电子测量与仪器学报

孙志刚,王国涛,高萌萌,郜雷阵,蒋爱平.基于kNN优化算法的密封电子设备多余物定位技术[J].电子测量与仪器学报,2021,35(3):94-104

基于kNN优化算法的密封电子设备多余物定位技术

Sealed electronic equipment loose particle positioning technology based on kNN algorithm of parameter optimization

DOI：

英文关键词:sealed electronic equipment loose particle kNN algorithm feature extraction positioning

基金项目:国家自然科学基金（51607059，51077022）、黑龙江省自然科学基金（QC2017059）、黑龙江省博士后基金（LBH Z16169）、黑龙江省高校基本科研业务费（HDRCCX-201604）、黑龙江省教育厅科技成果培育（TSTAU C2018016）、黑龙江大学校内项目（HDJMRH201912，2012TD007，QL2015）资助

作者	单位
孙志刚	1.黑龙江大学电子工程学院哈尔滨150080；
王国涛	1.黑龙江大学电子工程学院哈尔滨150080；2.哈尔滨工业大学军用电器研究所哈尔滨150001
高萌萌	1.黑龙江大学电子工程学院哈尔滨150080；
郜雷阵	2.哈尔滨工业大学军用电器研究所哈尔滨150001
蒋爱平	1.黑龙江大学电子工程学院哈尔滨150080；

Author	Institution
Sun Zhigang	1.Electronic Engineering College， Heilongjiang University, Harbin 150008, China;
Wang Guotao	1.Electronic Engineering College， Heilongjiang University, Harbin 150008, China;2.Military Apparatus Research Institute, Harbin Institute of Technology, Harbin 150001, China
Gao Mengmeng	1.Electronic Engineering College， Heilongjiang University, Harbin 150008, China;
Gao Leizhen	2.Military Apparatus Research Institute, Harbin Institute of Technology, Harbin 150001, China
Jiang Aiping	1.Electronic Engineering College， Heilongjiang University, Harbin 150008, China;

摘要点击次数: 883

全文下载次数: 925

中文摘要:

在密封电子设备的生产制造过程中，对多余物进行检测至关重要。微粒碰撞噪声检测法是我国军标规定的用于航天电子元器件多余物检测的方法。针对密封电子设备体积大和检测出的多余物位置难以确定的问题，使用参数优化的k邻近(kNN)算法对多余物进行定位。通过搭建定位实验系统和设计试件模型，得到多通道的多余物信号，提取性能优良的时域和频域特征作为kNN算法学习的数据集。采用网格搜索法寻找kNN算法最优的k值选择、距离度量和权重设置，然后采用参数优化的kNN算法分别建立平面与空间定位模型。实验结果表明，采用参数优化的kNN算法进行多余物定位，平面与空间定位精度分别达到8118%和7934%，有效提高了传统情况下的定位准确度。

英文摘要:

Detection of the loose particles is urgently required in the production of sealed electronic equipment. Particle impact noise detection is a national aerospace electronic component loose particles detection method. Aiming at the problem of the large volume of the sealed electronic equipment and the difficulty in determining the position of the loose particle, this paper uses the parameter optimized kNN algorithm to locate the loose particle. After building a positioning experiment system and designing a specimen model, a multi channel loose particle signal is obtained, and the time domain and frequency domain features with excellent performance are extracted as the data set for kNN algorithm learning. The grid search method is used to find the optimal k value selection, distance measurement and weight setting of the kNN algorithm, then the kNN algorithm of parameter optimization is used to establish the plane and space positioning models respectively. The experimental results show that using the kNN algorithm of parameter optimization for loose particle positioning, the accuracy of plane and space positioning reaches 8118% and 7934% respectively, which effectively improves the positioning accuracy under traditional conditions.

查看全文查看/发表评论下载PDF阅读器