NICOP - Development of algorithm to detect coastal changes and fishery activities on the South China Sea
Abstract
Technically, this proposal has a potential to make a significant contribution to the current state-of-art of MDA. While the use of AI such as NN in MDA has been the focus of several recent research projects, its efficacy has not been fully materialized. The proposed study is unique and innovative among the past studies of the use of NN in maritime vessel attributes. The past studies primarily focused on the geometrical patters and correlations (e.g., geometry of the vessel tracks). On the other hand, his study will take advantage of the temporal frequency of the signals obtained by the DNB (e.g., frequency of the white spots from lights) as the significant addition to the training algorithm. For this reason, there is a reasonable chance that this study can significantly improve the efficacy of NN in MDA.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N629091512074
Entities
People
- Ichio Asanuma
Organizations
- Office of Naval Research
- United States Navy
- University of Tokyo