Monitoring-System Development of Gillnet Using Artificial Neural Network Conference Paper uri icon

abstract

  • Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

name of conference

  • ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering

published proceedings

  • Volume 6B: Ocean Engineering

author list (cited authors)

  • Jin, C., Kim, H., Park, J., Kim, M., & Kim, K.

citation count

  • 0

complete list of authors

  • Jin, Chungkuk||Kim, HanSung||Park, JeongYong||Kim, MooHyun||Kim, Kiseon

publication date

  • August 2020