Research on Intelligent Extraction Techniques for Multi-Source Heterogeneous Experimental Test Image Data
Pages 101 - 108
Abstract
Within the realm of electric power, the empirical testing and detection operations encountered formidable issues encompassing a paucity of automation in experimental equipment, a pronounced degree of signal heterogeneity among sensors, limited resource management and sharing, and a dearth of sophistication. In the context of image data, grounded in the principles of projection and inclination, we have proffered a technological solution for processing the content within measurement experiments. We have devised hand-held devices that adeptly deconstruct experimental image data, harnessing a spectrum of algorithms and optical character recognition (OCR) capabilities to facilitate the extraction of invaluable insights from image-based experimental data.
References
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Index Terms
- Research on Intelligent Extraction Techniques for Multi-Source Heterogeneous Experimental Test Image Data
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Published In
November 2023
902 pages
ISBN:9798400716485
DOI:10.1145/3653081
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 May 2024
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- Research-article
- Research
- Refereed limited
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- State Grid Corporation science and technology project Metrology laboratory full scene perception and detection data sharing fusion technology research and application
Conference
IoTAAI 2023
IoTAAI 2023: 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
November 24 - 26, 2023
Nanchang, China
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