An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

Paul P. Lin; Kenol Jules

doi:10.1109/TIM.2002.806016

An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

Paul P. Lin, Kenol Jules

NASA Glenn Research Center

Research output: Contribution to journal › Article › peer-review

Abstract

An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and a back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

Original language	American English
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	51
DOIs	https://doi.org/10.1109/TIM.2002.806016
State	Published - Oct 1 2002

Keywords

Adaptive pattern recognition and classification
(APRC)
Back propagation neural network (BPNN); Learning vector quantization (LVQ)
Microgravity
Self-organizing feature map (SOFM)
Source detection
System monitoring

Disciplines

Computer and Systems Architecture
Space Vehicles

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10.1109/TIM.2002.806016License: CC BY

An Intelligent System for Monitoring the Microgravity EnvironmentFinal published version, 1.81 MBLicense: CC BY

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1174031&contentType=Journals+%26+Magazines&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A19%2Cp_Start_Page%3A1002%2Cp_Issue%3A5%2Cp_Volume%3A51%29

Cite this

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title = "An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station",

abstract = " An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and a back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.",

keywords = "Adaptive pattern recognition and classification, (APRC), Back propagation neural network (BPNN); Learning vector quantization (LVQ), Microgravity, Self-organizing feature map (SOFM), Source detection, System monitoring",

author = "Lin, \{Paul P.\} and Kenol Jules",

note = "Lin, P. P., \& Jules, K. (2002). An intelligent system for monitoring the microgravity environment quality on-board the International Space Station. Ieee Transactions on Instrumentation and Measurement, 51, 5, 1002-1009.",

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AU - Lin, Paul P.

AU - Jules, Kenol

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N2 - An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and a back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

AB - An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and a back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

KW - Adaptive pattern recognition and classification

KW - (APRC)

KW - Back propagation neural network (BPNN); Learning vector quantization (LVQ)

KW - Microgravity

KW - Self-organizing feature map (SOFM)

KW - Source detection

KW - System monitoring

UR - https://engagedscholarship.csuohio.edu/enme_facpub/12

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DO - 10.1109/TIM.2002.806016

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JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

ER -

An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

Abstract

Keywords

Disciplines

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