论文标题
强大的海洋浮标位置用于使用辍学的船舶检测
Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means
论文作者
论文摘要
海洋浮标通过检测附近的渔船来帮助与非法，未报告和不受监管的（IUU）捕鱼的战斗。然而，海洋浮标可能会因自然原因和破坏浮标而受到破坏。在本文中，我们将海洋浮标的放置作为聚类问题，并提出辍学k-均值和辍学的K-Median，以提高放置鲁棒性，以使浮标破坏。 我们使用历史自动识别系统（AIS）数据模拟了西非附近的加蓬海水中的船舶通过，然后将辍学K-均值的船检测概率与经典的K-均值和辍学的K-Median与经典K-Median进行了比较。有5个浮标，由经典K均值，辍学K-均，经典K-Median和辍学的K-Median计算的浮标安排具有38％，45％，48％和52％的船舶检测概率。
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%.
