Pengfei Jia

Electronic Nose Feature Extraction Methods: A Review

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications.

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

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

When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

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

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.

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas.

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to improve QPSO’s performance in global searching, and this novel QPSO is denoted as EQPSO during which we can guarantee the particles are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iteration. We also discuss this way of computing the local attractor in mathematics. The results of test functions are compared between EQPSO and other optimization techniques (including six different PSO and seven different optimization algorithms), and the results found by the EQPSO are better than other considered methods.

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.

A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections

An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.

Highly Sensitive Humidity Sensor Based on Oblique Carbon Nanoplumes

In this work, we fabricated three carbon nanoplume structured samples under different temperatures using a simple hot filament physical vapor deposition (HFPVD) process, and investigated the role of surface morphology, defects, and graphitic content on relative humidity (RH) sensing performances. The Van der Drift growth model and oblique angle deposition (OAD) technique of growing a large area of uniformly aligned and inclined oblique arrays of carbon nanoplumes (CNPs) on a catalyst-free silicon substrate was demonstrated. The optimal growing temperature of 800 °C was suitable for the formation of nanoplumes with larger surface area, more defect sites, and less graphitic content, compared to the other samples that were prepared. As expected, a low detection limit, high response, capability of reversible behavior, and rapid response/recovery speed with respect to RH variation, was achieved without additional surface modification or chemical functionalization. The holes’ depletion has been described as a RH sensing mechanism that leads to the increase of the conduction of the CNPs with increasing RH levels.