Referências

ABOT, J. et al. Foil Strain Gauges Using Piezoresistive Carbon Nanotube Yarn: Fabrication and Calibration. Sensors, v. 18, n. 2, p. 464, 5 fev. 2018.

AFOLABI, I. et al. Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions. IEEE Communications Surveys & Tutorials, v. 20, n. 3, p. 2429–2453, 2018.

AHMED, M.; MAHMOOD, A. N.; HU, J. A survey of network anomaly detection techniques. Journal of Network and Computer Applications, v. 60, p. 19–31, 2016.

ALCÁCER, V.; CRUZ-MACHADO, V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal, v. 22, n. 3, p. 899–919, jun. 2019.

ALPAYDIN, E. Introduction to machine learning. Third edition ed. Cambridge, Massachusetts: The MIT Press, 2014.

AMERSHI, S. et al. Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). Anais... In: 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP). Montreal, QC, Canada: IEEE, maio 2019Disponível em: . Acesso em: 15 jul. 2020

AZZI, R.; CHAMOUN, R. K.; SOKHN, M. The power of a blockchain-based supply chain. Computers & Industrial Engineering, v. 135, p. 582–592, set. 2019.

BANKS, J. et al. Failure Modes and Predictive Diagnostics Considerations for Diesel Engines. p. 11, 2011.

BARYANNIS, G. et al. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, v. 57, n. 7, p. 2179–2202, 3 abr. 2019.

BECK, R. Beyond Bitcoin: The Rise of Blockchain World. Computer, v. 51, n. 2, p. 54–58, fev. 2018.

BMWI. Technology Scenario ‘Artificial Intelligence in Industrie 4.0’. Berlim: Federal Ministry for Economic Affairs and Energy, mar. 2019. Disponível em: . Acesso em: 15 jul. 2020.

BRASIL. Perspectivas de especialistas brasileiros sobre a manufatura avançada no brasil: um relato de workshops realizados em sete capitais brasileiras em contraste com as experiências internacionais. Disponível em: . Acesso em: 15 jul. 2020.

BRASIL. Plano de CT&I para Manufatura Avançada no Brasil. Disponível em: . Acesso em: 15 jul. 2020.

BUCZAK, A. L.; GUVEN, E. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, v. 18, n. 2, p. 1153–1176, 2016.

CIORTEA, A.; MAYER, S.; MICHAHELLES, F. Repurposing Manufacturing Lines on the Fly with Multi-agent Systems for the Web of Things. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. Anais...: AAMAS ’18.Stockholm, Sweden: International Foundation for Autonomous Agents and Multiagent Systems, 9 jul. 2018. Acesso em: 15 jul. 2020

CONCEIÇÃO, A. F. DA et al. Electronic Health Records using Blockchain Technology. arXiv:1804.10078 [cs], 26 abr. 2018.

CORREA DA SILVA, F. S. et al. On the insufficiency of ontologies: problems in knowledge sharing and alternative solutions. Knowledge-Based Systems, v. 15, n. 3, p. 147–167, mar. 2002.

CORREA DA SILVA, F. S.; ROBERTSON, D. S.; VASCONCELOS, W. W. LS2C - A Platform for Norm Controlled Social Computers. (B. Duval et al., Eds.)Agents and Artificial Intelligence. Anais...: Lecture Notes in Computer Science.Cham: Springer International Publishing, 2015

DOPICO, M. et al. A vision of industry 4.0 from an artificial intelligence point of view - ProQuest. Disponível em: . Acesso em: 15 jul. 2020.

ELSTNER, M. Use cases of extended reality in the construction industry. fi=AMK-opinnäytetyö|sv=YH-examensarbete|en=Bachelor’s thesis|. Disponível em: . Acesso em: 15 jul. 2020.

EURAMET, E. A. OF N. M. Publishable Summary for 17IND12 Met4FoF Metrology for the Factory of the Future. Disponível em: . Acesso em: 15 jul. 2020.

FRASER, M. S.; ANASTASELOS, T.; RAVIKUMAR, R. The disruption in oil and gas upstream business by Industry 4.0. Disponível em: . Acesso em: 15 jul. 2020.

