• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Neutron-gamma discrimination method based on blind source separation and machine learning

    2021-03-18 13:27:28HananArahmaneElMehdiHamzaouiYannBenMaissaRajaaCherkaouiElMoursli
    Nuclear Science and Techniques 2021年2期

    Hanan Arahmane? El-Mehdi Hamzaoui? Yann Ben Maissa ?Rajaa Cherkaoui El Moursli

    Abstract The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks. Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination. However, their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals. The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup. Here, a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization (NTF) as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine (SVM) to identify and discriminate these components. In addition to these two main methods, we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model. The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise. We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM. Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance. In this framework, the obtained results have verified a suitable bias–variance trade-off value. We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate. The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality (figure of merit of 2.20).

    Keywords Blind source separation · Nonnegative tensor factorization (NTF) · Support vector machines (SVM) ·Continuous wavelets transform (CWT)·Otsu thresholding method

    1 Introduction

    Although organic scintillator detectors have been commonly used for neutron measurement systems owing to their high efficiency,they are too sensitive to gamma rays.Several methods have been proposed to reduce the effect of gamma rays on the neutron detection results. Pulse shape discrimination (PSD) [1] is a popular method used for neutron-gamma discrimination. Several digital and analog PSD approaches have been proposed to perform this task,such as the rise time[2],charge comparison(CCM)[3,4],zero-crossing[5,6],pulse gradient analysis(PGA)[7],and wavelet transform[8].However,these methods are limited in discriminating small difference pulses, as well as multiple pulses.

    With progress in data acquisition systems, new possibilities in digital pulse processing have opened up for organic scintillator detectors [9]. Currently, machine learning has proven to be a powerful tool in the analysis of radiation data.Its algorithms use simple and direct methods to learn information from a dataset without a predetermined model [10]. According to a priori knowledge in the modeling procedure, machine learning methods can be divided into two classes: supervised machine learning using a priori knowledge and unsupervised machine learning without prior knowledge.

    In most neutron spectroscopy applications, PSD is used to separate neutrons from gamma rays based on the time and energy features of the digitized pulses. However,their effectiveness is associated with a number of elements(i.e.,prior information) that can be divided into two categories.First, the experimental setup, which includes the type of scintillator detector used, experimental needs, and data acquisition system. Second, the processing phase, which represents the signal-to-noise ratio (SNR), and the difference between the pulses, as well as pile-up. The main challenge in these studies involves the means to realizing fast and accurate neutron-gamma discrimination without any a priori information on the signal to be analyzed (i.e.,processing phase), as well as the experimental setup.

    To overcome the ineffectiveness of PSD’s performance,other methods that combine PSD with machine learning algorithms, namely, support vector machines (SVMs),have been proposed [11–15]. Nevertheless, the SVM application does not follow certain paramount criteria such as data balance,cross-validation process,and bias-variance analysis, which provide more accuracy and credibility for neutron-gamma discrimination.

    In this study,a novel method was proposed on the basis of machine learning to obtain fast and accurate neutrongamma discrimination without any a priori information on the signal to be analyzed,as well as the experimental setup.First, we adopted the nonnegative tensor factorization(NTF)model as a blind source separation (BSS)technique to extract the original components from the mixture recorded at the output of a stilbene scintillator detector(45 mm × 45 mm). Second, an SVM was used to identify and discriminate these components. However, before that,Mexican-hat-function-based continuous wavelet transform(CWT) was utilized as a spectral analysis to characterize these extracted components within the time–frequency domain. The resulting scalograms are viewed as images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of neutrons and gamma-ray components from the background. We subsequently used principal component analysis(PCA)to select the most significant of these features to form the training and testing datasets for SVM classification purposes. We employed bias-variance analysis to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance. The achieved results have proven a suitable bias–variance trade-off value in this regard. We actually achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The performance of our proposed method was evaluated and validated by comparing it to the CCM via the figure of merit (FOM).With an FOM value of 2.20,the comparison verified the superiority of the SVM-based over NTF in terms of discrimination quality.

    An overview of the present paper is presented here.First, we discuss selected works on this framework in Sect. 2.Then,we focus on describing the analyzed signals,principle of the proposed method, and CCM method used for comparative validation in Sect. 3. Next, we comprehensively discuss the results of the proposed method in Sect. 4 before concluding the paper.

