Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Kin Gwn"'
Publikováno v:
International Journal of Prognostics and Health Management, Vol 7, Iss 4 (2016)
The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and
Externí odkaz:
https://doaj.org/article/a744c00a6d474aeab7a05cca8735a81e
Autor:
Lore, Kin Gwn, Reddy, Kishore K.
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. In offline learning, one can inspect historical training data to ident
Externí odkaz:
http://arxiv.org/abs/1910.08438
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. This pap
Externí odkaz:
http://arxiv.org/abs/1805.12296
A significant challenge in energy system cyber security is the current inability to detect cyber-physical attacks targeting and originating from distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible loads, and electric vehic
Externí odkaz:
http://arxiv.org/abs/1709.08830
Autor:
Balu, Aditya, Ghadai, Sambit, Lore, Kin Gwn, Young, Gavin, Krishnamurthy, Adarsh, Sarkar, Soumik
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In thi
Externí odkaz:
http://arxiv.org/abs/1612.02141
Akademický článek
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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. In this regard, root-cause analysis
Externí odkaz:
http://arxiv.org/abs/1605.06421
Autor:
Lore, Kin Gwn, Stoecklein, Daniel, Davies, Michael, Ganapathysubramanian, Baskar, Sarkar, Soumik
Deep learning became the method of choice in recent year for solving a wide variety of predictive analytics tasks. For sequence prediction, recurrent neural networks (RNN) are often the go-to architecture for exploiting sequential information where t
Externí odkaz:
http://arxiv.org/abs/1605.05368
This paper proposes an end-to-end convolutional selective autoencoder approach for early detection of combustion instabilities using rapidly arriving flame image frames. The instabilities arising in combustion processes cause significant deterioratio
Externí odkaz:
http://arxiv.org/abs/1603.07839
Effective human-machine collaboration can significantly improve many learning and planning strategies for information gathering via fusion of 'hard' and 'soft' data originating from machine and human sensors, respectively. However, gathering the most
Externí odkaz:
http://arxiv.org/abs/1512.07592