Self-expression-Based Abnormal Odor Detection

Abnormal odors (e.g., perfume, alcohol) show strong sensor response, such that they deteriorate the usual usage of E-nose for target odor analysis. An intuitive idea is to recognize abnormal odors and remove them online. A known truth is that the kinds of abnormal odors are countless in real-world scenarios. Therefore, general pattern classification algorithms lose effect because it is expensive and unrealistic to obtain all kinds of abnormal odors data. In this chapter, we propose two simple yet effective methods for abnormal odor (outlier) detection. (1) A self-expression model (SEM) with l1/l2-norm regularizer is proposed, which is trained on target odor data for coding and then a very few abnormal odor data is used as prior knowledge for threshold learning. (2) Inspired by self-expression mechanism, an extreme learning machine (ELM)-based self-expression (SE2LM) is presented. Experiments on several datasets by an E-nose system fabricated in our laboratory prove that the proposed SEM and SE2LM methods are significantly effective for real-time abnormal odor detection.

[1]  Hermann Kaindl,et al.  Self-Representation for Self-Configuration and Monitoring in Agent-Based Flexible Automation Systems , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Chunfang Liu,et al.  Object Classification and Grasp Planning Using Visual and Tactile Sensing , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Alexandre Perera,et al.  Drift compensation of gas sensor array data by Orthogonal Signal Correction , 2010 .

[5]  Vittoria Bruni,et al.  An Improvement of Kernel-Based Object Tracking Based on Human Perception , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  David Zhang,et al.  Correcting Instrumental Variation and Time-Varying Drift: A Transfer Learning Approach With Autoencoders , 2016, IEEE Transactions on Instrumentation and Measurement.

[7]  Fengchun Tian,et al.  A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors , 2014 .

[8]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[9]  Chi-Man Vong,et al.  Local Receptive Fields Based Extreme Learning Machine , 2015, IEEE Computational Intelligence Magazine.

[10]  Liu Junhua,et al.  Drift reduction of gas sensor by wavelet and principal component analysis , 2003 .

[11]  H. Troy Nagle,et al.  Enhancing multiple classifier system performance for machine olfaction using odor-type signatures , 2007 .

[12]  Ilkay Ulusoy,et al.  Railway Fastener Inspection by Real-Time Machine Vision , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Guang-Bin Huang,et al.  What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle , 2015, Cognitive Computation.

[14]  David Zhang,et al.  Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[15]  Lei Zhang,et al.  A novel background interferences elimination method in electronic nose using pattern recognition , 2013 .

[16]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[17]  Lei Zhang,et al.  On-line sensor calibration transfer among electronic nose instruments for monitoring volatile organic chemicals in indoor air quality , 2011 .

[18]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[19]  David Zhang,et al.  LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation , 2016, IEEE Transactions on Image Processing.

[20]  David Zhang,et al.  Robust Visual Knowledge Transfer via Extreme Learning Machine-Based Domain Adaptation , 2016, IEEE Transactions on Image Processing.

[21]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[22]  David Zhang,et al.  Calibration transfer and drift compensation of e-noses via coupled task learning , 2016 .

[23]  Lei Zhang,et al.  A novel pattern mismatch based interference elimination technique in E-nose , 2016 .

[24]  David Zhang,et al.  Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems , 2015, IEEE Transactions on Instrumentation and Measurement.

[25]  R. Huerta,et al.  Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization , 2016 .

[26]  Pere Caminal,et al.  Drift Compensation of Gas Sensor Array Data by Common Principal Component Analysis , 2010 .