Optics and Precision Engineering, Volume. 32, Issue 23, 3457(2024)
Aero-engine nacelle acoustic hole detection system integrating improved semi-supervised segmentation method
[1] D M NARK, M G JONES. An investigation of bifurcation acoustic treatment effects on aft-fan engine nacelle noise(2019).
[2] B M HOWERTON, M G JONES.
[3] B MEI, Z S LIANG, W D ZHU et al. Positioning variation synthesis for an automated drilling system in wing assembly. Robotics and Computer-Integrated Manufacturing, 67, 102044(2021).
[4] B MEI, W D ZHU. Accurate positioning of a drilling and riveting cell for aircraft assembly. Robotics and Computer-Integrated Manufacturing, 69, 102112(2021).
[5] A HERNANDEZ, A MAGHAMI, M KHOSHDARREGI. A machine vision framework for autonomous inspection of drilled holes in CFRP panels, 669-675(2020).
[6] D X GENG, Y H LIU, Z Y SHAO et al. Delamination formation, evaluation and suppression during drilling of composite laminates: a review. Composite Structures, 216, 168-186(2019).
[7] A CAGGIANO, R ANGELONE, R TETI. Image analysis for CFRP drilled hole quality assessment. Procedia CIRP, 62, 440-445(2017).
[8] T H ZHANG, B MEI, L QIAO et al. Detection method for composite hole guided by texture boundary. Journal of Zhejiang University (Engineering Science), 54, 2294-2300(2020).
张太恒, 梅标, 乔磊. 纹理边界引导的复合材料圆孔检测方法. 浙江大学学报(工学版), 54, 2294-2300(2020).
[9] C G LI, Y C LEI, Z C YOU et al. Vision-based defect measurement of drilled CFRP composites using double-light imaging. IEEE Transactions on Instrumentation and Measurement, 72, 3514809(2023).
[10] A MAGHAMI, M SALEHI, M KHOSHDARREGI. Automated vision-based inspection of drilled CFRP composites using multi-light imaging and deep learning. CIRP Journal of Manufacturing Science and Technology, 35, 441-453(2021).
[11] O CHAPELLE, B SCHOLKOPF, E ZIEN. Semi-supervised learning. IEEE Transactions on Neural Networks, 20, 542(2009).
[12] 王晨, 罗文山, 陆鹏飞. 基于半监督学习-多通道卷积神经网络的加氢裂化产品性质预测. 石油学报(石油加工), 39, 97-108(2023).
CH WANG, W SH LUO, P F LU et al. Prediction of hydrocracking product properties based on semi-supervised learning-multichannel convolutional neural network. Acta Petrolei Sinica (Petroleum Processing Section), 39, 97-108(2023).
[13] S ZHANG, F YE, B N WANG et al. Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models. IEEE Sensors Journal, 21, 6476-6486(2021).
[14] SH YANG. Research on Inspection Method of Composite Circular Hole Based on Deep Learning(2019).
杨爽. 基于深度学习的复合材料圆孔检测方法研究(2019).
[15] A PELÁEZ-VEGAS, P MESEJO, J LUENGO. A survey on semi-supervised semantic segmentation. arXiv, 09899(2023).
[16] L H YANG, L QI, L T FENG et al. Revisiting weak-to-strong consistency in semi-supervised semantic segmentation, 7236-7246(2023).
[17] K SIMONYAN, A ZISSERMAN. Very deep convolutional networks for large-scale image recognition. Computer Science(2014).
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Qingyu DONG, Biao MEI, Yun FU, Rongjin YANG, Weidong ZHU. Aero-engine nacelle acoustic hole detection system integrating improved semi-supervised segmentation method[J]. Optics and Precision Engineering, 2024, 32(23): 3457
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Received: Jul. 1, 2024
Accepted: --
Published Online: Mar. 10, 2025
The Author Email: MEI Biao (mechme@126.com)