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Análisis cuantitativo con diagrama de precedencias de condiciones productivas actuales de 5 líneas de ensamble de amortiguadores mediante el método de balanceo peso posicional

José Humberto Vergara García (2023, [Tesis de maestría])

En este trabajo de investigación se empleó el método de Helgeson & Birnie para realizar el balanceo por peso posicional de cinco líneas de ensamble modular de amortiguadores de la empresa ZF Suspension Technology Guadalajara S.A. de C.V. La metodología empleada permitió establecer las condiciones actuales de operación de las cinco líneas. Mediante el uso del método mencionado se encontró que en la mayoría de las líneas de ensamble analizadas sus tareas se encuentran correctamente balanceadas y ordenadas, corroborando así el buen trabajo realizado al momento de su instalación y puesta en marcha. Si bien en cualquier proceso de ensamble siempre hay oportunidades de mejora, contar con líneas de ensamble bien balanceadas permite a la empresa tener una base sólida para la producción de este tipo de componentes automotrices.

The Helgeson & Birnie method was employed in this research for balancing, by positional weight, five modular strut assembly production lines at ZF Suspension Technology Guadalajara S.A. De C.V. The proposed methodology allowed to know the current operating conditions of the production lines. It was found that most of the analyzed production lines are correctly balanced suggesting a correct commissioning and start up procedure performed when the lines were initially installed. Although every assembling line always can be improved, having well-balanced assembly lines provides a solid base line for any automotive manufacturing company.

Líneas de ensamble modular Método de Helgeson & Birnie Peso posicional Final assembly lines Helgeson & Birnie method Positional weight INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.

Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])

Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.

rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting.

Sajad Sabzi Razieh Pourdarbani Mohammad Hossein Rohban Alejandro Fuentes_Penna José Luis Hernández-Hernández Mario Hernández Hernández (2021, [Artículo])

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network¿imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network¿harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the Knearest- neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network¿biogeography-based optimization (ANNBBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

artificial neural network cucumber hyperspectral imaging majority voting nitrogen INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS