ArticleSensors (Basel, Switzerland)2018
Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor.Materials (Basel, Switzerland) · 2024Review
- Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning.Mikrochimica acta · 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 New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm.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
- Research on a Gas Concentration Prediction Algorithm Based on Stacking.Sensors (Basel, Switzerland) · 2021Article
- Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme.Sensors (Basel, Switzerland) · 2021Article
- Robust Estimation of Carbon Monoxide Measurements.Sensors (Basel, Switzerland) · 2020Article
- A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose.Sensors (Basel, Switzerland) · 2019Article
- A Gas Mixture Prediction Model Based on the Dynamic Response of a Metal-Oxide Sensor.Micromachines · 2019Article
- Identification and Statistical Analysis of Impulse-Like Patterns of Carbon Monoxide Variation in Deep Underground Mines Associated with the Blasting Procedure.Sensors (Basel, Switzerland) · 2019Article
- A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification.Sensors (Basel, Switzerland) · 2019Article
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4 authors.
Funding
Abstract
As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH₄ as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH₄ concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.
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