    2 Related works

    Only a few limited works have adopted PSD and SVM in addressing the neutron-gamma discrimination challenge,online or offline [11–14]. The results achieved by these research works are satisfactory, with each one introducing a specific approach to improving neutron-gamma discrimination. However, their effectiveness is associated with various factors, including the PSD requirement of a priori information on the signal to be analyzed, choice of scintillator detector, and acquisition system. Besides these factors,SVM application does not respect some key points such as bias-variance analysis [8–11] and cross-validation process [10–14]. In this section, we present and discuss a selection of these and other works.

    Sanderson et al.(2012)[11]applied SVM to determine a PSD classifier. The authors demonstrated that the SVM method coupled with CCM enhances detection performance and provides more precise estimates by considering the necessity of contaminating the training data needed for the SVM. However, the SVM solution proposed by the authors does not meet the basic criteria for its application,such as the cross-validation procedure, computation of generalization performance, and bias-variance analysis.

    Yu et al. (2015) [12] proposed the SVM method in conjunction with the moment analysis method (MAM) to achieve neutron-gamma discrimination of pulses from an organic liquid scintillator. They used the CCM method to discriminate neutrons and gamma-ray pulses, which form the training and testing datasets for the SVM.Then,MAM was applied to create the feature vectors for each pulse in the datasets. The authors demonstrated that the SVM classifier coupled with MAM has exhibited a great ability to separate the neutrons and gamma-ray pulses while providing the classification accuracy for each pulse type.However, its performance is limited because of the neglected data balance in the prediction phase, as well as the evaluation of the SVM model complexity using biasvariance analysis.

    Zhang et al. (2018) [13] presented a method based on the SVM discriminator for discriminating neutrons from gamma-ray backgrounds and enhancing the performance of the time-of-flight neutron detector (EJ-299-33 plastic scintillator with PSD property). The proposed method has been implemented in field-programmable gate arrays(FPGAs) to detect neutrons in mixed radiation fields. The obtained results show that real-time neutron-gamma discrimination was achieved with a discrimination accuracy of 99.1%, which could be better with bias-variance analysis.

    Zhang et al. (2019) [14] presented a direct method to discriminate nuclear pulse shapes based on PCA and SVM.The authors determined that the training and testing accuracies of SVM classifiers are all above 94.7%provided appropriate kernels are well selected. However, the performance accuracy was less than that obtained by Zhang et al. (2018) and Yu et al. (2015). Furthermore, the selection of the Gaussian kernel most adapted to their study was not based on the grid search and stratified K-fold crossvalidation used to set its hyperparameters.

    In our previous work [16], we introduced a novel method that combines nonnegative matrix factorization(NMF) with SVM to perform neutron-gamma discrimination at the output of a stilbene detector. We used the Otsu thresholding method based on CWT to extract the main features of neutrons and gamma-ray signals that have been extracted by the NMF method. These features were then fed into a nonlinear SVM classifier to perform neutrongamma discrimination. To achieve this, a Gaussian kernel function was selected using grid search and stratified Kfold cross-validation. The proposed method obtained a good SVM prediction model with a suitable classification rate of 99.93%.

    Via the analysis of the works mentioned in this section,we can consider that of Arahmane et al.,who presented an approach that does not require any a priori information on the signal to be analyzed, as well as on the experimental setup. In addition, it obeys the true working process of SVM with an operational prediction model that has provided a better true neutron-gamma classification rate of 99.93%.

    To achieve more efficiency and accuracy of this neutron-gamma discrimination process, we have altered the first block of our neutron signal processing chain, as described in [16], which was formed by the second-order NMF separation method. Therefore, in this study, a nonnegative parallel factor analysis model (PARAFAC),denoted by the NTF-2 model, is used to achieve a 3D nonnegative tensor factorization of the signals recorded at the output of the stilbene detector. The aim of using the NTF-2 method is based mainly on the fact that it considers both the space and time correlations between the variables more precisely [17], which is very important from the perspective of data processing to optimize the performance of the SVM model.

    3 Materials and methods

    In this section, we present the dataset used for the evaluation of the proposed method, and then the principle of this method, as well as the CCM method, which is used later for comparative verification.

