Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Observer-based controller for unmanned aerial vehicles in reforestation applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-28) Muñoz Sepúlveda, Gustavo Alberto; Lozoya, Rafael Camilo; emimmayorquin; Castañeda, Herman; School of Engineering and Sciences; Campus Monterrey; Abaunza, HernánThis study presents a breakthrough in unmanned aerial vehicle (UAV) technology, showcas- ing the efficacy of a custom-designed controller and observer in the context of reforestation initiatives. Through meticulous experimentation and analysis, the study demonstrates the ob- server’s adeptness in mitigating external disturbances, thereby enhancing the precision and stability of UAV operations. This technological advancement not only holds promise for diverse practical applications but also holds profound implications for environmental con- servation efforts, particularly reforestation. Reforestation plays a pivotal role in mitigating climate change, preserving biodiversity, and safeguarding ecosystems. By leveraging UAV technology, this study propels forward the efficacy and efficiency of reforestation endeavors, laying the groundwork for future innovations in UAV-based interventions. The findings affirm the viability of the proposed controller and observer framework, highlighting its potential to revolutionize environmental monitoring, conservation, and sustainable resource management practices. This abstract encapsulates the significance of integrating cutting-edge technology with environmental conservation efforts, underscoring the pivotal role of UAVs in fostering a more sustainable future.
- The use of multispectral images and deep learning models for agriculture: the application on Agave(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Montán López, José Alberto; FALCON MORALES, LUIS EDUARDO; 168959; Falcón Morales, Luis Eduardo; puelquio, emipsanchez; Sánchez Ante, Gildardo; Roshan Biswal, Rajesh; Sossa Azuela, Huan Humberto; Escuela de Ingeniería y Ciencias; Campus Estado de MéxicoAgave is an important plant for Mexico, country considered as center of biological diversity of agave, in addition, one variety is used for production of tequila, an important product that brings money to the country. Demand of product has led farmers to pay more attention to plantation and to reduce quality. We can find several solutions regarding agricultural filed such as identification of weed and classification of species implementing aerial imagery along with machine and deep learning reaching good results. However, there are few solutions applied directly on agaves to monitor they health. Moreover, there is not a public dataset about agaves for the purpose of this work, for this reason we have worked to collect data using a drone equipped with a multispectral camera capable to capture five different channels of a different wavelength of the light spectrum. This dataset contains 7ha of agave information into five channels provided by the multispectral camera as well as three Vegetation Indices that were computed from the multispectral bands. In this work, we explore the use of recent deep learning (DL) algorithms as well as traditional machine learning (ML) algorithms to segment agaves based on health using aerial multispectral images. On the experiments we found out that ML algorithms were able to segment just one of the two classes defined for agaves. On the experiments of DL models we could define the size of the images we wanted to train where a size of 500x500 was the best for this problem. Experiments for both types of algorithms were done using many combinations of channels such as use just vegetation indices or using all available bands on the dataset. On the other hand, Vision Transformer (ViT) Segmenter model reached an accuracy of 92.96% using vegetation indices data while the best ML algorithm was Random Forest using the five bands captured by the drone reaching 88.06% accuracy. We also test the models using traditional RGB images to compare against multispectral images and see if there is an actual advantage on the use of this type of technology. Results show us that when we introduce the variable of health into agaves, i.e. we have two classes of agaves, models that have additional bands can get better results. Thus, the use of multispectal images actually increase the performance of all models, including ML and DL, for identification of more than one class of agave.
- A multiobjective mathematical model for a multimodal transportation problem in Humanitarian Logistics(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-03) Romero Mancilla, Marisol Sarai; HERNANDEZ RUIZ, KENNETH EDGAR; 331807; Hernández Ruiz, Kenneth Edgar; emipsanchez; Leal Coronado, Mariel Adriana; School of Engineering and Sciences; Campus Ciudad de México; Huerta Muñoz, Diana LucíaFrom 2001 to 2013, 4 out of 32 major public health emergencies worldwide were caused by earthquakes. The destruction caused by earthquakes generates problems like lack of shelters to accommodate people, food, and water shortage, but above all, the need to distribute emergency supplies for the most affected people when the roads are damaged (Lurie et al., 2013). The research papers Drone Delivery Models for Healthcare (Scott & Scott, 2017) and A metaheuristic algorithm to solve the selection of transportation channels in supply chain design (Olivares-Benitez et al., 2013) were of great influence in the development of this project. The purpose of this research is to formulate a mathematical model that minimizes the delivery time and the transportation costs of emergency medical supplies in the most critical stage of an earthquake, to save as many lives as possible. Moreover, this study pretends to encourage other researchers to expand the area of knowledge in Humanitarian Logistics. To fulfill the aforementioned, a mathematical model that incorporates the combination of land and air transportation was developed and solved with the optimization software Gurobi. Subsequently, the model was applied to a case study and to analyze the results, a Pareto Front was constructed. To taste the efficiency of the model, instances of different sizes were used. The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time, on the flip side, the model performed a in shorter CPU time with medium and small instances than with large instances.