Zhang, W, Liu, T, Ye, L, Ueland, M, Forbes, SL & Su, SW 2019, 'A novel data pre-processing method for odour detection and identification system', SENSORS AND ACTUATORS A-PHYSICAL, vol. 287, pp. 113-120.View/Download from: UTS OPUS or Publisher's site
Liu, T, Zhang, W, McLean, P, Ueland, M, Forbes, SL & Su, SW 2018, 'Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines', International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1309-1320.View/Download from: UTS OPUS or Publisher's site
© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs) to detect the target odor. GAs are applied to select the informative features and the optimal model parameters of FSVMs. FSVMs are adopted as fitness evaluation criterion and the sequent odor classifier, which can reduce the outlier effects and provide a robust and accurate classification. This proposed algorithm has been compared with some commonly used learning algorithms, such as support vector machine, the k-nearest neighbors and other combination algorithms. This study is based on experimental data collected from the response of the UTS NOS.E, which is the electronic nose system developed by the University of Technology Sydney NOS.E team. In comparison with other approaches, the experiment results show that the proposed odor classification algorithm can significantly improve the classification accuracy by selecting high-quality features and reach to 92.05% classification accuracy.
Zhang, W, Liu, T, Zhang, M, Zhang, Y, Li, H, Ueland, M, Forbes, SL, Rosalind Wang, X & Su, SW 2018, 'NOS.E: A New Fast Response Electronic Nose Health Monitoring System', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 4977-4980.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. We present a practical electronic nose (e-nose) sys-tem, NOS.E, for the rapid detection and identification of human health conditions. By detecting the changes in the composition of an individual's respiratory gases, which have been shown to be linked to changes in metabolism, e-nose systems can be used to characterize the physical health condition. We demonstrated our system's viability with a simple data set consists of breath collected under three different scenarios from one volunteer. Our preliminary results show the popular classifier SVM can discriminate NOS.E's responses under the three scenarios with high performance. In future work, we will aim to gather a more varied data set to test NOS.E's abilities.
Zhang, W, Szymanski, J, Chiu, C, Chaczko, Z, Su, S & Zhou, J 2017, 'How the Internet of Things is Changing Teaching of Technical Subjects at UTS', The 5th Asia-Pacific Conference on Computer-Aided System Engineering, Asia-Pacific Conference on Computer Aided System Engineering, APCASE Foundation, Guilin, Guangxi, China, pp. 71-77.View/Download from: UTS OPUS