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Temperature modulation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensor
Sensors and Actuators B 114 (2006) 1059–1063
Temperature modulation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensor J.R. Huang a,b,∗ , G.Y. Li a , Z.Y. Huang a , X.J. Huang a , J.H. Liu a b
a Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, PR China Department of Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, PR China
Received 20 May 2005; received in revised form 28 July 2005; accepted 29 July 2005 Available online 3 October 2005
Abstract Stannic oxide sensors were developed to monitor CO of 10–250 ppm concentrations. Cross sensitivities of these sensors against 100–2000 ppm methane can be suppressed by evaluating the features extracted from the sensor signals. For this purpose, the working temperature of the sensors was modulated between 250 and 300 ◦ C, and the dynamic responses were measured to different concentrations of CO, CH4 , and their mixtures were measured. The discrete wavelet transform (DWT) was used to extract important features from the sensor responses. These features were then input to the pattern recognition (neural) method. The species considered can be discriminated with a 100% success rate by using a back propagation network and the concentrations of the gases studied can also be accurately predicted. © 2005 Elsevier B.V. All rights reserved. Keywords: SnO2 gas sensor; Carbon monoxide; Temperature modulation; Wavelet transform; Selectivity; Artificial neural network
1. Introduction SnO2 -based semiconductor gas sensors are widely used to measure CO and other reducing gases in air. It operates on the principle that the sensor resistance changes in the presence of reducing or oxidizing gases. Presently well-known advantages include their low costs and high sensitivities; while disadvantages concern their lack of stability and selectivity [1]. To achieve high CO selectivity of SnO2 gas sensors different methods were used in the past. These include optimization of the doping [2–4], the operation temperature [5] or the substrate geometry [6].Finally, several independent signals from a sensor have been evaluated to achieve selectivity by a pattern-recognition algorithm [7,8]. The different strategies reported, however, have been applied with limited success. A recent approach consists of analyzing the dynamic response of a sensor in order to obtain a new set of parameters specific to target gases. An easily implemented method is based on changes in operating temperature for generating suitable response transients [9,10]. Previous work of this kind has shown that a modulation of the sensor working temper-
∗
Corresponding author. Tel.: +86 551 5591 132; fax: +86 551 5592 420. E-mail address: [email protected] (J.R. Huang).
0925-4005/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2005.07.070
ature leads to a response pattern that is characteristic of the gas species under investigation [11,12]. The most commonly used method to extract important features from the response signals of temperature-modulated gas sensors is the fast Fourier transform (FFT). An alternative way for decomposing a signal into its constituent parts is the discrete wavelet transform (DWT). The main difference between the two methods is that DWT provides both frequency and temporal information of the signal, while FFT gives only frequency information for the complete duration of the signal so that the temporal information is lost. The artificial neural network also had been used to improve the CO selectivity of SnO2 gas sensors [13]. In this work the temperature modulation was used for both objectives; the decrease of power consumption and the increase of selectivity by application of pattern recognition and DWT. We detect CO, CH4 , and their mixture with dry air by using a single temperature-modulated SnO2 gas sensor. The sensor was operated in the dynamic mode affected by square voltage pulses applied to its heating element, thus modulating its temperature between 250 and 300 ◦ C. The sensor signals were decomposed by the DWT, and the features extracted were used as inputs into the pattern recognition method (artificial neural network) for identification purposes. Finally, we trained
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J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
Fig. 1. The experimental set-up.
