Chapters authored
Three Dimensional Reconstruction Strategies Using a Profilometrical Approach based on Fourier Transform
Part of the book: Fourier Transforms
Advances in Airborne Pollution Forecasting Using Soft Computing Techniques
Part of the book: Air Quality
Greenhouse Fuzzy and Neuro-Fuzzy Modeling Techniques
Part of the book: Fuzzy Logic
New Strategy to Approach the Inverse Kinematics Model for Manipulators with Rotational Joints
The chapter describes a new strategy to approach the solution of the inverse kinematics problem for robot manipulators. A method to determine a polynomial model approximation for the joints positions is described by applying the divided differences with a new point of view for lineal path in the end-effector of the robot manipulator. Results of the mathematical approach are analysed by obtaining the kinematics inverse model and the approximate model for lineal trajectories of a manipulator for three degrees of freedom. Finally, future research approaches are commented.
Part of the book: Automation and Control Trends
A Transformer-Based Architecture for Airborne Particles Forecasting: Case Study – PM2.5 in Mexico City
View all chaptersIn this comprehensive research project, our goal is to predict the concentration levels of PM2.5, a critical air pollutant, in Mexico City. To address this challenge, we use an innovative approach based on the transformer model, specifically a modified version called the Informer. This project focuses on improving air quality prediction, a key step in tackling public health concerns and aiding decision-making in environmental management in one of the world’s most densely populated cities. We trained the Informer model using a robust dataset of historical air quality records and evaluated its performance with standard metrics: mean absolute error (MAE) and mean squared error (MSE). The results showed MAE values of 4.6266 and 5.5844, and MSE values of 40.7972 and 55.4009 for each monitoring station, demonstrating the model’s effectiveness in predicting PM2.5 levels. These results highlight the potential of the Informer in enhancing air quality management strategies. We also compared the Informer’s performance with the LSTM model, showing that the Informer not only competes with but may outperform the LSTM in air quality prediction tasks. This underscores the promise of the Informer for future environmental monitoring.
Part of the book: Artificial Intelligence Annual Volume 2024