Three-Dimensional X-Ray Micro-Tomography Based Microstructure and Mechanical Performance of Hybrid Laser Welded AA7020

胡雅楠,; 吴圣川,; 张思齐,; 焦汇胜,; 付亚楠,

doi:10.3788/cjl201643.0103007

Cited by 2 publications

(1 citation statement)

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“…Many researchers have previously contributed to predictive modeling through data analysis. For example, Hu et al utilized machine learning to predict the mechanical properties of metal in additive manufacturing [2]. Tang et al compared traditional methods with machine learning algorithms for predicting the adaptive weighting combination of rock mechanics parameters in the Mahu Depression Fengcheng Formation [3].…”

Section: Introductionmentioning

confidence: 99%

Revealing the Close Connection Between Tennis Match Momentum and Victory Through Machine Learning Methods

Yu,

Wang,

Dong

2024

HSET

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In competitive sports, especially in tennis matches, momentum is considered a key factor influencing the outcome. However, there is widespread controversy surrounding the definition of momentum, its quantification methods, and its impact on match results. We compared different models (LightGBM, SVM, XGBoost, MLP, logistic regression) using five-fold cross-validation to predict changes in momentum in tennis matches based on a given dataset. The results showed that LightGBM performed best with an accuracy of 77.6%. We selected LightGBM to predict the momentum score for each point of player 1 in the final match. It was found that the average momentum scores in the second, third, and fifth sets were greater than 0.5, which aligned with the actual outcomes. Furthermore, through Pearson correlation analysis and OLS validation of the relationship between momentum changes and match results, we obtained a p-value below 0.05, a Pearson correlation coefficient of 0.59, and an F-statistic of 376.6 in the OLS model, indicating a strong positive correlation between the two.

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Section: Introductionmentioning

confidence: 99%