| Autor: |
Chee-Siang Leow, Haruka Matsukura, Hiromitsu Nishizaki, Christian Bilgera, Akifumi Yamamoto, Naoki Sawada, Hiroshi Ishida, Maki Sawano |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). |
| DOI: |
10.1109/isoen.2019.8823160 |
| Popis: |
In outdoor environments, fluctuating airflow and gas distribution make gas source localization (GSL) tasks difficult. In our research, we use neural networks (NNs) to overcome these difficulties by applying long short-term memory deep neural networks (LSTM-DNNs) to time series data taken from a gas sensor array and anemometer to estimate the position of a gas source. In this paper, we present NNs for GSL with the ability to use various length input data and estimate a gas source location each time-step. In doing so, we were able to estimate the location of a gas source within 40 time-steps (20 s) and achieved (using 300 time-steps) an estimation accuracy of 95%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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