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Research of line-of-sight angle on aero-optic imaging deviation for vehicles flying at 40—60 km

Optoelectronics Letters 2023年第4期19卷 242-247页

作者： XU Liang ZHAO Shiwei WANG Tao Tianjin Key Laboratory of Complex Control Theory and Application School of Electronic Engineering and AutomationTianjin University of TechnologyTianjin 300384China Foreign Cooperation Projects Department PetroChina Dagang Oilfield CompanyTianjin 300280China

Aero-optical imaging deviation is a kind of optical effect,aiming at a conical *** paper analyzes the influence of the aircraft on the imaging deviation with the change of altitude and line-of-sight *** this paper,the relationships between imaging deviation and altitude as well as line-of-sight angle are analyzed,and the coupling relationship between altitude and line-of-sight angle is *** give the imaging deviation results of 5°—75°line-of-sight *** analysis shows that when the line-of-sight angle is in the range of 5°—35°,the imaging deviation is more sensitive,while the line-of-sight angle is in the range of 35°—75°,the imaging deviation is relatively flat,and the imaging deviation does not gradually decrease with the increase of line-of-sight angle,but there is a turning point in the middle of 5°—75°.The imaging deviation decreases first and then increases,and the turning point of line-of-sight angle is closely related to height.

关键词： deviation angle turning

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Graph convolutional network soft sensor for process quality prediction

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Journal of Process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Stochastic High-order Fully-actuated Systems: Model, Equivalence and Stabilization

Stochastic High-order Fully-actuated Systems: Model, Equival...

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2022 Chinese automation Congress, CAC 2022

作者： Liu, Xueqing Chen, Maoyin Sheng, Li Zhou, Donghua Tsinghua University Department of Automation Beijing China College of Control Science and Engineering Qingdao China

ISBN: (纸本)9781665465335

This paper develops a stochastic high-order fully-actuated systems model that complements the existing highorder fully-actuated system methodology. Different from the deterministic model, stochastic signals can be considered in the proposed version. By employing a high-order operator, the equivalent control and stabilization control laws are obtained to guarantee the global asymptotical stability in probability of the closed-loop system. Finally, the simulation results show the effectiveness of the proposed control schemes. © 2022 IEEE.

关键词： Stochastic systems

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Deep learning-based digital twin for intelligent predictive maintenance of rapier loom

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9409-9430页

作者： Xiao, Yanjun Li, Rui Zhao, Yue Wang, Xiaoliang Liu, Weiling Peng, Kai Wan, Feng School of Mechanical Engineering Hebei University of Technology Tianjin China Career Leader Intelligent Control Automation Company Jiangsu Province Suqian China Tianjin Key Lab Power Transmiss & Safety Technol Department State Key Lab Reliabil & Intellectual Elect Tianjin China

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from "regular maintenance" and "post-event maintenance" to "predictive maintenance". In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of "combining the real with the virtual and controlling the real". Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. © 2024 – IOS Press.

关键词： Forecasting

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Model of atomized droplets average particle size and verification of eco-friendly hybrid lubrication(CAMQL)

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摩擦（英文版） 2025年第5期13卷 149-164页

作者： Mingzheng Liu Changhe Li Dongzhou Jia Xin Liu Yanbin Zhang Min Yang Xin Cui Teng Gao Yusuf Suleiman Dambatta Runze Li Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of EducationQingdao University of TechnologyQingdao 266520China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of EducationQingdao University of TechnologyQingdao 266520China Qingdao Jimo Qingli Intelligent Manufacturing Industry Research Institute Qingdao 266201China College of Mechanical Engineering and Automation Liaoning University of TechnologyJinzhou 121001China School of Mechanical Engineering Dalian University of TechnologyDalian 116081China School of Engineering Department of Biomedical EngineeringMassachusetts Institute of TechnologyBoston 02139USA

In the precision cutting of difficult-to-process metals,surface thermal damage to a workpiece is a significant technical *** clean minimum quantity lubrication(MQL)technology,which replaces traditional pouring cooling,is used,inadequate heat dissipation remains an *** air MQL(CAMQL),an eco-friendly technology,can enhance the heat transfer performance of the lubricating film in the cutting zone,offering excellent cooling and lubrication ***,the influence of jet and temperature parameters on the average particle size and distribution characteristics of atomized droplets is not well *** study first analyzes the evolution of lubricant physical properties and establishes a quantitative mapping relationship between cryogenic air temperature and physical parameters of ***,the unstable fluctuation in the annular liquid film at the two-phase flow nozzle exit is observed and analyzed.A thickness model of annular liquid film is developed,revealing the effect of airflow field on the annular liquid ***,a model for the average particle size of atomized droplets under CAMQL is *** analysis and validation experiments under different working conditions show that the measured values align with the theoretical *** an air pressure of 0.4 MPa and an air flow temperature of-50 °C,the droplet particle size is 133.5 μm,with an error of 8.2%.The effect of air pressure on particle size is greater than that of air flow ***,the distribution spans of droplet size under different conditions are analyzed,and the results demonstrated that low temperatures help shorten the interval between particle sizes and improve the relative uniformity of particle size *** research provides a theoretical basis for the application of CAMQL technology in the cutting process.

关键词： cryogenic air minimum quantity lubrication(MQL) atomization average particle size mathematical model

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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Robust controller Design for Longitudinal and Lateral Model of High-Performance Fixed-Wing UAVs

Robust Controller Design for Longitudinal and Lateral Model ...

引用

International Conf on Electrical and Electronic engineering (ICEEE)

作者： Burak Isci Levent Ucun Department of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

This study focuses on designing and analyzing $H_{\infty}$ and $H_{2}$ control algorithms for fixed-wing unmanned aerial vehicles (UAVs) with reference tracking control objective under disturbances. Two different robust control strategies, developed based on the linearized UAV mathematical model, are implemented in the inner loop defined as the angular velocity control loop in the nonlinear mathematical model to enhance the performance of attitude angular velocity by improving the robustness against disturbances. This papaer also includes disturbance effects on UAVs such as gust model and time-dependent change of the centre of gravity model. The results of the two different control algorithms are compared and presented in terms of time-domain specifications.

关键词： Robust control Uncertainty Simulation Transfer functions Autonomous aerial vehicles Mathematical models Stability analysis Robustness Time-domain analysis Gravity

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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