scholarly journals Deep Learning for the Prediction of Temperature Time Series in the Lining of an Electric Arc Furnace for Structural Health Monitoring at Cerro Matoso (CMSA)

2020 ◽  
Vol 2 (1) ◽  
pp. 23
Author(s):  
Jersson X. Leon-Medina ◽  
Ricardo Cesar Gomez Vargas ◽  
Camilo Gutierrez-Osorio ◽  
Daniel Alfonso Garavito Jimenez ◽  
Diego Alexander Velandia Cardenas ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Electric Furnace ◽  
Health Monitoring ◽  
Electric Arc ◽  
Temperature Behavior ◽  
Furnace Lining ◽  
Input Variables ◽  
Deep Learning Model

Cerro Matoso SA (CMSA) is located in Montelibano, Colombia. It is one of the biggest producers of ferronickel in the world. The structural health monitoring process performed in the electric arc furnaces at CMSA is of great importance in the maintenance and control of ferronickel production. The control of thermal and dimensional conditions of the electric furnace aims to detect and prevent failures that may affect its physical integrity. A network of thermocouples distributed radially and at different heights from the furnace wall, are responsible for monitoring the temperatures in the electric furnace lining. In order to optimize the operation of the electric furnace, it is important to predict the temperature at some points. However, this can be difficult due the number of variables which it depends on. To predict the temperature behavior in the electric furnace lining, a deep learning model for time series prediction has been developed. Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and other combinations were tested. GRU characterized by its multivariate and multi output type had the lowest square error. A study of the best input variables for the model that influence the temperature behavior is also carried out. Some of the input variables are the power, current, impedance, calcine chemistry, temperature history, among others. The methodology to tune the parameters of the GRU deep learning model is described. Results show an excellent behavior for predicting the temperatures 6 h into the future with root mean square errors of 3%. This model will be integrated to a software that obtains data for a time window from the Distributed Control System (DCS) to feed the model. In addition, this software will have a graphical user interface used by the operators furnace in the control room. Results of this work will improve the process of structural control and health monitoring at CMSA.

scholarly journals Aggregating Time Series and Tabular Data in Deep Learning Model for University Students’ GPA Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Harjanto Prabowo ◽  
Alam A. Hidayat ◽  
Tjeng Wawan Cenggoro ◽  
Reza Rahutomo ◽  
Kartika Purwandari ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
University Students ◽  
Learning Model ◽  
Tabular Data ◽  
Deep Learning Model

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

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