Pei-Feng Qi

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

A New Method Combining KECA-LDA With ELM for Classification of Chinese Liquors Using Electronic Nose

We proposed a hybrid algorithm by combining kernel entropy component analysis (KECA) with linear discriminant analysis (LDA), namely, KECA-LDA for feature reduction in electronic-nose systems. It combined the advantages of KECA and LDA. Then, the data extracted by KECA-LDA were inputted to extreme learning machine (ELM) for classification. In order to examine the performance of the proposed method, eight types of strong-flavor Chinese liquors were tested using an electronic nose (e-nose) system designed by ourselves, and the results after cross validation showed that features extracted by KECA-LDA were more beneficial to classification than KECA, and the performance of ELM was better than that of backpropagation neural network. The highest classification rate by KECA-LDA-ELM was 100%. In conclusion, an e-nose combined with KECA-LDA and ELM is a feasible method to classify Chinese liquors.

2-D Wind Velocity Measurement Using a Vertically Suspended Optical Fiber Combined With a Photosensor Array

A novel low-cost 2-D wind velocity (speed and direction) sensor was designed based on the principles of statics and optics. A vertically suspended optical fiber was employed in the designed sensor as the sensitive element, which could be deflected by the applied force of airflow in 2-D horizontal planes. An optical signal emitted from the end of the fiber was projected onto a charge coupled device (CCD) array detector, which could recognize the offset magnitude and direction through image processing. The mathematic expression between the displacement of the light spot projected onto the CCD and the wind speed was derived. Preliminary wind tunnel experiments validated the correctness of the theoretical derivation, and the results also showed that the designed sensor had good precision (particularly in low wind speed).

A Bio-Inspired Breathing Sampling Electronic Nose for Rapid Detection of Chinese Liquors

The rapid detection of Chinese liquors is one of the most demanded techniques of the China light industry and electronic noses (e-noses) have been used to solve this problem. However, these e-noses using classic dynamic sampling (CDS) method consume quite long sampling time. In this paper, a novel bio-inspired breathing sampling (BBS) method is proposed to shorten the sampling time. The proposed BBS is a bionic multi-stage sampling method that imitates the biological process of continuous short breathings. Based on the CDS and BBS methods, the same data processing procedures are employed to process the sampled data. Compared with the CDS, the sampling time of the BBS is much shorter and the extracted features of the BBS are simpler. To test the performance of the two sampling methods, three classification methods are employed to identify seven kinds of Chinese liquors using the self-designed electronic nose. The experimental results indicate that the BBS method achieves not only rapider detection but also higher classification accuracy.

Combining PSO-KECA with ELM in an electronic nose for classification of Chinese liquors

We designed an electronic nose to classify different Chinese liquors. Kernel entropy component analysis (KECA) was applied to reduce the dimensionality of data sets. In order to avoid the blindness of parameter setting, particle swarm optimization (PSO) algorithm was employed to optimize parameters in KECA. At last, we adopted extreme learning machine (ELM) as a classifier to classify eight kinds of strong-flavor Chinese liquors. The results indicate that ELM has a better performance for classification of Chinese liquors with different brands than back propagation neural network (BPNN). The highest classification rate by ELM is 97.5%.

A wind estimation method for quadrotors using inertial measurement units

Wind estimation is quite important for the control and applications of quadrotors. On the basis of the freestream-velocity estimation method using inclination-angle measurement, this paper proposes a correction method considering the accelerations influence on the quadrotor. Both the original and correction methods use the data that totally come from the inertial measurement units (IMUs) of the quadrotor. Kinetic model and controller of the quadrotor are illustrated. The original estimation method and the correction one are theoretically analyzed and verified through simulation. The result demonstrates that the correction method improves the wind estimation accuracy remarkably.

A portable E-nose system for classification of Chinese liquor

In this paper, a portable electronic nose (e-nose), being highlighted with high speed and portability, was developed to classify Chinese liquor. It consisted of sampling module, gas chamber and ARM processor. First, two cascaded chambers wrapped with thermostat (Silicone heating band) were designed, one for rapid evaporation, and the other for reaction of the liquor samples. Then a novel bio-inspired segmented breathing sampling method was proposed, which can notably enrich the available dynamic information in limited time. Consequently, by taking the advantage of fast online algorithm, one test process was limited in 4.5 minutes, including sample evaporation, sequential sampling, data output and cleanout. Besides, the developed e-nose was easily carried (1.9 Kg including battery). To examine the performance of the e-nose, six kinds of Chinese liquors were tested (each for 20 times) using optimized support vector machine (SVM), and the accuracy rate was 90.8%.

Using Spike-Based Bio-Inspired Olfactory Model for Data Processing in Electronic Noses

Conventional electronic noses need complicated data preprocessing and tedious feature reduction steps for different sensors and applications. To overcome the drawbacks, a bio-inspired data processing method using a spike-based olfactory model is proposed in this paper, which consists of spike encoding by virtual olfactory receptor neurons (VORNs) and subsequent processing in a bionic olfactory bulb (BOB) model. Each VORN transduces the continuous sensor responses into spike time points, which are relayed to the BOB to enhance the operation efficiency. It is easy to extract useful features from BOB’s outputs due to their specific oscillation patterns, which simplifies the subsequent steps of feature generation. Three classification methods are used to identify seven Chinese liquors. The experimental results show that the proposed method achieves a better classification performance than the conventional methods.

Which sampling method more suitable for portable electronic noses: Classic or bio-inspired one?

The purpose of this paper is to find a suitable sampling method for portable electronic noses. For this reason, two sampling methods, namely classic dynamic sampling (CDS) method and bio-inspired breathing sampling (BBS) method, were studied using a portable e-nose designed by us. In order to test the performance of the two sampling methods, the same data processing procedures were employed to handle the sampled data, and two classification methods were used to identify seven kinds of Chinese liquors. The results show that the BBS not only spends shorter sampling time with fewer samples per second, but also obtains higher classification accuracy. Therefore, the proposed BBS is more suitable for portable e-noses.

Design of a portable electronic nose for real-fake detection of liquors

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.