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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,...
Characterizing nonlinear dynamic behaviors in the 3D near-surface wind field is a challenging problem of significant importance. The study of pollutant transport, wind power evaluation, forest damage assessment, among others, would benefit from this research. In this paper, we first use a high-resolution 3D ultrasonic anemometer to simultaneously measure three wind components. Then the multiscale...
Quantitatively characterizing wind variation series in different environments is an important problem with significant engineering and industrial applications. We systematically carry out indoor and outdoor experiments separately to record two groups of wind speed time series. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first investigate the fluctuating behaviors from...
Noises exist in many measured systems inevitably. Therefore, noise resistance ability analysis is of great importance to accurately evaluate the performance of information analysis methods. In this paper, we systematically test the noise resistance ability of visibility graph (VG) and its generalization, i.e., limited penetrable visibility graph (LPVG). Taking the Lorenz system as the example, we...
Characterizing the time series measured from different flow environments is of great importance in diverse research fields. In this paper, we first systematically record three groups of 3D wind speed data from indoor and outdoor environments separately. Then, we employ a modality transition-based approach for mapping the experimental multivariate data into a directed weighted complex network. For...
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...
Odor/gas dispersion in atmospheric boundary layer is mainly controlled by airflow. The analysis of the wind speed/direction properties is helpful for understanding the odor/gas dispersion mechanism. Twenty groups of wind speed/direction time series are measured using an ultrasonic anemometer. Multi-scale entropies of wind-speed series in along-wind and across-wind directions are analyzed. The results...
Credit insurance market is the reason of self-organization system which lies in the process of self-organization in the marketing operation and evolution. The way to realize the credit insurance marketing stability and order is to reduce the entropy of the marketing system by the material, energy and information to the credit insurance market from the outside world. Dealing with correctly the relationship...
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