    3.1 Dataset characteristics

    Fig.1 Example of two consecutive stilbene scintillator output signals

    In this study,the datasets are composed of 100 neutrons and gamma-ray signals of 1000 samples each. Figure 1 illustrates an example of 2 consecutive stilbene scintillator output signals. The pulses were obtained based on the following experimental setup: Cf-252 as a mixed neutrongamma-emitting source and Na-22 as the gamma only source measured through a stilbene crystal scintillator with dimensions of 45 mm × 45 mm, a RCA7265 photomultiplier tube (PMT) [18], and data acquisition system. It should be noted that the aim of using Na-22 is to form pure gamma-ray signal set used in the CWT processing step as a reference to confirm the characterization task. The PMT output is connected directly to the ACQIRIS DP210-U1068A using single-ended impedance matching. The ACQIRIS DP210-U1068A is the acquisition system used to digitize the output pulses with 8-bits resolution at a sampling rate of 1 GSamples/s. It is worth noting that the digitizer signal quality is measured by the signal-to-quantization noise ratio(SQNR),which accurately estimates the quality of a b-bit digitizer output [19] as expressed below:SQNR=1.76+6.02b. (1)

    This corresponds to the fact that the SQNR increases by approximately 6 dB for every bit added to the digitizer word length [19]. Therefore, the sampling rate chosen in our case implies that the analyzed signals have low amplitude.This choice allows us to prove the performance ability of our proposed method in this constraint.

    The collected data were stored in a 64-bit computer with 16 Go of RAM for offline processing according to the processing method illustrated in Fig. 1.

    3.2 Our NTF/SVM method for neutron/gamma discrimination

    3.2.1 Method overview

    Figure 2 illustrates the steps of our proposed method.We focus on the description of the NTF-2 model. Before that, we provide a brief overview of the other tools used because they are widely described in the literature.

    ? The CWT is a technique used to carry out signal analysis when the signal frequency varies over time[20,21].It is adopted to cut-up the signal using a set of wavelet functions by shifting (time) and scaling (frequency) to a mother wavelet. In our approach, we adopted the Mexican-hat function of the CWT [20] as the mother wavelet because the signal shape is similar to a Gaussian distribution with a long tail on one side[16].

    ? The Otsu thresholding method enables the determination of an optimal threshold value by minimizing the within-class variance[22].The selection of the optimal threshold is based on the prior calculation of the graylevel histogram of an image.

    ? PCA is used in image and signal processing for dimensionality reduction and feature selection [23].Its main goal is to determine a few linear combinations of the principal components, in which their directions are orthogonal to explain the variance in the data [24].

    ? SVM was adopted to solve a two-classification problem by determining optimal separation hyperplanes as linear or nonlinear classifiers with maximum margin in a multi-dimensional space [25, 26]. The transformation of the data from the input space into a highdimensional space requires kernels [27]. The generalization performance evaluation of the SVM model is performed via bias-variance analysis. Determining an optimal bias-variance trade-off helps to achieve good results on unseen datasets[28],thus avoiding deceptive results owing to the inability of the classifier to perform learning generalization (i.e., overfitting phenomenon)[29].3.2.2 NTF model

    NTF (or nonnegative PARAFAC) is a model with nonnegative factor matrices. It is a blind source separation(BSS) method composed of an unsupervised machine learning class used for feature extraction and dimensionality reduction[24].It should be noted that the BSS method[30,31]is adopted in signal processing to recover a source signal from a mixture of signals recorded by a sensor without any information about the source signals and/or the mixing procedure. The outputs of digital systems are mostly multi-dimensional and discontinuous. They typically represent one or more variables at a discontinuous set of positions in time and space,and thus are perfect for NTF analysis [32].

    Fig. 2 Flowchart of proposed neutron-gamma discrimination process

    where αA, αB, αCare parameters of nonnegative regularization.

    The alternating least squares (ALS) method is the most common approach for solving this optimization problem[24] and thus solving the NTF problem. In this approach,we compute the cost function gradient relative to each individual matrix,assuming that the others are independent and fixed [24].

    To extract original sources from the recorded mixed signals in this research work,we selected an NTF-2 model as a more suitable model owing to the form of the column observation vectors.

