GTCC-based BiLSTM deep-learning framework for respiratory sound classification using empirical mode decomposition

2021 ◽  
Author(s):  
S. Jayalakshmy ◽  
Gnanou Florence Sudha
Keyword(s):  
Deep Learning ◽  
Empirical Mode Decomposition ◽  
Respiratory Sound ◽  
Learning Framework ◽  
Sound Classification ◽  
Mode Decomposition

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

Environmental sound classification using optimum allocation sampling based empirical mode decomposition

2020 ◽  
Vol 537 ◽  
pp. 122613 ◽  
Author(s):  
Saad Ahmad ◽  
Shubham Agrawal ◽  
Samta Joshi ◽  
Sachin Taran ◽  
Varun Bajaj ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimum Allocation ◽  
Environmental Sound ◽  
Sound Classification ◽  
Mode Decomposition

Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

2017 ◽  
Vol 54 ◽  
pp. 246-255 ◽  
Author(s):  
Xueheng Qiu ◽  
Ye Ren ◽  
Ponnuthurai Nagaratnam Suganthan ◽  
Gehan A.J. Amaratunga
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Empirical Mode Decomposition ◽  
Time Series Forecasting ◽  
Load Demand ◽  
Mode Decomposition

scholarly journals Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yang Cao ◽  
Xiaokang Zhou ◽  
Ke Yan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Surface Settlement ◽  
Training Data ◽  
Tunnel Construction ◽  
Mode Decomposition ◽  
Short Period

Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.

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