Empowered edge intelligent aquaculture with lightweight Kubernetes and GPU-embedded
Author:
Funder
National Science and Technology Council (NSTC) Taiwan
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11276-023-03592-2.pdf
Reference24 articles.
1. Huang, C. T., et al. (2022). Economic feasibility assessment of cage aquaculture in offshore wind power generation areas in Changhua County. Taiwan. Aquaculture, 548, 737611. https://doi.org/10.1016/J.AQUACULTURE.2021.737611
2. Gladju, J., Kamalam, B. S., & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2, 100061. https://doi.org/10.1016/J.ATECH.2022.100061
3. Zhao, S., et al. (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 540, 736724. https://doi.org/10.1016/J.AQUACULTURE.2021.736724
4. Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022). Towards edge computing in intelligent manufacturing: Past, present and future. Journal Of Manufacturing Systems, 62, 588–611. https://doi.org/10.1016/J.JMSY.2022.01.010
5. Gutierrez-Torre, A., et al. (2022). Automatic distributed deep learning using resource-constrained edge devices. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3098973
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