the artificial neural network by using selected wavelet coefficients to predict the CO concentration in a high background of methane. 2. Experimental The thick film sensors were made by depositing thick films of tin oxide on ceramics substrates. The devices and the preparation of the sensitive materials were described elsewhere [14]. The grain sizes of the materials were around 20 and 50 nm. A headspace sample (HP-7694) was used to inject the gases measured into a 4500 ml sensor test chamber, where a single SnO2 gas sensor was set. Dry air, CO, CH4 and binary mixtures were measured. A square input voltage (20 mHz, frequency generator: HP 3325B, power supply: HP 6035A) was applied to the sensor heating resistor, which allowed the working temperature of the device to be modulated in the range 250–300 ◦ C. The output voltage was monitored, acquired and stored in a personal computer for further analysis. Fig. 1 shows the experimental set-up. The measurement process was as follows: dry air at a constant flow rate of 10 ml s−1 was used as carrier a gas. Data acquisition started 80 s before the injection of a test gas sample into the air-flow. The sampling rate was set at two samples per second and the whole process took 6 min to be completed. Measurements were carried out with a single gas and gas mixtures (two gases) in dry air. The concentrations were from 10 to 250 ppm for CO gases and from 100 to 2000 ppm for CH4 gases. The number of experiments carried out for binary mixtures was 80. 3. Results and discussion 3.1. The dynamic response to different gases In order to clearly distinguish the dynamic response features of gas to be detected, experimental conditions were set as follows: rectangular wave modulation, a dutyfactor of 30/(30 + 20), and an applied potential of 7 V. Fig. 2 shows the typical time dependences of the sensor output voltage realized by a single SnO2 sensor upon exposure to dry air, CO, CH4 , and their mixtures of various concentrations. It is noted that different characteristic responses are given by a single SnO2 sensor, i.e. the temperature dependence of
the sensor conductance are different in temperature dependence in the presence of the adsorbed gases under the experimental conditions. In this study, dry air, CO, CH4 , and the binary mixtures were obviously distinguished by controlling the temperature and the temperature modulating frequency. On the other hand, the characteristic response to each gas was attributable to the difference in the adsorption or desorption kinetics and the oxidative reaction kinetics of the test gases on the sensor surface. It is widely accepted that the key process in the response of a semiconductor oxide to a reducing gas is the modulation of the adsorbed oxygen species (such as O2− , O− or O2 )concentration. Although the sensing mechanism upon exposure to reducing gases have not been clear, it is affirmative that there exists a complicated adsorption/desorption or oxidative reaction kinetics on the sensor surface. The response curve also changed regularly with increasing the concentration of the detected gases, i.e. the characteristic peak of the response curve became higher as the amount of the target gases increased. Comparing with the response curve in dry air, one can also observe the high sensitivity to CO and CH4 . 3.2. Feature extraction Sensor responses contain numerous non-stationary or transitory characteristics (e.g. drift, trends and abrupt changes). These characteristics may be a very important part of the signal and Fourier analysis is not suitable for detecting them. While FFT gives frequency information for the complete duration of the signal (temporal information is lost), wavelet analysis provides both frequency and temporal information. DWT coefficients provide ’fingerprints’ that are characteristic of the concentration level and gas measured. Here, we used the DWT to extract important features from the dynamic response of the sensor. Because of its desirable properties of orthogonality, approximation quality, redundancy and numerical stability, the wavelet base constructed by Daubechies has become the foundation for signal analysis in a wide range of applications [15]. For this reason, the Daubechies family of wavelets was selected to perform the analysis. Specifically, to compute the DWT the 4th order Daubechies (db4) was selected as the analyzing wavelet, because it is the first ’smooth’ wavelet of the family. The features (DWT coefficients) extracted
J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
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Fig. 2. Response curves of a SnO2 gas sensor to (a) dry air, (b) CO, (c) CH4 , and (d) their mixtures of various concentrations in the dry air. Experimental conditions: rectangular voltage, a dutyfactor of 30/(30 + 20), applied potential of 7 V.
