Spacial-temporal Traceability for Cyber-Physical Industry 4.0 Systems
Author(s)
Zhao, Zhiheng
Zhang, Mengdi
Huang, George Q.
Xu, Gangyan
Chen, Qiqi
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Abstract
In this research, we first delineate and propose universal and interoperable spatial-temporal elements for cyber-physical industrial 4.0 systems (CPIS). A multi-modal bionic learning method for indoor positioning is developed to estimate the accurate and reliable location in a durable manner. Proximity, mobility, and contextual reasoning mechanisms are introduced to capture interplay, evolution, and synchronization among objects at operational level. To verify and evaluate the efficacy of our proposed solution, we implement it in a real-life case company and conduct a comparison study. Our results indicate that the proposed method outperforms the current indoor positioning methods and represents a significant step forward in achieving spatial-temporal traceability in CPIS.
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Date
2023-06
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Resource Type
Text
Resource Subtype
Poster