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Table 1 Reconstruction error (NPMSE, %) and computation time for different kernel factors and different signal to noise ratios (SNR, dB).
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Next, kernel factor analysis is performed for dimension reduction to remove the irrelevant and redundant features.
A new nonlinear feature selection and fusion method, named kernel factor analysis, is proposed to mitigate the aforementioned challenge.
2, the details of the kernel factor analysis-based virtual sensing model are then discussed in Sect.
A third order kernel factor is synthesised from this first order kernel and is processed along with the third order kernel for estimation of the non-linear parameter.
The kernel factor analysis only needs to address the kernel matrix K, which is different from the traditional FA and has a more efficient learning process.
We demonstrated that the kernel factor was to precisely control the ratio of the lateral and vertical etching rate to achieve the desirable geometries.
As shown in Fig. 2, target detection is the first step and a kernel factor in the ground vision-based guidance.
Next, a new nonlinear feature selection and fusion method, named kernel factor analysis (KFA), is proposed to reduce the feature dimensionality.
By taking into account the high computational efficiency and nonlinear projection ability of kernel functions, a kernel factor analysis is investigated for feature selection and representation in gearbox condition monitoring.
The developed virtual sensing technique is experimentally evaluated in spiral bevel gear wear tests, and the results show that the developed kernel factor analysis method outperforms the state-of-the-art feature selection techniques in terms of virtual sensing model accuracy.
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