Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing

Qian F, Jin Y, Yu X, Tang Y, Marin GB (2024)
IEEE Transactions on Neural Networks and Learning Systems 35(3): 2914-2916.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Qian, Feng; Jin, YaochuUniBi ; Yu, Xinghuo; Tang, Yang; Marin, Guy B.
Abstract / Bemerkung
The digitization of process manufacturing involves converting information and knowledge into a digital format through technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. This transformation promotes extension and optimization within the industrial, supply, and value chains, aiming to enhance decision-making efficiency, enable agile operations, and ensure information security and privacy. However, the current learning and operational approaches in the process industry remain rooted in traditional informatization, falling short of the vision for digital transformation. To address this gap, it is crucial to implement fusion analysis, deepen understanding, adopt autonomous learning, and enable intelligent optimization based on life-cycle data. Therefore, it is of fundamental importance to realize the transformation of process manufacturing toward digitalization and intelligentization, i.e., the use of artificial intelligence with decision-making capability, via new learning theories, methods, and algorithms.
Erscheinungsjahr
2024
Zeitschriftentitel
IEEE Transactions on Neural Networks and Learning Systems
Band
35
Ausgabe
3
Seite(n)
2914-2916
ISSN
2162-237X
eISSN
2162-2388
Page URI
https://pub.uni-bielefeld.de/record/2987476

Zitieren

Qian F, Jin Y, Yu X, Tang Y, Marin GB. Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing. IEEE Transactions on Neural Networks and Learning Systems. 2024;35(3):2914-2916.
Qian, F., Jin, Y., Yu, X., Tang, Y., & Marin, G. B. (2024). Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing. IEEE Transactions on Neural Networks and Learning Systems, 35(3), 2914-2916. https://doi.org/10.1109/TNNLS.2024.3362091
Qian, Feng, Jin, Yaochu, Yu, Xinghuo, Tang, Yang, and Marin, Guy B. 2024. “Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing”. IEEE Transactions on Neural Networks and Learning Systems 35 (3): 2914-2916.
Qian, F., Jin, Y., Yu, X., Tang, Y., and Marin, G. B. (2024). Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing. IEEE Transactions on Neural Networks and Learning Systems 35, 2914-2916.
Qian, F., et al., 2024. Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing. IEEE Transactions on Neural Networks and Learning Systems, 35(3), p 2914-2916.
F. Qian, et al., “Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing”, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, 2024, pp. 2914-2916.
Qian, F., Jin, Y., Yu, X., Tang, Y., Marin, G.B.: Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing. IEEE Transactions on Neural Networks and Learning Systems. 35, 2914-2916 (2024).
Qian, Feng, Jin, Yaochu, Yu, Xinghuo, Tang, Yang, and Marin, Guy B. “Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing”. IEEE Transactions on Neural Networks and Learning Systems 35.3 (2024): 2914-2916.
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