Transformer-based dual-input deep learning framework for remaining useful life prediction using sensor fusion and attention mechanism
待确认
10
由 serhg q 发布于 2026/2/1 16:48:54
Sage
应助信息
等待求助人确认
求助人未确认前，本求助不能再次应助。36小时后系统将自动确认应助成功。本求助将自动关闭。
3小时前
龟小乌
24b9306eff6311f0a58f34735aa3bc31.pdf
6小时前
Book学术机器人
2026/2/1 16:48:55
未找到该文献，机器人已退出，请等待人工下载
2026/2/1 16:48:55
Book学术AI机器人收到请求，开始寻找文献
2026/2/1 16:48:55
已向机器人发送请求
6小时前
serhg q
发布求助
上一个求助:
其它数据库Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary
下一个求助:SageA novel sparse Gaussian process regression with time-aware spatiotemporal kernel for remaining useful life prediction and uncertainty quantification of bearings
其它数据库Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary
下一个求助:SageA novel sparse Gaussian process regression with time-aware spatiotemporal kernel for remaining useful life prediction and uncertainty quantification of bearings
京公网安备 11010802042870号
