Sánchez-Gómez, Claudia
Main Affiliation
Preferred name
Sánchez-Gómez, Claudia
Official Name
Sánchez Gómez, Claudia Nallely
ORCID
0000-0001-6316-5214
Researcher ID
DTU-3417-2022
Scopus Author ID
57201851558
35 results
Now showing 1 - 10 of 35
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Item type:Publication, Identification of potential areas for reforestation applying the analytic hierarchy process (AHP), case study from the valley of Aguascalientes, Mexico(Informa UK Limited, 2025-08-11); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Comparison of Multi-Objective Optimizers for Dynamic Balancing of Six-Bar Watt Linkages Using a Fully Cartesian Model(MDPI AG, 2025-07-04); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimización del balanceo de un mecanismo plano mediante redistribución de masas(2022); ; ; 9 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gradient Descent-Based Optimization Method of a Four-Bar Mechanism Using Fully Cartesian Coordinates(2019); ; ; <jats:p>Machine vibrations often occur due to dynamic unbalance inducing wear, fatigue, and noise that limit the potential of many machines. Dynamic balancing is a main concern in mechanism and machine theory as it allows designers to limit the transmission of vibrations to the frames and base of machines. This work introduces a novel method for representing a four-bar mechanism with the use of Fully Cartesian coordinates and a simple definition of the shaking force (ShF) and the shaking moment (ShM) equations. A simplified version of Projected Gradient Descent is used to minimize the ShF and ShM functions with the aim of balancing the system. The multi-objective optimization problem was solved using a linear combination of the objectives. A comprehensive analysis of the partial derivatives, volumes, and relations between area and thickness of the counterweights is used to define whether the allowed optimization boundaries should be changed in case the mechanical conditions of the mechanism permit it. A comparison between Pareto fronts is used to determine the impact that each counterweight has on the mechanism’s balancing. In this way, it is possible to determine which counterweights can be eliminated according to the importance of the static balance (ShF), dynamic balance (ShM), or both. The results of this methodology when using three counterweights reduces the ShF and ShM by 99.70% and 28.69%, respectively when importance is given to the static balancing and by 83.99% and 8.47%, respectively, when importance is focused on dynamic balancing. Even when further reducing the number of counterweights, the ShF and ShM can be decreased satisfactorily.</jats:p>Scopus© Citations 11 14 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Damage Importance Analysis for Pavement Condition Index Using Machine-Learning Sensitivity Analysis(2024); Jonás Velasco<jats:p>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.</jats:p>36 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 12 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, I3GO+ at RICATIM 2017: A semi-supervised approach to determine the relevance between images and text-annotations(2017)Scopus© Citations 1 12 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scopus© Citations 3 25 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Consumer Acceptances Through Facial Expressions of Encapsulated Flavors Based on a Nanotechnology Approach(2018); ; ; Scopus© Citations 2 10 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scopus© Citations 18 39 2