scholarly journals Prediction of high frequency resistance in polymer electrolyte membrane fuel cells using Long Short Term Memory based model

Energy and AI ◽  
2020 ◽  
pp. 100045
Author(s):  
Tong Lin ◽  
Leiming Hu ◽  
Willetta Wisely ◽  
Xin Gu ◽  
Jun Cai ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Polymer Electrolyte ◽  
High Frequency ◽  
Short Term Memory ◽  
Polymer Electrolyte Membrane ◽  
Short Term ◽  
Term Memory ◽  
Electrolyte Membrane ◽  
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.

scholarly journals A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
A. V. Medvedev ◽  
G. I. Agoureeva ◽  
A. M. Murro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Short Term Memory ◽  
Global Network ◽  
Short Term ◽  
Term Memory ◽  
Network Training ◽  
High Frequency Oscillations ◽  
Long Short Term Memory

AbstractOver the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50–500 events (per class) from all patients from the 1st dataset. This ‘global’ network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.

SHORT-TERM FINANCIAL TIME SERIES ANALYSIS WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

2021 ◽  
Vol 3 (4) ◽  
pp. 165-177
Author(s):  
M. V. Labusov ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Frequency ◽  
Short Term Memory ◽  
Financial Time Series ◽  
Short Term ◽  
Term Memory ◽  
Financial Time ◽  
Long Short Term Memory ◽  
In The Beginning

The process of creating a long short-term memory neural network for high-frequency financial time series analyzing and forecasting is considered in the article. The research base is compiled in the beginning. Further the estimation of long short-term memory neural network parameters is carried out on the learning subsamples. The forecast of future returns signs is made for the horizon of 90 minutes with the estimated neural network. In conclusion the trading strategy is formulated.

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