| Autor: |
Shengshi Yao, Yanpeng Lu, Kai Niu, Jincheng Dai, Chao Dong, Ping Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Fundamental Research, Vol 4, Iss 1, Pp 8-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3258 |
| DOI: |
10.1016/j.fmre.2023.06.003 |
| Popis: |
With the advent of the Internet of Everything (IoE), the concept of fully interconnected systems has become a reality, and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before. To address the challenges posed by massive data traffic, we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network. In particular, the scheme is featured with task-orientation and collaborative processing. To illustrate its applicability, we provide examples of time series and images, as typical industrial data sources, for practical tasks, such as lifecycle estimation and surface defect detection. Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging, compared to conventional methods, which is crucial for handling the demands of future interconnected industrial networks. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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