no code implementations • 6 Jan 2021 • Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo, Paige K Boor, Soumalya Sarkar, Satyanarayanan R Chakravarthy, Soumik Sarkar
3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability.
no code implementations • 6 Feb 2017 • Adedotun Akintayo, Soumik Sarkar
The algorithm minimizes model complexity and captures data likelihood.
no code implementations • 3 Feb 2017 • Zhanhong Jiang, Chao Liu, Adedotun Akintayo, Gregor Henze, Soumik Sarkar
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems.
no code implementations • 17 Aug 2016 • Vikas Chawla, Hsiang Sing Naik, Adedotun Akintayo, Dermot Hayes, Patrick Schnable, Baskar Ganapathysubramanian, Soumik Sarkar
In this paper, we propose a data-driven approach that is "gray box" i. e. that seamlessly utilizes expert knowledge in constructing a statistical network model for corn yield forecasting.
no code implementations • 25 Mar 2016 • Adedotun Akintayo, Kin Gwn Lore, Soumalya Sarkar, Soumik Sarkar
With such a training scheme, the selective autoencoder is shown to be able to detect subtle instability features as a combustion process makes transition from stable to unstable region.
no code implementations • 25 Mar 2016 • Adedotun Akintayo, Nigel Lee, Vikas Chawla, Mark Mullaney, Christopher Marett, Asheesh Singh, Arti Singh, Greg Tylka, Baskar Ganapathysubramaniam, Soumik Sarkar
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks.
6 code implementations • 12 Nov 2015 • Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar
In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation.