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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Li, Tingxin Liu, Weihua Liu, Xinpeng Yang, Xianqiang Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Research Center of Intelligent Control and Systems Zhejiang University Yongjiang Laboratory Hangzhou China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665464543

Modern surface mount circuit board assemblies require more advanced defect detection methods. While deep learning algorithms have great potential for PCBA inspection, their detection accuracy in complex background situations is still limited. To overcome this problem, we propose a deep learning-based PCBA detection model (PCB-Net) to achieve accurate classification and localization of components on PCB. Firstly, for similar objects under complex background interference, this paper proposes a backbone network consisting of a generalized efficient aggregation network and a context converter to effectively extract local and global information. This integration aims to enhance the expressiveness of the network. Secondly, a multi-scale attention mechanism is designed to improve the feature extraction ability of the network on the target and suppress the interference of complex backgrounds. Finally, a C2fHB lightweight module was designed to improve the model's extraction of component features using the HorNet structure. Experimental results show that our proposed model is an effective PCBA detection method, as it can accurately detect tiny components in complex backgrounds, efficiently obtain component class and location information, and remain detection efficiency. © 2024 IEEE.

关键词： Surface mount technology

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A type-independent unknown parameters estimation method for lead component 50

A type-independent unknown parameters estimation method for ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Yang, Hong Liu, Xinpeng Liu, Weihua Yang, Xianqiang Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Control Science and Engineering Dalian University of Technology Dalian China Research Center of Intelligent Control and Systems Yongjiang Laboratory Zhejiang University Ningbo China

ISBN: (纸本)9781665464543

To meet the growing demands in the electronics manufacturing industry for smaller, more reliable, and flexible component packages, this research introduces a novel algorithm designed to automatically measure the geometric parameters of lead components. The algorithm can address the challenges associated with measuring these parameters in surface mount technology (SMT), overcoming the inefficiencies and inaccuracies of traditional manual measurement methods. This innovative approach combines an adaptive threshold segmentation algorithm with a multilevel grayscale range, enabling robust binary segmentation that adapts to variations in illumination. Additionally, by employing conditional filtering and state tracking, the algorithm facilitates the extraction of lead groups and individual leads, allowing for precise evaluation of component parameters through sub-pixel interpolated fitting. Extensive testing under various illumination conditions across multiple types of packaged and standard components has confirmed the algorithm's high accuracy and robustness. These results highlight its efficacy as a measurement solution suitable for industrial-grade applications. © 2024 IEEE.

关键词： Surface mount technology

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Power Flow Analytical Solutions and Multi-dimensional Voltage Stability Boundaries Based on Multivariate Quotient-difference Method

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1168-1178页

作者： Chengxi Liu Qiupin Lai School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Wuhan 430072China IEEE

This paper proposes a novel Multivariate Quotient-Difference(MQD)method to obtain the approximate analytical solution for AC power flow ***,in the online environment,the power flow solutions covering different operating conditions can be directly obtained by plugging values into multiple symbolic variables,such that the power injections and consumptions of selected buses or areas can be independently *** method first derives a power flow solution through a Multivariate Power Series(MPS).Next,the MQD method is applied to transform the obtained MPS to a Multivariate Pad´e Approximants(MPA)to expand the Radius of Convergence(ROC),so that the accuracy of the derived analytical solution can be significantly *** addition,the hypersurface of the voltage stability boundary can be identified by an analytical formula obtained from the coefficients of *** direct method for power flow solutions and voltage stability boundaries is fast for many online applications,since such analytical solutions can be derived offline and evaluated online by only plugging values into the symbolic variables according to the actual operating *** proposed method is validated in detail on New England 39-bus and IEEE 118-bus systems with independent load variations in multi-regions.

