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Geosynthetic-reinforced and pile-supported (GRPS) embankments are increasingly used in recent years to reduce the differential settlement and increase the bearing capacity of the embankments. A new theoretical model is proposed to calculate the tensile force distribution and cross-sectional configuration of the geosynthetic reinforcement used in the Geosynthetic-reinforced and flexible pile-supported GRRPS and Geosynthetic-reinforced and rigid pile-supported GRFPS embankments. The accuracy of the proposed methods is verified by the results from laboratory model tests, centrifuge model tests and theoretical models in the literature. It is found that the tensile forces along the geosynthetic are distributed unevenly both in GRRPS and GRFPS embankments with the maximum and minimum magnitudes located at the edge and centre of the pile cap, respectively. The soil arching effect contributes more to the bearing capacity of the GRPS embankment than the membrane effect. It is found through the analyses of the centrifuge and model tests data that the soil arching effect contributes 65–75% of the total load transfer efficiency. • A theoretical method is proposed to analyze the geosynthetic reinforcement used in the GRRPS and GRFPS embankments. • The accuracy of the proposed methods is verified using the results from model tests and theoretical models in the literature. • The tensile forces are nonlinearly distributed along geosynthetic with their maximum magnitudes located at the edge of pile cap. • It is found that the soil arching effect contributes 65–75% of the total load transfer efficiency. [ABSTRACT FROM AUTHOR]

Radiation computation is very important for high energy density experiments design in the laser-driven Inertial Confinement Fusion. The view-factor based models are often used to calculate the radiation on the capsule inside a hohlraum. However, it usually takes much time to solve them when the number of equations is very large. In this paper, an efficient iteration approach GPU is presented. The core idea is: (1) guaranteed symmetry, strictly diagonally dominant, and positive definite properties underlying the models are described, (2) a preconditioned conjugate gradient iteration approach is presented to compute the radiation based on such guaranteed properties, and (3) such approach is then parallelized and implemented for GPU so that the large scale models, especially for the non-linear model, can be efficiently solved in reasonable time. Finally, two experimental targets for Shenguang laser facilities built in China are demonstrated and compared to validate the efficiency of the presented approach. The results show that, the models’ computation (1) can be speeded up with successive over-relax iteration method by eight times as compared with Cholesky factorization based direct approach, (2) can be accelerated more with the preconditioned conjugate gradient iteration approach by almost eight times, and (3) can be further accelerated about 2 to 4 times as it parallelized and run on the GPU, which enables the large scale models, can be efficiently solved in reasonable time on the usual desktop computers. [ABSTRACT FROM AUTHOR]

Computation-intensive analyses/simulations are becoming increasingly common in engineering design problems. To improve the computation efficiency, surrogate models are used to replace expensive simulations of engineering problems. This paper proposes a new high-fidelity surrogate modeling approach which is called the Sparsity-promoting Polynomial Response Surface (SPPRS). In the SPPRS model, a series of Legendre polynomials is selected as basis functions, and its number is compatible with the sample size so as to enhance the expression ability for complex functional relationships. The coefficients associated with basis functions are estimated using a “sparsity-promoting” regression approach which is an ensemble of two techniques: least squares and ℓ 1 -norm regularization. As a result, only these basis functions relevant to explain the function relationship are picked out, and that dedicates to ease the problem of overfitting for training points. With the sparsity-promoting regression approach, such a surrogate model intends to capture both the global trend of the functional variation and a reasonable local accuracy in the neighborhood of training points. Additionally, Latin hypercube design (LHD) is proved conducive to improving the predictive capability of our model. The SPPRS is applied to seven benchmark test functions and a complex engineering problem. The results illustrate the promising benefits of this novel surrogate modeling technique. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods. [Copyright &y& Elsevier]

In this paper, we present a new -spline surface reconstruction approach, called dynamic surface reconstruction, aiming to close the sensing-and-modeling loop in 3D digitization. At its core, this approach uses a recursive least squares method, the Kalman filter, to dynamically reconstruct the -spline surface as the surface data are acquired. That is, the acquired data are dynamically incorporated into the surface model and the updated surface model is then used to dynamically guide further data acquisition. It thus enables a closed-loop shape sensing-and-modeling methodology for 3D digitization. Our technical contribution lies on the exploitation of the recursive nature of the Kalman filter for -spline surface reconstruction. This enables dynamic parameterization of data points, dynamic determination of next optimal sensing locations, and low-discrepancy based efficient sensing and reconstruction. Experiments demonstrate that such dynamic surface reconstruction leads to more efficient data acquisition and better surface reconstruction. [Copyright &y& Elsevier]

