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dc.contributor.advisor1Santos, Alyson de Jesus dos Santos-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/5998752909180697pt_BR
dc.contributor.referee1Santos, Alyson de Jesus dos-
dc.contributor.referee1Latteshttp://lattes.cnpq.br/5998752909180697pt_BR
dc.contributor.referee2Guerreiro, Gabriel Rebello-
dc.contributor.referee3Souza, Daniel Fonseca de-
dc.contributor.referee3Latteshttp://lattes.cnpq.br/4043793492782488pt_BR
dc.creatorFeitosa, Paulo Rafael Rodrigues-
dc.date.accessioned2024-09-20T15:15:36Z-
dc.date.available2024-09-20-
dc.date.available2024-09-20T15:15:36Z-
dc.date.issued2024-08-05-
dc.identifier.citationFeitosa, Paulo Rafael Rodrigues. Simulação de um sistema automatizado de inspeção de rótulos de baterias utilizando Webots. 77f. 2024. Monografia (Engenharia de Controle e Automação) - Instituto Federal de Educação, Ciência e Tecnologia do Amazonas, Manaus, 2024.pt_BR
dc.identifier.urihttp://repositorio.ifam.edu.br/jspui/handle/4321/1510-
dc.description.abstractThis work consists of a simulation in Webots of a visual inspection system for information printed on cell phone battery labels using pad printing. The environment includes an entry conveyor where the batteries are inserted. A camera attached to a UR5e robot captures images of the batteries and sends them to the inspection algorithm developed in Python. The inspection algorithm comprises an OCR (Optical Character Recognition) system to check if the textual information is present on the label and a classification model to verify if the symbols have been printed on the label. The UR5e robot then separates the approved and rejected batteries.pt_BR
dc.description.resumoEste trabalho consiste em uma simulação no Webots de um sistema de inspeção visual para informações gravadas em rótulos de baterias de celular por meio de tampografia. O ambiente possui uma esteira de entrada onde as baterias são inseridas. Uma câmera acoplada a um robô UR5e captura as imagens das baterias e as envia para o algoritmo de inspeção feito em Python. O algoritmo de inspeção é composto de um sistema OCR (Optical Character Recognition) para verificar se as informações textuais estão presentes no rótulo, e um modelo de classificação para verificar se os símbolos foram gravados no rótulo. O robô UR5e então separa as baterias aprovadas e reprovadas.pt_BR
dc.description.provenanceSubmitted by Darlene Rodrigues (darlene.rodrigues@ifam.edu.br) on 2024-09-20T15:15:36Z No. of bitstreams: 1 SIMULAÇÃO DE UM SISTEMA AUTOMATIZADO DE INSPEÇÃO DE RÓTULOS DE BATERIAS UTILIZANDO WEBOTS_FEITOSA_2024.pdf: 2974382 bytes, checksum: bcfc6db97699d37eeab4384c6e6d99ba (MD5)en
dc.description.provenanceMade available in DSpace on 2024-09-20T15:15:36Z (GMT). No. of bitstreams: 1 SIMULAÇÃO DE UM SISTEMA AUTOMATIZADO DE INSPEÇÃO DE RÓTULOS DE BATERIAS UTILIZANDO WEBOTS_FEITOSA_2024.pdf: 2974382 bytes, checksum: bcfc6db97699d37eeab4384c6e6d99ba (MD5) Previous issue date: 2024-08-05en
dc.languageporpt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentCampus Manaus Distritopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
dc.publisher.initialsInstituto Federal do Amazonaspt_BR
dc.publisher.initialsIFAMpt_BR
dc.publisher.initialsEngenharia de Controle e Automaçãopt_BR
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Examining the Values Reflected by Children during AI Problem Formulation. arXiv preprint arXiv:2309.15839, 2023. DWIVEDI, Utkarsh et al. Exploring machine teaching with children. In: 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2021. p. 1-11. FORCHHAMMER, Stephan et al. Development of an image analysis-based prognosis score using Google’s teachable machine in melanoma. Cancers, v. 14, n. 9, p. 2243, 2022. GONZALEZ, R. C., WOODS, R.E., EDDINS, S. L. Digital image processing using Matlab. New Jersey, Pearson Prentice Hall, 2004. ISBN 0-13-008519-7. HOM, Julie et al. Facilitating clinical research through automation: Combining optical character recognition with natural language processing. Clinical Trials, v. 19, n. 5, p. 504-511, 2022. KALSHETTY, Ashwini; RAKSHIT, Sutapa. Use case of no code machine learning tools for medical image classification. 