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.

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 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.

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.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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