Publications by authors named "Honghan Ye"
Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability.
View Article and Find Full Text PDFScientists have expressed concern that the risk of flawed decision making is increased through the use of preprint data that might change after undergoing peer review. This Health Policy paper assesses how COVID-19 evidence presented in preprints changes after review. We quantified attrition dynamics of more than 1000 epidemiological estimates first reported in 100 preprints matched to their subsequent peer-reviewed journal publication.
View Article and Find Full Text PDFArticle Synopsis
- The paper discusses a method to reduce thermal errors in machine tools by accurately modeling and compensating for temperature changes, which is crucial for maintaining machine accuracy.
- It introduces an approach based on ambient temperature intervals (ATIs), using the C-Means clustering algorithm to identify these intervals, which helps improve prediction accuracy and robustness of thermal error models.
- The ATI model outperforms traditional low and high temperature models, showing significant improvements in prediction accuracy (20.6% to 41.7% higher) and robustness (48.8% to 62.0% higher), demonstrating its effectiveness across varying seasonal temperatures.