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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Shangbin, Jiao Dongbin, Hou Ding, Liu Weichao, Huang Natl. and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China

ISBN: (纸本)9781538637579

Based on the analysis of the Cusp magnetic field suppression convection principle, the simulation model of superconducting Cusp magnetic field is built by the finite element method. In the case of satisfying the magnetic field strength of the crucible wall and giving the excitation current, the number of layers of the superconducting coil is calculated by using the established model. The influence of coil spacing, thickness of shielding thickness, horizontal distance between coil and shielding thickness and longitudinal distance on magnetic induction strength, horizontal distance and longitudinal distance between coil and the shielding are analyzed under the condition that the number of superconducting coils is constant. The influence of the force on the coil, the influence of the thickness of the shielding body on the magnetic leakage strength of the magnetic field. The simulation results show that the optimized Cusp magnetic field structure parameters can be used in the actual design. © 2018 IEEE.

关键词： Finite element method

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Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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International Journal of Agricultural and Biological engineering 2017年第5期10卷 177-185页

作者： Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects *** the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor *** research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral *** involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition *** maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects *** performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关键词： distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification

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Temperature and strain sensitivities of surface and hybrid acoustic wave Brillouin scattering in optical microfibers

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Chinese Physics B 2022年第9期31卷 333-339页

作者： Yi Liu Yuanqi Gu Yu Ning Pengfei Chen Yao Yao Yajun You Wenjun He Xiujian Chou Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control SystemMinistry of Education and Shanxi ProvinceTaiyuan 030024China Taiyuan University of Technology Institute of Optoelectronic EngineeringCollege of Physics and OptoelectronicsTaiyuan 030024China Strong Digital Technology Co. Ltd.(Thinvent)Nanchang 410000China North University of China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationTaiyuan 030051China North University of China College of Mechatronics EngineeringTaiyuan 030051China North University of China Science and Technology on Electronic Test and Measurement LaboratorySchool of Instrument and ElectronicsTaiyuan 030051China

Temperature and strain sensitivities of surface acoustic wave(SAW)and hybrid acoustic wave(HAW)Brillouin scat-tering(BS)in 1μm-1.3μm diameter optical microfibers are *** contrast to stimulated Brillouin scattering(SBS)from bulk acoustic wave in standard optical fiber,SAW and HAW BS,due to SAWs and HAWs induced by the coupling of longitudinal and shear waves and propagating along the surface and core of microfiber respectively,facilitate innovative detection in optical microfibers *** highest temperature and strain sensitivities of the hybrid acoustic modes(HAMs)are 1.082 MHz/℃and 0.0289 MHz/με,respectively,which is suitable for microfiber sensing applica-tion of high temperature and strain ***,the temperature and strain sensitivities of the SAMs are less affected by fiber diameter changes,ranging from 0.05 MHz/℃/μm to 0.25 MHz/℃/μm and 1×10^(-4) MHz/με/μm to 5×10^(-4) MHz/με/μm,*** can be found that that SAW BS for temperature and strain sensing would put less stress on manufacturing constraints for optical ***,the simultaneous sensing of temperature and strain can be realized by SAW and HAW BS,with temperature and strain errors as low as 0.30℃-0.34℃and 14.47με-16.25με.

关键词： Brllouin scatering surface acoustic waves hybrid acoustic waves optical microfiber sensing

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Multi-task Learning Based Single Temporal Remote Sensing Image Change Detection 12

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Yang, Peixuan Cao, Yichao Lu, Xiaobo College of Software Engineering Southeast University JiangSu NanJing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education JiangSu NanJing China School of Automation Southeast University JiangSu NanJing China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images' semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset. © 2023 ACM.