关键词： Analytical power flow solution multivariate power series multivariate Padéapproximants multivariate quotient-difference multi-dimensional voltage stability boundary

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Impacts of near-M_(s)austempering treatment on microstructure evolution and bainitic transformation kinetics of a medium Mn steel

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Journal of Iron and Steel research International 2025年第1期32卷 249-259页

作者： Yong-gang Yang Xin-yue Liu Rui-zhi Li Yu-lai Chen Hong-xiang Wu Guo-min Sun Zhen-li Mi National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology BeijingBeijing 100083China Technology Center Ningbo Iron&Steel Co.Ltd.Ningbo 315800ZhejiangChina

The microstructure evolution and bainitic transformation of an Fe-0.19C-4.03Mn-1.48Si steel subjected to near-M_(s)austempering treatment were systematically investigated by combining dilatometer,X-ray diffraction,and electron *** additional austempering treatments with isothermal temperatures above M_(s)were used as *** show that the incubation period for the bainitic transformation occurs when the medium Mn steel is treated with the austempering temperature above M_(s).However,when subjected to near-M_(s)isothermal treatment,the medium Mn steel does not show an incubation period and has the fastest bainitic transformation ***,the largest volume fraction of bainite with a value of 74.7%is obtained on the condition of near-M_(s)austempering treatment after cooling to room *** and microstructure evolution analysis indicates that the elimination of the incubation period and the fastest rate of bainitic transformation are related to the preformed *** advent of preformed martensite allows the specimen to generate more bainite in a limited *** bainitic ferrite nucleation at austenite grain boundaries and through autocatalysis at ferrite/austenite interfaces,a model is established to understand the kinetics of bainite formation and it can describe the nucleation rate of bainitic transformation well when compared to the experimental results.

关键词： Medium manganese steel Bainitic transformation Microstructure Near-M_(s)austempering Transformation modeling

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Steady-state and dynamic simulation of gas phase polyethylene process

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Chinese Journal of Chemical engineering 2024年第11期75卷 110-120页

作者： Xiaodong Hong Wanke Chen Zuwei Liao Xiaoqiang Fan Jingyuan Sun Yao Yang Chunhui Zhao Jingdai Wang Yongrong Yang State Key Laboratory of Chemical Engineering College of Chemical and Biological EngineeringZhejiang UniversityHangzhou 310027China Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province ZJU-Hangzhou Global Scientific and Technological Innovation CenterHangzhou 311215China Ningbo Research Institute Zhejiang UniversityNingbo 315100China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

Gas-phase polyethylene(PE)processes are among the most important methods for PE production.A deeper understanding of the process characteristics and dynamic behavior,such as properties of PE and reactor stability,holds substantial interest for both academic researchers and *** this study,both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform,which can be used to study a variety of operation tasks for commercial solution polyethylene processes,such as new product development,process control and real-time *** copolymerization kinetic parameters are fitted by industrial ***,a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed *** accuracy in predicting melt index and density of the polymer,and the dynamic behavior of the developed models are verified by real plant ***,the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments,since temperature control is important in industrial *** results reveal that all four schemes effectively track the setpoint ***,only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

关键词： Gas-phase polyethylene process Steady state Dynamic modeling control

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Multi-Motor Synchronous control Strategy Based on Fuzzy Internal Model PID and Virtual Spindle Synchronous control

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Progress in Electromagnetics research C 2025年 152卷 187-195页

作者： Yan, Wei Li, Shasha College of Mechanical and Electronic Engineering Huanghe Jiaotong University Jiaozuo454950 China Henan Engineering Technology Research Center on Intelligent Manufacturing Technology and Equipment Jiaozuo454950 China

To achieve high-precision synchronous control of multiple motors, this study utilizes a permanent magnet synchronous motor as a case study. It adopts a fuzzy internal model proportional-integral-derivative algorithm with integral separation for single-motor control. On this basis, the virtual spindle synchronization strategy of multi-motor synchronous control and fuzzy control algorithm are further introduced to adjust the feedback torque compensation coefficient dynamically, optimizing the virtual spindle synchronization strategy. The results showed that in single-motor control, the dual closed-loop fuzzy proportional-integral control algorithm achieved a torque fluctuation error of 4 N · m when the load torque changed significantly. The fuzzy internal model proportional-integral-derivative control algorithm with integral separation had a relatively smooth adjustment process, and the maximum torque fluctuation did not exceed 1 N · m. In multi-motor synchronous control, the improved virtual spindle synchronous control strategy had a synchronization error of only 14.2 r/min between motor 1 and motor 2, as well as between motor 1 and motor 3. The single-motor and multi-motor synchronous control strategies used in the study have high control accuracy and response efficiency, which is conducive to improving the synchronization accuracy and coordination between motors. The improved strategy provides a reliable control scheme for industrial automation systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Permanent magnets

