Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería / International Journal of Numerical Methods for Calculation and Design in Engineering

RIMNI - An International Journal of Numerical Methods for Calculation and Design in Engineering (Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería) is a peer-reviewed Open Access journal, founded in 1985. RIMNI publishes articles written in Spanish and English. The journal's scope includes Computational and Numerical Models of Engineering Problems, Development and Application of Numerical Methods, Advances in Software, Computer Design Innovations, Soft Computing, Machine Learning, Artificial Intelligence, etc. RIMNI is an essential ... show more

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Optimizing Convolutional Neural Network-Long Short-Term Memory Architecture with Additive Attention Mechanism for Stock Price Prediction
B. Wu, Y. Qi, G. Fu

Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. Online First, 47

Abstract
Stock price fluctuations reflect market expectations for the economic situation and company profits. Accurately predicting stock prices has become a hot topic in academia. With the rapid development of artificial intelligence, many researchers are starting to use machine learning algorithms to predict stock prices. In this paper, a new time series prediction model, the combination of the convolutional neural network and long shortterm memory neural network with additive attention mechanism (CNNLSTM-AAM), is proposed for stock price prediction. It can combine the advantages of the convolutional neural network (CNN), the long shortterm memory (LSTM) neural network, and the additive attention mechanism (AAM), and better capture nonlinear features of time series data. In the simulation analysis, we select sample data of three stocks (Vanke A, Shanghai International Port Group, and China Merchants Bank) and three stock price indexes (China Securities 500 Index, Shanghai Stock Exchange 50 Index, and Growth Enterprise Index) in the Chinese stock market for comparative analysis, and use mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) as the evaluation indexes. The CNN-LSTM-AAM model has the best prediction ability relative to the CNN and CNN-LSTM models. In addition, we also find that the prediction ability of the CNN-LSTM-AAM model for different stock data sets is different under the existing parameter conditions. For a specific dataset, the parameter conditions of the CNN-LSTM-AAM model need to be further adjusted to achieve the best prediction effect. Based on the above findings, the CNN-LSTM-AAM model has better performance and higher accuracy, and can provide credible decision-making basis and research methods for investors, financial institutions, and regulators.
Abstract
Stock price fluctuations reflect market expectations for the economic situation and company profits. Accurately predicting stock prices has become a hot topic in academia. [...]
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A Numerical Study of the Impact of Bending and Torsional Stiffness on the Static Aeroelastic Characteristics of a Large Aspect Ratio Composite Wing
S. qiao, F. Wang, P. Wang

Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. Online First, 46

Abstract
The bending and torsional stiffness influence structural deformation to varying degrees. Utilizing the coupled numerical method of Computational Fluid Dynamics and Computational Structural Dynamics (CFD/CSD), a comprehensive study was conducted on the impact of bending and torsional stiffness on the static aeroelastic behavior of a high-aspect-ratio wing, employing FLUENT software across diverse flight conditions. The study summarized this influence by comparing the computational outcomes. The results indicate that as the angle of attack rises, the lift increment diminishes gradually until the angle reaches 14 degrees, rendering the strategy of enhancing bending and torsional stiffness to gain more lift ineffective. Between 2 and 14 degrees of angle of attack, the lift difference between the scenarios decreases from 23.98% to 7.31%. At higher Mach numbers and lower angles of attack, augmenting wing stiffness significantly boosts wing lift. Optimal lift-to-drag characteristics are achieved at approximately 6 degrees of angle of attack. By averagely increasing the wing’s bending and torsional stiffness by 8.28% and 5.22%, respectively, the lift-to-drag characteristics can be enhanced by 5.27% at a low angle of attack of 0.75 Mach. The disparity in maximum deflection between the two stiffness wings is most pronounced at higher flight speeds and smaller angles of attack, with the opposite trend observed for the difference in maximum torsion angle. The key findings presented in this paper can expedite the integrated design of stiffness for this type of wing structure by providing vital technical insights.OPEN ACCESS Received: 24/07/2024 Accepted: 22/10/2024
Abstract
The bending and torsional stiffness influence structural deformation to varying degrees. Utilizing the coupled numerical method of Computational Fluid Dynamics and Computational [...]
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