.../01457638708962781 [39] Pedregosa , F. , Varoquaux , G. , Gramfort , A. , Michel , V. , Thirion , B. , Grisel , O. , Blondel , M. , , 2011 , “ Scikit-Learn. Machine Learning in Phyton ,” J. Mach. Learn. Res. , 12 , pp. 2825 – 2830 . https://scikit-learn.org/stable/ [40...

... parameters on heat treatment quality. This analysis was integrated with machine learning technology to guide heat treatment production. The research findings indicate that when the number of workpieces remains constant, their position has a significant impact on airflow velocity distribution, heating rate...

... interest in the application of machine learning techniques to oscillating heat pipe modeling. Our survey of the literature has revealed that machine learning techniques have successfully been used to predict different aspects of the operation of these devices. However, many fundamental questions...

...B. C. Anilkumar; Ranjith Maniyeri; S. Anish The development of prediction models for solar thermal systems has been a research interest for many years. The present study focuses on developing a prediction model for solar box cookers (SBCs) through computational and machine learning (ML) approaches...

... will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates...

... on only a single image rather than a sequence of images or signal processing. Successful identification of wave behavior using image classification serves as a steppingstone for further machine learning integration in RDE research and development of comprehensive real-time diagnostics. Email...