基于图像识别技术的随机电缆束分布参数建模与统计分析

Q4 Engineering
Rong Fan, Zhong Longquan, Liu Qiang, Yan Liping, Zhao Xiang
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引用次数: 0

摘要

本文提出了一种实际弯曲随机捆扎线束的建模方法。首先,基于图像识别技术,利用实际线束的两张侧视图和俯视图,重建弯曲线束轴线的三维坐标;然后基于随机传递路径法实现了线束的随机捆扎。基于这种建模方法,本文通过蒙特卡洛模拟分析了弯曲随机线束分布参数的统计特征,发现自感、互感和互电容沿线路的变化趋势与线束高度的变化趋势一致,而自电容的变化趋势相反;自电容、自感和互感的变化系数与线束高度呈负相关;捆绑随机性不明显。它会改变自感和自电容的平均值,但会降低互感和互感的平均值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and statistical analysis of distribution parameters of random cable bundles based on image recognition technology
In this paper, a modeling method of actual bending random bundled wire harness is proposed. Firstly, based on image recognition technology, the three-dimensional coordinates of bending wire harness axis are reconstructed by using two photos of actual wire harness in side view and top view; then the random bundled wire harness is realized based on random transfer path method. Based on this modeling method, this paper analyzes the statistical characteristics of distribution parameters of bending random wire harness by Monte Carlo simulation, and finds that the variation trend of self inductance, mutual inductance and mutual capacitance along the line is consistent with the variation trend of wire harness height, while the trend of self capacitance is opposite; the coefficient of variation of self capacitance, self inductance and mutual inductance has negative correlation with wire harness height; the bundling randomness is not obvious It will change the mean value of self inductance and self capacitance, but reduce the mean value of mutual capacitance and mutual inductance.
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来源期刊
强激光与粒子束
强激光与粒子束 Engineering-Electrical and Electronic Engineering
CiteScore
0.90
自引率
0.00%
发文量
11289
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