Commit cc102d9f authored by Evan Mottais's avatar Evan Mottais
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Ajoute des références manquantes

parent 8c345bea
......@@ -232,39 +232,70 @@ En conclusion, un système de vidéosurveillance pour la détection de chutes av
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@Article{Caesarendra2010,
author = {Caesarendra, Wahyu and Niu, Gang and Yang, Bo-Suk},
title = {Machine condition prognosis based on sequential Monte Carlo method},
journaltitle = {Expert Systems with Applications},
date = {2010-03-15},
volume = {37},
number = {3},
pages = {2412--2420},
issn = {0957-4174},
doi = {10.1016/j.eswa.2009.07.014},
url = {http://www.sciencedirect.com/science/article/pii/S0957417409006782},
urldate = {2017-10-25},
__markedentry = {[emottais:]},
abstract = {Machine condition prognosis is an important part of the decision-making in condition-based maintenance. By predicting the degradation of working conditions of machinery, it can organize a predictive maintenance program and prevent production loss. For complex systems, the trending data of the performance degradation is nonlinear over time known as a time series. This paper proposes a prognosis algorithm applied in a real dynamic system. Sequential Monte Carlo method, also known as a particle filter, can be used in nonlinear systems without any assumption of linearity. It is based on the sequential important sampling and resampling algorithm, which represents the posterior probability density function by a set of randomly drawn samples (called particles) and their associated weights. The prediction estimations are computed based on those samples and their weights. The real trending data of low methane compressors acquired from condition monitoring routines is employed for evaluating the proposed method. The results show that the proposed method offers a potential to predict the trending data in real systems of machine condition prognosis.},
file = {ScienceDirect Full Text PDF:/home-reseau/emottais/Documents/zotero_ok/storage/IUDECK4G/Caesarendra et al. - 2010 - Machine condition prognosis based on sequential Mo.pdf:application/pdf},
keywords = {Machine condition prognosis, Particle filter, Sequential importance sampling and resampling ({SIRs}), Sequential Monte Carlo method},
shortjournal = {Expert Systems with Applications},
author = {Caesarendra, Wahyu and Niu, Gang and Yang, Bo-Suk},
title = {Machine condition prognosis based on sequential Monte Carlo method},
journaltitle = {Expert Systems with Applications},
date = {2010-03-15},
volume = {37},
number = {3},
pages = {2412--2420},
issn = {0957-4174},
doi = {10.1016/j.eswa.2009.07.014},
url = {http://www.sciencedirect.com/science/article/pii/S0957417409006782},
urldate = {2017-10-25},
abstract = {Machine condition prognosis is an important part of the decision-making in condition-based maintenance. By predicting the degradation of working conditions of machinery, it can organize a predictive maintenance program and prevent production loss. For complex systems, the trending data of the performance degradation is nonlinear over time known as a time series. This paper proposes a prognosis algorithm applied in a real dynamic system. Sequential Monte Carlo method, also known as a particle filter, can be used in nonlinear systems without any assumption of linearity. It is based on the sequential important sampling and resampling algorithm, which represents the posterior probability density function by a set of randomly drawn samples (called particles) and their associated weights. The prediction estimations are computed based on those samples and their weights. The real trending data of low methane compressors acquired from condition monitoring routines is employed for evaluating the proposed method. The results show that the proposed method offers a potential to predict the trending data in real systems of machine condition prognosis.},
file = {ScienceDirect Full Text PDF:/home-reseau/emottais/Documents/zotero_ok/storage/IUDECK4G/Caesarendra et al. - 2010 - Machine condition prognosis based on sequential Mo.pdf:application/pdf},
keywords = {Machine condition prognosis, Particle filter, Sequential importance sampling and resampling ({SIRs}), Sequential Monte Carlo method},
shortjournal = {Expert Systems with Applications},
}
@InProceedings{Scovanner2007,
author = {Scovanner, Paul and Ali, Saad and Shah, Mubarak},
title = {A 3-dimensional Sift Descriptor and Its Application to Action Recognition},
booktitle = {Proceedings of the 15th {ACM} International Conference on Multimedia},
date = {2007},
series = {{MM} '07},
publisher = {{ACM}},
location = {New York, {NY}, {USA}},
isbn = {978-1-59593-702-5},
pages = {357--360},
doi = {10.1145/1291233.1291311},
url = {http://doi.acm.org/10.1145/1291233.1291311},
author = {Scovanner, Paul and Ali, Saad and Shah, Mubarak},
title = {A 3-dimensional Sift Descriptor and Its Application to Action Recognition},
booktitle = {Proceedings of the 15th {ACM} International Conference on Multimedia},
date = {2007},
series = {{MM} '07},
publisher = {{ACM}},
location = {New York, {NY}, {USA}},
isbn = {978-1-59593-702-5},
pages = {357--360},
doi = {10.1145/1291233.1291311},
url = {http://doi.acm.org/10.1145/1291233.1291311},
urldate = {2017-10-25},
abstract = {In this paper we introduce a 3-dimensional (3D) {SIFT} descriptor for video or 3D imagery such as {MRI} data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D {SIFT} is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.},
}
@Misc{Prisme2016_ProbablyWrong_CheckTheDate,
author = {Images et Réseaux},
title = {Appel à projets à destination des PME (PRISME)},
year = {2016},
}
@Article{Kim2010,
author = {Kim, Eunju and Helal, Sumi and Cook, Diane},
title = {Human Activity Recognition and Pattern Discovery},
journaltitle = {{IEEE} pervasive computing / {IEEE} Computer Society [and] {IEEE} Communications Society},
date = {2010},
volume = {9},
number = {1},
pages = {48},
issn = {1536-1268},
doi = {10.1109/MPRV.2010.7},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023457/},
urldate = {2017-10-25},
__markedentry = {[emottais:]},
file = {PubMed Central Full Text PDF:/home-reseau/emottais/Documents/zotero_ok/storage/J4B5Z4GR/Kim et al. - 2010 - Human Activity Recognition and Pattern Discovery.pdf:application/pdf},
pmcid = {PMC3023457},
pmid = {21258659},
shortjournal = {{IEEE} Pervasive Comput},
}
@InProceedings{Elhedda2003,
author = {Elhedda, Walid and Kalti, Karim and Hamrouni, Kamel},
title = {analyse des performances de filtres en traitement d'images},
date = {2003-03-22},
__markedentry = {[emottais:6]},
abstract = {In this paper we introduce a 3-dimensional (3D) {SIFT} descriptor for video or 3D imagery such as {MRI} data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D {SIFT} is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.},
file = {Full Text PDF:/home-reseau/emottais/Documents/zotero_ok/storage/SJC3JR8T/Elhedda et al. - 2003 - analyse des performances de filtres en traitement .pdf:application/pdf},
}
@Comment{jabref-meta: databaseType:biblatex;}
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