Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

Wen, Ting Bin; Jia, Yan; Huang, Daoyu; Lü, Kun; Deng, Changjian; Zeng, Tanyue; Song, Yu; He, Ziming

doi:10.3390/s18020388

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…However, different ways of adding weights make the WKFDA algorithm focus on learning sample features differently. For example, [27] added weights to each kernel function. The purpose was to introduce the prior knowledge of samples to enhance the learning of sample features in the WKFDA algorithm.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

et al. 2019

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Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). When the labeled samples satisfy a certain requirement, S2DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS2DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S2DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.

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Section: Proposed Methodsmentioning

confidence: 99%

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

et al. 2019

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“…A method combining Weighted Kernels Fisher Discriminant Analysis (WKFDA) with Quantum-behaved Particle Swarm Optimization (QPSO) and reprocessing of an original eigenmatrix using QWKFDA was proposed by Li, Z.H. et al [ 36 ], improving the accuracy of feature parameter extraction in the prediction of wound infection and inflammable gases. Reference [ 37 ] proposed a hybrid gas detection method based on one-class support vector machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array

Zhao

Chen

et al. 2018

Sensors

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As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

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“…The reason could be that there were differences between the real response models of the sensor arrays and the mathematical models of these methods. Recently, researchers have attempted to solve the problem through new techniques, such as feature extraction (selection) [ 33 , 34 , 35 , 36 ], nonlinear modification [ 35 , 37 ], interference suppression algorithms [ 24 ] and so on. These techniques, to some extent, can improve the discrimination performance of electronic noses, but do not essentially solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

Guo

Haohui

Jia

et al. 2018

Sensors

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The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.

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Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

Cited by 12 publications

References 24 publications

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

A Novel Semi‐Supervised Learning Method Based on Fast Search and Density Peaks

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

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