In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis (PCA) subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit (CPU). This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm.

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Hollow fiber membranes are used in industrial processes widely. Porosity is one of the important parameters affecting the humidification performance of hollow fiber membrane components. The aim of this study was to analyze the effect of porosity of hollow fiber membrane on humidification performance. In order to perform this analysis, a model based on the finite element method was used to simulate numerically the heat and mass transfer under 6 porosity conditions. Five working conditions with different air flow was considered in order to get more data. The results show that when the porosity increases from 0.35 to 0.8, the humidification performance is greatly improved. However, when it increases from 0.8 to 0.9, the humidification performance is almost unchanged. Considering the humidification performance and support strength of hollow fiber membrane, it is suggested to control the porosity of hollow fiber membrane between 0.65 and 0.8.

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