Results of Oil Condition Recognition Accuracy in the Training Set Under Different Algorithms Across Iteration Cycles.

<p>Results of Oil Condition Recognition Accuracy in the Training Set Under Different Algorithms Across Iteration Cycles.</p>

Saved in:

Bibliographic Details
Main Author:	Shiya Gao (12693389) (author)
Other Authors:	Xin Guan (6987) (author), Xiaojing Cao (11668306) (author), Zhili Bai (20933580) (author), Caimeng Wang (20933583) (author), Yun Zhan (5225441) (author), Haiyang Yu (543050) (author)
Published:	2025
Subjects:	Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified decision support technology 08 %, respectively offshore oil monitoring mobilenet v2 network mobilenet v2 architecture oil condition monitoring detailed feature fusion average loss value classification model based mobilenet v2 oil condition loss value xlink "> sustainable utilization study aims results demonstrate proposed model model utilizes model constructed input images faster r experimental assessments experiment develops enterprises aiming development process deep learning carbon neutrality approximately 0 algorithms remains 90 %.
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items