KEYWORDS: Fuzzy logic, Education and training, Neural networks, Feature extraction, Principal component analysis, Data modeling, Convolution, Spectral data processing, Defect detection, Deep learning
To address the problem that a single discriminant index is difficult to comprehensively evaluate the quality of tangelo, this paper explores a tangelo grading method based on deep neural network and fuzzy decision. Firstly, a tangelo grading model based on deep neural network and fuzzy decision was constructed, and its structure and functional characteristics were given; Secondly, based on Standard Normal Variate (SNV) and Principal Component Analysis (PCA), the spectral space construction mechanism of tangelo dry water defect characteristics is given. Then, a deep neural network model of dry water defect grade of tangelo is constructed; Thirdly, the Soluble Solids Content (SSC) feature spectral space extraction model based on Multivariate Scattering Correction (MSC) and PCA was constructed. A deep neural network regression model for SSC content of tangelo was constructed; Finally, based on fuzzy logic, a fusion decision classifier for tangelo grading was constructed. The experimental results show that the proposed method can realize the comprehensive decision of tangelo quality and effectively improve the utilization rate of fruit.
The phase transition of water vapour is typically accompanied by a change in the water vapour isotopes. The dynamics, transpiration, and condensation of water vapour in the atmosphere can also be revealed by measuring water vapour isotopes in the atmosphere. This information is crucial for understanding the water cycle in the atmosphere. Fourier Transform Infrared (FTIR) spectroscopy is widely used to monitor atmospheric trace gases. This study is based on near-infrared solar absorption spectra collected by portable Fourier Transform Infrared spectrometer (FTS) to observe the column concentration results of H2O and HDO. And the column isotope ratio δD is calculated by H2O and HDO results. The fitted root-mean-square errors (RMSE) of the spectral retrieval window of H2O and HDO were 0.107% and 0.175%, respectively. And the mean retrieval error for H2O and HDO was (0.59 ± 0.21) % and (0.94 ± 0.20) %, respectively. The calculated error of δD was 0.0035‰, which shows a high level of observational accuracy. The time series of δD obtained from September 2016 to December 2017 with a varied in the range of -5.69‰ to -369.19‰. And the lowest δD observed in January with a mean value of (-249.63 ± 32.35) ‰ and the highest δD observed in July with a mean value of (-38.61 ± 2.43) ‰, the time series show a clear seasonal variation. The observations demonstrate the capability of the FTIR spectrometer to observe the stable isotope and isotope ratio δD of atmospheric water vapour with accuracy and precision.
Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. However,
the performance of DE is sensitive to the mutation and crossover strategies and their associated parameters. In this
paper, a kind of scale factor generating scheme within the process of search is proposed, named MSFDE, to enhance
the performance of DE. In this method, the scale factor is a D dimensional matrix which component is a random
number for each difference vector during the iteration. The proposed scheme has been evaluated on a test-suite of 25
benchmark functions provided by CEC 2005 special session on real parameter optimization. The results of the
experiments indicate that MDVDE is competitive to classical DE and some other variants on different strategies.
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