Frequency selection with oscillatory neurons for engine misfire detection

Dae Eun Kim, Jaehong Park

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Detecting novelties over time series data is of practical interest in many signal processing applications. Especially engine misfire detection is one of the great issues in automobile systems to inform incomplete engine exhaustion to cause environmental problem and also to guarantee safe operation of vehicles. It requires continuous monitoring of the system in real-time to detect deviations from the normal signal patterns. This paper presents a special frequency selection method based on recurrent neural networks consisting of oscillatory neurons, and applies the method with genetic algorithm to engine misfire detection problem by observing engine speed in automobile system.

Original languageEnglish
Pages2649-2652
Number of pages4
Publication statusPublished - 1999 Dec 1
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

Fingerprint

Neurons
Engines
Automobiles
Recurrent neural networks
Time series
Signal processing
Genetic algorithms
Monitoring

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Kim, D. E., & Park, J. (1999). Frequency selection with oscillatory neurons for engine misfire detection. 2649-2652. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .
Kim, Dae Eun ; Park, Jaehong. / Frequency selection with oscillatory neurons for engine misfire detection. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .4 p.
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abstract = "Detecting novelties over time series data is of practical interest in many signal processing applications. Especially engine misfire detection is one of the great issues in automobile systems to inform incomplete engine exhaustion to cause environmental problem and also to guarantee safe operation of vehicles. It requires continuous monitoring of the system in real-time to detect deviations from the normal signal patterns. This paper presents a special frequency selection method based on recurrent neural networks consisting of oscillatory neurons, and applies the method with genetic algorithm to engine misfire detection problem by observing engine speed in automobile system.",
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Kim, DE & Park, J 1999, 'Frequency selection with oscillatory neurons for engine misfire detection', Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, 99/7/10 - 99/7/16 pp. 2649-2652.

Frequency selection with oscillatory neurons for engine misfire detection. / Kim, Dae Eun; Park, Jaehong.

1999. 2649-2652 Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .

Research output: Contribution to conferencePaper

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N2 - Detecting novelties over time series data is of practical interest in many signal processing applications. Especially engine misfire detection is one of the great issues in automobile systems to inform incomplete engine exhaustion to cause environmental problem and also to guarantee safe operation of vehicles. It requires continuous monitoring of the system in real-time to detect deviations from the normal signal patterns. This paper presents a special frequency selection method based on recurrent neural networks consisting of oscillatory neurons, and applies the method with genetic algorithm to engine misfire detection problem by observing engine speed in automobile system.

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Kim DE, Park J. Frequency selection with oscillatory neurons for engine misfire detection. 1999. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .