Drift compensation of gas sensor array data by common principal component analysis

Ziyatdinov, Andrey; Marco, S.; Chaudry, A.; Persaud, Krishna; Caminal, P.; Perera, Alexandre

doi:10.1016/j.snb.2009.11.034

Cited by 187 publications

(88 citation statements)

References 14 publications

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“…The cluster centers were used as a basis to define a FIS which was subsequently used to explore and understand environmental patterns over the years. Set 1 data is used in the Wavelet -FCM -FIS based initial water data understanding and modelling where as Set 2 and Set 3 are used to measure the sensory drift over the years and to test prediction performance of the system [10][11][12][13].…”

Section: Data and Systemmentioning

confidence: 99%

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Dutta¹

2013

IJCA

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A novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT-FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 -11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

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Section: Data and Systemmentioning

confidence: 99%

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Dutta¹

2013

IJCA

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“…Among the above-mentioned methods, discrete wavelet transform is more flexible because it can analyze the signal at different frequency bands with different resolutions. In case of E-nose measurements, since the drift effects are correlated, the multivariate methods allow capturing more information from all the sensors permitting modeling more complex or nonlinear drift effects [12]. In the literature around the E-nose research different multivariate methods can be found.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems

et al. 2017

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Metal oxide sensors are the most often used in electronic nose devices because of their high sensitivity, long lifetime, and low cost. However, these sensors suffer from a lack of response stability making the electronic nose systems useless in industrial applications. The sensor instabilities are particularly caused by incomplete recovery process producing gradual drifts in the sensor responses. This paper focuses on a signal processing method combining baseline manipulation and orthogonal signal correction technique in order to reduce effectively the drift impact from the sensor outputs. The proposed signal processing is explored using experimental data obtained from a gas sensor array responding to various concentrations of pine essential oil vapors. Partial Least Square method is then applied on the corrected dataset to establish a regression model for the estimation of gas concentration. In this work, we show essentially how our drift correction approach can help to improve significantly the stability of the regression model, while ensuring good accuracy.

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“…Moreover, the second idea is collecting drift trace from the former testing process. The common methods used for extracting drift signals include Principal Component Analysis (PCA), (6)(7)(8) Independent Component Analysis (ICA), (9) and Partial Least-Squares (PLS). (10) Aside from these methods, the Orthogonal Signal Correction (OSC) is superior to drift correction as shown in a recent study by Padilla et al (11) Generally speaking, all the component correction methods aim to find one preferable direction to eliminate drift.…”

Section: Introductionmentioning

confidence: 99%

“…(10) Aside from these methods, the Orthogonal Signal Correction (OSC) is superior to drift correction as shown in a recent study by Padilla et al (11) Generally speaking, all the component correction methods aim to find one preferable direction to eliminate drift. However, the drift direction varies with time and gas type in the mapping space, (6) and consecutive groups of samples are needed for predicting each drift direction. Thus, a great deal of calculation is performed during the testing process.…”

Section: Introductionmentioning

confidence: 99%

A Novel Retraining Method of Multiple Self-Organizing Maps for Gas Sensor Drift Compensation

2013

Sensors and Materials

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The drift effect of gas sensors severely affects the performance of an electronic nose, because the primarily built pattern recognition models degrade over time. A multiple self-organizing map (MSOM) network is an adaptive approach to compensate for gas sensor drift by self-retraining during the test phase. However, the conventional local retraining method of multiple self-organizing maps may lose drift information if the retraining is carried out with successive homogeneous samples for a long time. In this paper, we propose a novel global retraining method to keep each retraining vector (RV) fresh over time. Compared with the local retraining approach, the new method updates all the retraining vectors after one of them has been replaced. Experimental results demonstrate that the global retraining method retains the network recognition ability on drift effect, whereas the local retraining and adaptive resonance theory methods show high error rates. Finally, a discussion on the retraining rate is given to optimize the process speed of the MOSM network with the global retraining strategy.

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Drift compensation of gas sensor array data by common principal component analysis

Cited by 187 publications

References 14 publications

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems

A Novel Retraining Method of Multiple Self-Organizing Maps for Gas Sensor Drift Compensation

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