The Application of Piecewise Regularization Reconstruction to the Calibration of Strain Beams.

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies.

Application of vision measurement model with an improved moth-flame optimization algorithm.

An improved moth-flame optimization (IMFO) algorithm is proposed to increase the location accuracy of a vision measurement system. This algorithm can optimize the initial pose parameters by improving a series of random solutions to the required precision. A measurement experiment system of space manipulator is designed to precision test. The IMFO algorithm is evaluated on 23 benchmark functions and measurement experiments for pose, and the results are verified by a comparative study with self-adaptive differential evolution (SaDE), moth-flame optimization (MFO), and proactive particle swarm optimization (PPSO). The statistical results of the benchmark functions show that the IMFO algorithm can provide very promising and competitive results. Additionally, the experimental results of pose measurement show that the accuracy of the IMFO algorithm is approximately twice higher than that of other three algorithms. All in all, the experiments indicate that the IMFO algorithm has a good optimization ability to complete the visual identification accurately.