Alessandro Massaro - Electronics in Advanced Research Industries

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Electronics in Advanced Research Industries: краткое содержание, описание и аннотация

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A one-of-a-kind examination of the latest developments in machine control 
 
In 
, accomplished electronics researcher and engineer Alessandro Massaro delivers a comprehensive exploration of the latest ways in which people have achieved machine control, including automated vision technologies, advanced electronic and micro-nano sensors, advanced robotics, and more. 
The book is composed of nine chapters, each containing examples and diagrams designed to assist the reader in applying the concepts discussed within to common issues and problems in the real-world. Combining electronics and mechatronics to show how they can each be implemented in production line systems, the book presents insightful new ways to use artificial intelligence in production line machines. The author explains how facilities can upgrade their systems to an Industry 5.0 environment. 
Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances A thorough introduction to the state-of-the-art in a variety of technological areas, including flexible technologies, scientific approaches, and intelligent automatic systems Comprehensive explorations of information technology infrastructures that support Industry 5.0 facilities, including production process simulation Practical discussions of human-machine interfaces, including mechatronic machine interface architectures integrating sensor systems and machine-to-machine (M2M) interfaces In-depth examinations of internet of things (IoT) solutions in industry, including cloud computing IoT Perfect for professionals working in electrical industry sectors in manufacturing, production line manufacturers, engineers, and members of R&D industry teams, 
 will also earn a place in libraries of technicians working in the process industry.

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47 47 Xu, X., Zheng, H., Guo, Z. et al. (2019). SDD‐CNN: small data‐driven convolution neural networks for subtle roller defect inspection. Applied Sciences 9 (1364): 1–16.

48 48 Perez, H., Tah, J.H.M., and Mosavi, A. (2019). Deep learning for detecting building defects using convolutional neural networks. Sensors 19 (3556): 1–22.

49 49 Lin, C.‐S., Huang, Y.‐C., Chen, S.‐H. et al. (2018). The application of deep learning and image processing technology in laser positioning. Applied Sciences 8 (1542): 1–13.

50 50 Iskra, P. and Hernàndez, R.E. (2012). Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction. Wood Science and Technology 46 (1): 115–128.

51 51 Iskra, P. and Hernàndez, R.E. (2009). The influence of cutting parameters on the surface quality of routed paper birch and surface roughness prediction modeling. Wood and Fiber Science 41 (1): 28–37.

52 52 Contuzzi, N., Massaro, A., Manfredonia, I. et al. (2019). A decision making process model based on a multilevel control platform suitable for Industry 4.0. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

53 53 De Smedt, J., Hasić, F., Vanden Broucke, S.K.L.M., and Vanthienen, J. (2017). Towards a holistic discovery of decisions in process‐aware information systems. Proceedings of the International Conference on Business Process Management, Barcelona, Spain (10–15 September 2017). Cham: Springer Nature.

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57 57 Birglen, L. (2019). Design of a partially‐coupled self‐adaptive robotic finger optimized for collaborative robots. Autonomous Robots 43 (2): 523–538.

58 58 Jamshidi, P., Cámara, J., Schmerl, B. et al. (2019). Machine learning meets quantitative planning: enabling self‐adaptation in autonomous robots. Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self‐Managing Systems, Montreal, Canada (25–26 May 2019). Piscataway, NJ: IEEE.

59 59 Khalid, A., Kirisci, P., Ghrairi, Z. et al. (2016). A methodology to develop collaborative robotic cyber physical systems for production environments. Logistics Research 9 (23): 1–15.

60 60 Alcácer, V. and Cruz‐Machado, V. (2019). Scanning the Industry 4.0: a literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal 22 (1): 899–919.

61 61 Vidal, F., Álvarez, M., González, R. et al. (2011). Development of a flexible and adaptive robotic cell for small batch manufacturing. Contemporary Materials 2 (1): 1–12.

62 62 Wang, S., Wan, J., Li, D., and Zhang, C. (2016). Implementing smart factory of industry 4.0: an outlook. International Journal of Distributed Sensor Networks 12 (1): 1–10.

63 63 Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B. et al. (2019). The potential of additive manufacturing in the smart factory industrial 4.0: a review. Applied Sciences 9 (3865): 1–34.

64 64 Shahzad, A. and Mebarki, N. (2016). Learning dispatching rules for scheduling: a synergistic view comprising decision trees, tabu search and simulation. Computers 5 (3): 1–16.

65 65 Zhang, X., Xiao, L., and Kan, J. (2015). Degradation prediction model based on a neural network with dynamic windows. Sensors 15 (3): 6996–7015.

66 66 Pynam, V., Spanadna, R.R., and Srikanth, K. (2018). An extensive study of data analysis tools (Rapid Miner, Weka, R Tool, Knime, Orange). International Journal of Computer Science and Engineering 5 (9): 4–11.

67 67 Massaro, A., Maritati, V., Savino, N. et al. (2018). A study of a health resources management platform integrating neural networks and DSS telemedicine for homecare assistance. Information 9 (176): 1–20.

68 68 Nwankpa, C.E., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378v1.

69 69 Agostinelli, F., Hoffman, M., Sadowski, P., and Baldi, P. (2015). Learning activation functions to improve deep neural networks. arXiv:1412.6830v3.

70 70 Wan, Y., Li, Y., Song, Y., and Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences 10 (5): 1–20.

71 71 Harmon, M. and Klabjan, D. (2017). Activation ensembles for deep neural networks. arXiv:1702.07790v1.

72 72 Guarnieri, S., Piazza, F., and Uncini, A. (1999). Multilayer feedforward networks with adaptive spline activation function. IEEE Transactions on Neural Networks 10 (3): 672–683.

73 73 Biau, G. (2012). Analysis of a random forests model. Journal of Machine Learning Research 13: 1063–1095.

74 74 Massaro, A., Maritati, V., Giannone, D. et al. (2019). LSTM DSS automatism and dataset optimization for diabetes prediction. Applied Sciences 9 (17): 1–22.

75 75 Massaro, A., Meuli, G., and Galiano, A. (2018). Intelligent electrical multi outlets controlled and activated by a data mining engine oriented to building electrical management. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 7 (4): 1–20.

76 76 Massaro, A., Lisco, P., Lombardi, A. et al. (2019). A case study of research improvements in a service industry upgrading the knowledge base of the information system and the process management: data flow automation, association rules and data mining. International Journal of Artificial Intelligence and Applications (IJAIA) 10 (1): 25–46.

77 77 Massaro, A., Vitti, V., Lisco, P. et al. (2019). A business intelligence platform implemented in a big data system embedding data mining: a case of study. International Journal of Data Mining & Knowledge Management Process (IJDKP) 9 (1): 1–20.

78 78 Massaro, A., Leogrande, A., Lisco, P. et al. (2019). Innovative BI approaches and methodologies implementing a multilevel analytics platform based on data mining and analytical models: a case of study in roadside assistance services. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 8 (1): 17–36.

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