期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Sequence-To-Sequence Learning for Online Imputation of Sensory Data
1
作者 kaitai tong Teng LI 《Instrumentation》 2019年第2期63-70,共8页
Online sensing can provide useful information in monitoring applications,for example,machine health monitoring,structural condition monitoring,environmental monitoring,and many more.Missing data is generally a signifi... Online sensing can provide useful information in monitoring applications,for example,machine health monitoring,structural condition monitoring,environmental monitoring,and many more.Missing data is generally a significant issue in the sensory data that is collected online by sensing systems,which may affect the goals of monitoring programs.In this paper,a sequence-to-sequence learning model based on a recurrent neural network(RNN)architecture is presented.In the proposed method,multivariate time series of the monitored parameters is embedded into the neural network through layer-by-layer encoders where the hidden features of the inputs are adaptively extracted.Afterwards,predictions of the missing data are generated by network decoders,which are one-step-ahead predictive data sequences of the monitored parameters.The prediction performance of the proposed model is validated based on a real-world sensory dataset.The experimental results demonstrate the performance of the proposed RNN-encoder-decoder model with its capability in sequence-to-sequence learning for online imputation of sensory data. 展开更多
关键词 DATA IMPUTATION RECURRENT NEURAL Network Sequence-To-Sequence Learning SEQUENCE Prediction
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部