scholarly journals Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

2021 ◽  
Vol 13 (9) ◽  
pp. 4905
Author(s):  
Chen Cao ◽  
Xiangbin Wu ◽  
Lizhi Yang ◽  
Qian Zhang ◽  
Xianying Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Earthquake Risk ◽  
Earthquake Activity ◽  
Earthquake Catalog ◽  
Short Term ◽  
Term Memory ◽  
Earthquake Migration ◽  
Long Short Term Memory

Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.

scholarly journals GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 774
Author(s):  
Tingliang Liu ◽  
Jing Yan ◽  
Yanxin Wang ◽  
Yifan Xu ◽  
Yiming Zhao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Short Term Memory ◽  
Partial Discharge ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Analysis Methods ◽  
Voltage Signal ◽  
Long Short Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.

scholarly journals Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Can Yang ◽  
Junjie Zhai ◽  
Guihua Tao
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Movement Direction ◽  
Short Term ◽  
Price Movement ◽  
Term Memory ◽  
Stock Indices ◽  
Long Short Term Memory

The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

scholarly journals Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

2021 ◽  
Vol 14 (1) ◽  
pp. 166
Author(s):  
Xuan Zhang ◽  
Chun Zhu ◽  
Manchao He ◽  
Menglong Dong ◽  
Guangcheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Discrete Element ◽  
Slope Instability ◽  
Monitoring Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.

scholarly journals Using Artificial Intelligence for Pattern Recognition in a Sports Context

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3040 ◽  
Author(s):  
Ana Cristina Nunes Rodrigues ◽  
Alexandre Santos Pereira ◽  
Rui Manuel Sousa Mendes ◽  
André Gonçalves Araújo ◽  
Micael Santos Couceiro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Positional Data

Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.

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