[Multi Spectral Detection of Ethanol Content in Gasoline Based on SiPLS Feature Extraction and Information Fusion].

The ethanol content in ethanol gasoline was detected with ultraviolet/visible(UV/vis) and near-infrared (NIR) spectroscopy while information fusion technology and synergy interval PLS(SiPLS) algorithm were used as the feature extraction method with the establishment of partial least squares(PLS) regression model. Using the information fusion theory, UV/vis and NIR spectra were used for data fusion, the data level fusion (Low level data fusion, LLDF) and feature level fusion(Mid-level data fusion, MLDF) model were established. The results were compared with the single source modelwith low level data fusion before vector normalization(LLDF-VN1) selected for the optimal model. Finally, the optimal model was tested using the spectral data collected from the samples of high ethanol content and commercial gasoline. The results showed that both UV/vis and NIR can be used to detect and provide good prediction results, whereas direct fusion of the UV/vis and NIR spectral data provided the best results in the regression model based on the calibration set, with the highest correlation coefficient rc, the smallest Biasc and RMSECV values, as 0.999 9, 0.125 8 and 0.000 6, respectively. And the prediction effect of the model of LLDF-VN1(low level data fusion before vector normalization) was the best, r(p)=0.999 1，Bias(p)=0.352 7，RMSEP=-0.073 8. In the verification of the optimal model (LLDF-VN1) by the self distribution solution, rp=0.999 7, Bias(p)=0.102 2, RMSEP=0.329 1; and that for gasoline sold on market, r(p)=0.990 1, RMSEP=0.675 1, Bias(p)=0.892 7, respectively. It showed that the data level fusion based on UV/vis and NIR spectral information could be used to detect the content of ethanol in ethanol-gasoline quickly and accurately, achieving a wide range of ethanol concentration detection, which laid a foundation for further realization of the rapid detection of substances in the blended fuel oil.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

[A Multi-Peak Brillouin Scattering Spectrum Feature Extraction Method Based on Multi-Criteria Decision-Making and Particle Swarm Optimization-Levenberg Marquardt Hybrid Optimization Algorithm].

As to fitting the multi-peaks Brillouin scattering spectrum with traditional method, the maximum power point is usually selected as the benchmark while other extreme value points which are less than the maximum power are lost. The fitting curve only has one peak because the multi-peaks Brillouin scattering spectrum is simplified into the highest peak and several small peaks. So it will lead to the loss of useful information. In order to improve the feature extraction accuracy of Brillouin scattering spectrum, a hybrid optimization algorithm named MCDM-PSO-LM algorithm is presented based on MCDM and PSO-LM algorithm. The MCDM algorithm can identify and locate the peaks and valleys of multi-peaks Brillouin scattering spectrum accurately. The PSO-LM hybrid algorithm can realize the curve fitting on every peak and valley, and it can seach the center frequency shift of each peak. The PSO-LM hybrid algorithm can solves these disadvantages, which PSO algorithm premature convergence to local minimum and LM algorithm depends on the initial value problem. It can also combine the global search ability of PSO algorithm and the local search ability of LM algorithm. Compared with traditional algorithms, MCDM-PSO-LM algorithm can ensure the solving speed and accuracy to the optimal value, and the analytical solution will be close to the optimal value sufficiently. So it improves the operation ability. With different signal to noise ratio and linewidth, the results of frequency shift and temperature error show that the MCDM-PSO-LM method can locate every peak and valley of multi-peaks Brillouin scattering spectrum accurately. Thus, it can be used for the feature extraction of multi-peaks Brillouin scattering spectrum. The recognition effect of this method is obviously better than that of traditional algorithms and it can improve the accuracy of information analysis.

[Refractive Index Insensitive Temperature Sensor Based on Cascading Single Mode Fiber with Few Mode Fiber].

