Now showing 1 - 10 of 32
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Modeling of Amorphous-Carbon Cells for Molecular Dynamics Simulations

2019 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , ORTIZ-MEDINA, JOSUE

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Analysis of wind missing data for wind farms in Isthmus of Tehuantepec

2018 , Sánchez-Gómez, Claudia , J. Enriquez-Zarate , Velázquez, Ramiro , Mario Graff , S. Sassi

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Heidegger and the simile of the cave. The assumptions of its interpretation

2020 , Domínguez-Soberanes, Julieta , Sánchez-Gómez, Claudia , Orvañanos-Guerrero, María T.

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Sustainability of Urban Parks: Applicable Methodological Framework for a Simple Assessment

2023 , Teresa González-ramírez , Berger, Pia , Sánchez-Gómez, Claudia , Faezeh Mahichi

Urban parks are central to advancing urban sustainability and improving overall quality of life by providing green spaces that promote physical and mental well-being, mitigate environmental issues, and foster community cohesion. However, there is a lack of methodologies that measure these benefits and provide a sustainability rating. In this study, we propose a valuable tool for measuring the sustainability level of urban parks: low (0–50%), medium (51–79%), and high (80–100%). It employs effective and affordable measures for the daily management of urban parks. It is rooted in the three pillars of sustainability: environmental, social, and economic. We have defined 19 indicators (e.g., renewable energy and energy efficiency, environmental impact on society) and 50 criteria (e.g., clean energy generation, water workshops). A multi-criteria analysis facilitated the selection process for these indicators and criteria. This methodology is developed by characterizing and systematically documenting the park’s day-to-day operations. We present a case study of Cárcamos Park in Guanajuato, Mexico. Through this real-life scenario, we demonstrate our methodology’s high applicability and effectiveness. The sustainability assessment of Cárcamos Park reveals a level of 57%, with the environmental pillar at 47.7%, the economic pillar at 49%, and the social pillar at 75%. The adaptability of our methodology during the design phase of new parks plays a crucial role in shaping sustainable park layouts. Park managers can apply our procedure to any park, evaluate their sustainability status, and detect areas of opportunity.

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Damage Importance Analysis for Pavement Condition Index Using Machine-Learning Sensitivity Analysis

2024 , Alejandro Pérez Carvajal , Sánchez-Gómez, Claudia , Jonás Velasco

The Pavement Condition Index (PCI) is a prevalent metric for assessing the condition of rigid pavements. The PCI calculation involves evaluating 19 types of damage. This study aims to analyze how different types of damage impact the PCI calculation and the impact of the performance of prediction models of PCI by reducing the number of evaluated damages. The Municipality of León, Gto., Mexico, provided a dataset of 5271 records. We evaluated five different decision-tree models to predict the PCI value. The Extra Trees model, which exhibited the best performance, was used to assess the feature importance of each type of damage, revealing their relative impacts on PCI predictions. To explore the potential for reducing the complexity of the PCI evaluation, we applied Sequential Forward Search and Brute Force Search techniques to analyze the performance of models with various feature combinations. Our findings indicate no significant statistical difference in terms of Mean Absolute Error (MAE) and the coefficient of determination (R2) between models trained with 13 features compared to those trained with all 17 features. For instance, a model using only eight damages achieved an MAE of 4.35 and an R2 of 0.89, comparable to the 3.56 MAE and 0.92 R2 obtained with a model using all 17 features. These results suggest that omitting some damages from the PCI calculation has a minimal impact on prediction accuracy but can substantially reduce the evaluation’s time and cost. In addition, knowing the most significant damages opens up the possibility of automating the evaluation of PCI using artificial intelligence.

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Estimating the Maximum Power of a Concentrator Photovoltaic Module through Random Forest

2023 , Sánchez-Gómez, Claudia , Pedro M. Rodrigo

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Optimal Design of Sliding Mode Control Combined with Positive Position Feedback

2018 , Enríquez-Zárate, J. , Trujillo, Leonardo , Hernández, C. , Sánchez-Gómez, Claudia

This work focuses on the application of a discontinuous controller combined with a type of modal control using hybrid optimization techniques to tune the parameters of the controller. The case study is a civil structure with three floors, on which the performance of the control scheme is evaluated by applying an external harmonic force at the ground floor of the structure. The active control is designed to reduce the displacement of the civil structure and the vibrations of the overall system. The Differential Evolution method with the Interior Point Algorithm are used to tune the parameters of the proposed controller, with the goal of maximizing performance relative to hand-tuned parameters. The numerical results are presented comparing the performance of the control in open and closed loop, considering the optimized values of the control parameters.

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Night club recommendation system based on decision trees

2021 , Sánchez-Gómez, Claudia , Jose-Carlos Delgado-Gomez , Pablo Ramirez-Espana , Luis Garcia-Zermeno , Samantha Licea , Domínguez-Soberanes, Julieta

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Robust multiband image segmentation method based on user clues

2017 , Sánchez-Gómez, Claudia , Mariano Rivera , Velázquez, Ramiro

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Landscape images distance using kullback leibler divergence

2018 , Sánchez-Gómez, Claudia , Mario Graff , Domínguez-Soberanes, Julieta , Gutiérrez, Sebastián