    3.3 Alternative method for comparative validation:CCM

    3.4 Comparative validation metric

    To assess the discrimination ability of the method, we used the figure of merit (FOM) [8, 9, 13] as a discrimination quality metric, formulated as:

    where SNγis the separation between the gamma-ray and neutron peaks, and FWHMNand FWHMγrepresent their full widths at half maximum (FWHM).We used the Gaussian function to fit the distribution of the neutron and gamma-ray events. A higher FOM value indicates greater quality discrimination.

    Fig. 3 Example of pulse processing using CCM/PSD method

    4 Results and discussion

    In this section, we present the results of the proposed method mainly based on the SVM and NTF machine learning methods to perform fast and accurate neutrongamma discrimination. The obtained results for each step of our proposed method are as follows:

    4.1 Step1: NTF processing

    The NTF-2 model applied to these overlapped sources provides a solution to our BSS problem. To validate and determine the accuracy of the separation quality, we compute the signal-to-interference ratio (SIR) to estimate the original sources that comprise the detector mixed output signals. Figure 4 illustrates that the recorded mixed signals are formed by two independent components (ICs:2nd and 5th). We performed the separation task with a mean SIR value of approximately 76 dB, which reflects a very good signal processing performance, as shown in Fig. 4. In fact, SIR ≥30 dB indicates an optimal separation performance and perfect reconstruction of the original sources [33, 38]. This can also be justified by the fact that the nonnegative tensor factorization methods adopt more projection axes than 2-D to achieve the blind separation task.This allows information to be extracted from different projections (tensors) and therefore results in coherent components that are more independent of each other.

    4.2 Step 2: Mexican-hat function-based CWT processing

    The characterization of both ICs (IC2and IC5) was carried out using a Mexican-hat-function-based CWT. We determined that IC2and IC5have one high-energy zone situated in the same scale range of 5–38 and at different time ranges of 700–720 ms and 300–320 ms, respectively(see Fig. 5). The comparison of both obtained scalograms with those of pure neutrons and gamma-ray signals demonstrates that IC2and IC5represent the neutrons and gamma-ray signals, respectively.

    The results of the NMF application[16]have shown that the scalograms of the neutron signal are formed by two main high-energy bands situated at ranges of approximately 0–25 ms and 700–720 ms.These energies appear in the scale range of 7–43 (Fig. 6a). The gamma-ray signal scalograms have only one high-energy band situated at a range of approximately 300–320 ms and scales of 6–35 scales (Fig. 6b). These differences appear owing to the high level of accuracy that the NTF-2 provides whileextracting the independent components that form the recorded signals. Therefore, the true energy of the signal appears only on its corresponding scalogram.

    Fig. 4 (Color online) Plot of SIR as a function of column index in the original mixing matrix-A

    Fig.5 (Color online)Scalograms of neutron(IC2)(a)and gamma-ray(IC5)(b)signals resulting from the application of Mexican-hat-functionbased CWT for the NTF-2 application

    Fig.6 (Color online)Scalograms of neutron(a)and gamma-ray(b)signals resulting from the application of Mexican-hat-function-based CWT for the NMF application

    4.3 Step 3: otsu thresholding processing

    We conclude that TNeutron=TGamma=0.5098 (Fig. 7a,b)is the optimal threshold that can be used to segment the neutrons and gamma-ray images (Fig. 7c, d). From each image, we have extracted 13 geometrical features that represent: ‘‘ConvexArea’’, ‘‘Area’’, ‘‘Eccentricity’’,‘‘EquivDiameter’’, ‘‘MajorAxisLength’’, ‘‘FilledArea’’,‘‘MinorAxisLength’’, ‘‘Extent’’, ‘‘PerimeterOld’’, ‘‘Orientation’’, ‘‘Solidity’’, ‘‘EulerNumber’’, and ‘‘Perimeter’’[16].

    4.4 Step 4: principle components analysis processing

    To enhance the prediction ability of the SVM model,we adopted principle component analysis (PCA) to select significant features among the 13 extracted features. We inferred that ‘‘Area’’, ‘‘MajorAxisLength’’, and ‘‘MinorAxisLength’’ are the most useful features (Fig. 8).

    4.5 Step 5: SVM processing

    We use the three selected features as a vector of attributes that are implanted in the SVM model for training,cross-validation, and testing the SVM model.

