|Prediction of Dinghy Boom Direction Using Intelligent Predictor
Yeong-Hyeon Byeon, Myung-Won Lee, Jae-Neung Lee, and Keun-Chang Kwak*
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.368-376, 2018
Abstract : "In this study, an intelligent predictor is designed for predicting the direction of dinghy booms and coaching
dinghy sailing using the designed predictor with information obtained from multiple sensors attached to the
dinghy and the sailor. For this purpose, we designed a Takagi-Sugeno-Kang-based linguistic model with interval
prediction based on fuzzy granulation, which can be realized by using a context-based fuzzy c-means clustering.
The sensors used in this study are cameras, GPS, and an anemometer. The GPS and cameras were attached to the
dinghy, and the anemometer was installed on a separate boat near the dinghy. Features are extracted from obtained
data to interpret them discretely without sequential data. The boom direction was predicted by collecting information
from the dinghy driven by an expert wearing a marked suit to predict the optimal direction. The constructed
database was randomly divided into a training set (60%) and a validation set (40%) for a 10-fold cross-validation.
The experimental results revealed that in the prediction of the dinghy boom direction, the proposed predictor showed
performance improvements of 25.2% and 17.9% on the training and validation sets, respectively, when compared
to the previous predictors."
Dinghy, fuzzy granulation, intelligent predictor, linguistic model, prediction of boom direction.
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