Identifying Reliability High-Correlated Gates of Logic Circuits With Pearson Correlation Coefficient

Shi, Zhanhui; Xiao, Jie; Jiang, Jianhui; Zhang, Ying; Zhou, Yuhao

doi:10.1109/tcsii.2023.3334390

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2024

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Cited by 7 publications

References 12 publications

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Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots

Ma,

Li,

Yang

et al. 2024

J Bionic Eng

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Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots

Ma,

Li,

Yang

et al. 2024

J Bionic Eng

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A hybrid model based on novel SVM-SVR and weighted combination strategy for build-up rate prediction

Wang,

Geng,

Zhang

et al. 2024

Meas. Sci. Technol.

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The build-up rate prediction is of great importance for trajectory control in the field of drilling. However, it is very difficult to achieve accurate prediction due to the complexity, nonlinearity, and multiple uncertainties of the drilling system. As a consequence, a novel hybrid prediction model is proposed, which uses multiple feature selection methods, the model combination strategy based on machine learning, and three prediction models to improve the prediction accuracy of the build-up rate. More precisely, correlation analysis, importance analysis, and statistical analysis are employed to ensure the effectiveness of feature selection. Then, a novel classification prediction model called SVM-SVR is proposed to improve the accuracy of samples with the higher build-up rate. Subsequently, the SVR optimized by grey wolf optimizer (GWO-SVR) and back propagation (BP) neural network are constructed. Finally, the three models are integrated by a weighted combination method based on SVR to realize the accurate prediction of the build-up rate. To verify the performance of the hybrid model, the data of the Z48 well in Sichuan province is used, and the results show that the hybrid model can reduce by 22.7% in mean absolute error (MAE) and 32% in mean square error (MSE) when compared with the existing models.

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Intelligent Analysis and Software Development of Converter Transformer Failure Cases Based on Pearson Correlation Coefficient

He,

Zhang,

Cao

et al. 2024

2024 3rd Asian Conference on Frontiers of Power and Energy (ACFPE)

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Identifying Reliability High-Correlated Gates of Logic Circuits With Pearson Correlation Coefficient

Cited by 7 publications

References 12 publications

Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots

Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots

A hybrid model based on novel SVM-SVR and weighted combination strategy for build-up rate prediction

Intelligent Analysis and Software Development of Converter Transformer Failure Cases Based on Pearson Correlation Coefficient

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