by DWT were used for the qualitative and quantitative analysis of the gases studied. The DWT was computed over a single period of the sensor response (100 samples). A level 3 decomposition of the sensor response was selected, because it was found that the wavelet coefficients showed significant differences between the gases to be discriminated. Fig. 3 shows 31wavelet coefficients of the third level decomposition for dry air, CO, CH4 , and the binary mixtures. From Fig. 3, it can be derived that the 14th–26th wavelet coefficients carry important information to discriminate the test gases. 3.3. Qualitative analysis Qualitative analyses were performed with the aim at discrimination between dry air, CO, and CH4 gases using a single sensor. To this end, the coefficients (14th–26th) extracted from the dynamic sensor responses by using DWT methods were fed into the artificial neural network. The data matrices must be normalized because the neural network requires that its input data lie in the range [0,1].To classify gases by using neural network the transformed data sets were first column normalized (the values of elements in column j are divided by the maximum value to be
found in column j) between 0 and 1 and then used for training and testing the neural network. Both the training and testing data set consist of input and output vectors. Each input vector has a corresponding output vector. The output vectors corresponding the gases in the training data set are fixed as 0, 1. Table 1 shows the expectation output of BP neural network. A three-layer feedforward neural network with sigmoidal activation was designed to learn the features of the sensor responses. Neural network simulation was implemented in C language on a Pentium-based computer. As there were 13 features and 2 gases, input and output nodes were fixed as 13 and 3. The number of hidden layer neurons was fixed as 4 by trials. After the network was built, learning was performed by using back-propagation algorithm. Learning parameters such as learning rate (α) and momentum term (η) were optimized by grid experiments for a fixed small number of learning cycles. A point on the grid corresponding to the minimum mean square error (MSE) represents the best choice of learning parameters. Table 1 Expectation output of BP neural network Test gas Expectation output
Dry air 000
CO 001
CH4 010
CO/CH4 001
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Fig. 3. Wavelet coefficients of a third level decomposition of the sensor responses in the presence of (a) dry air, (b) CO, (c) CH4 , and (d) their mixtures.
The optimum α and η were found to be 0.001 and 0.0001. Once the optimum values of the learning parameters were available, the network was trained for a larger number of iterations. The maximum number of iterations was fixed up to 10,000. In this artificial neural network, the MSE reached 4.83 × 10−3 after the 10,000 learning cycles. The results of the simulation experiments are shown in Table 2 For this kind of a single sensor-2 gases problem, the neural network gave 100% classification for the transformed data sets. 3.4. Quantitative analysis Quantitative analyses were performed with the aim at prediction the concentrations of the test gases by using a single sensor. The normalized concentrations of the testing gas and the global normalized (the values of elements in the matrix are divided by the maximum value to be found in the matrix) wavelet coefficients 14th–26th from dynamic sensor responses extracted by using DWT methods were fed into the artificial neural network. It is important to notice that the two normalizations are not equivalent. While the column normalization scales all the columns in the data matrix independently and therefore the differences in ‘intensity’ between different columns are lost, the global normalization keeps these differences unchanged. Each Table 2 Gas recognition results of BP neural network Input gas
Dry air CO CH4 CO/CH4
System output Dry air
CO
CH4
2 0 0 0
0 20 0 60
0 0 15 0
Fig. 4. Predicted results of (a) the CO concentrations in air, (b) the CO concentration in a different high background of methane.
