In recent years, with the further development of big data analysis and artificial intelligence technology, many intelligent methods of machine learning have been used in actual production links. The alloy absorption rate refers to the ratio of the weight of the alloying element absorbed by the molten steel during the deoxidation alloying to the total weight of the added element. The main work of this paper is to predict the absorption rate of C and Mn in the iron and steel industry, and realize the automatic optimization and cost control of alloy ingredients in smart factories. However, in the process of molten steel deoxidation and alloying, the alloy absorption rate is affected by many factors, and it is difficult to determine it by an explicit expression. First of all, this paper analyzes the factors that may affect the absorption rate of C and Mn from the perspectives of mechanism and quantification. Secondly, we utilize the canonical correlation analysis method (CCA) to screen out all factors with a correlation coefficient greater than 0.8 as the main influencing factors of element absorption. Finally, comparing the results of the BP and Elman network models, this paper employs the generalized regression neural network (GRNN) to accurately predict the absorption rate.
In order to quickly and effectively detect lung information in different medical images, this paper designs an improved VGG16 image-based lung opacity classification detection method based on deep transfer learning. This paper applies offline data enhancement technology to increase the number of samples, improves VGG, and employs transfer learning to train the lung recognition model. The results show that the improved VGG16 network has an accuracy rate of 85% for the classification and recognition of lung pictures, and can accurately detect lung pathological mutation information.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.