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A Reduced Complexity Two-Stage Neural Classifier for Quantified Identification of Gases/Odors Using an Integrated Thick Film Sensor Array neural network comprising of 42 neurons in five layers, gaining better performance than respective principal component analysis (PCA) and projection pursuit network (PPN) methods for data information compaction, and they finally trained a neural network for IGS using second-order scaled conjugate gradient (SCG) method and refined through vanilla back-propagation algorithm. M. Pardo et al. [8] have gained enhancement through data pre-processing for classification of different brands of Espresso coffee with an electronic nose using PCA, up to 87.5% of classification performance, being the best than using Self Organizing maps (SOM). T. Roppel et al. [9] have established that data pre-processing using pulse coupled neural network (PCNN), rank order filtering (ROF) and centroid proximity metric (CPM) outperforms principal component analysis (PCA) for data pre-processing for neural quantifier in succeeding stage and have compared respective improvement in concentration identification up to 96% of correct identification. This is also recommended that while classifying a data, the process of classification be divided in two steps: super-class and sub-class, and then the super-class should be explored and this approach has been quoted as the ‘spirit of hierarchical classification' [10], [11]. Better generalization ability of a two-stage network has also been utilized by Henkel et al. [12] for monitoring carbon dioxide and relative humidity but with step quantification for relative humidity in seven classes (from 0 to 60%) and gradual quantification of CO2 from 0 to 650 ppm facing variety of constraints and processed the data set for each stage with discordant reservations. In recent literature, Acetone, Carbon Tetra-chloride, Ethyl Methyl Ketone and Xylene have been shown to score high hazard rankings among major industrial pollutants, considering its toxicity, persistence and exposure potential, evaluated through many parameters like Worker Exposure Hazard, Human Health Effect, Environmental Hazard Value and Total Hazard Value Score. Thereby, the need of accurately identified quantification of these gases using minimal complexity networks has gained importance for the development of real-time high-speed IGS for its use in hazard prone industrial situations [13] – [16]. Considering various aspects of previously published literature, and human and ecological health ranking of aforesaid industrial pollutants, we are motivated to attempt an n-stage neural network to achieve higher degree of quantified identification of the gases/odors with much simpler network architecture, involving only a few neurons and layers. 2. THE EXPERIMENT To take up an experiment over the concept of effectiveness in ‘reducedcomplexity over convergent neuronal pathway’, an n-stage neural network for quantified identification of individual gas/odor is considered. We have utilized the responses of sensor array for Acetone, Carbon Tetra-chloride, Ethyl Methyl Ketone and Xylene, as reported by Nayak et al. [17]. The sensor used in their experiments consisted of an array of four sensors with an integrated heater on a substrate. Four different sensors forming an array were fabricated with SnO2 as a base material and doped with different materials, namely, ZnO (sensor A), undoped SnO2 (sensor B), MoO (sensor C) and CdS (sensor D). The sensor array was fabricated using thick film technology. The fabrication process is described in detail in reference [17]. The test rig used to collect sensor array response data and experimental conditions in the laboratory have also been described in the above reference. The four gases/odors used in the referred experiment are Acetone (ace), Ethyl Methyl Ketone (emk), Carbon Tetra-chloride International Journal of the Computer, the Internet and Management Vol.18.No.3 (September - December, 2010) pp 30-37 31
N. S. Rajput, V. N. Mishra, and K. P. Singh (car) and Xylene (xyl) and the outputs measured were percent change in resistance of different sensors when exposed to various concentrations of these gases. As mentioned earlier, data used in the present analyses are derived from the results of [17], therefore, the sensor characteristics from [17] have been reproduced here for ready reference in Figure 1(a) to 1(d). Using ‘Precision Image Digitizer ver. 1.3.0.0’, the graphs of Fig. 1(a). to 1(d). were imported and three categories of data were generated viz. Data set I (with 8 vectors for ace, 10 vectors for car, 12 vectors for emk and 12 vectors for xyl), Data set II (with 8 vectors for ace, 9 vectors for car, 7 vectors for emk and 8 vectors for xyl) and Data set III (with 3 vectors for ace, 4 vectors for car, 6 vectors for emk and 5 vectors for xyl). All of these vectors were uncorrelated and were generally uniformly spaced all over the concentration axis of the graphs at Figs. 1(a). to 1(d). From raw data, normalized data were developed by shifting the origin to match the graph’s origin and the data were scaled over the axes ranges of the graph, as the data points were in terms of respective pixel values of the software window. For all data points and graph windows, origin shifting and axes scaling was carried through (1), (2) and (3), … (1), Here, for all data pixels, is the true pixel coordinate w.r.t. the origin of the respective graph, is the pixel coordinate w.r.t. the origin of the software window where the graph is imported and , is the origin of the actual graph while is always the origin of the window where the graph is imported for digitization. are the true maximum values of x-axis pixel and y-axis pixel, respectively. and are the represented as maximum ranges of percentage concentration along the x-axis and the maximum percentage change in resistance represented along the y-axis, respectively; taken appropriately for each gas/odor. and is the concentration of a particular gas/odor and the percentage change of sensor element resistance for the data point of the graph, plotted along x-axis and y-axis for each gas/odor and for each sensor element individually. The normalized ‘sensor response vs. gas concentration’ data was hence intrinsically obtained for all the four gases/odors viz. ace, car, emk and xyl. These data were used for training and testing of the neural network thus conceived. 3. THE CONCEPT ANN and … (2) … (3). Inspired by the general architecture of Mammalian Olfactory System [1] and following the principles of odor coding [18] and the spirit of hierarchical classification [11], we divided the task of classified quantification into a two stage task wherein first stage should classify the data into its classes. Thence, the duly classified data from individual class should be quantified using respective quantifier trained for that class and the neural net used for this should be of simplest configuration in terms of perceptron 32
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