A refractive index insensitive temperature sensor is proposed base on cascading single mode fiber with few mode fiber(FMF). During the sensor preparation, the splicing current is set to 100 mA, and a section of FMF is no core-offset splicing between two single-mode fibers. Therefore, it can motivate the transmission mode preferably and form optical fiber Mach-Zehnder interferometer. The mode phase difference in FMF will be changed according to the outside environment. It will cause interference fringe shift. The parameter to be measured can be achieved by detecting the amount shift of interference spectrum. The FMF can transmit four modes with LP01, LP11, LP21, LP02. The transmission spectrum is also analyzed, which shows that they have two modes of LP01 and LP11 in sensor with the length of 81.5 mm. In the refractive index and temperature sensing experiment, the cascading FMF sensor with the length of 81.5 mm is used. The results show that the transmission spectrum of sensor appears obvious blue shift as temperature is increasing, the temperature sensitivity can be up to -85.9 pm·â„ƒ-1 within the range of 27.6~93.8 ℃ with good linearity. The refractive index sensitivity is 3.697 34 nm·RIU-1 within the range of 1.347 1~1.443 9. There is no obvious shift phenomenon in the transmission spectrum with the feature of refractive index insensitive. Therefore, compared with the traditional cladding mode and multimode interferometric fiber-optic sensor, the proposed sensor based on FMF is easier to control and analyze transmission mode has the advantages of simple structure, easy process and high sensitivity. It can avoid cross-sensitivity between temperature and refractive index measurement. Thus, it can be used for temperature detection of power system, biomedicine, aerospace and other fields.

[The High Precision Analysis Research of Multichannel BOTDR Scattering Spectral Information Based on the TTDF and CNS Algorithm].

Traditional BOTDR optical fiber sensing system uses single channel sensing fiber to measure the information features. Uncontrolled factors such as cross-sensitivity can lead to a lower scattering spectrum fitting precision and make the information analysis deflection get worse. Therefore, a BOTDR system for detecting the multichannel sensor information at the same time is proposed. Also it provides a scattering spectrum analysis method for multichannel Brillouin optical time-domain reflection (BOT-DR) sensing system in order to extract high precision spectrum feature. This method combines the three times data fusion (TTDF) and the cuckoo Newton search (CNS) algorithm. First, according to the rule of Dixon and Grubbs criteria, the method uses the ability of TTDF algorithm in data fusion to eliminate the influence of abnormal value and reduce the error signal. Second, it uses the Cuckoo Newton search algorithm to improve the spectrum fitting and enhance the accuracy of Brillouin scattering spectrum information analysis. We can obtain the global optimal solution by smart cuckoo search. By using the optimal solution as the initial value of Newton algorithm for local optimization, it can ensure the spectrum fitting precision. The information extraction at different linewidths is analyzed in temperature information scattering spectrum under the condition of linear weight ratio of 1:9. The variances of the multichannel data fusion is about 0.0030, the center frequency of scattering spectrum is 11.213 GHz and the temperature error is less than 0.15 K. Theoretical analysis and simulation results show that the algorithm can be used in multichannel distributed optical fiber sensing system based on Brillouin optical time domain reflection. It can improve the accuracy of multichannel sensing signals and the precision of Brillouin scattering spectrum analysis effectively.

[A Brillouin Scattering Spectrum Feature Extraction Based on Flies Optimization Algorithm with Adaptive Mutation and Generalized Regression Neural Network].

According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.

[The nitrate and temperature impact analysis in the detection of COD by UV spectrum].

Configured standard solution of chemical oxygen demand with potassium hydrogen phthalate was used as experimental subjects, collected ultraviolet absorption spectra of the standard solution in the range of 1,800 mg x L(-1), were collected, and PLS (partial least squares) algorithm was used to establish the correction model of different spectral region, the results showed that. The model in the spectral region of 265-310 nm had the highest correlation and smallest error; In order to eliminate the impact of nitrates and temperature on the detection of the COD , studied the changes of the UV absorption spectrum with different concentrations of sodium standard solution and different temperature. The results showed that absorption of nitrate in 208-238 nm was apparent, and the model for spectral region of 265-310 nm was free from the influence of nitrate; In the full range of spectrum, temperature rising leads to an increase in absorbance, thus the temperature compensation model was established for the different spectral region through predictive analysis.