GAMER, T. et al. The autonomous industrial plant – future of process engineering, operations and maintenance. Journal of Process Control, v. 88, p. 101–110, abr. 2020.

GELERNTER, D. Mirror worlds, or the day software puts the universe in a shoebox-: how it will happen and what it will mean. New York Oxford: Oxford University Press, 1991.

GHOLAMINEJAD, A.; POSHTAN, J. A comparison between some pattern recognition based fault diagnosis methods of induction motor. 2017 5th International Conference on Control, Instrumentation, and Automation (ICCIA). Anais...IEEE, 2017

GILMAN, E.; BARTH, D. Zero Trust Networks. [s.l.] O’Reilly Media, Incorporated, 2017.

GÖKALP, E.; ÇOBAN, S.; GÖKALP, M. O. Acceptance of Blockchain Based Supply Chain Management System: Research Model Proposal. 2019 1st International Informatics and Software Engineering Conference (UBMYK). Anais...IEEE, 2019

GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. [s.l.] MIT Press, 2016.

GRIEVES, M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. Disponível em: . Acesso em: 15 jul. 2020.

HALLIKAS, J. et al. Assessing Benefits of Information Process Integration in Supply Chains. Procedia Manufacturing, v. 39, p. 1530–1537, 2019.

HU, A. et al. Intelligent condition assessment of industry machinery using multiple types of signal from monitoring systems. Measurement, v. 149, p. 107018, 2020.

IEEE. Big Data Analytics in the Smart Grid. [s.l.] IEEE Smart Grid Big Data Working Group, 2018. Disponível em: . Acesso em: 15 jul. 2020.

KHUVIS, S. et al. A continuous integration-based framework for software management. In: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning). [s.l: s.n.]. p. 1–7.

KORPELA, K.; HALLIKAS, J.; DAHLBERG, T. Digital supply chain transformation toward blockchain integration. proceedings of the 50th Hawaii international conference on system sciences. Anais...2017

LEE, J. et al. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, v. 18, p. 20–23, 2018.

LÉO, P. et al. Rapid detection of corrosion inducing microorganisms using antibodies as bioreceptors for its application into microdevices. . In: RIO OIL & GAS EXPO AND CONFERENCE. Rio de Janeiro: Instituto Brasileiro de Petróleo, Gás e Biocombustíveis, 2016

LI, C. et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing, v. 76, p. 283–293, 2016.

LIANG, Q. et al. In-line inspection solution for codes on complex backgrounds for the plastic container industry. Measurement, v. 148, p. 106965, 2019.

LIM, K. Y. H.; ZHENG, P.; CHEN, C.-H. A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, p. 1–25, 2019.

LIN, C.; ZHANG, Z. W. A Two-Tier Blockchain Architecture for the Digital Transformation of Multilateralism. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Anais...IEEE, 2020

LONG, Q.; SONG, K.; YANG, S. Semantic Modeling for the Knowledge Framework of Computational Experiments and Decision Making for Supply Chain Networks. IEEE Access, v. 7, p. 46363–46375, 2019.

MAKONIN, S.; POPOWICH, F.; GILL, B. The cognitive power meter: Looking beyond the smart meter. 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Anais...IEEE, 2013

MALLER, M. et al. Sonic: Zero-knowledge SNARKs from linear-size universal and updatable structured reference strings. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. Anais...2019

MARSLAND, S. Machine learning: an algorithmic perspective. [s.l.] CRC press, 2015.

MIERS, C. et al. Análise de Mecanismos para Consenso Distribuído Aplicados a Blockchain. Minicursos do XIX Simpósio Brasileiro de de Segurança da Informação e de Sistemas Computacionais (SBSeg’19), v. 12, p. 91–139, 2019.

MITCHELL, T. et al. Never-ending learning. Communications of the ACM, v. 61, n. 5, p. 103–115, 2018.

