GAS IDENTIFICATION WITH MICROELECTRONIC GAS SENSOR IN PRESENCE OFDRIFT USING ROBUST GMM

Sofiane Brahim-Belhouari, Amine Bermak and Philip C. H. Chan

Hong Kong University of Science and Technology,Electrical and Electronic Engineering Department

Clear Water Bay, Kowloon, Hong Kong, SAR.

ABSTRACT

The pattern recognition problem for real life applications ofgas identification is particularly challenging due to the smallamount of data available and the temporal variability of theinstrument mainly caused by drift. In this paper we present agas identification approach based on class-conditional den-sity estimation using Gaussian mixture models (GMM). Adrift counteraction approach based on extracting robust fea-ture using a simulated drift is proposed. The performanceof the retrained GMM shows the effectiveness of the newapproach in improving the classification performance in thepresence of artificial drift.

1. INTRODUCTIONGas identification on a real-time basis is very critical for avery wide range of applications in the civil and military en-vironments. The past decade has seen a significant increasein the application of multi-sensor arrays to gas classificationand quantification. Most of this work has been focused onsystems using microelectronic gas sensors featuring smallsize and low-cost fabrication, making them attractive forconsumer applications. A number of interesting applica-tions have also emerged in the last decade whether relatedto hazard detection, poisonous and dangerous gases or toquality and environmental applications such as air qualitycontrol. Furthermore, in the next decade, increased interestin mass production of reliable microelectronic gas sensorsis expected in automation process manufacturing as well asin the automotive industry as recent requirements for activeprotection of passengers in vehicles from inherent pollutantsare being introduced. Unfortunately, the present gas sensorshave a lack of selectivity and therefore respond similarly toa wide variety of gases. Figure 1 illustrates an example of amicroelectronic gas sensor’s response to carbon monoxideCO, with 4 different concentrations of methane CH�. Itcan be shown from this simple illustration that differentCOconcentrations may result in similar voltages at the output ofthe sensor depending on the concentration of the interferinggas. This makes the problem of detecting and further quan-tifying a gas mixture a challenging task.

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Fig. 1. Sensor responses as a function of CO concentration for differentconcentrations of CH� .

The idea to combine an array of sensors with a patternrecognition algorithm to improve the selectivity of the sin-gle gas sensor has been widely accepted and being used byresearchers in this field. In fact, an array of different gassensors is used to generate a unique signature for each gas.After a preprocessing stage, the resulting feature vector isused to solve a given classification problem, which consistsof identifying an unknown sample as one from a set of pre-viously learned gases. Significant research work has beencarried out during the last decade in gas detection, prepro-cessing algorithms as well as pattern recognition classifica-tion [1] [2]. Unfortunately, the relative success of gas iden-tification systems using the pattern recognition algorithmsis primarily based upon laboratory measurements in well-controlled environments. Among the most serious limita-tion of actual gas classification and quantification systemsis the inherent drift of gas sensors, which shows significanttemporal variations of the sensor response when exposedto identical atmospheres. These drifts are due to unknowndynamic processes in the sensor system (e.g. poisoning orageing of sensors) or environmental changes (e.g. tempera-ture and pressure conditions). As a result of drift, the clus-ter distribution in the feature space becomes unstable over

time, making useless the decision surface built by the classi-fier during the training phase. Therefore, after certain time,drift impairs the pattern recognition ability of the system,which needs to be completely retrained.

In this paper we will present a gas classification ap-proach based on class-conditional density estimation usingGaussian mixture models (GMM). The proposed classifieris found to outperform both Multi Layer Perceptron (MLP)and K Nearest Neighbors (KNN). A drift counteraction ap-proach based on extending the training data set using a sim-ulated drift is proposed. The performance of the retrainedGMM shows the effectiveness of the new approach in im-proving the classification performance in the presence of ad-ditive as well as multiplicative drift. Section 2 of the paperbriefly presents the theoretical background of GMM classi-fier. Section 3 describes the classification results with andwithout drift using GMM classifier. This section also de-scribes the experimental set-up used to sample the data fromthe microelectronic gas sensor array. Section 4 concludesthe paper.