J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
input vector also had a corresponding output (target) vector. The output vectors corresponding to the concentrations of the CO gas in the training data sets were normalized between 0 and 1. The prediction in this case was performed by a 13 × 2 × 1 feed forward neural network with sigmoidal activation. The number of hidden layer neurons was fixed as 2 by trials. The optimum α and η were found to be 0.001 and 0.0001. The maximum number of iterations was fixed up to 100,000. The MSE decreased to 0.1745 when the training was stopped. The test data were put into the neural network after the training and the output vectors represented the gases concentrations. The results of the simulation experiment are shown in Fig. 4. From Fig. 4a one can observe that the most predicted concentrations of the pure gas are close to the real values, and only few samples are far from the real concentrations. The mean absolute proportional error was 10.97% and the maximal mean proportional error was 63.33% (the first sample). From Fig. 4b one can observe that the most predicted concentrations of the pure gas are also close to the real values. The mean absolute proportional error was 10.79% and the maximal mean proportional error was 52.48% (the first sample). For this kind of a single temperature-modulated SnO2 -based sensor, a neural network can accurately predict the concentrations of the gases studied for the transformed data sets. 4. Conclusion The selectivity of SnO2 sensors for CO detection was sufficiently improved by applying artificial neural networks to the features extracted from the sensor signals. This procedure was necessary to improve discrimination of CO from methane. Both gases could be recognized by their different response shapes after switching the operation temperature. The sensor signals were decomposed by the DWT, and the matrix (the features extracted from the sensor signals) normalized by using a column normalization or a global normalization was used as inputs into the artificial neural network for identification and quantification purposes. The artificial neural network can give 100% classification of two gases and a sufficiently good prediction of the CO concentration. The results of the simulation experiments showed that CO gases could be detected with good selectivity and sensitivity in a high background of CH4 in air. Acknowledgements This work was financially supported by the National Natural Science Foundation of China (No. 60374049) and National High Technology Research and Development Program of China (863 Program No. 2004AA302030), which are gratefully acknowledged. References [1] K. Ihokura, J. Watson, Stannic Oxide Gas Sensors, Principles and Applications, CRC press, Boca Raton, FL, 1994. [2] G.G. Mandayo, E. Casta¨no, F.J. Gracia, A. Cirera, A. Cornet, J.R. Morante, Strategies to enhance the carbon monoxide sensitivity of tin oxide thin films, Sens. Actuators B, Chem. 95 (2003) 90–96.
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[3] P. M´enini, F. Parret, M. Guerrero, K. Soulantic, L. Erades, A. Maisonnat, B. Chaudret, CO response of a nanostructured SnO2 gas sensor doped with palladium and platinum, Sens. Actuators B, Chem. 103 (2004) 111– 114. [4] O. Wurzinger, G. Reinhardt, CO-sensing properties of doped SnO2 sensors in H2 -rich gases, Sens. Actuators B, Chem. 103 (2004) 104–110. [5] A. Fort, M. Gregorkiewitz, N. Machetti, S. Rocchi, B. Serrano, L. Tondi, N. Ulivieri, V. Vignoli, G. Faglia, E. Comini, Selectivity enhancement of SnO2 sensors by means of operating temperature modulation, Thin Solid Films 418 (2002) 2–8. [6] P. Montmeat, R. Lalauze1, J.P. Viricelle, G. Tournier, C. Pijolat, Model of the thickness effect of SnO2 thick film on the detection properties, Sens. Actuators B, Chem. 103 (2004) 84–90. [7] A. Zafer, K. Thomas, G. Andrea, S. Andreas, Low power virtual sensor array based on a micromachined gas sensor for fast discrimination between H2 , CO and relative humidity, Sens. Actuators B, Chem. 100 (2004) 240– 245. [8] A.K. Srivastava, Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network, Sens. Actuators B, Chem. 96 (2003) 24–27. ˘ [9] A. Ortega, S. Marco, A. Perera, T. Sundic, A. Pardo, J. Samitier, An intelligent detector based on temperature modulation of a gas sensor with a digital signal processor, Sens. Actuators B, Chem. 78 (2001) 32–39. [10] X.-J. Huang, F.-L. Meng, Z.-X. Pi, W.-H. Xu, J.-H. Liu, Gas sensing behavior of a single tin dioxide sensor under dynamic temperature modulation, Sens. Actuators B, Chem. 99 (2004) 444–450. [11] X.-J. Huang, J.-H. Liu, D.-L. Shao, Z.-X. Pi, Z.-L. Yu, Rectangular mode of operation for detecting pesticide residue by using a single SnO2 -based gas sensor, Sens. Actuators B, Chem. 96 (2003) 630–635. [12] Y.-F. Sun, X.-J. Huang, F.-L. Meng, J.-H. Liu, Study of influencing factors of dynamic measurements based on SnO2 gas sensor, Sensors 4 (2004) 95–104. [13] M. Schweizer-Berberich, M. Zdralek, U. Weimar, W. Gopel, T. Viard, D. Martinez, A. Seube, A. Peyre-Lavigne, Pulsed mode of operation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensors, Sens. Actuators B, Chem. 65 (2000) 91–93. [14] A. Khodadadi, S.S. Mohajerzadeh, Y. Mortazavi, A.M. Miri, Cerium oxide/SnO2 -based semiconductor gas sensor, Sens. Actuators B, Chem. 80 (2001) 267–271. [15] I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, vol. 61, pp. 194–202, 1994.
