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Found 7 papers, 1 papers with code
Implicit Context-aware Learning and Discovery for Streaming Data Analytics
The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task.
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms.
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
Data-driven root-cause analysis for distributed system anomalies
Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems.
Deep Action Sequence Learning for Causal Shape Transformation
Deep learning became the method of choice in recent year for solving a wide variety of predictive analytics tasks.
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.
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.
