Artificial neural network surrogate models are often used in the design optimization of the automotive semi-active suspension system. To realize the desired damping force, a surrogate model needs to be constructed to approximate the regulating mechanism of the hydraulic adjustable damper. However, very few of the existing studies discuss how to guarantee the modeling accuracy. To this end, this work constructs a novel surrogate model by using radial basis function neural network. Meanwhile, an adaptive modeling method based on modified hyperband and trust-region-based mode pursuing sampling is presented. Concretely, modified hyperband is used to fast select a seed model by early-stopping and dynamic resource allocation. Mode pursuing sampling is then performed in the neighborhood of the seed model, to systematically generate more sample points while statistically covering the entire neighborhood (i.e., trust region). In particular, in the mode pursuing sampling procedure, quadratic regression is performed around the current optimum as the second detection. Moreover, as the position or size of the trust region changes, the sampling and detection process iterate until the accuracy of the model is no longer improved. To avoid falling into the local optimum, the seed model selection and mode pursuing sampling process iterate until the termination criterion is met. The experimental results show that compared with the benchmarks, the modeling accuracy of hydraulic adjustable damper can be improved by up to 48%, and the iteration resources can be reduced by up to 84%.

The one-dimensional velocity interferometer system for any reflector (VISAR) is widely used in inertial confinement fusion (ICF). Compressed ultrafast photography (CUP) is recently combined with VISAR to enable two-dimensional imaging, in which, the time-varied two-dimensional fringe images reconstruction from one image is a challenging problem. In this Letter, a new l ₁ norm based sparse reconstruction approach is presented, to the best of our knowledge, in which: (1) fringe images are sparsely represented with a few coefficients over the discrete cosine transformation (DCT) basis; (2) the image reconstruction is formulated as an l ₁ norm based sparse coefficients optimization problem; and (3) the two-step iterative shrinkage/threshold algorithm combined with a soft-thresholding operator is proposed to efficiently solve such a problem. Finally, the results show that, compared with the usual method, reconstructed fringe images with clear boundaries and good continuity are obtained. Additionally, the maximum relative error of the velocity is reduced from 14% to 8%, which is a reduction of approximately half.

We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.