Biographies Jiarui Huang received his MS degree in synthetic organic chemistry from Nanjing University of technology, China, in 2003. Then he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Guangyi Li received his BS Degree in College of Mechanical and Electrical Engineering from China University of Petroleum, in 2003. Now, he is a MS student in Institute of Intelligent Machines, CAS, China. Zhongying Huang received his BS Degree in Department of Materials from Hefei University of Technology, China, in 2002, now a MS student in Institute of Intelligent Machines, CAS, China. Xingjiu Huang received his MS degree in electrochemistry from Wuhan University, China, in 2001. Since 2002, he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Jinhuai Liu received his BS Degree in Department of Chemistry from Yunnan Agricultural University, China, in 1982. He is currently a professor at the Institute of Intelligent Machines, CAS, China. He has performed research on semiconductor gas sensor since 1982, and also performed a research in the field of intelligent materials.
Temperature modulation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensor J.R. Huang a,b,∗ , G.Y. Li a , Z.Y. Huang a , X.J. Huang a , J.H. Liu a b
a Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, PR China Department of Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, PR China
Received 20 May 2005; received in revised form 28 July 2005; accepted 29 July 2005 Available online 3 October 2005
Abstract Stannic oxide sensors were developed to monitor CO of 10–250 ppm concentrations. Cross sensitivities of these sensors against 100–2000 ppm methane can be suppressed by evaluating the features extracted from the sensor signals. For this purpose, the working temperature of the sensors was modulated between 250 and 300 ◦ C, and the dynamic responses were measured to different concentrations of CO, CH4 , and their mixtures were measured. The discrete wavelet transform (DWT) was used to extract important features from the sensor responses. These features were then input to the pattern recognition (neural) method. The species considered can be discriminated with a 100% success rate by using a back propagation network and the concentrations of the gases studied can also be accurately predicted. © 2005 Elsevier B.V. All rights reserved. Keywords: SnO2 gas sensor; Carbon monoxide; Temperature modulation; Wavelet transform; Selectivity; Artificial neural network
1. Introduction SnO2 -based semiconductor gas sensors are widely used to measure CO and other reducing gases in air. It operates on the principle that the sensor resistance changes in the presence of reducing or oxidizing gases. Presently well-known advantages include their low costs and high sensitivities; while disadvantages concern their lack of stability and selectivity [1]. To achieve high CO selectivity of SnO2 gas sensors different methods were used in the past. These include optimization of the doping [2–4], the operation temperature [5] or the substrate geometry [6].Finally, several independent signals from a sensor have been evaluated to achieve selectivity by a pattern-recognition algorithm [7,8]. The different strategies reported, however, have been applied with limited success. A recent approach consists of analyzing the dynamic response of a sensor in order to obtain a new set of parameters specific to target gases. An easily implemented method is based on changes in operating temperature for generating suitable response transients [9,10]. Previous work of this kind has shown that a modulation of the sensor working temper-
∗
Corresponding author. Tel.: +86 551 5591 132; fax: +86 551 5592 420. E-mail address: [email protected] (J.R. Huang).
