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With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. In order to effectively make up for the impact of the insufficiency of the collection process on the quality of the final panoramic image of Lingnan cultural heritage, it is necessary to minimize the irregular rotation of the camera and collect images according to the overlapping area between adjacent images of appropriate size. In order to make Lingnan cultural heritage panoramic images have better visual effects, it is necessary to preprocess the images before image registration and fusion. Image preprocessing mainly includes image denoising and image projection transformation. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. In order to generate each component classifier, we determine the overlap area of the two images according to the matched SIFT feature points and determine the best stitching line during the implementation of stitching. In this paper, the number of pixels in the first row of the overlapping area is used to determine the candidate stitching line column, and the best stitching line position should be determined in consideration of the smallest color difference in the stitching area and the most similar texture on both sides. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This research will promote the development of Lingnan cultural heritage.

This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimization problem, several unconstrained optimization problems and one typical simulation-based optimization problem, i.e., (s, S)-inventory problem.

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General Purpose Graphics Units (GPGPUS) have seen a tremendous rise in scientific computing application. To fully utilize the powerful parallel computing ability of GPU， and combine the isolation characteristic of virtualization, a GPU virtualization method that supports dynamic scheduling and multiuser concurrency is proposed. For multi-task of GPU general computing programs in virtualization environment, the existing GPU scheduling algorithms have been improved for achieving a more fine-grained and more accurate load evaluation. For large-scale computing programs, we present a method for multi-GPU collaborative computing in virtualization environment, which can effectively deals with accelerating the large-scale program on multi-GPU within a single node. In the experiments, we make verifications by using the representative scientific computing examples, such as classical matrix calculation and discrete Fourier transformation. The experimental results prove that with the increasing of the calculation scale, the speedup can go up and finally close to the numbers of GPU.