Khung tham chiếu và kế hoạch tổng thể cho trí tuệ nhân tạo công nghiệp (I-AI) trong các kịch bản ứng dụng mới

Xuejiao T, Xiaofeng H, Yang L (2013) Overview of big data research. Journal of System Simulation. http://en.cnki.com.cn/article_en/cjfdtotal-xtfz2013s1035.htm Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554 https://www.researchgate.net/publication/7017915 Farid AM (2017) Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J Intell Manuf 28(2):353–369. https://doi.org/10.1007/s10845-014-0983-7 Yang F, Zhang R, Yao Y, Yuan Y (2016) Locating the propagation source on complex networks with propagation centrality algorithm. Knowl-Based Syst 100(C):112–123 https://www.researchgate.net/publication/297597666 Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw 32(1):96–101 https://www.researchgate.net/publication/322728184 Moor J (2006) The Dartmouth College artificial intelligence conference: the next fifty years. AI Mag 27(4):87–91 http://www.dartmouth.edu/~ai50/homepage.html Hassler S (2016) Marvin Minsky and the pursuit of machine understanding - making machines-and people-think [spectral lines] IEEE Spectrum 53 (3):7–7. https://www.researchgate.net/publication/296693726 Boyd AB, Crutchfield JP (2016) Maxwell Demon Dynamics: Deterministic Chaos, the Szilard Map, and the Intelligence of Thermodynamic Systems. Phys Rev Lett 116(19):190601 https://www.researchgate.net/publication/278413864 Kuipers B, Feigenbaum EA, Hart PE, Nilsson NJ (2017) Shakey: from conception to history. AI Mag 38(1):88–103 https://www.researchgate.net/publication/315737206 Zhuanng Y-t, Wu F, Chen C, Pan Y-h (2017) Challenges and opportunities: from big data to knowledge in AI2.0. Front Inform Tech El 18 (1):3–14. https://doi.org/10.1631/FITEE.1601883 Li W, Wu WJ, Wang HM, Cheng XQ, Chen HJ, Zhou ZH, Ding R (2017) Crowd intelligence in AI 2.0 era. Frontiers of information technology. Electron Eng 18(1):15–43. https://doi.org/10.1631/FITEE.1601859 Peng Y-x, Zhu W-w, Zhao Y, Xu C-s, Huang Q-m, Lu H-q, Zheng Q-h (2017) Cross-media analysis and reasoning: advances and directions. Front Inform Tech El 18(1):44–57. https://doi.org/10.1631/FITEE.1601787 Zheng NN, Liu ZY, Ren PJ, Yong-Qiang MA, Chen ST, Si-Yu YU, Xue JR, Chen BD, Wang FY (2017) Hybrid-augmented intelligence: collaboration and cognition. Front Inform Tech El 18(2):153–179. https://doi.org/10.1631/FITEE.1700053 Liu W, Cheraghi SH (2006) A hybrid approach to nonconformance tracking and recovery. J Intell Manuf 17(1):149–162. https://doi.org/10.1007/s10845-005-5518-9 Zhang T, Li Q, Zhang CS, Liang HW, Li P, Wang TM, Li S, Zhu YL, Wu C (2017) Current trends in the development of intelligent unmanned autonomous systems. Front Inform Tech El 18(1):68–85. https://doi.org/10.1631/2FFITEE.1601650 Chen T, Wang Y-C, Lin Z (2017) Predictive distant operation and virtual control of computer numerical control machines. J Intell Manuf 28(5):1061–1077. https://doi.org/10.1007/s10845-014-1029-x https://link.springer.com/article/10.1007%2Fs10845-014-1029-x York WW, Ekbia HR (2013) Slippage in cognition, perception, and action: from aesthetics to artificial intelligence. Beyond Artificial Intelligence: Contemplations, Expectations; Applications 4(1):27–47 https://www.researchgate.net/publication/302212164 Wagman BM (2019) Artificial intelligence and human cognition. Q Rev Biol 68(1) https://www.abc-clio.com/Praeger/product.aspx?pc=C2353C Mazinan AH (2012) On the practice of artificial intelligence based predictive control scheme: a case study. Appl Intell 36(1):178–189. https://doi.org/10.1007/s10489-010-0253-0 Cook DJ, Varnell RC (2011) Adaptive