Prof. Dr. Luis F. Luque-Vega
Dept. of Technological and Industrial Processes, Instituto Tecnológico y de Estudios Supreriores de Occidente (ITESO), Tlaquepaque, Jalisco, Mexico
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Academic Career Length
15Years (2012-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 March 2012 in Journal of the Franklin Institute
Journal Article
Luis F. Luque-VegaBCB. Castillo-Toledo
B. Castillo-Toledo
ALAlexander G. Loukianov
Alexander G. Loukianov
1 April 2014
Conference Paper
Luis F. Luque-VegaBCBernardino Castillo-Toledo
Bernardino Castillo-Toledo
ALAlexander Loukianov
Alexander Loukianov
Luis Enrique González JiménezThe inspection of high voltage power transmission lines is mainly carried out by manned aerial vehicles or foot patrol. However, these maintenance methodologies for inspection are somehow inefficient and expensive. Moreover, helicopter assisted inspection endangers the human life. Recently, unmanned aerial vehicles have been under development in several research centers all over the world due to its potential applications. In this paper, we present an unmanned aerial system based on the quadrotor helicopter for high voltage power line inspection. Our interest is to equip the quadrotor helicopter with the necessary payload in order to be able to carry out a qualitative inspection, therefore the hardware architecture of the aerial robotic system is presented. Finally, some experimental results are shown to demonstrate the feasibility of the proposed system.
Journal Article
Luis F. Luque-VegaDMDavid A. Michel-Torres
David A. Michel-Torres
Emmanuel Lopez-Neri
Miriam A. Carlos-MancillaLuis Enrique González JiménezHumanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented.
1 July 2016 in Journal of the Franklin Institute
Journal Article
JGJ. Ghommam
J. Ghommam
Luis F. Luque-VegaBCB. Castillo-Toledo
B. Castillo-Toledo
MSM. Saad
M. Saad
Journal Article
JGJuan Manuel Esparza Gómez
Juan Manuel Esparza GómezLuis F. Luque-Vega
Héctor Alonso Guerrero OsunaRCRocío Carrasco-Navarro
Rocío Carrasco-Navarro
Fabián García-VázquezLSLuis Octavio Solís-Sánchez
Luis Octavio Solís-Sánchez +3
One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse’s internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R2, RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year’s four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R2 = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of ±2 °C hysteresis.
Journal Article
Fabián García-VázquezJPJesús R. Ponce-González
Jesús R. Ponce-GonzálezHéctor Alonso Guerrero OsunaRCRocío Carrasco-Navarro
Rocío Carrasco-NavarroLuis F. Luque-Vega
German Díaz-Flórez +3
Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models’ performance using R, RMSE, MAE, and MAPE metrics, considering an acceptability interval of ±2 C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms.
About Me
Luis F. Luque-Vega was born in Guasave, Sinaloa, Mexico, in 1985. He received his B.S. degree in Electronic Engineering from the Technological Institute of Culiacan, Sinaloa, Mexico, in 2006, and both his M.Sc. and PhD. degrees in Electrical Engineering with an orientation in automatic control from the Center for Research and Advanced Studies of the National Polytechnic Institute CINVESTAV Campus Guadalajara, Jalisco, Mexico, in 2010 and 2014, respectively. In addition, he completed a post-doctorate stage at the Department of Electronics, Systems and Informatics of the Western Institute of Technology and Higher Education ITESO, San Pedro Tlaquepaque, Jalisco, Mexico, in 2015. He is currently an associate professor at the Department of Technological and Industrial Processes of ITESO. He is a certified Jalisco Innovation Agent by the Ministry of Innovation, Science and Technology of Jalisco SICyT through the Monterrey Institute of Technology and Higher Education ITESM Guadalajara Campus, and he has earned a distinction from the National System of Researchers, Level I, by the National Council of Science and Technology, CONACyT, Mexico. His research interests include engineering education, robotics, drones, computer vision, robust nonlinear control, intelligent control, and distributed control applied robotic systems. Dr. Luque-Vega became a senior member of IEEE in 2020, and he is currently the treasurer of the IEEE Guadalajara Section.
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