0925-4005/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2005.07.070
ature leads to a response pattern that is characteristic of the gas species under investigation [11,12]. The most commonly used method to extract important features from the response signals of temperature-modulated gas sensors is the fast Fourier transform (FFT). An alternative way for decomposing a signal into its constituent parts is the discrete wavelet transform (DWT). The main difference between the two methods is that DWT provides both frequency and temporal information of the signal, while FFT gives only frequency information for the complete duration of the signal so that the temporal information is lost. The artificial neural network also had been used to improve the CO selectivity of SnO2 gas sensors [13]. In this work the temperature modulation was used for both objectives; the decrease of power consumption and the increase of selectivity by application of pattern recognition and DWT. We detect CO, CH4 , and their mixture with dry air by using a single temperature-modulated SnO2 gas sensor. The sensor was operated in the dynamic mode affected by square voltage pulses applied to its heating element, thus modulating its temperature between 250 and 300 ◦ C. The sensor signals were decomposed by the DWT, and the features extracted were used as inputs into the pattern recognition method (artificial neural network) for identification purposes. Finally, we trained
1060
J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
Fig. 1. The experimental set-up.
the artificial neural network by using selected wavelet coefficients to predict the CO concentration in a high background of methane. 2. Experimental The thick film sensors were made by depositing thick films of tin oxide on ceramics substrates. The devices and the preparation of the sensitive materials were described elsewhere [14]. The grain sizes of the materials were around 20 and 50 nm. A headspace sample (HP-7694) was used to inject the gases measured into a 4500 ml sensor test chamber, where a single SnO2 gas sensor was set. Dry air, CO, CH4 and binary mixtures were measured. A square input voltage (20 mHz, frequency generator: HP 3325B, power supply: HP 6035A) was applied to the sensor heating resistor, which allowed the working temperature of the device to be modulated in the range 250–300 ◦ C. The output voltage was monitored, acquired and stored in a personal computer for further analysis. Fig. 1 shows the experimental set-up. The measurement process was as follows: dry air at a constant flow rate of 10 ml s−1 was used as carrier a gas. Data acquisition started 80 s before the injection of a test gas sample into the air-flow. The sampling rate was set at two samples per second and the whole process took 6 min to be completed. Measurements were carried out with a single gas and gas mixtures (two gases) in dry air. The concentrations were from 10 to 250 ppm for CO gases and from 100 to 2000 ppm for CH4 gases. The number of experiments carried out for binary mixtures was 80. 3. Results and discussion 3.1. The dynamic response to different gases In order to clearly distinguish the dynamic response features of gas to be detected, experimental conditions were set as follows: rectangular wave modulation, a dutyfactor of 30/(30 + 20), and an applied potential of 7 V. Fig. 2 shows the typical time dependences of the sensor output voltage realized by a single SnO2 sensor upon exposure to dry air, CO, CH4 , and their mixtures of various concentrations. It is noted that different characteristic responses are given by a single SnO2 sensor, i.e. the temperature dependence of
the sensor conductance are different in temperature dependence in the presence of the adsorbed gases under the experimental conditions. In this study, dry air, CO, CH4 , and the binary mixtures were obviously distinguished by controlling the temperature and the temperature modulating frequency. On the other hand, the characteristic response to each gas was attributable to the difference in the adsorption or desorption kinetics and the oxidative reaction kinetics of the test gases on the sensor surface. It is widely accepted that the key process in the response of a semiconductor oxide to a reducing gas is the modulation of the adsorbed oxygen species (such as O2− , O− or O2 )concentration. Although the sensing mechanism upon exposure to reducing gases have not been clear, it is affirmative that there exists a complicated adsorption/desorption or oxidative reaction kinetics on the sensor surface. The response curve also changed regularly with increasing the concentration of the detected gases, i.e. the characteristic peak of the response curve became higher as the amount of the target gases increased. Comparing with the response curve in dry air, one can also observe the high sensitivity to CO and CH4 . 3.2. Feature extraction Sensor responses contain numerous non-stationary or transitory characteristics (e.g. drift, trends and abrupt changes). These characteristics may be a very important part of the signal and Fourier analysis is not suitable for detecting them. While FFT gives frequency information for the complete duration of the signal (temporal information is lost), wavelet analysis provides both frequency and temporal information. DWT coefficients provide ’fingerprints’ that are characteristic of the concentration level and gas measured. Here, we used the DWT to extract important features from the dynamic response of the sensor. Because of its desirable properties of orthogonality, approximation quality, redundancy and numerical stability, the wavelet base constructed by Daubechies has become the foundation for signal analysis in a wide range of applications [15]. For this reason, the Daubechies family of wavelets was selected to perform the analysis. Specifically, to compute the DWT the 4th order Daubechies (db4) was selected as the analyzing wavelet, because it is the first ’smooth’ wavelet of the family. The features (DWT coefficients) extracted
J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
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Fig. 2. Response curves of a SnO2 gas sensor to (a) dry air, (b) CO, (c) CH4 , and (d) their mixtures of various concentrations in the dry air. Experimental conditions: rectangular voltage, a dutyfactor of 30/(30 + 20), applied potential of 7 V.
