This paper constructs an interactive offshore wind power resource assessment system based on the 30-year data of ERA5 wind farm from the European Center for Medium-Range Weather Forecasts (ECMWF), combined with the offshore wind power big data storage technology of Apache Cassandra database and the wind energy resource assessment model, and following the current national standards and technical specification documents. In this paper, 6 wind turbines in Changyi wind farm are randomly selected for authenticity, reasonableness and completeness analysis and verification, which verifies the feasibility of constructing wind farms in this sea area, and provides scientific site selection basis and wind resource assessment reference for the construction of offshore wind farms in China.
With the aggravation of marine oil spill pollution, the safety of the marine ecosystem and the development of the marine industry are threatened. Synthetic aperture radar (SAR) has become an important tool for detecting oil spill pollution. In order to further improve the accuracy of oil spill detection, this paper adopts Mask R-CNN model for oil spill detection of SAR oil spill images and improves the non-maximum suppression algorithm. Through experimental validation, the SAR image oil spill detection method based on the improved Mask R-CNN model proposed in this paper successfully improved the S2AR oil spill detection precision rate, recall rate and F1 score, in which the oil spill detection accuracy reached 91.5%, an improvement of 9.1% compared with the traditional model. Therefore, the research in this paper has certain practical significance and value for improving the application and promotion of SAR images in marine oil spill detection.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.