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Group Hybrid Coordination control of Multi-Agent Systems With Time-Delays and Additive Noises

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 737-748页

作者： Chuanjian Li Xiaofeng Zong School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China IEEE

A new kind of group coordination control problemgroup hybrid coordination control is investigated in this *** group hybrid coordination control means that in a whole multi-agent system(MAS)that consists of two subgroups with communications between them,agents in the two subgroups achieve consensus and containment,*** MASs with both time-delays and additive noises,two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case,*** developing a new analysis idea,some sufficient conditions and necessary conditions related to the communication intensity betw een the two subgroups are obtained for the following two types of group hybrid coordination behavior:1)Agents in one subgroup and in another subgroup achieve weak consensus and containment,respectively;2)Agents in one subgroup and in another subgroup achieve strong consensus and containment,*** is revealed that the decay of the communication impact betw een the two subgroups is necessary for the consensus-oriented ***,the validity of the group control results is verified by several simulation examples.

关键词： Additive noises consensus control containment control group hybrid coordination control multi-agent systems(MASs) time-delays

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Active Magnetic Compensation Based on Parametric Resonance Magnetometer

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 280-289页

作者：郭阳李绍良黄艺明骆曼箬刘华 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Spacefight Control Technology Shanghai Key Laboratory of Space Intelligent Control TechnologyShanghai Engineer Research Center of InertiaShanghai 201109China

Based on the parametric resonance magnetometer(PRM)theory,this paper establishes an experimen-tal system of *** experimental results are consistent with the theoretical predictions.A PRM has been developed with sensitivity of 0.5 pT/Hz^(1/2),which can detect the magnitude of residual magnetic field;furthermore,a proportion-integration-differentiation(PID)closed-loop magnetic compensation system of the residual magnetic field also has been *** with open-loop compensation,the PID closed-loop compensation reduces the average value of the residual magnetic field in the z-axis direction from 0.0244nT to-0.0023nT,and the mean-square error from 0.2083 nT to 0.0691 *** the same way,the average value of the residual magnetic field in the y-axis direction is reduced from 0.0816nT to-0.0042nT,and the mean-square error from 0.1316nT to 0.0461 *** magnitude of residual magnetic fields in both directions is decreased to the order of picotesla(pT).In addition,based on the signal waveforms of the magnetometer,a method of verifying the effect of magnetic compensation is proposed.

关键词： magnetometer parametric resonance nuclear magnetic resonance gyroscope magnetic compensa-tion proportion-integration-differentiation(PID)

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A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

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Autonomous intelligent Systems 2024年第1期4卷 109-125页

作者： Yichen Zhou Wenhe Han Heng Zhou Science and Technology Development Co. Ltd.of Shanghai Research Institute of Building SciencesShanghai 201506China Shanghai Engineering Research Center for Safety Intelligent Control of Building Machinery Shanghai 200032China

Customer maintenance is of vital importance to the enterprise *** assessment and efficientprediction for customer ordering behavior can offer better decision-making and reduce business costs *** to existing studies about customer behavior regularity segment and demand prediction most focus one-commerce and other fields with large amount of data,making them not suitable for small enterprises and datafeatures like sparsity and outliers are not mined when doing regularity ***,more and morecomplex network structures for demand prediction are proposed,which builds on the assumption that all thesamples have predictive value,ignoring the fine-grained analysis of different time series regularity with high *** with the above issues,a multi-step regularity assessment and joint prediction system for ordering time series *** extracting features,comprehensive assessment of customer regularity based on entropy weightmethod with the result of predictability quantification using K-Means clustering algorithm,real entropy,LZWalgorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to‘how highthe regularity of customers is’,filling the gap in the field of regularity quantification,but also provides a theoreticalbasis for demand prediction models *** models:Random Forest regression,XGBoost,CNN andLSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of theproposed regularity quantitation ***,a merged CNN-BiLSTM neural network model is established forpredicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms,which performs better on the data set compared to *** Forest is still used for prediction of customerswith high regularity due to its high training ***,the results of prediction,regularity quantificatio

关键词： Regularity quantification Customer assessment Time series prediction Machine learning Deep learning

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