by DWT were used for the qualitative and quantitative analysis of the gases studied. The DWT was computed over a single period of the sensor response (100 samples). A level 3 decomposition of the sensor response was selected, because it was found that the wavelet coefficients showed significant differences between the gases to be discriminated. Fig. 3 shows 31wavelet coefficients of the third level decomposition for dry air, CO, CH4 , and the binary mixtures. From Fig. 3, it can be derived that the 14th–26th wavelet coefficients carry important information to discriminate the test gases. 3.3. Qualitative analysis Qualitative analyses were performed with the aim at discrimination between dry air, CO, and CH4 gases using a single sensor. To this end, the coefficients (14th–26th) extracted from the dynamic sensor responses by using DWT methods were fed into the artificial neural network. The data matrices must be normalized because the neural network requires that its input data lie in the range [0,1].To classify gases by using neural network the transformed data sets were first column normalized (the values of elements in column j are divided by the maximum value to be
found in column j) between 0 and 1 and then used for training and testing the neural network. Both the training and testing data set consist of input and output vectors. Each input vector has a corresponding output vector. The output vectors corresponding the gases in the training data set are fixed as 0, 1. Table 1 shows the expectation output of BP neural network. A three-layer feedforward neural network with sigmoidal activation was designed to learn the features of the sensor responses. Neural network simulation was implemented in C language on a Pentium-based computer. As there were 13 features and 2 gases, input and output nodes were fixed as 13 and 3. The number of hidden layer neurons was fixed as 4 by trials. After the network was built, learning was performed by using back-propagation algorithm. Learning parameters such as learning rate (α) and momentum term (η) were optimized by grid experiments for a fixed small number of learning cycles. A point on the grid corresponding to the minimum mean square error (MSE) represents the best choice of learning parameters. Table 1 Expectation output of BP neural network Test gas Expectation output
Dry air 000
CO 001
CH4 010
CO/CH4 001
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Fig. 3. Wavelet coefficients of a third level decomposition of the sensor responses in the presence of (a) dry air, (b) CO, (c) CH4 , and (d) their mixtures.
The optimum α and η were found to be 0.001 and 0.0001. Once the optimum values of the learning parameters were available, the network was trained for a larger number of iterations. The maximum number of iterations was fixed up to 10,000. In this artificial neural network, the MSE reached 4.83 × 10−3 after the 10,000 learning cycles. The results of the simulation experiments are shown in Table 2 For this kind of a single sensor-2 gases problem, the neural network gave 100% classification for the transformed data sets. 3.4. Quantitative analysis Quantitative analyses were performed with the aim at prediction the concentrations of the test gases by using a single sensor. The normalized concentrations of the testing gas and the global normalized (the values of elements in the matrix are divided by the maximum value to be found in the matrix) wavelet coefficients 14th–26th from dynamic sensor responses extracted by using DWT methods were fed into the artificial neural network. It is important to notice that the two normalizations are not equivalent. While the column normalization scales all the columns in the data matrix independently and therefore the differences in ‘intensity’ between different columns are lost, the global normalization keeps these differences unchanged. Each Table 2 Gas recognition results of BP neural network Input gas
Dry air CO CH4 CO/CH4
System output Dry air
CO
CH4
2 0 0 0
0 20 0 60
0 0 15 0
Fig. 4. Predicted results of (a) the CO concentrations in air, (b) the CO concentration in a different high background of methane.
