This study analyzes the effect of momentum on the outcome of tennis matches, using the 2023 Wimbledon men's singles final as an example. The match data was preprocessed, including invalid sample treatment, outlier treatment, data normalization, feature screening and the innovative combination of wavelet analysis and Bayesian Optimization are used in data preprocessing. An ARIMA model was constructed to analyze the effect of match duration on the outcome. The study found that the longer the duration of the match, the more difficult it is to predict. A Pearson correlation analysis model was used to determine the correlation between the distance moved by players and wins and losses. The random forest model analysis showed that serve order, match point, and set point were the key factors influencing the match outcome. Additionally, Djokovic's momentum changes were evaluated through hierarchical analysis, and the model's accuracy was verified using the 2022 Qatar World Cup Final as an example. The study proposes a coaching strategy based on risky decision-making to help coaches effectively coach athletes during matches.

The findings of this study underscore the efficacy of integrating the time modelling capability of LSTM with the robustness of integrated methods to enhance the stability and operational reliability of the power grid. This study has contributed to the development of elastic DSE technology that can adapt to the complexity challenges of modern power grids.

This study compares the performance of the bandpass filtering method and the Prony algorithm in identifying parameters of power system electromechanical oscillation. The bandpass filtering method suppresses noise by selectively passing signals in a certain frequency range, while the Prony algorithm directly estimates the signal parameters. In simulations, we find that the Prony algorithm outperforms the bandpass filtering method in extracting signal features. To further improve the performance of the Prony algorithm, we introduced a genetic algorithm for global optimization. This study provides an important reference for the selection of parameter identification methods for power system oscillation analysis, which is of practical importance for improving system stability and security. In addition, this study provides a new perspective on the application of genetic algorithms in signal processing and a theoretical basis for further research in related fields.