System of Pressure Detection with IoT for Pins in Tail Pinch Test
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Publicado:
jun 2, 2022
Palabras clave:
Internet de las cosas, Redes Neuronales Artificiales, Instrumentación Electrónica, Prueba Tail Pinch
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Resumen
Resumen—El Internet de las Cosas (IoT) permite a los objetos conectarse e intercambiar datos de todo tipo usando Internet. Se trata de uno de los componentes de la denominada Industria 4.0, cuyo campo de trabajo son los sistemas ciber-físicos para la automatización de procesos. Pero IoT tiene aplicaciones en muchos otros campos además de la industria, como lo son la investigación y el desarrollo tecnológico en la automatización de laboratorios. Así se creó un sistema que mide y clasifica de manera automática la presión de pinzas usadas en la prueba Tail-Pinch. El objetivo fue crear un sistema que permitiera categorizar instrumentos de experimentación. De tal manera que las variaciones de medición se reduzcan mientras crece la confiabilidad del experimento. Para ello, se empleó una tarjeta Arduino ADK y un sensor resistivo de presión. Se empleó una red WIFI y un servicio de almacenamiento de datos en nube. La aplicación final utilizó 100 medidas de presión y una Red Neuronal Artificial para dar una categoría lingüística de salida a la pinza seleccionada.
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Aldana Franco, F., Peña Martínez, D., Hernández Lozano, M., & Aldana Franco, R. (2022). System of Pressure Detection with IoT for Pins in Tail Pinch Test. Ingenio Magno, 12(2), 7-20. Recuperado a partir de http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/2432
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Por medio del presente documento, certifico(amos) que el artículo que se presenta para posible publicación en la revista institucional INGENIO MAGNO del Centro de Investigaciones de Ingeniería Alberto Magno CIIAM de la Universidad Santo Tomás, seccional Tunja, es de mi (nuestra) entera autoría, siendo su contenido producto de mi (nuestra) directa contribución intelectual y aporte al conocimiento.
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Citas
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Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Industrial Science and Engineering, 8(1), 37-44.
González-Fernandez, I., Iglesias-Otero, M. A., Esteki, M., Moldes, O. A., Mejuto, J. C., & Simal-Gandara, J. (2018). A critical review on the use of artificial neural networks in olive oil production, characterization and authentication. Critical reviews in food science and nutrition, 1-14.
Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D. (2014). Human-machine-interaction in the industry 4.0 era. In Industrial Informatics, 2014 12th IEEE International Conference on(pp. 289-294). Ieee.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660.
Horton, P., Jaboyedoff, M., & Obled, C. (2018). Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps. Journal of hydrology, 556, 1220-1231.
Hu, M. H., Bashir, Z., Li, X. F., & O'byrne, K. T. (2016). Posterodorsal Medial Amygdala Mediates Tail‐Pinch Induced Food Intake in Female Rats. Journal of neuroendocrinology, 28(5).
Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on (pp. 1-4). IEEE.
Kagermann, H. (2015). Change through digitization—Value creation in the age of Industry 4.0. In Management of permanent change (pp. 23-45). Springer Gabler, Wiesbaden.
Kelly, S. D. T., Suryadevara, N. K., & Mukhopadhyay, S. C. (2013). Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sensors Journal, 13(10), 3846-3853.
Khan, G. M. (2018). Evolutionary computation. In Evolution of Artificial Neural Development (pp. 29-37). Springer, Cham.
Ki, Y. K., Heo, N. W., Choi, J. W., Ahn, G. H., & Park, K. S. (2018). An incident detection algorithm using artificial neural networks and traffic information. In Cybernetics & Informatics, 2018 (pp. 1-5). IEEE.
Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.
Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, 3-8.
Montes-González, F., & Aldana-Franco, F. (2011). The Evolution of Signal Communication for the e-puck Robot. In Mexican International Conference on Artificial Intelligence (pp. 466-477). Springer, Berlin, Heidelberg.
Montes-González, F., Palacios-Leyva, R., Aldana-Franco, F., & Cruz-Alvarez, V. (2011). The Coevolution of Behavior and Motivated Action Selection. In On Biomimetics. InTech.
Negrete, J. C., Kriuskova, E. R., Canteñs, G. D. J. L., Avila, C. I. Z., & Hernandez, G. L. (2018). Arduino Board in the Automation of Agriculture in Mexico, a Review. International Journal of Horticulture, 8.
Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121-139.
Pagliuca, P., Milano, N., & Nolfi, S. (2018). Maximizing adaptive power in neuroevolution. PloS one, 13(7), e0198788.
Palacios-Leyva, R., Aldana-Franco, F., Lara-Guzmán, B., & Montes-González, F. (2017). The impact of population composition for cooperation emergence in evolutionary robotics. International Journal of Combinatorial Optimization Problems and Informatics, 8(3), 20-32.
Pérez, M. S., & Carrera, E. (2015). Time synchronization in Arduino-based wireless sensor networks. IEEE Latin America Transactions, 13(2), 455-461.
Rabiei, Z., Naderi, S., & Rafieian-Kopaei, M. (2017). Study of antidepressant effects of grape seed oil in male mice using tail suspension and forced swim tests. Bangladesh Journal of Pharmacology, 12(4), 397-402.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Schmidt, R., Möhring, M., Härting, R. C., Reichstein, C., Neumaier, P., & Jozinović, P. (2015). Industry 4.0-potentials for creating smart products: empirical research results. In International Conference on Business Information Systems(pp. 16-27). Springer, Cham.
Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on (pp. 697-701). IEEE.
Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V. (2016). Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors Journal, 16(20), 7373-7380.
Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17-27.
Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. In Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on (pp. 2147-2152). IEEE.