| Autor: |
Chang IS; The Graduate School of Nano IT Design Fusion, Seoul National University of S&T, Seoul 01811, Republic of Korea., Byun SW; Digital Innovation Support Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea., Lim TB; Intelligent Information Research Division, Korea Electronics Technology Institute, Seongnam 13488, Republic of Korea., Park GM; Department of Smart ICT Convergence Engineering, Seoul National University of S&T, Seoul 01811, Republic of Korea. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jan 04; Vol. 24 (1). Date of Electronic Publication: 2024 Jan 04. |
| DOI: |
10.3390/s24010302 |
| Abstrakt: |
With the development of the field of e-nose research, the potential for application is increasing in various fields, such as leak measurement, environmental monitoring, and virtual reality. In this study, we characterize electronic nose data as structured data and investigate and analyze the learning efficiency and accuracy of deep learning models that use unstructured data. For this purpose, we use the MOX sensor dataset collected in a wind tunnel, which is one of the most popular public datasets in electronic nose research. Additionally, a gas detection platform was constructed using commercial sensors and embedded boards, and experimental data were collected in a hood environment such as used in chemical experiments. We investigated the accuracy and learning efficiency of deep learning models such as deep learning networks, convolutional neural networks, and long short-term memory, as well as boosting models, which are robust models on structured data, using both public and specially collected datasets. The results showed that the boosting models had a faster and more robust performance than deep learning series models. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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