关键词： Change detection

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Vibration fatigue analysis and research of URV shunt reactor 14

Vibration fatigue analysis and research of URV shunt reactor

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14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019

作者： Li, Shengmin Zheng, Zhicong Liang, Jining Xiao, Yamin Faculty of Automation and Information Engineering Xi'an University Ot Technology Xi'an710048 China Intelligent Information Processing Shaanxi Key Laboratory of Complex System Control Xi' an710048 China

ISBN: (纸本)9781538694909

Problems of vibration fatigue in UHV reactor is mentioned, comparative analysis of several commonly used fatigue analysis methods, finally, the modal superposition method is used to calculate the structural fatigue caused by vibration. Through analysis of reactor vibration data, the reason for using frequency domain signals as fatigue frequency domain load spectrum to calculate the service life of reactors. In order to perform reactor fatigue analysis, a finite element model of the reactor was first built using ANSYS Workbench finite element integration software, the frequency response function FRF is obtained by harmonic response analysis, and then the vibration time domain signal are transformed into frequency signal as load spectrum input with the power density spectrum, finally, nCode fatigue analysis software is used to estimate the service life of the reactor. © 2019 IEEE.

关键词： Finite element method

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Target Recognition Inference Based on Knowledge Graph and Graph Neural Network

Target Recognition Inference Based on Knowledge Graph and Gr...

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2024 China Automation Congress, CAC 2024

作者： Li, Wei Xu, Hongfeng Li, Yuan Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350368604

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Knowledge graph

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection 37

A Multi-Kernel Principal Component Analysis Method for Quali...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Mu, Lingxia Lei, Biyu Liu, Ding Xi'An University of Technology School of Automation and Information Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

ISBN: (纸本)9781665465366

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction. © 2022 IEEE.

关键词： Fault detection

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Modeling based on MOGA and the dynamic Ε -SVM for fermentation process

Modeling based on MOGA and the dynamic Ε -SVM for fermentat...

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6th World Congress on Intelligent control and Automation, WCICA 2006

作者： Xuejin, Gao Pu, Wang Chongzheng, Sun Yating, Zhang Huiqing, Zhang Yi, Jianqiang College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Key Laboratory of Complex System and Intelligence Science Chinese Academy of Sciences Beijing 100033 China

ISBN: (纸本)1424403324

To found the suitable models to describe the behavior of biochemistry systems, the dynamic Ε -SVM method was proposed on the basis of SVM. Each training sample uses different error. The existed methods for selecting the parameters of SVM not only consume time, but also are difficult to find the optimal parameters. The optimal parameters were automatically decided by using multi-object Genetic Algorithm (MOGA). A new modeling method that combined MOGA with the dynamic Ε -SVM was presented. The model for penicillin titer preestimate was developed by it in Matlab6.5 with data collected from real plant. The model possesses the strong capability of fitting and generalization. Experiments show that the dynamic Ε -SVM is superior to the standard SVM modeling method. MOGA is very feasible and efficient too. ©2006 IEEE.

关键词： Fermentation

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Research on data-based nonlinear fault prediction theory and methods in multi-transform domains for electromechanical equipment

Research on data-based nonlinear fault prediction theory and...

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23rd International Congress on Condition Monitoring and Diagnostic engineering Management, COMADEM 2010

作者： Wang, Shaohong Chen, Tao Xu, Xiaoli School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing China Beijing Key Laboratory on Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9784883254194

Most faults of large-scale electromechanical equipment are trendy ones, often have long course characteristics. As the fault information is usually lost in non-fault information of condition changes, the traditional methods are difficult to predict it effectively. To solve this problem, field data-based fault prediction theory and methods are studied primary for large rotating electromechanical equipment, and new nonlinear prediction way in multi-transform domains is proposed to achieve long course prediction for the equipment under variable operating conditions. In the new way fault feature extraction method of nonlinear dimensionality reduction is investigated to extract fault and potential fault sensitive information and to separate faulty development changes from non-fault energy changes. The research is important for large electromechanical equipment to achieve early fault prediction, guarantee safe operation, save maintenance costs, improve utilization and implement scientific maintenance.

关键词： Feature extraction

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