scholarly journals Cloud and IoT Based Temperature Prediction System for a Greenhouse Using Multivariate Convolutional Long Short Term Memory Network

2020 ◽  
Vol 10 (1) ◽  
pp. 189-194
Author(s):  
Zar Zar Oo ◽  
◽  
Sabai Phyu
Keyword(s):  
Short Term Memory ◽  
Prediction System ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Research on Winding Temperature Prediction of UHV Transformer Based on Convolutional Long Short-Term Memory Network

2021 ◽  
pp. 109-120
Author(s):  
Weiqing Lin ◽  
Xiren Miao ◽  
Sa Xiao ◽  
Hao Jiang ◽  
Shengbin Zhuang
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Regional soil moisture prediction system based on Long Short-Term Memory network

2022 ◽  
Vol 213 ◽  
pp. 30-38
Author(s):  
Nemanja Filipović ◽  
Sanja Brdar ◽  
Gordan Mimić ◽  
Oskar Marko ◽  
Vladimir Crnojević
Keyword(s):  
Soil Moisture ◽  
Short Term Memory ◽  
Prediction System ◽  
Short Term ◽  
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.

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