Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks
Hazard monitoring systems play a key role in ensuring people’s safety. The problem of detecting dangerous levels of methane concentration in a coal mine was a subject of IJCRS’15 Data Challenge competition. The challenge was to predict, from multivariate time series data collected by sensors, if met...
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| Published in | Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp. 475 - 484 |
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| Main Authors | , |
| Format | Book Chapter |
| Language | English |
| Published |
Cham
Springer International Publishing
2015
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| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 331925782X 9783319257822 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-319-25783-9_42 |
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| Abstract | Hazard monitoring systems play a key role in ensuring people’s safety. The problem of detecting dangerous levels of methane concentration in a coal mine was a subject of IJCRS’15 Data Challenge competition. The challenge was to predict, from multivariate time series data collected by sensors, if methane concentration reaches a dangerous level in the near future. In this paper we present our solution to this problem based on the ensemble of Deep Neural Networks. In particular, we focus on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells. |
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| AbstractList | Hazard monitoring systems play a key role in ensuring people’s safety. The problem of detecting dangerous levels of methane concentration in a coal mine was a subject of IJCRS’15 Data Challenge competition. The challenge was to predict, from multivariate time series data collected by sensors, if methane concentration reaches a dangerous level in the near future. In this paper we present our solution to this problem based on the ensemble of Deep Neural Networks. In particular, we focus on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells. |
| Author | Pawłowski, Krzysztof Kurach, Karol |
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| Copyright | Springer International Publishing Switzerland 2015 |
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| DOI | 10.1007/978-3-319-25783-9_42 |
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| EISBN | 9783319257839 3319257838 |
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| Editor | Yu, Hong Yao, Yiyu Hu, Qinghua Grzymala-Busse, Jerzy W. |
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| EndPage | 484 |
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| Notes | K. Pawłowski and K. Kurach—Both authors contributed equally. |
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| PublicationSubtitle | 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings |
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| RelatedPersons | Kleinberg, Jon M. Hartmanis, Juris Mattern, Friedemann Goos, Gerhard Kittler, Josef Weikum, Gerhard Naor, Moni Mitchell, John C. Terzopoulos, Demetri Pandu Rangan, C. Kanade, Takeo Hutchison, David Tygar, Doug |
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| Snippet | Hazard monitoring systems play a key role in ensuring people’s safety. The problem of detecting dangerous levels of methane concentration in a coal mine was a... |
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| SubjectTerms | Ensemble methods Hazard monitoring systems Machine learning Recurrent neural networks Time series forecasting |
| Title | Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks |
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