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Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression. / Kalmuk, Alexander; Granichin, Oleg; Granichina, Olga; Ding, Mingyue.

2016 AMERICAN CONTROL CONFERENCE (ACC). IEEE Canada, 2016. стр. 6839-6844 (Proceedings of the American Control Conference).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Kalmuk, A, Granichin, O, Granichina, O & Ding, M 2016, Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression. в 2016 AMERICAN CONTROL CONFERENCE (ACC). Proceedings of the American Control Conference, IEEE Canada, стр. 6839-6844, 2016 American Control Conference (ACC), Boston, Соединенные Штаты Америки, 6/07/16.

APA

Kalmuk, A., Granichin, O., Granichina, O., & Ding, M. (2016). Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression. в 2016 AMERICAN CONTROL CONFERENCE (ACC) (стр. 6839-6844). (Proceedings of the American Control Conference). IEEE Canada.

Vancouver

Kalmuk A, Granichin O, Granichina O, Ding M. Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression. в 2016 AMERICAN CONTROL CONFERENCE (ACC). IEEE Canada. 2016. стр. 6839-6844. (Proceedings of the American Control Conference).

Author

Kalmuk, Alexander ; Granichin, Oleg ; Granichina, Olga ; Ding, Mingyue. / Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression. 2016 AMERICAN CONTROL CONFERENCE (ACC). IEEE Canada, 2016. стр. 6839-6844 (Proceedings of the American Control Conference).

BibTeX

@inproceedings{62989c719a5841ed8af1b8f6ac469064,
title = "Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression",
abstract = "The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algorithm is used to detect abrupt changes in the obtained data using an adaptive threshold. The estimation of this adaptive threshold is presented. This approach is then applied to demonstrate the slowdown detection of a small autonomous vehicle.",
keywords = "THRESHOLD",
author = "Alexander Kalmuk and Oleg Granichin and Olga Granichina and Mingyue Ding",
year = "2016",
language = "Английский",
series = "Proceedings of the American Control Conference",
publisher = "IEEE Canada",
pages = "6839--6844",
booktitle = "2016 AMERICAN CONTROL CONFERENCE (ACC)",
address = "Канада",
note = "null ; Conference date: 06-07-2016 Through 08-07-2016",

}

RIS

TY - GEN

T1 - Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression

AU - Kalmuk, Alexander

AU - Granichin, Oleg

AU - Granichina, Olga

AU - Ding, Mingyue

PY - 2016

Y1 - 2016

N2 - The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algorithm is used to detect abrupt changes in the obtained data using an adaptive threshold. The estimation of this adaptive threshold is presented. This approach is then applied to demonstrate the slowdown detection of a small autonomous vehicle.

AB - The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algorithm is used to detect abrupt changes in the obtained data using an adaptive threshold. The estimation of this adaptive threshold is presented. This approach is then applied to demonstrate the slowdown detection of a small autonomous vehicle.

KW - THRESHOLD

M3 - статья в сборнике материалов конференции

T3 - Proceedings of the American Control Conference

SP - 6839

EP - 6844

BT - 2016 AMERICAN CONTROL CONFERENCE (ACC)

PB - IEEE Canada

Y2 - 6 July 2016 through 8 July 2016

ER -

ID: 74015207