Towards Automatic Robotic NDT Dense Mapping for Pipeline Integrity Inspection
暂无分享,去创建一个
[1] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[2] Zheng Liu,et al. State of the art review of inspection technologies for condition assessment of water pipes , 2013 .
[3] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[4] Xinjun Wu,et al. An improved ferromagnetic material pulsed eddy current testing signal processing method based on numerical cumulative integration , 2015 .
[5] Alen Alempijevic,et al. Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.
[6] Jian Ji,et al. Probabilistic physical modelling of corroded cast iron pipes for lifetime prediction , 2017 .
[7] J Valls Miro,et al. Towards Optimized and Reconstructable Sampling Inspection of Pipe Integrity for Improved Efficiency of NDT , 2016 .
[8] Xinjun Wu,et al. Assessment of wall thinning in insulated ferromagnetic pipes using the time-to-peak of differential pulsed eddy-current testing signals , 2012 .
[9] Yihua Kang,et al. Pulsed eddy current signal processing method for signal denoising in ferromagnetic plate testing , 2010 .
[10] Jaime Valls Miro,et al. 3D Point Cloud Upsampling for Accurate Reconstruction of Dense 2.5D Thickness Maps , 2014 .
[11] Li Xie,et al. Heavy-duty omni-directional Mecanum-wheeled robot for autonomous navigation: System development and simulation realization , 2015, 2015 IEEE International Conference on Mechatronics (ICM).
[12] Bing Qiao,et al. Kinematic Model of a Four Mecanum Wheeled Mobile Robot , 2015 .