Name
Abstract | ||
|---|---|---|
Learning the movement of the oil spills is a challenging task. To address this, we propose an algorithm that extracts particles from the expansion aspect of an oil spill and tracks the location of the particles in time-series of oil spill data. Using principal component analysis, the oil spill data were divided, and the particles matched; this approach aims to minimize the variance of the distance of each oil spill through the use of a genetic algorithm and principal component analysis. Using the oil spill visualization data set in the previous study, the algorithm was determined to be suitable for oil spill monitoring, with the average data error of the particles 3.2%.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3377929.3389902 | GECCO '20: Genetic and Evolutionary Computation Conference
Cancún
Mexico
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7127-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeon-Chang Lee | 1 | 0 | 0.68 |
| Hwi-Yeon Cho | 2 | 0 | 1.35 |
| Yong-Hyuk Kim | 3 | 355 | 40.27 |