized to reduce the feature set obtained from the time domain., frequency domain., and time-frequency domain. Subsequently., an adaptive stage partitioning is achieved based on kernel density estimation (KDE). Furthermore., a directed spatial-temporal graph (STG) containing both structural and sequential information is innovatively proposed., which adequately reflects the degradation process of bearings. Finally., combining graph attention network (GAT) and gate recurrent unit (GRU)., evolutionary-GAT (E-GAT) is constructed to explore RUL prediction from both local and global perspectives. The effectiveness and superiority of the proposed method are verified through accelerated bearing life experiments.

关键词： Degradation Time-frequency analysis Refining Rolling bearings process control Predictive models Logic gates

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Partial Representation-Based Oil-Gas-Water Flow State Monitoring via Dual-Modal Detection

Partial Representation-Based Oil-Gas-Water Flow State Monito...

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IEEE Instrumentation and Measurement Technology Conference

作者： Zhao Li Chao Tan Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

The detection and monitoring of oil-gas-water three-phase flow is a hot and difficult topic in oil-gas well industry. Concentrating on combining the three sub-states of oil, gas and water to form a whole flow state, a monitoring framework based on partial representation is proposed. In this framework, a differential pressure (DP) -pulse wave ultrasonic Doppler (PWUD) system is designed for obtaining information of three-phase structure and the intrinsic relationships among different phases, so as to accurately characterize the sub-states of oil, gas and water. Some information approaches such as Variational Mode Decomposition (VMD) and statistics method are applied to detection signal analysis to further realize the conversion of data to features. On the basis of obtained state features, the parts-based Nonnegative Matrix Factorization (NMF) is adopted to model and monitor the flow states in oil-gas-water flow process. The proposed novel scheme about partial representation-based horizontal oil-gas-water flow state monitoring via dual-modal detection is verified by dynamic experiments on three-phase flow loop at Tianjin University.

关键词： Industries Oils Focusing Feature extraction Acoustics Matrix decomposition Doppler effect

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Deep Neovascularization Segmentation by Learning Graphical Connectivity in Optical Coherence Tomography

Deep Neovascularization Segmentation by Learning Graphical C...

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IEEE Instrumentation and Measurement Technology Conference

作者： Lin Ding Shangjie Ren Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Segmentation of neovascularization (NV) within plaques from medical images can assist physicians in locating, evaluating, and diagnosing vessel lesions and plaque abnormalities. It has been demonstrated that NV serves as a crucial indicator of plaque vulnerability. Current convolutional neural network based (CNN-based) methods primarily rely on learning local appearance on a regular image grid without considering the structural characteristics of NV. To solve this problem, graph convolutional networks (GCN) are integrated into a unified convolutional architecture to effectively combine local image information with graph connection information. This integration enables us to achieve NV segmentation in Optical Coherence Tomography (OCT) images by leveraging various types of features. GCN learns vertex features on this irregular pattern so that when a vertex is surrounded by NV nodes, it becomes more likely to be labeled as NV. Simultaneously, the spatial attention mechanism is used in the coding-decoding structure to emphasize the effective feature information in the feature mapping and enhance the representation of NV-related features in the image. The proposed method achieves accurate end-to-end segmentation of NV in OCT images.

关键词： Image segmentation Visualization Accuracy Optical coherence tomography Semantic segmentation Data models Lesions

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Nonlinear control of Crane systems Based on Intelligence Computing of Disturbance Observer Under Mismatched Disturbance 22

Nonlinear Control of Crane Systems Based on Intelligence Com...

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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Wang, Tianlei Wang, Yiyan Xu, Meng Tian, Jiajie Zhang, Jingling Hong, Zhiyong Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen529020 China

ISBN: (纸本)9798331509712

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness. © 2024 IEEE.

关键词： Gantry cranes

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Deep Canonical Variate Analysis with Interpretable Attribute Guidance for Three-Phase Flow process Monitoring

Deep Canonical Variate Analysis with Interpretable Attribute...