parallel iterative deepening search. J Artif Intell Res 9(1):139–166 https://www.researchgate.net/publication/51893578 Guo R, Palmerbrown D, Lee SW, Cai FF (2014) Intelligent diagnostic feedback for online multiple-choice questions. Artif Intell Rev 42(3):369–383. https://doi.org/10.1007/s10462-013-9419-6 Froese T, Ziemke T (2009) Enactive artificial intelligence: investigating the systemic organization of life and mind. Artif Intell 173(3):466–500 https://www.researchgate.net/publication/220546367 Schölkopf B (2015) Artificial intelligence: learning to see and act. Nature 518(7540):486. https://www.researchgate.net/publication/272837230–487 Stocker M (2016) Decision-making: be wary of ‘ethical’ artificial intelligence. Nature 540(7634):525–525 https://www.researchgate.net/publication/311781254 Bryson J, Winfield A (2017) Standardizing ethical design for artificial intelligence and autonomous systems. Computer 50(5):116–119 https://www.researchgate.net/publication/316898639 Goertzel B, Orseau L, Snaider J (2006) Artificial general intelligence. IEEE Trans Comput 10(3):295–299. https://doi.org/10.1007/2F978-3-319-09274-4 Russell S (2017) Artificial intelligence: the future is superintelligent. Nature 548(7669):520–521 https://www.researchgate.net/publication/319368099 Manju A, Nigam MJ (2014) Applications of quantum inspired computational intelligence: a survey. Artif Intell Rev 42(1):79–156. https://doi.org/10.1007/s10462-012-9330-6 Pentland A (1999) Perceptual intelligence. Commun ACM 43(3):74–88 https://www.researchgate.net/publication/221568749 Cassimatis NL (2006) A cognitive substrate for achieving human-level intelligence. AI Mag 27(2):45–56 https://aaai.org/ojs/index.php/aimagazine/article/download/1879/1777 Beaulac CD, Larribe F (2017) Narrow artificial intelligence with machine learning for real time estimation of a mobile agent’s location using hidden Markov models. International Journal of Computer Games Technology 2017(2017-02-14). http://cn.arxiv.org/abs/1802.03417):1–10 Kuehnberger KU, Rudolph S, Wang P (2013) Report on the sixth conference on artificial general intelligence. AI Mag 34(4):123–125 https://www.researchgate.net/publication/295572346 Davis E (2015) Ethical guidelines for a superintelligence. Artif Intell 220(C):121–124 https://dl.acm.org/citation.cfm?id=2741446 Yang J, Kim E, Hur M, Cho S, Han M, Seo I (2018) Knowledge extraction and visualization of digital design process. Expert Syst Appl 92:206–215. https://doi.org/10.1016/j.eswa.2017.09.002 https://www.sciencedirect.com/science/article/pii/S0957417417306036 Li YW, Jiang WF, Yang L, Wu T (2018) On neural networks and learning systems for business computing. Neurocomputing 275:1150–1159. https://doi.org/10.1016/j.neucom.2017.09.054 https://www.sciencedirect.com/science/article/pii/S0925231217315734 Anghel CI, Ozunu A (2006) Prediction of gaseous emissions from industrial stacks using an artificial intelligence method. Chem Pap 60(6):410–415. https://doi.org/10.2478/s11696-006-0075-z Hokey M (2015) Genetic algorithm for supply chain modelling: basic concepts and applications. Int J Serv Oper Manag (Switzerland) 22(2):143–164 https://www.researchgate.net/publication/220691204 Hu ZY, Yang JM, Zhao ZW, Sun H, Che HJ (2016) Multi-objective optimization of rolling schedules on aluminum hot tandem rolling. Int J Adv Manuf Technol 85(1–4):85–97. https://doi.org/10.1007/s00170-015-7909-1 Cupek R, Ziebinski A, Drewniak M, Fojcik M (2018) Improving KPI based performance analysis in discrete, multi-variant production. Intelligent Information and Database Systems 10th Asian Conference, ACIIDS 2018 Proceedings: