Consecutive Missing Data Recovery Method Based on Long-Short Term Memory Network

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Shijia Zhu ◽  
Zhiwei Yang ◽  
Hao Liu ◽  
Tianshu Bi
Keyword(s):  
Missing Data ◽  
Short Term Memory ◽  
Data Recovery ◽  
Short Term ◽  
Recovery Method ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems

2020 ◽  
pp. 147592172093281
Author(s):  
Linchao Li ◽  
Haijun Zhou ◽  
Hanlin Liu ◽  
Chaodong Zhang ◽  
Junhui Liu
Keyword(s):  
Missing Data ◽  
Empirical Mode Decomposition ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Measured Signal ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

scholarly journals Synthetic Stage of Charge Estimation for Lithium-ion Batteries Based on Long Short-Term Memory Network Modeling and Adaptive H-Infinity Filter

Energy ◽  
2021 ◽  
pp. 120630
Author(s):  
Zheng Chen ◽  
Hongqian Zhao ◽  
Xing Shu ◽  
Yuanjian Zhang ◽  
Jiangwei Shen ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Network Modeling ◽  
Lithium Ion ◽  
Short Term ◽  
H Infinity ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

2021 ◽  
Vol 60 ◽  
pp. 608-619
Author(s):  
Xianli Liu ◽  
Shaoyang Liu ◽  
Xuebing Li ◽  
Bowen Zhang ◽  
Caixu Yue ◽  
...  
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Tool Wear Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Wear Monitoring ◽  
Memory Network ◽  
Long Short Term Memory
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