A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Jia, Pengfei; Huang, Tailai; Duan, Shukai; Ge, Lingpu; Jia, Yan; Wang, Lidan

doi:10.3390/s16030370

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…However, they can be useful alternatives. Recently, Jia et al proposed a novel semi-supervised electronic nose learning technique to classify indoor pollution gases [15]. The experimental set-up of this work is rather ideal: in a temperature-humidity controlled chamber, the e-nose system, including 3 Figaro MOX sensors, was exposed to clear air for 2 minutes before the target gas was presented for 4 minutes, and then the sensor array was exposed to clean air for 9 minutes again to recover the baseline.…”

Section: Unsupervised Gas Discriminationmentioning

confidence: 99%

A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments

Fan

Bennetts

Schaffernicht

et al. 2018

Sensors and Actuators B: Chemical

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Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

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Section: Unsupervised Gas Discriminationmentioning

confidence: 99%

A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments

Fan

Bennetts

Schaffernicht

et al. 2018

Sensors and Actuators B: Chemical

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“…A concrete description of the E-nose system and the experimental procedure has been expounded in our previous research [ 37 ]. Here we simply describe the details which are different from the previous experiment.…”

Section: E-nose System and Experimentsmentioning

confidence: 99%

“…This leads to the waste of experimental samples. On the other hand, there is plenty of information on unlabeled samples, which can effectively enhance the performance of E-nose [ 37 ]. Additionally, unlabeled samples are often easier to obtain and require less time to train the E-nose.…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs

Huang

Jia

et al. 2016

Sensors

Self Cite

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Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and there are some cases where the gas samples in the real world are complex and unlabeled. As a result, it is necessary to make an E-nose that cannot only classify unlabeled samples, but also use these samples to modify its classification model. In this paper, we first introduce a semi-supervised learning algorithm called S4VMs and improve its use within a multi-classification algorithm to classify the samples for an E-nose. Then, we enhance its performance by adding the unlabeled samples that it has classified to modify its model and by using an optimization algorithm called quantum-behaved particle swarm optimization (QPSO) to find the optimal parameters for classification. The results of comparing this with other semi-supervised learning algorithms show that our multi-classification algorithm performs well in the classification system of an E-nose after learning from unlabeled samples.

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“…Apparently, exploiting unlabeled samples to help supervised classifier learning is a promising solution to solve the scarcity of labeled samples and has been a hot research topic in recent years. To take full advantage of the underlying classification information from the unlabeled samples, semi-supervised learning-based classifier design cause great attention and many successful cases have been reported in the literature, see [ 37 , 38 , 39 , 40 ]. Roughly speaking, current semi-supervised learning methods can be categorized into three groups: the first are the generative model-based semi-supervised learning methods.…”

Section: Related Workmentioning

confidence: 99%

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

Liu

Tang

et al. 2016

Sensors

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Abstract:The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

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A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Cited by 6 publications

References 31 publications

A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments

A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

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