LNAI 10752:661–673. https://doi.org/10.1007/978-3-319-75420-8_62 Abellan-Nebot JV, Subiron FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257. https://doi.org/10.1007/s00170-009-2191-8 Mundada V, Narala SKR (2018) Optimization of milling operations using artificial neural networks (ANN) and simulated annealing algorithm (SAA). Materials Today-Proceedings 5(2):4971–4985 http://www.researchgate.net/publication/323990064 Ahmed AN, Noor CWM, Allawi MF, El-Shafie A (2018) RBF-NN-based model for prediction of weld bead geometry in shielded metal arc welding (SMAW). Neural Comput Applic 29(3):889–899. https://doi.org/10.1007/s00521-016-2496-0 Gershwin SB (2018) The future of manufacturing systems engineering. Int J Prod Res 56(1–2):224–237. https://doi.org/10.1080/00207543.2017.1395491 https://www.researchgate.net/publication/320903701 Wang HR, Chen HF, Fu GL, Xiao HP (2016) Relationship between grinding process and the parameters of subsurface damage based on the image processing. Int J Adv Manuf Technol 83(9–12):1707–1715. https://doi.org/10.1007/s00170-015-7691-0 Xu K, Xu Y, Zhou P, Wang L (2018) Application of RNAMlet to surface defect identification of steels. Opt Lasers Eng 105:110–117. https://doi.org/10.1016/j.optlaseng.2018.01.010 https://www.researchgate.net/publication/325500736 Kang S (2018) On effectiveness of transfer learning approach for neural network-based virtual metrology modeling. IEEE Trans Semicond Manuf 31(1):149–155. https://doi.org/10.1109/tsm.2017.2787550 https://www.researchgate.net/publication/322093298 Fernandes H, Zhang H, Figueiredo A, Malheiros F, Ignacio LH, Sfarra S, Ibarra-Castanedo C, Guimaraes G, Maldague X (2018) Machine learning and infrared thermography for fiber orientation assessment on randomly-oriented strands parts. Sensors 18(1). https://doi.org/10.3390/s18010288 https://www.researchgate.net/publication/322625184 Bai Y, Sun ZZ, Deng J, Li L, Long JY, Li C (2018) Manufacturing quality prediction using intelligent learning approaches: a comparative study. Sustainability 10(1). https://doi.org/10.3390/su10010085 https://www.researchgate.net/publication/322241384 Valluri A, North MJ, Macal CM (2009) Reinforcement learning in supply chains. Int J Neural Syst 19(5):331–344. https://doi.org/10.1142/s0129065709002063 https://www.researchgate.net/publication/38063993 Kaneko Y, Yada K (2016) A deep learning approach for the prediction of retail store sales. In: Domeniconi C, Gullo F, Bonchi F et al (eds) 2016 Ieee 16th international conference on data mining workshops. International conference on data mining workshops, pp 531–537. https://doi.org/10.1109/icdmw.2016.154 https://www.researchgate.net/publication/313454339 Kesheng W, Yi W (2018) How AI affects the future predictive maintenance: a primer of deep learning. Advanced manufacturing and automation VII:1-9. https://doi.org/10.1007/978-981-10-5768-7_1. https://www.researchgate.net/publication/323107352 Mesloub H, Benchouia MT, Golea A, Golea N, Benbouzid MEH (2017) A comparative experimental study of direct torque control based on adaptive fuzzy logic controller and particle swarm optimization algorithms of a permanent magnet synchronous motor. Int J Adv Manuf Technol 90(1–4):59–72. https://doi.org/10.1007/s00170-016-9092-4 Chen T (2008) A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction. Int J Adv Manuf Technol 37(7–8):782–792. https://doi.org/10.1007/s00170-007-1007-y Chen TT, Tian XC (2014) An intelligent self-learning method for dimensional error pre-compensation in CNC grinding. Int J Adv Manuf Technol 75(9–12):1349–1356. https://doi.org/10.1007/s00170-014-6249-x