2. GAUSSIAN MIXTURE MODELSThe objective of pattern recognition is to set a decision rule,which optimally partitions the data space into c regions, onefor each class Ck . A pattern classifier generates a class la-bel for an unknown feature vector x � Rd from a discreteset of previously learned classes. The most general classi-fication approach is to use the posterior probability of classmembership ��Ckjx�. To minimize the probability of mis-classification one should consider the maximum a posteriorrule and assign x to class C�k:

C�k � argmaxk

���Ckjx�� � argmaxk

���xjCk���Ck�� (1)

where ��xjCk� is the class-conditional density and ��Ck�is the prior probability. In the absence of prior knowledge,��Ck� can be approximated by the relative frequency of ex-amples in the dataset. One way to build a classifier is toestimate the class-conditional densities by using represen-tation models for how each pattern class populates the fea-ture space. In this approach, classifier systems are built byconsidering each of the class in turn, and estimating the cor-responding class-conditional densities ��xjCk� from data.The most widely used method of nonparametric density es-timation is the K Nearest Neighbors (KNN). Despite thesimplicity of the algorithm, it often performs very well andis an important benchmark method. However, one draw-back of KNN is that all the training data must be stored, anda large amount of processing is needed to evaluate the den-sity for a new input pattern. An alternative is to combinethe advantages of both parametric and nonparametric meth-ods, by allowing a very general class of functional forms inwhich the number of adaptive parameters can be increasedto build more flexible models. This leads us to a powerful

technique for density estimation, called mixture model [3].In our work we focus on semiparametric models based onGaussian mixture distributions.In a Gaussian mixture model, a probability density functionis expressed as a linear combination of basis functions. Amodel with M components is decribed as mixture distribu-tion [3]:

��x� �MX

j��

��j���xjj� (2)

where ��j� are the mixing coefficients and the parametersof the component density functions��xjj� vary with j. Eachmixture component is defined by a Gaussian parametric dis-tribution in d dimensional space:

��xjj� ��

����d��j�j j���expf�

�

��x� �j�

����

j �x� �j�g

The parameters to be estimated are the mixing coefficients��j�, the covariance matrix �j and the mean vector �j .The method for training mixture model is based on maxi-mizing the data likelihood. The log likelihood of the dataset�x�� ����xn�, which is treated as an error, is defined by:

l �

nX

i��

log��xi� (3)

A specialized method is commonly used to produce opti-mum parameters, known as the expectation-maximisation(EM) algorithm [4].

3. EXPERIMENTAL RESULTS3.1. Data DescriptionMeasurements have been done using an experimental setupconsisting of a special sensor chamber equipped with gaspumps and mass flow controllers as well as a data acqui-sition board (Figure 2). The sensor array composed of 8

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Fig. 2. Scheme of the experimental setup. MFC stands for Mass FlowController.

micro-hotplate based SnO� thin film gas sensors, have beenused [5]. Four sensors with Pt/SnO� sensing film, two withAu/SnO� sensing film and the other two with Pt/Cu(0.16wt%)-SnO�. The sensors’ operating temperature was cho-sen to be ���oC for the purpose of good sensitivity to thestudied gases. The sensors output are raw voltage measure-ments in the form of exponential-like curves, as shown inFigure 3. Gases used in the experiment are methane, car-

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Fig. 3. Raw response of an array of 8 microelectronic gas sensors.

bon monoxide, hydrogen, and two binary mixtures: one ofmethane and carbon monoxide and another of hydrogen andcarbon monoxide. Vapors were injected into the gas cham-ber at a flow rate determined by the mass flow controllers(MFC). Concentration ranges are reported in Table 1. The

Gas Concentration range (ppm)CO 25-200CH� 500-4000CO & CH� 25-200 & 500-4000H� 500-2000CO & H� 25-200 & 500-2000

Table 1. Gases and their concentration ranges.

steady state values of the array sensor were recorded whileperiodically injecting different gases. A gas data set of 220examples was created to evaluate the performance of differ-ent pattern recognition systems. Each example consists of 8sensor transients, with i � �� ���� NT samples per transientdenoted by v�ti�. We subtracted the baseline of each sen-sor in order to reduce the effects of the additive sensor drift.Since our goal is the qualitative classification of patterns, anormalization procedure is used in order to reduce the in-fluence of concentrations and non-linearities. Each inputpattern is divided by its Euclidean norm.

3.2. Classification without driftPrior to applying the GMM classifier, a dimensionality re-duction technique namely PCA was used in order to per-form redundancy removing and feature reduction. Figure 4presents the two-dimensional PCA scores for all the studiedgas sensors steady state voltage. We can note that the de-cision boundaries are not well defined due to a strong over-lapping.

