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Found 7 papers, 1 papers with code
3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability.
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
The algorithm minimizes model complexity and captures data likelihood.
Energy Prediction using Spatiotemporal Pattern Networks
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems.
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
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.
Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video
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.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks.
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
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.
