Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Soheil Bahrampour"'
Autor:
Christoph Lang, Autumn Maruniak, Sam Kavusi, Soheil Bahrampour, Christopher Johnson, Habib Ahmad, Nadezda Fomina, Ronald W. Davis
Publikováno v:
Lab on a Chip. 16:2236-2244
Solution pH is a powerful tool for regulating many kinds of chemical activity, but is generally treated as a static property defined by a pre-selected buffer. Introducing dynamic control of pH in space, time, and magnitude can enable richer and more
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16bfea3b3374983de6ff96b06a015925
https://doi.org/10.1039/c6lc00421k
https://doi.org/10.1039/c6lc00421k
Publikováno v:
International Journal of Mining Science and Technology, Vol 25, Iss 6, Pp 905-913 (2015)
Measurement while drilling systems are becoming an important part of excavation operations for rock characterization and ground support design that require reliable information on rock strength and location & frequency of joints or voids. This paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e9aa09119037e5678d7a61c1855d9f2
http://www.sciencedirect.com/science/article/pii/S2095268615001512
http://www.sciencedirect.com/science/article/pii/S2095268615001512
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction
Publikováno v:
ICASSP
In this paper, we consider the problem of event prediction with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex dependencies between the variables combined with asynchronicity and sparsit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08f00a59045c3541bcbd6c37ad47689c
https://doi.org/10.1109/icassp.2017.7953302
https://doi.org/10.1109/icassp.2017.7953302
This chapter studies the problem of time-series classification and presents an overview of recent developments in the area of feature extraction and information fusion. In particular, a recently proposed feature extraction algorithm, namely symbolic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::048c32792b579462768dd224375217a2
https://doi.org/10.1142/9789813144552_0007
https://doi.org/10.1142/9789813144552_0007
Publikováno v:
Pattern Recognition Letters. 34:2126-2134
This paper addresses the problem of target detection and classification, where the performance is often limited due to high rates of false alarm and classification error, possibly because of inadequacies in the underlying algorithms of feature extrac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b05a550405492d6d49f53d7f57c389e
https://doi.org/10.1016/j.patrec.2013.06.021
https://doi.org/10.1016/j.patrec.2013.06.021
Publikováno v:
Applied Intelligence. 35:269-284
In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bf575b0d38bdc8a0a70dca393f9df8e
https://doi.org/10.1007/s10489-010-0219-2
https://doi.org/10.1007/s10489-010-0219-2
Publikováno v:
IFAC Proceedings Volumes. 42:858-863
This paper presents a set of new fault detection and isolation (FDI) approaches based on a Modified Gath-Geva (MGG) Clustering approach. The proposed approaches are formulated in the forms of a combined principal component analysis (PCA)-MGG or nonli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86c2a0ea4a5701a3953800872ba4e39c
https://doi.org/10.3182/20090630-4-es-2003.00141
https://doi.org/10.3182/20090630-4-es-2003.00141
Publikováno v:
IFAC Proceedings Volumes. 42:852-857
This paper proposes some new fusion architectures to enhance the diagnostic operation of a principal component analysis (PCA)-based fault diagnosis and isolation (FDI) system. The first approach presents a serial classifier fusion methodology by inco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d85cf7434ef8f36d60429db675a612e
https://doi.org/10.3182/20090630-4-es-2003.00140
https://doi.org/10.3182/20090630-4-es-2003.00140
Publikováno v:
ICASSP
Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under $\ell
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bd6b59d4916a8b6bfa305659f1d974e
http://arxiv.org/abs/1502.03126
http://arxiv.org/abs/1502.03126
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for singl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d7432a5dfae3a8f29902320bc6cae15
http://arxiv.org/abs/1502.01094
http://arxiv.org/abs/1502.01094