Pubblicazione scientifica

Improving point-based AIS trajectory classification with partition-wise gated recurrent units

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità
sicurezza e qualità sui luoghi di lavoro

RIASSUNTO

We present Partition-wise Gated Recurrent Units (pGRUs) for point-based trajectory classification to detect real-world trawler fishing activities in the ocean. We propose partition-wise activation functions integrated with Gated Recurrent Units that partitions each feature and uses independent parameters to model distinct regions of the feature space. This approach enables us to leverage the benefits of deep learning on a low-dimensional and heterogeneous feature space by mapping the low-dimensional features into another space that can be better separated with piece-wise learnable parameters. We show that the proposed partition-wise activation functions can approximate a wide array of functions, including conventional activations such as sigmoid and hyperbolic tangent. Our experimental results demonstrate that pGRU learns the probability distribution from data and achieves substantial improvements over the state-of-the-art systems on the task of trawler fishing activity detection.

DATA

Data di pubblicazione: 01/05/2017

AUTORI

XIANG JIANG
XUAN LIU
ERICO N DE SOUZA
BAIFAN HU
DANIEL L SILVER
STAN MATWIN

ENTE DI AFFERENZA

INST BIG DATA ANALYTICS DALHOUSIE UNIV HALIFAX NS CANADA
JODREY SCH OF COMPUT SCI ACADIA UNIV WOLFVILLE NS CANADA

RIVISTA

2017 International Joint Conference on Neural Networks (IJCNN) (Page(s): 4044-4051)

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