Imaging Characteristic Analysis Method for Large-Aperture Diffractive Optical System

For the aim of analyzing focusing field characteristics of radially polarized beams, a conjoint analyzing method is presented based on LabVIEW and MATLAB. The main program is programmed in the LabVIEW environment, connecting with MATLAB program through ActiveX control. Experimental results show that the strength of the radial component in the center is zero and the strength of the axial component in the center is the largest. The conjoint analysis method realized characteristic analysis of focusing field for radially polarized beams, laying the groundwork for beam transmission of the late application.

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Wavefront Reconstruction Error Analysis Method for Diffraction Optical System

Acta Optica Sinica ◽

10.3788/aos201939.1105002 ◽

2019 ◽

Vol 39 (11) ◽

pp. 1105002

Author(s):

乔凯 Qiao Kai ◽

智喜洋 Zhi Xiyang ◽

杨冬 Yang Dong ◽

于頔 Yu Di ◽

王达伟 Wang Dawei

Keyword(s):

Error Analysis ◽

Optical System ◽

Reconstruction Error ◽

Analysis Method ◽

Wavefront Reconstruction

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Short-term Load Forecasting Model Considering Meteorological Factors

E3S Web of Conferences ◽

10.1051/e3sconf/201911802050 ◽

2019 ◽

Vol 118 ◽

pp. 02050

Author(s):

Xi Yunhua ◽

Zhu Haojun ◽

Dong Nan

Keyword(s):

Decision Tree ◽

Meteorological Factors ◽

Load Forecasting ◽

Forecasting Model ◽

Analysis Method ◽

Decision Tree Algorithm ◽

Characteristic Analysis ◽

Short Term ◽

Tree Algorithm ◽

Short Term Load Forecasting

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified.

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In-plane dynamic response of a hydraulic pipe subjected to random vibration

Engineering Computations ◽

10.1108/ec-10-2019-0442 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Wei Qu ◽

Huailiang Zhang ◽

Wei Li ◽

Ling Peng ◽

Wenqian Sun

Keyword(s):

Random Vibration ◽

Tunnel Boring Machine ◽

Transmission Efficiency ◽

Hydraulic Systems ◽

Analysis Method ◽

The Finite Element Method ◽

Characteristic Analysis ◽

Content Type ◽

Tunnel Boring ◽

Selection Of

Purpose To improve the transmission efficiency and reduce the damage to pipes in the hydraulic systems of tunnel boring machine subjected to random vibration, this paper aims to propose a novel dynamic characteristic analysis method that considers random vibration. Design/methodology/approach A fluid-structure interaction motion equation of the pipe is established by using Hamilton’s principle. The finite element method and discrete analysis method of random vibration are used to construct a model of the dynamic behavior of the pipe. Findings The influences of fluid parameters and external excitation parameters on the dynamic characteristics of pipes are analyzed. The experimental results are found to be in good agreement with the simulation results, which demonstrates that the proposed analytical method can provide a theoretical reference for the design and selection of hydraulic pipes subjected to random vibration. Originality/value The proposed method can be regarded as a future calculation method for pipes subjected to random vibration, and the transmission efficiency of the pipe can be improved.

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