Highlights • A high sparse response surface method based on combined bases is proposed. • Sparsest solution is relaxed to ℓp -norm (p= 1/2) minimum solution. • Cross-validation method is proposed to select the initial value. • High sparse representation decreases the number of sampling and improves the accuracy of response surface. Abstract Product optimization requires many times of simulation which is often time-consuming. The sparse response surface, which is constructed over single orthogonal polynomial bases and sparse coefficients from a few samplings, is employed to reduce simulation times. However, it still requires many samplings for response surface of complex products. In this paper, a High Sparse Response Surface (HSRS) method based on combined bases is proposed with the following main contributions: (1) compared with a single base, a base dictionary is combined with a variety of different base functions, and maybe construct sparser response surface by less expressive bases, which reduced the number of sampling and improved the approximation accuracy, (2) ℓ p -norm (p =1/2) minimum solution, which is calculated by the Conjugate Gradient-FOCal Underdetermined System Solver (CG-FOCUSS) method, is used to approximate the sparest solution through calculating cost and coefficient sparsity trade-off, and (3) cross-validation is employed to select good initial value to obtain approximation optimal solution, which reduces the influence of the initial value on the CG-FOCUSS algorithm result. Finally, HSRS is applied to three benchmark test functions and two engineering problem, and the results are compared with the single base sparse response surface. The results show that (1) about 14.3% to 44.4% sample points can be reduced for HSRS to achieve the same accuracy of single base sparse response surface, (2) the accuracy of HSRS with cross-validation can be increased by about 20.31% to 40.81%. [ABSTRACT FROM AUTHOR]

Response surface method is often employed in simulation based design and optimization for complex products. The sparsity of response surface on the mathematic basis has been explored to accurately represent the variation between design variables and performance response with only a few design points, which is very beneficial to efficient design optimization. Due to the selected basis, it may lead to a large deviation, or under-fitting of the reconstructed response surface since the number of sampling points is often smaller than its sparseness. In this paper, a quasi-sparse response surface is presented to trade-off the sparsity and variation of response surface by introducing coefficient shrinkage regularization and uniformly sampling for the design points, which enables more atoms in the basis included to accurately and robustly reconstruct the surface. The group of basis atoms which are correlated with sampling points instead of the most correlated one are all selected to uniform express the sampling points, and the coefficients of basis atoms are shrunk to improve the prediction performance of the model. Finally, 9 benchmark functions and 1 engineering applications are utilized to demonstrate the significance of the presented approach by comparing with other normally used response surface models, The results shows that the accuracy and robustness of the reconstructed response surface is superior than those of other response surface approaches. [ABSTRACT FROM AUTHOR]

• Efficient camera calibration approach presented for quality inspection of products. • Such calibration is formulated as a non-linear multi-parameter optimization problem. • Multi-parameters are initialized with K-SVD dictionary learning and online one image. • Achieves comparable calibration with only one image as that of usual methods with 9 images. Camera calibration is essential for accurate product visual inspection. In this paper, a novel efficient camera calibration approach based on K-Singular Value Decomposition (K-SVD) sparse dictionary learning is presented, in which, (1) the nonlinear optimization model with ten calibration parameters is formulated, (2) a large amount of images of checkerboards are acquired offline at different locations in the working volume of the camera to construct a sparse dictionary through K-SVD sparse dictionary learning, which enables such non-linear optimization model appropriately initialized and efficiently converges, and (3) in addition, the sparse dictionary can be further updated with the new acquired image so that it can be used to timely characterize the camera variation due to lens aging, and still be used to obtain appropriate initial values. Finally, three experiments are demonstrated to validate the efficiency of the presented approach. The results show that (1) the camera can be accurately calibrated with only one image, which can save almost more than 60 percent calibration time, and is very beneficial to online calibration for quality inspection, and (2) the calibration is much more accurate than that of traditional approaches with only one image having identical grid pixels. [ABSTRACT FROM AUTHOR]

• An efficient GPU parallel optimal point searching approach is proposed. • Branch and bound is used in the proposed approach. • Interval arithmetic is employed to delimit the subsets after branching. • The proposed method can be works on PC with a GPU-capable graphics card. Response surface-based simulation optimization method is widely used in the design of complex products for its low-cost in optimization target valuation. However, when design variables increase, it often takes considerable time for high-dimensional response surface to search the optimal point, which falls easily into the local optimum due to the large search space. To solve these problems, a GPU (Graphic Processing Unit) parallel optimization based on branch and bound is proposed in this paper, of which the main algorithm flow can be described as the following steps: the optimization space is branched to subsets and mapped to different GPU threads; the Chebyshev response surface is constructed within the threads; the compact convex hull of the subsets are obtained through the interval operation, and the optimization space is reduced on a large scale by pruning; all subsets that may contain optimal design points are efficiently obtained by repeating spatial subdivision and demarcation; finally, all the reserved subsets are mapped to different GPU threads, and all global optimization design points are obtained through sequential quadratic programming and comparative analysis. [ABSTRACT FROM AUTHOR]