In order to compare the performance of different classi-fiers, a 10-fold cross validation approach was used allowingto overcome the problem of the limited data set typicallyavailable in gas sensors applications. It is well known that

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Fig. 4. PCA results for the microelectronic sensor array steady statevoltage. Measurement type,CO (circles), CH� (plus signs), mixture CO-CH� (diamonds),H� (triangles) and mixture CO-H� (squares).

the performance of a given classifier depends on the numberof principal components. In order to obtain a more objec-tive comparison, we reported in Figure 5, the classificationsuccess of GMM, KNN and MLP as function of the numberof principal components. The best performance is achievedusing GMM with a success rate of ����� obtained for 5principal components.

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Fig. 5. Accuracy as a function of the number of principal components.

3.3. Classification with driftAmong the most serious limitation of actual gas sensors isthe drift problem, which shows significant temporal varia-tions of the sensor response when exposed to identical at-mospheres. Drift problem can be explained as a randomtemporal variation of the sensor response when exposed tothe same gases under identical conditions. It can affect boththe baseline (additive) and the sensitivity of the sensor (mul-tiplicative). Figure 6 illustrates an example of an additivedrift problem in which we have reported the real responseof the sensor as function of the concentration of gases pe-riodically injected into a gas chamber in which the sensors

are being placed. We can note that the baseline responseof the sensor is shifted which complicates the classificationproblem even further.

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Fig. 6. Additive drift affecting the sensor baseline.

The drift causes temporal variations of the pattern dis-tribution in the feature space. This makes obsolete the de-cision surface obtained during the training phase and henceretraining the entire system is necessary.

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Fig. 7. Classification performance as function of drift (expressed in %)before (dashed) and after (solid) retraining.

To compensate for the patterns dispersion movement,we propose to extract robust features by generating simu-lated drift. The efficiency of this procedure has been testedagainst simulated linear drift. The drift has been modelledas vd�t� � v����t�where v is the sensor output before thedrift experiment and �tmax was chosen randomly for eachsensor [6]. Drift varying between 0 and 30% has been artifi-cially generated. The performance of the best classifier wasevaluated over the drifted measurements. Figure 7 showsthat drift affects the recognition ability of the GMM as theclassification success declines significantly (dashed line offigure 7). The drift counteraction strategy is to retrain GMMusing drifted sensor responses (solid line of figure 7). Theperformance of the retrained GMM was evaluated using the

10-fold cross validation method. It is shown that the coun-teraction procedure improves the performance of GMM inpresence of ��� drift by a factor of over ���. The finalassessment of this procedure has to be achieved by testing itover real sensor’s drift data.

4. CONCLUSION

In this paper we presented a gas identification approach basedon class-conditional density estimation using Gaussian mix-ture models (GMM). The proposed classifier is shown tooutperform both KNN and MLP for gas sensors data set col-lected from an integrated gas sensor array. It was howeverfound that the drift seriously degrades the classification per-formance of GMM. A drift counteraction approach basedon extracting robust feature using a simulated drift was pro-posed. The performance of the retrained GMM was evalu-ated using a cross validation method which shows a gain ofover 35% obtained for up to 30% drift. The test of the pro-posed approach for real drift values obtained for exampleby varying the operating temperatures is underway.

AcknowledgmentsThe work described in this paper was supported by a DirectAllocation Grant (project No. DAG02/03.EG05) and a PDFGrant.

5. REFERENCES

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[2] M. Pardo and G. Sberveglieri, ”Learning from data: Atutorial with emphasis on modern pattern recognitionmethods”, IEEE Sensors Journal, vol. 2, NO. 3, pp.189-202, 2002.

[3] D.M. Titterington, A.F.M. Smith and U.E. Makov,“Statistical analysis of finite mixture distributions,”John Wiley, New York, 1985.

[4] C. M. Bishop, “Neural networks for pattern recogni-tion,” Clarendon press, Oxford, 1995.

[5] P. C.H. Chan, G. Yan, L. Sheng, R. K. Sharma, Z.Tang, J.K.O. Sin, I-M. Hsing, Y. Wang, “An inte-grated gas sensor technology using surface micro-machining,” Sensors and Actuators B 82, pp. 277-283,2002.

[6] Marco, S.; Ortega, A.; Pardo, A.; Samitier, J., “Gasidentification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts,”IEEE Transactions on Instrumentation and Measure-ment, Volume: 47 Issue: 1 , PP. 316 -321, Feb. 1998.