<p class="icsmabstract">Los sistemas de producción han evolucionado los últimos años gracias a avances tecnológicos recientes e innovaciones en el proceso de manufactura. El termino Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas, centros de investigación y universidades, sin existir un consenso generalmente aceptado del término. Como resultado es difícil diseñar e implementar soluciones de Industria 4.0 a nivel académico, científico o empresarial. La contribución de este documento se centra en proporcionar un análisis del significado e implicaciones de Industria 4.0 y exponer de forma detallada 17 principios de diseño fundamentales obtenidos a través de un estudio de mapeo sistemático. Estos principios son eficiencia, integración, flexibilidad, descentralización, personalización, virtualización, seguridad, es holística, orientada a servicios, ubicua, colaborativa, modular, robusta, utiliza información en tiempo real, toma decisiones optimizadas por datos, equilibra la vida laboral y es autónoma e inteligente. A través de estos principios, ingenieros e investigadores están capacitados para investigar e implementar escenarios apropiados de Industria 4.0.</p>
Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19, together with deep learning models, eliminating disadvantages such as the scarcity of RT-PCR test kits, their elevated costs, and the long wait for results. The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics in binary and 4-classes classification scenarios. Classification accuracy was improved, and overfitting was prevented by following 2 actions: (1) increasing the data set size while the classification scenarios were balanced; and (2) adding regularization techniques and performing hyperparameter optimization. Additionally, the network capacity and size in the models were reduced as much as possible, making the final models a perfect option to be deployed locally on devices with limited capacities and without the need for Internet access. The impact of key hyperparameters was tested using modern deep learning packages. The final models obtained a classification accuracy of 99,17 and 94,03% for the binary and categorical scenarios, respectively, achieving superior performance compared to other studies in the literature, and requiring a significantly lower number of parameters. The models can also be placed on a digital platform to provide instantaneous diagnostics and surpass the shortage of experts and radiologists.
This article shows the development of a new methodology whose purpose is to support and guide senior managers of manufacturing industries for the improvement and migration of their organizations to industry 4.0 (I4.0). This methodology is the result of a pure research developed in the last 3 years (2019-2022). Which is based on the study of science and engineering tools that have served in solving problems for manufacturing organizations in the last 100 years and are still valid and are key to the complete technological adoption and migration towards industry 4.0. As a result of the development of the current research, the following 3 new concepts to the scientific and industrial community are presented: 1.- The house of improvement and migration towards I4.0. 2.- Methodology of the 4 phases for the improvement and migration towards I4.0 and 3.- The cycle for the improvement and migration to I4.0. It is hoped that through the uses and applications of these concepts, manufacturing organizations will be able to migrate in a gradual, measurable and controllable way to Industry 4.0. Key Words: Industry 4.0, business management, methodology, migration, strategic objectives.
El término Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas y centros de investigación pero aún se encuentra dentro de sus primeras etapas de implementación. Además, las compañías enfrentan dificultades al desarrollar soluciones para Industria 4.0, sin estar seguras de cómo afrontar sus requerimientos básicos. El diseño de una arquitectura de referencia aborda explícitamente este problema, apoya a los profesionales en la implementación de soluciones siendo la base del desarrollo y proporciona un soporte ante los desafíos que la Industria 4.0 representa. Por lo tanto, la contribución de este documento se centra en diseñar una arquitectura de referencia para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos, mostrando su viabilidad a través de la implementación en un caso de uso: Agricultura 4.0. Mediante esta arquitectura, ingenieros e investigadores podrán enfrentar los desafíos actuales de la producción inteligente, así como investigar, desarrollar e implementar soluciones (aplicaciones y sistemas) guiadas, estandarizadas y a costos accesibles, que cumplan los requerimientos que gobiernan Industria 4.0.
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