Optimization of Extracted Features from an Explosive-Detecting Electronic Nose Using Genetic Algorithm

The use of an electronic nose in detecting explosives has gained attention among researchers. This paper aims to optimize the extraction of features generated from a predetermined explosive-detecting electronic nose setup by using a genetic algorithm. A genetic algorithm (GA) is used to minimize the errors such as the mean error within explosive types, the mean error between explosive types and the classification error. The GA optimization program is implemented for each feature extraction technique, namely, principal component analysis (PCA) and linear discriminant analysis (LDA). As a result, the proponents were able to optimize the extracted features into a single point that can truly classify each explosive type. PCA is more preferred than LDA for practical purposes.

[1]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.

[2]  Selena Sironi,et al.  Electronic Noses for Environmental Monitoring Applications , 2014, Sensors.

[3]  Argel A. Bandala,et al.  Pre-Harvest Factors Optimization Using Genetic Algorithm for Lettuce , 2018 .

[4]  Bipan Tudu,et al.  Instrumental testing of tea by combining the responses of electronic nose and tongue , 2012 .

[5]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[6]  H. T. Nagle,et al.  Using neural networks and genetic algorithms to enhance performance in an electronic nose , 1999, IEEE Transactions on Biomedical Engineering.

[7]  Argel A. Bandala,et al.  Design of a Fuzzy-Genetic Controller for an Articulated Robot Gripper , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[8]  Elmer P. Dadios,et al.  A kNN-based approach for the machine vision of character recognition of license plate numbers , 2017, TENCON 2017 - 2017 IEEE Region 10 Conference.

[9]  Simon X. Yang,et al.  Sensor Array Optimization of Electronic Nose for Detection of Bacteria in Wound Infection , 2017, IEEE Transactions on Industrial Electronics.

[10]  Lei Zhao,et al.  Optimization of electronic nose sensor array by genetic algorithms in Xihu-Longjing Tea quality analysis , 2013, Math. Comput. Model..

[11]  Louis A. Tamburino,et al.  Evolutionary Optimization of Gaussian Windowing Functions for Data Preprocessing , 2003, Int. J. Artif. Intell. Tools.

[12]  Elmer P. Dadios,et al.  Automated traffic violation apprehension system using genetic algorithm and artificial neural network , 2016, 2016 IEEE Region 10 Conference (TENCON).

[13]  Argel A. Bandala,et al.  Optimization of decentralized information dissemination in quadrotor swarm using genetic algorithm , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[14]  Ricardo Gutierrez-Osuna,et al.  A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[15]  S. Osowski,et al.  Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose , 2012 .

[16]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[17]  Stanislaw Osowski,et al.  Differential electronic nose of two chemo sensor arrays for odor discrimination , 2010 .

[18]  Peter J Sterk,et al.  An electronic nose in the discrimination of patients with asthma and controls. , 2007, The Journal of allergy and clinical immunology.

[19]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..