@proceedings {7238,
	title = {Adaptive Sparse On-line Sequential Autoencoder for Sensors Measurements Compression Applied to Military Aircraft Engines},
	journal = {8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT{\textquoteright}2020) April14-16},
	year = {2020},
	address = {Algiers, Algeria},
	abstract = {In this work a new data-driven compression approach is presented. The compression algorithm is an autoencoder trained with an improved On-line sequential Extreme Learning Machine (OS-ELM). First, a dynamic adaptation of the training algorithm towards the newly coming data is achieved by integrating an updated selection strategy (USS) and dynamic forgetting function (DDF). Second, Singular Value Decomposition (SVD) is involved to enhance hidden layer representation via sparse mapping. This new developed autoencoder (ASOS- AE) is compared with the ordinary OS-ELM autoencoder (OS-AE) and proved its accuracy in CMAPSS dataset (Commercial Modular Aero-Propulsion System Simulation). The C-MAPSS software has revisions in civil and military applications. In the present work we used the military version of its applications.},
	url = {https://www.researchgate.net/publication/340732102_Adaptive_Sparse_On-line_Sequential_Autoencoder_for_Sensors_Measurements_Compression_Applied_to_Military_Aircraft_Engines},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Ouahab KADRI}
}