    Fig.7 (Color online)Gray-level histograms of neutron(a)and gamma-ray(b)signals.Binary images of neutron(c)and gamma-ray(d)signals

    Fig. 8 (Color online) Feature selection of neutron (a) and gamma-ray (b) signals in the binary image, using PCA method

    Fig. 9 (Color online) Evaluation of SVM model using bias-variance analysis based on the cross-validation process

    From the perspective of bias-variance analysis, a good bias–variance trade-off value between train and test errors was determined as log2(C)=5, which demonstrated the capability of our SVM model for maximizing its generalization performance and thus minimizing the prediction error.

    To examine the performance of our SVM model, we tested 1000 new images (i.e., 500 images for neutrons and 500 images for gamma rays).We successfully classified 99 images with a high classification rate (99.96%), which completes the efficiency proof of the neutron-gamma discrimination process introduced. Compared to the results obtained in[16],the NMF-SVM method allowed the use of the same kernel function (Gaussian) as a neutron-gamma discrimination with a rate of 99.93%. This rate was achieved using C = 32 and γ=0.5 as the best parameters of the Gaussian function and with a generalization error of 0.065%.

    4.6 Performance evaluation

    To evaluate and validate the performance of the proposed method, we compared it to CCM/PSD. Figure 10 shows the bi-parametric histogram of the tail-to-total integral as a function of the total integral obtained from a stilbene crystal scintillator and PMT. We can see that the neutron and gamma-ray regions can be identified visually.As illustrated, there are two classes: the upper class represents neutron pulses and the lower class corresponds to gamma-ray pulses. Therefore, this representation allows the qualitative assessment of the effectiveness of neutrongamma discrimination methods.

    To quantify the obtained results by using NTF/SVM compared to the CCM, the FOM was used (Fig. 11). The computation of the FOM value is determined from the analysis of the tail-to-total integral histogram,which shows that NTF/SVM has a higher FOM(2.20)when compared to the CCM (FOM = 0.99). This clearly indicates the charge distribution of the neutron and gamma-ray events. Consequently, this distribution confirms the results achieved in Fig. 10, thus validating the previous results.

    As stated above and based on the achieved results,using the NTF model in the separation task is more efficient than using the NMF algorithm,[13]and it is evident through the higher classification rate obtained (99.96%). From the separation perspective, the NTF model (or PRAFAC model) adopts 3D projection matrices, and thus the separation task is more accurate than the NMF that uses 2D projection matrices. Despite this difference, the obtained results with NMF and/or NTF algorithms of the BSS methods, coupled with SVM, prove its ability to perform accurate neutron-gamma discrimination with a true classification rate as high as that of conventional methods such as a common PSD standard technique. The comparison results show that the FOM provided by the SVM-based NTF method is superior.

    5 Conclusion

    In this study, we proposed a novel method for neutrongamma discrimination without any a priori information on the signal to be analyzed using NTF/BSS (unsupervised learning method) and SVM (supervised learning method).The first method aims to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector,while the second method aimsto classify these components. In addition to SVM, we applied the Mexican-hat-function-based CWT, Otsu thresholding, and PCA methods to improve the prediction ability of our SVM model. Furthermore, we used biasvariance analysis to evaluate the SVM model complexity as it provided an optimal level with the highest possible generalization performance. Furthermore, we compared our method with CCM/PSD for validation. The FOM values obtained using the SVM-based NTF method were determined to be significantly higher (FOM = 2.20) than those obtained using CCM. Therefore, all obtained results clearly indicate that SVM based on the NTF method proposed in this study can provide neutron/gamma PSD ability with very high resolution. Presently, this method can become one of the most effective methods for neutron measurement systems, using the digital signal processing technique, as well as the stilbene crystal organic detector.

    Fig. 10 (Color online) Biparametric histogram of tail-tototal integral as a function of the total integral obtained from a stilbene crystal scintillator and PMT via SVM based on NTF(a) and CCM/PSD (b)

    Fig.11 (Color online)Histogram analysis of tail-to-total integral obtained from a stilbene crystal scintillator and PMT via SVM based on NTF(a) and CCM/PSD (b)

    The promising results obtained encouraged us to validate our proposed process experimentally with other organic scintillator detectors (i.e., plastic and liquid) and time-of-flight methods, as well as testing it in low activity or random detection. We also aim to introduce other artificial intelligence methods to improve the performance of the entire processing method. Furthermore, our NMF/NTF combined with SVM approaches is implemented on DSP and FPGA-based cards to perform real-time tests.

