Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments
| Autor: | Maki Sawano, Akifumi Yamamoto, Haruka Matsukura, Hiroshi Ishida, Christian Bilgera |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Real-time computing Airflow 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry gas detection Article Analytical Chemistry Sensor array sensor networks Anemometer 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Time series Instrumentation gas source localization Artificial neural network 010401 analytical chemistry metal oxide gas sensors Atomic and Molecular Physics and Optics CNN-LSTM 0104 chemical sciences Recurrent neural network machine learning 020201 artificial intelligence & image processing Wireless sensor network artificial neural networks |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 18 Issue 12 Sensors, Vol 18, Iss 12, p 4484 (2018) |
| ISSN: | 1424-8220 |
| Popis: | Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks. |
| Databáze: | OpenAIRE |
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