J.R. Huang et al. / Sensors and Actuators B 114 (2006) 1059–1063
input vector also had a corresponding output (target) vector. The output vectors corresponding to the concentrations of the CO gas in the training data sets were normalized between 0 and 1. The prediction in this case was performed by a 13 × 2 × 1 feed forward neural network with sigmoidal activation. The number of hidden layer neurons was fixed as 2 by trials. The optimum α and η were found to be 0.001 and 0.0001. The maximum number of iterations was fixed up to 100,000. The MSE decreased to 0.1745 when the training was stopped. The test data were put into the neural network after the training and the output vectors represented the gases concentrations. The results of the simulation experiment are shown in Fig. 4. From Fig. 4a one can observe that the most predicted concentrations of the pure gas are close to the real values, and only few samples are far from the real concentrations. The mean absolute proportional error was 10.97% and the maximal mean proportional error was 63.33% (the first sample). From Fig. 4b one can observe that the most predicted concentrations of the pure gas are also close to the real values. The mean absolute proportional error was 10.79% and the maximal mean proportional error was 52.48% (the first sample). For this kind of a single temperature-modulated SnO2 -based sensor, a neural network can accurately predict the concentrations of the gases studied for the transformed data sets. 4. Conclusion The selectivity of SnO2 sensors for CO detection was sufficiently improved by applying artificial neural networks to the features extracted from the sensor signals. This procedure was necessary to improve discrimination of CO from methane. Both gases could be recognized by their different response shapes after switching the operation temperature. The sensor signals were decomposed by the DWT, and the matrix (the features extracted from the sensor signals) normalized by using a column normalization or a global normalization was used as inputs into the artificial neural network for identification and quantification purposes. The artificial neural network can give 100% classification of two gases and a sufficiently good prediction of the CO concentration. The results of the simulation experiments showed that CO gases could be detected with good selectivity and sensitivity in a high background of CH4 in air. Acknowledgements This work was financially supported by the National Natural Science Foundation of China (No. 60374049) and National High Technology Research and Development Program of China (863 Program No. 2004AA302030), which are gratefully acknowledged. References [1] K. Ihokura, J. Watson, Stannic Oxide Gas Sensors, Principles and Applications, CRC press, Boca Raton, FL, 1994. [2] G.G. Mandayo, E. Casta¨no, F.J. Gracia, A. Cirera, A. Cornet, J.R. Morante, Strategies to enhance the carbon monoxide sensitivity of tin oxide thin films, Sens. Actuators B, Chem. 95 (2003) 90–96.
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Biographies Jiarui Huang received his MS degree in synthetic organic chemistry from Nanjing University of technology, China, in 2003. Then he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Guangyi Li received his BS Degree in College of Mechanical and Electrical Engineering from China University of Petroleum, in 2003. Now, he is a MS student in Institute of Intelligent Machines, CAS, China. Zhongying Huang received his BS Degree in Department of Materials from Hefei University of Technology, China, in 2002, now a MS student in Institute of Intelligent Machines, CAS, China. Xingjiu Huang received his MS degree in electrochemistry from Wuhan University, China, in 2001. Since 2002, he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Jinhuai Liu received his BS Degree in Department of Chemistry from Yunnan Agricultural University, China, in 1982. He is currently a professor at the Institute of Intelligent Machines, CAS, China. He has performed research on semiconductor gas sensor since 1982, and also performed a research in the field of intelligent materials.