LAPSE:2020.0921
Published Article
LAPSE:2020.0921
Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production
Yifeng Liu, Wei Zhang, Wenhao Du
August 5, 2020
Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.
Keywords
deep-learning, few-shot learning, image classification, real-time systems
Suggested Citation
Liu Y, Zhang W, Du W. Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production. (2020). LAPSE:2020.0921
Author Affiliations
Liu Y: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Zhang W: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Du W: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Journal Name
Processes
Volume
8
Issue
6
Article Number
E649
Year
2020
Publication Date
2020-05-29
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8060649, Publication Type: Journal Article
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LAPSE:2020.0921
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doi:10.3390/pr8060649
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Aug 5, 2020
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Calvin Tsay
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