Deep learning for monitoring cyber-physical systems

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[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Sébastien Candel,et al.  Modeling of premixed swirling flames transfer functions , 2011 .

[4]  Raman Sujith,et al.  Loss of Chaos in Combustion Noise as a Precursor of Impending Combustion Instability , 2013 .

[5]  S. Carpenter,et al.  Early-warning signals for critical transitions , 2009, Nature.

[6]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7]  S. Sen,et al.  Dynamic Characterization of a Ducted Inverse Diffusion Flame Using Recurrence Analysis , 2018 .

[8]  Motoaki Kawanabe,et al.  How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..

[9]  Miroslav Krstic,et al.  An adaptive algorithm for control of combustion instability , 2004, Autom..

[10]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[11]  Yoshua Bengio,et al.  Attention-Based Models for Speech Recognition , 2015, NIPS.

[12]  Frederica Darema,et al.  Dynamic Data Driven Applications Systems: New Capabilities for Application Simulations and Measurements , 2005, International Conference on Computational Science.

[13]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[14]  Soumik Sarkar,et al.  LLNet: A deep autoencoder approach to natural low-light image enhancement , 2015, Pattern Recognit..

[15]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[16]  Yi Li,et al.  Bayesian automatic relevance determination algorithms for classifying gene expression data. , 2002, Bioinformatics.

[17]  Paul Kuentzmann,et al.  Unsteady Motions in Combustion Chambers for Propulsion Systems , 2006 .

[18]  A. Hussain.  Coherent structures—reality and myth , 1983 .

[19]  R. I. Sujith,et al.  Multifractality in combustion noise: predicting an impending combustion instability , 2014, Journal of Fluid Mechanics.

[20]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[21]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[22]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[23]  Jason Weston,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[24]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[25]  Steven C. Fisher,et al.  Remembering the Giants: Apollo Rocket Propulsion Development , 2012 .

[26]  Or Biran,et al.  Explanation and Justification in Machine Learning : A Survey Or , 2017 .

[27]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[28]  P. Holmes,et al.  The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows , 1993 .

[29]  A. Dowling.  Nonlinear self-excited oscillations of a ducted flame , 1997, Journal of Fluid Mechanics.

[30]  P. Schmid,et al.  Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.

[31]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[32]  Asok Ray,et al.  Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis , 2015, Annual Conference of the PHM Society.

[33]  T. Miyano,et al.  Dynamic properties of combustion instability in a lean premixed gas-turbine combustor. , 2011, Chaos.

[34]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Detection and Analysis of Combustion Instability From Hi-Speed Flame Images Using Dynamic Mode Decomposition , 2016 .

[36]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[37]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[38]  Pramod K. Varshney,et al.  Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory , 2018, ArXiv.

[39]  Rayleigh.  The Explanation of Certain Acoustical Phenomena , 1878, Nature.

[40]  Tim Lieuwen,et al.  Flame transfer function saturation mechanisms in a swirl-stabilized combustor , 2007 .

[41]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[42]  Yoshua Bengio,et al.  Artificial neural networks and their application to sequence recognition , 1991 .

[43]  Asok Ray,et al.  Symbolic dynamic analysis of complex systems for anomaly detection , 2004, Signal Process..

[44]  Phil Blunsom,et al.  Reasoning about Entailment with Neural Attention , 2015, ICLR.

[45]  Maria A. Heckl,et al.  Active Control of the Noise from a Rijke Tube , 1988 .

[46]  Soumik Sarkar,et al.  Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video , 2015, CoCo@NIPS.

[47]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[48]  Dimitry M. Gorinevsky,et al.  Amplitude and phase control in active suppression of combustion instability , 2012, 2012 American Control Conference (ACC).

[49]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[51]  Christopher Joseph Pal,et al.  Describing Videos by Exploiting Temporal Structure , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[52]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[53]  J. Janicka,et al.  Experimental characterization of onset of acoustic instability in a nonpremixed half-dump combustor. , 2007, The Journal of the Acoustical Society of America.

[54]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[55]  Kin Gwn Lore,et al.  Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder , 2020 .

[56]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[57]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[58]  S. Candel,et al.  A unified framework for nonlinear combustion instability analysis based on the flame describing function , 2008, Journal of Fluid Mechanics.

[59]  Klaus-Robert Müller,et al.  Learning how to explain neural networks: PatternNet and PatternAttribution , 2017, ICLR.