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IEEE Instrumentation and Measurement Technology Conference

作者： Linghan Li Shumei Zhang Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Oil-gas-water three-phase flow has multiple flow states that exhibits dynamic, nonlinearity and instantaneity. Real-time monitoring and analysis of flow state are crucial for ensuring safe operation of industries. However, there is no universal definition for three-phase flow states due to complex flow characteristics and structures. Hence, labeling three-phase flow states by categories is impractical. For a comprehensive monitoring and analysis, the flow states should be described from multiple aspects, such as the dominant phase and interphase structure. To address the above issues, a method called deep canonical variate analysis with attribute guidance (Ag-DCVA) is proposed, which provides an innovative process monitoring paradigm that designs attributes for process description. To effectively guide the model for feature embedding, an attribute guidance module is introduced to increase supervision information at both the class and attribute levels. Then, the convolutional neural networks (CNN) are developed to learn nonlinear transformations with an objective of maximizing serial correlation for effectively encoding attributes and characterizing flow states. Finally, the attribute evolution heat map offers a convincing and comprehensive analysis of the flow process. Besides, the extracted features can be input to classifiers to identify typical flow states. Extensive experiments demonstrate that Ag-DCVA outperforms existing methods, and it can monitor the flow process comprehensively and minutely while providing reasonable explanations.

关键词： process monitoring Industries Correlation Semantics Sensor phenomena and characterization Feature extraction Vectors

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Robust Platoon control of Industrial Heavy-Load Autonomous Vehicles 12

Robust Platoon Control of Industrial Heavy-Load Autonomous V...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences China Department of Automation 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 Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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Image Reconstruction in Ultrasonic Transmission Tomography Using L1/L2 Regularization

Image Reconstruction in Ultrasonic Transmission Tomography U...

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IEEE Instrumentation and Measurement Technology Conference

作者： Aoyu Li Guanghui Liang Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

As a non-invasive and non-radiating imaging technology, ultrasound transmission tomography (UTT) has gained broad attention in industrial and biomedical fields. Image reconstruction plays a vital role in UTT. A novel $\boldsymbol{L}_{\mathbf{1}}/\boldsymbol{L}_{\mathbf{2}}$ regularization method aiming to solve the UTT inverse problem based on total variation regularization is proposed, which divided the variation into its $\boldsymbol{L}_{\mathbf{1}}-\mathbf{norm}$ and $\boldsymbol{L}_{\mathbf{2}}-\mathbf{norm}$ forms and made a ratio. Then, the ratio term is used as the regularization term in UTT inverse problem, and the alternating direction method of multipliers (ADMM) algorithm is adopted to solve it. The simulation and experimental results demonstrate that the proposed method successfully improves the imaging accuracy and the effect of image visualization compared to traditional image reconstruction methods.

关键词： Visualization Accuracy Ultrasonic imaging TV Inverse problems Tomography Convex functions

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Global-Local State Analysis and Monitoring-Based Velocity Measurement of Oil-Gas-Water Three-Phase Flow

SSRN

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SSRN 2024年

作者： Li, Zhao Tan, Chao Zhang, Shumei Dong, Feng Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China School of Information and Intelligent Engineering Tianjin Renai College Tianjin China

The mixture fluids of oil-gas-water metamorphose through change of phase, properties and structure. This poses a challenge to decipher their dynamic entanglement for phase velocity measurement. It is evident that progressively grasp of process states provides valuable prior information for further flow velocity measurement. As a significant flow characteristic, multiscale gives unique inspiration for flow state monitoring and velocity measurement from a global-local view. In this work, empirical wavelet transform-based Hilbert-Huang transform (EWT-based HHT) and multiscale robust empirical permutation entropy (Ms-rePE) are employed for the analysis of CWUD and conductance signals respectively, achieving the joint characterization of oil-gas-water flow structure by acoustic-electrical dual-mode information. On this basis, a data-mechanism dual-driven model for flow velocity measurement is established under a unified multiscale framework. And a novel approach of hypergraph-based global-local structure analysis (HGLSA) is proposed to simultaneously extract global-local information embodied in the complex flow process. The proposed scheme of global-local state monitoring-based flow velocity measurement is verified by dynamic experiment of oil-gas-water three phase flow. © 2024, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Differentiator-Based Adaptive H8 Tracking control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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