    Author contributionsAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by HA. The first draft of the manuscript was written by HA, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

    听说在线观看完整版免费高清| 欧美日韩综合久久久久久| av国产精品久久久久影院| 欧美日韩精品成人综合77777| 亚洲精品aⅴ在线观看| 大话2 男鬼变身卡| 国产熟女欧美一区二区| 看免费成人av毛片| 搞女人的毛片| h日本视频在线播放| 亚洲,欧美,日韩| 免费人成在线观看视频色| 51国产日韩欧美| 国产中年淑女户外野战色| 热99国产精品久久久久久7| 亚洲内射少妇av| 最近最新中文字幕免费大全7| 欧美精品一区二区大全| 欧美精品一区二区大全| av播播在线观看一区| 99热国产这里只有精品6| 日韩制服骚丝袜av| 高清日韩中文字幕在线| 成人国产av品久久久| 精品人妻熟女av久视频| 成年人午夜在线观看视频| 狂野欧美白嫩少妇大欣赏| 麻豆成人午夜福利视频| 亚洲精品第二区| 51国产日韩欧美| 99热全是精品| 国产精品熟女久久久久浪| 亚洲成人久久爱视频| 在线免费观看不下载黄p国产| 人妻系列 视频| 久久久久网色| 黄色视频在线播放观看不卡| 最近的中文字幕免费完整| 日韩一区二区三区影片| 一级毛片我不卡| 乱系列少妇在线播放| 熟女人妻精品中文字幕| 日韩亚洲欧美综合| 亚洲国产精品成人久久小说| 99热全是精品| 蜜桃久久精品国产亚洲av| 在线观看人妻少妇| 欧美极品一区二区三区四区| 国产精品国产三级国产专区5o| 狂野欧美白嫩少妇大欣赏| 欧美3d第一页| 亚洲国产最新在线播放| 在线观看三级黄色| 2021天堂中文幕一二区在线观| 看免费成人av毛片| 国产 一区 欧美 日韩| 国产美女午夜福利| 视频中文字幕在线观看| 国产精品一及| 婷婷色麻豆天堂久久| 国产成人freesex在线| 欧美高清性xxxxhd video| 国产在视频线精品| 在线观看三级黄色| 2022亚洲国产成人精品| 99re6热这里在线精品视频| 极品教师在线视频| 亚洲婷婷狠狠爱综合网| 一级毛片 在线播放| 边亲边吃奶的免费视频| 欧美老熟妇乱子伦牲交| 国产又色又爽无遮挡免| 国产毛片a区久久久久| 国产老妇女一区| 久久久久久九九精品二区国产| 街头女战士在线观看网站| 国产永久视频网站| 在线看a的网站| 久久99精品国语久久久| 少妇被粗大猛烈的视频| 亚洲国产精品专区欧美| 精品久久久久久电影网| 又粗又硬又长又爽又黄的视频| 青春草亚洲视频在线观看| 久久久久久久午夜电影| av在线播放精品| 99久久精品国产国产毛片| 99热全是精品| 亚洲美女搞黄在线观看| 成年女人在线观看亚洲视频 | 国产男人的电影天堂91| 亚洲欧洲日产国产| 最近中文字幕高清免费大全6| videossex国产| 简卡轻食公司| 性色av一级| 97在线人人人人妻| a级一级毛片免费在线观看| 91精品一卡2卡3卡4卡| 色综合色国产| 身体一侧抽搐| 亚洲欧美日韩无卡精品| 草草在线视频免费看| 日韩av免费高清视频| 激情五月婷婷亚洲| 18禁在线无遮挡免费观看视频| 亚洲成人久久爱视频| 久热久热在线精品观看| 日本猛色少妇xxxxx猛交久久| 亚洲激情五月婷婷啪啪| 少妇丰满av| 国产黄a三级三级三级人| 久久99热这里只有精品18| 欧美bdsm另类| av又黄又爽大尺度在线免费看| 亚洲最大成人av| 