System of Pressure Detection with IoT for Pins in Tail Pinch Test
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Published:
Jun 2, 2022
Keywords:
IoT, Artificial Neural Networks, Electronic Instrumentation, Tail Pinch, Test
Main Article Content
Abstract
Internet of Things (IoT) allows to interconnect and to interchange objects data through Internet. This component is part of a global term denominated Industry 4.0 that involved cyber-physical components for automation in industry. But IoT can be applied to different fields in research and technological developing as laboratory automation. Thus, it was developed a system that measure and classify pins used on Tail Pinch Test automatically. Our objective was to create a system that allow to categorize automatically instruments that are used in experiments. Thus, measurements variations are reduced, and the reliability of experiments grows. The system was based on Arduino ADK and resistive sensor to acquire pressure data. Also, a WIFI network and cloud computing service was used in the system. The application layer processed 100 pressure measurements and classified in linguistic values a pin using an Artificial Neural Network.
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How to Cite
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. Retrieved from http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/2432
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Articulos
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With this document, I/We certify that the article submitted for possible publication in the institutional journal INGENIO MAGNO of the Research Center Alberto Magno CIIAM of the University Santo Tomás, Tunja campus, is entirely of my(our) own writing, and is a product of my(our) direct intellectual contribution to knowledge.
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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.
Bagal, S., Dhobale, J., Sarve, A., Satone, R., Faizan, M., & Pande, S. (2018). Arduino Based Automatic Plant Watering System. International Journal of Engineering Science, 16342.
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.