2021. KHASTGIR, Siddartha et al. Calibrating trust through knowledge: Introducing the concept of informed safety for automation in vehicles. Transportation research part C: emerging technologies, v. 96, p. 290-303, 2018. KOERT, Dorothea et al. Multi-channel interactive reinforcement learning for sequential tasks. Frontiers in Robotics and AI, v. 7, p. 97, 2020. LAIQUE, Sobia Nasir et al. Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports. Gastrointestinal endoscopy, v. 93, n. 3, p. 750-757, 2021. LOCKS, Francisco et al. Biomechanical exposure of industrial workers–Influence of automation process. International Journal of Industrial Ergonomics, v. 67, p. 41-52, 2018. MORDVINTSEV, A., ABID, K. OpenCV-Python Tutorials Documentation, Release 1, 2017. NGUYEN, Anthony et al. Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principle. BMJ open, v. 10, n. 6, p. e037740, 2020. OPENCV. About. 2024. Disponível em: < https://opencv.org/about/>. Acesso em: 24 jun. 2024. Oscar Flues. O que é tampografia. 2016. Disponível em: https://oscarflues.com.br/#o-que-e-tampografiaa17c-ab62. Acesso em 19 Jun. 2024. Pytesseract Documentation 9.2.0. 2024. Disponível em:< https://pytesseract.readthedocs.io/en/latest/. Acesso em 06 Jun 2024. PYTESSERACT. PYTESSERACT 9.2.0. 2024. Disponível em:< https://github.com/madmaze/pytesseract. Acesso em 06 Jun 2024. PYTHON. HOME. 2024. Disponível em: < https://www.python.org/>. Acesso em: 24 jun. 2024. QUEIROZ, J. E. R., GOMES, H. Introdução ao Processamento Digital de Imagens. Revista RITA, Vol. 3. Número 1, 2001. RADI, Marwan et al. Telepresence in industrial applications: implementation issues for assembly tasks. Presence: Teleoperators and Virtual Environments, v. 19, n. 5, p. 415-429, 2010. RUSSEL, S. NORVIG, P. Artificial intelligence: a modern approach. New Jersey, Pearson Prentice Hall, 2010, 3° Edição. ISBN-13: 978-0-13-604259-4. SAMPLE, Pamela A. et al. Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. Investigative ophthalmology & visual science, v. 45, n. 8, p. 2596-2605, 2004. SARZYNSKI, Erin et al. Beta testing a novel smartphone application to improve medication adherence. Telemedicine and e-Health, v. 23, n. 4, p. 339-348, 2017. SAUER, Juergen; NICKEL, Peter; WASTELL, David. Designing automation for complex work environments under different levels of stress. Applied ergonomics, v. 44, n. 1, p. 119-127, 2013. SCHWABE, Henrik; CASTELLACCI, Fulvio. Automation, workers’ skills and job satisfaction. Plos one, v. 15, n. 11, p. e0242929, 2020. SVENSSON, Åsa et al. Automation, teamwork, and the feared loss of safety: Air traffic controllers’ experiences and expectations on current and future ATM systems. In: Proceedings of the 32nd European Conference on Cognitive Ergonomics. 2021. p. 1-8. TRUCCO, E. VERRI, A. Introductory Techniques for 3-D Computer Vision. New Jersey, Pearson Prentice Hall, 1998. ISBN-10 0132611082. TSAO, Liuxing et al. Modelling performance during repetitive precision tasks using wearable sensors: A data-driven approach. Ergonomics, v. 63, n. 7, p. 831-849, 2020. WEBOTS. Cloud. 2024. Disponível em: < https://webots.cloud/>. Acesso em: 26 jun. 2024. WEBOTS. Cloud. 2024. Disponível em: < https://webots.cloud/>. Acesso em: 26 jun. 2024. WELFARE, Katherine S. et al. Consider the human work experience when integrating robotics in the workplace. In: 2019 14th ACM/IEEE international conference on human-robot interaction (HRI). IEEE, 2019. p. 75-84.pt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectWebotspt_BR
dc.subjectPythonpt_BR
dc.subjectPytesseractpt_BR
dc.subjectTeachable Machinept_BR
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::ELETRONICA INDUSTRIAL, SISTEMAS E CONTROLES ELETRONICOSpt_BR
dc.titleSimulação de um sistema automatizado de inscrições de rótulos de baterias webotspt_BR
dc.typeTrabalho de Conclusão de Cursopt_BR
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