MONDRAGON, A. E. C.; MONDRAGON, C. E. C.; CORONADO, E. S. Feasibility of Internet of Things and Agnostic Blockchain Technology Solutions: A Case in the Fisheries Supply Chain. 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). Anais...IEEE, 2020

NI, D.; XIAO, Z.; LIM, M. K. A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, v. 11, n. 7, p. 1463–1482, jul. 2020.

PAN, S.; ZHONG, R. Y.; QU, T. Smart product-service systems in interoperable logistics: Design and implementation prospects. Advanced Engineering Informatics, v. 42, p. 100996, 2019.

QUEIROZ, M. M.; WAMBA, S. F. The Role of Digital Connectivity in Supply Chain and Logistics Systems: A Proposed SIMPLE Framework. Conference on e-Business, e-Services and e-Society. Anais...Springer, 2020

ROSE, S. et al. Zero Trust Architecture. [s.l: s.n.]. Disponível em: . Acesso em: 15 jul. 2020.

ROUSSEAUX, F. BIG DATA and Data-Driven Intelligent Predictive Algorithms to support creativity in Industrial Engineering. Computers & Industrial Engineering, v. 112, p. 459–465, out. 2017.

RUIZ, M. et al. Wind turbine fault detection and classification by means of image texture analysis. Mechanical Systems and Signal Processing, v. 107, p. 149–167, jul. 2018.

RUSSELL, S. J.; NORVIG, P. Artificial intelligence: a modern approach. Fourth edition ed. Hoboken: Pearson, 2020.

SCHEID, E.; RODRIGUES, B.; STILLER, B. Toward a Policy-based Blockchain Agnostic Framework. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Anais... In: 2019 IFIP/IEEE SYMPOSIUM ON INTEGRATED NETWORK AND SERVICE MANAGEMENT (IM). abr. 2019

SCHWAB, K. The fourth industrial revolution. First U.S. edition ed. New York: Crown Business, 2017.

SIADATI, H.; SAKET, B.; MEMON, N. Detecting malicious logins in enterprise networks using visualization. 2016 IEEE Symposium on Visualization for Cyber Security (VizSec). Anais... In: 2016 IEEE SYMPOSIUM ON VISUALIZATION FOR CYBER SECURITY (VIZSEC). Baltimore, MD, USA: IEEE, out. 2016Disponível em: . Acesso em: 15 jul. 2020

SIEGWART, R.; NOURBAKHSH, I. R.; SCARAMUZZA, D. Introduction to autonomous mobile robots. 2nd ed ed. Cambridge, Mass: MIT Press, 2011.

SOUZA, W. A. et al. A NILM Dataset for Cognitive Meters Based on Conservative Power Theory and Pattern Recognition Techniques. Journal of Control, Automation and Electrical Systems, v. 29, n. 6, p. 742–755, dez. 2018.

SULTANA, T.; LOCORO, A.; DA SILVA, F. S. C. Time Accounting System: Validating a Socio-technical Solution for Service Exchange in Local Communities. (C. Rossignoli, F. Virili, S. Za, Eds.)Digital Technology and Organizational Change. Anais...: Lecture Notes in Information Systems and Organisation.Cham: Springer International Publishing, 2018

SUTTON, R. S.; BARTO, A. G. Reinforcement learning: an introduction. Second edition ed. Cambridge, Massachusetts: The MIT Press, 2018.

TALARI, S. et al. A Review of Smart Cities Based on the Internet of Things Concept. Energies, v. 10, n. 4, p. 421, 23 mar. 2017.

WRIGHT, L.; DAVIDSON, S. How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, v. 7, n. 1, p. 13, dez. 2020.

YIN, J.; ZHAO, W. Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach. Engineering Applications of Artificial Intelligence, v. 56, p. 250–259, nov. 2016.

ZHANG, J. M. et al. Machine Learning Testing: Survey, Landscapes and Horizons. IEEE Transactions on Software Engineering, p. 1–1, 2020.

ZHANG, Y.; HUANG, T.; BOMPARD, E. F. Big data analytics in smart grids: a review. Energy Informatics, v. 1, n. 1, p. 8, dez. 2018.