Non‐intrusive reduced‐order modeling using convolutional autoencoders
Sign Up to like & getrecommendations! Published in 2022 at "International Journal for Numerical Methods in Engineering"
DOI: 10.1002/nme.7072
Abstract: The use of reduced‐order models (ROMs) in physics‐based modeling and simulation almost always involves the use of linear reduced basis (RB) methods such as the proper orthogonal decomposition (POD). For some nonlinear problems, linear RB… read more here.
Keywords: reduced order; order; intrusive reduced; convolutional autoencoders ... See more keywords
Compression of EMG Signals Using Deep Convolutional Autoencoders
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3142034
Abstract: Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet… read more here.
Keywords: performance; deep convolutional; compression; convolutional autoencoders ... See more keywords
Toward Generalized Change Detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2019.2936771
Abstract: Ongoing planetary exploration missions are returning large volumes of image data. Identifying surface changes in these images, e.g., new impact craters, is critical for investigating many scientific hypotheses. Traditional approaches to change detection rely on… read more here.
Keywords: convolutional autoencoders; change detection; transfer learning; image ... See more keywords
Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders
Sign Up to like & getrecommendations! Published in 2022 at "Biomedical Optics Express"
DOI: 10.1364/boe.476233
Abstract: Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how… read more here.
Keywords: spatio spectral; segmentation; high dimensionality; end ... See more keywords