RESUMO
In the field of the absorption spectrum, especially for direct tunable diode laser absorption spectroscopy (dTDLAS) technology, the integrated area of the absorption spectrum is needed to be measured accurately for calculating the temperature and the component concentration of the flow field. Doing single optical path absorption spectroscopic measurement in the non-uniform flow field, spectral lineshape broadening is varied with the flow changes, in previous research reports, researchers mainly use single Voigt or Lorentz profile to fit absorbance curve or use directly integral to obtain the integrated area of the absorption spectrum. There are some shortcomings in these methods, resulting in certain error between the fitting result and the actual area, which is not conducive to the accurate measurement of flow field parameters. Firstly, the error is analyzed theoretically, and then, we adopt the simulation method to obtain the error size of the method. Finally, we proposed the Voigt wings fitting absorbance method to reduce the fitting error. The operation of Voigt wings fitting method is to Select the wings of the spectral line, and then use Voigt profile fitting, The difference between the two wings was used the numerical integral method to calculate area, the integrated area is sum of Voigt profile fitting area and numerical integral area. We have used water vapor as the target gas, with eight absorption lines which have different low-level states energy from HITRAN 2012 database being selected-, building two kinds of non-uniform flow field model base on the flat flame furnace, and through the method of segmentation to equivalent processing the no uniformity of flow field. Using Voigt profile fitting method, numerical integral method and Voigt profile wings fitting method to obtain the integral area of models, the error size is obtained by comparing with the theoretical value. As the result of contrast, the fitting error of Voigt profile fitting method is large and related to the different absorption line, the error of numerical integral method is biggest but it is nothing to do with absorption line, the fitting error of Voigt profile wings fitting method is least and stable. By force of contrast, we determined the appropriate method to obtain integral area in the different non-uniform flow field, which is beneficial to obtain accurate integrated area and flow field parameters.
RESUMO
We specify water vapor among combustion products as the target gas based on tunable diode absorption spectroscopy in this paper. The direct absorption signals of water vapor after being processed can be used to calculate the gas concentration distributions and temperature distributions of the combustion region of methane and air flat flame furnace via algebraic reconstruction technique (ART). In the numerical simulation, reconstruction region is a grid of five by five, we assume a temperature and water vapor concentration distribution of 25 grid, then simulate different direction laser rays which cross the combustion region, generating projection of each ray, by ART reconstruction algorithm, it turns out that the temperature and water vapor distribution reconstruction error is less than 1%. In the experiment, we chose a distributed-feedback laser to scan the target gas H2O7 153.722, 7 153.748 and 7 154.354 cm(-1) as absorbtion line pair to measure temperature of the flame, we consider the former two line as one absorbtion line. By Stages multi-directional scanning, the authors abtain 16 different regions distributin of temperature and gas concentration of furnace when we collecte 30 different angle data by spectral data processing, reconstruction algorithm, two absorbtion line ratio method for temperature sensing, finding the temperature and water concentration are higher in the center than in the edge, it turns out that the reconstruction algorithm is good enough to achieve the distributions of gas concentration and temperature of the combustion region.
RESUMO
Tunable diode laser absorption spectroscopy (TDLAS) has been developed to realize the real-time and dynamic measurement of the combustion temperature, gas component concentration, velocity and other flow parameters, owing to its high sensitivity, fast time response, non-invasive character and robust nature. In order to obtain accurate water vapor concentration at high temperature, several absorption spectra of water vapor near 1.39 µm from 773 to 1273 K under ordinary pressure were recorded in a high temperature experiment setup using a narrow band diode laser. The absorbance of high temperature absorption spectra was calculated by combined multi-line nonlinear least squares fitting method. Two water vapor absorption lines near 7154.35 and 7157.73 cm(-1) were selected for measurement of water vapor at high temperature. A model method for high temperature water vapor concentration was first proposed. Water vapor concentration from the model method at high temperature is in accordance with theoretical reasoning, concentration measurement standard error is less than 0.2%, and the relative error is less than 6%. The feasibility of this measuring method is verified by experiment.
RESUMO
Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .