scholarly journals Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4342 ◽  
Author(s):  
Jongwon Park ◽  
Kyushik Min ◽  
Hayoung Kim ◽  
Woosung Lee ◽  
Gaehwan Cho ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Short Term Memory ◽  
Classification Performance ◽  
Sensor Data ◽  
Short Term ◽  
Feature Selection Technique ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Real Vehicle

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.

Ionospheric TEC prediction using Long Short-Term Memory deep learning network

2021 ◽  
Vol 366 (1) ◽  
Author(s):  
Zhichao Wen ◽  
Shuhui Li ◽  
Lihua Li ◽  
Bowen Wu ◽  
Jianqiang Fu
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

2018 ◽  
Vol 99 ◽  
pp. 24-37 ◽  
Author(s):  
Κostas Μ. Tsiouris ◽  
Vasileios C. Pezoulas ◽  
Michalis Zervakis ◽  
Spiros Konitsiotis ◽  
Dimitrios D. Koutsouris ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

scholarly journals Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Jinghe Yuan ◽  
Rong Zhao ◽  
Jiachao Xu ◽  
Ming Cheng ◽  
Zidi Qin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Intensity ◽  
Short Term Memory ◽  
Short Term ◽  
Learning Network ◽  
The Past ◽  
Unsupervised Deep Learning ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Membrane Protein Interaction

AbstractWe propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so neither predefined state number nor pre-labelling were required. With the bidirectional Long Short-Term Memory (biLSTM) networks as the hidden layers, both the past and future context can be used fully to improve the prediction results and can even extract information from the noise distribution. The method was validated with the synthetic dataset and the experimental dataset of monomeric fluorophore Cy5, and then applied to extract the membrane protein interaction dynamics from experimental data successfully.

scholarly journals Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1894
Author(s):  
Xiaosheng Peng ◽  
Kai Cheng ◽  
Jianxun Lang ◽  
Zuowei Zhang ◽  
Tao Cai ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Wind Farm ◽  
Short Term Memory ◽  
High Dimensional ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Input Parameters ◽  
Long Short Term Memory

Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch of the power system, but it faces two challenges: (1) The dimensions of the input parameters for WPP of wind farm clusters are very high so that the input parameters contain irrelevant or redundant features; (2) it is difficult to build a holistic WPP model with high-dimensional input parameters for wind farm clusters. To overcome these challenges, a novel short-term WPP model for wind farm clusters, based on sequential floating forward selection (SFFS) feature selection and bidirectional long short-term memory (BLSTM) deep learning, is proposed in this paper. First, more than 300,000 input features of the wind farm cluster are constructed. Second, the SFFS method is applied to sort the high-dimensional features and analyze the rule that the forecasting accuracy changes with the number of features to obtain the optimal number of features and feature sets. Finally, based on the results of feature selection, BLSTM is applied to build a WPP model for wind farm clusters with a combination of feature selection and deep learning. This case study shows that (1) SFFS is an effective method for selecting the core features for WPP of wind farm clusters; (2) BLSTM shows not only higher WPP accuracy than long short-term memory and backpropagation neural network but also outstanding performance in terms of reducing the phase errors of WPP.

scholarly journals Research on power demand forecasting based on attention mechanism and deep learning network

2022 ◽  
Vol 355 ◽  
pp. 02022
Author(s):  
Chenglong Zhang ◽  
Li Yao ◽  
Jinjin Zhang ◽  
Junyong Wu ◽  
Baoguo Shan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Attention Mechanism ◽  
Power Demand ◽  
Short Term ◽  
Time Step ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Deep Learning Model

Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

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