Design of Sea Ice Monitoring UAV Platform Based on Machine Learning
Abstract Machine learning, as one of the most currently remarkably intelligent techniques, has achieved great success in many applications. It makes mechanical instruments and equipment become more automated. In order to realize the intelligent monitoring of polar glacier movement and environmental...
| Published in: | Journal of Physics: Conference Series |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1088/1742-6596/1654/1/012067 https://iopscience.iop.org/article/10.1088/1742-6596/1654/1/012067/pdf https://iopscience.iop.org/article/10.1088/1742-6596/1654/1/012067 |
| Summary: | Abstract Machine learning, as one of the most currently remarkably intelligent techniques, has achieved great success in many applications. It makes mechanical instruments and equipment become more automated. In order to realize the intelligent monitoring of polar glacier movement and environmental data, this paper designs a buoy platform that can be equipped with a small environmental monitoring UAV (Unmanned Aerial Vehicle) based on automated ice station technology. On the basis of the original environmental monitoring capabilities of the platform, the target structure is designed according to the needs of the landing of the UAV. Based on the SVM (Support Vector Machine) algorithm, the conditions are predicted whether to start the UAV according to the environment parameters around the platform, which reduce the risk of damage to the UAV due to wind and snow. The superiority of the SVM algorithm in UAV environmental recognition has been verified through field experiments. |
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