ArticleSensors (Basel, Switzerland)2019
A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm.Scientific reports · 2025Article
- Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry.Sensors (Basel, Switzerland) · 2023Review
- A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function.Sensors (Basel, Switzerland) · 2023Article
- Sensor Data Prediction in Missile Flight Tests.Sensors (Basel, Switzerland) · 2022Article
- Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor.Sensors (Basel, Switzerland) · 2022Article
- Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks-VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50-were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.
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