观看美女的网站| 在现免费观看毛片| 国产欧美日韩一区二区三区在线 | 久久精品国产鲁丝片午夜精品| 男人添女人高潮全过程视频| 国产毛片a区久久久久| 欧美精品人与动牲交sv欧美| 国产 一区精品| 黑人高潮一二区| 欧美日韩视频高清一区二区三区二| 国语对白做爰xxxⅹ性视频网站| 午夜激情福利司机影院| 午夜精品国产一区二区电影 | av天堂中文字幕网| 日韩一本色道免费dvd| 国产真实伦视频高清在线观看| 99久久中文字幕三级久久日本| 深爱激情五月婷婷| 成年人午夜在线观看视频| 99热这里只有精品一区| 久久精品熟女亚洲av麻豆精品| 五月开心婷婷网| 国产黄色免费在线视频| 久久久久久伊人网av| 少妇人妻一区二区三区视频| 高清日韩中文字幕在线| 日本色播在线视频| 又粗又硬又长又爽又黄的视频| 久久精品久久精品一区二区三区| 五月天丁香电影| 国产精品一二三区在线看| 九九爱精品视频在线观看| 波多野结衣巨乳人妻| 夜夜看夜夜爽夜夜摸| 只有这里有精品99| 久久久久久久久大av| 天美传媒精品一区二区| 久久久精品欧美日韩精品| 色婷婷久久久亚洲欧美| 久久99热6这里只有精品| 99热这里只有是精品50| 欧美变态另类bdsm刘玥| 老司机影院毛片| 国产精品无大码| 欧美精品国产亚洲| 久久韩国三级中文字幕| 麻豆乱淫一区二区| 中文在线观看免费www的网站| 熟女av电影| 99视频精品全部免费 在线| 在线观看av片永久免费下载| 视频区图区小说| 一级a做视频免费观看| 国产成人a区在线观看| 国产成人免费观看mmmm| 亚洲婷婷狠狠爱综合网| 亚洲精品国产色婷婷电影| 国国产精品蜜臀av免费| 美女国产视频在线观看| 男女下面进入的视频免费午夜| 日韩电影二区| 网址你懂的国产日韩在线| 亚洲电影在线观看av| 大话2 男鬼变身卡| 在线精品无人区一区二区三 | 爱豆传媒免费全集在线观看| 久久久精品免费免费高清| 中文字幕人妻熟人妻熟丝袜美| 国产伦在线观看视频一区| 久久久久久九九精品二区国产| 只有这里有精品99| 少妇人妻久久综合中文| 听说在线观看完整版免费高清| 少妇人妻久久综合中文| av.在线天堂| 网址你懂的国产日韩在线| 国产亚洲av嫩草精品影院| 99热这里只有精品一区| 久久久欧美国产精品| 黄色日韩在线| 精华霜和精华液先用哪个| av在线天堂中文字幕| 五月天丁香电影| 一级爰片在线观看| 精品久久久久久久人妻蜜臀av| 国产大屁股一区二区在线视频| 亚洲久久久久久中文字幕| 国产精品久久久久久精品电影小说 | 免费大片18禁| 午夜激情福利司机影院| 中文字幕久久专区| 国产男女超爽视频在线观看| 视频区图区小说| 一级毛片久久久久久久久女| 少妇人妻精品综合一区二区| 青春草视频在线免费观看| 欧美三级亚洲精品| 久久久成人免费电影| 国产成人a∨麻豆精品| 美女视频免费永久观看网站| 欧美人与善性xxx| 神马国产精品三级电影在线观看| 中文精品一卡2卡3卡4更新| 午夜老司机福利剧场| 日本三级黄在线观看| www.av在线官网国产| 精品人妻视频免费看| 国产男人的电影天堂91| 成年人午夜在线观看视频| 精品一区二区三卡| www.av在线官网国产| 亚洲怡红院男人天堂| 在线观看免费高清a一片| av线在线观看网站| 秋霞伦理黄片| 一个人看视频在线观看www免费| 99久久精品一区二区三区| 在线观看人妻少妇| 最近中文字幕2019免费版| 亚洲精品成人久久久久久| 中国国产av一级| 美女高潮的动态| 看免费成人av毛片| 视频区图区小说| 久久国内精品自在自线图片| 中文在线观看免费www的网站| 精品熟女少妇av免费看| 亚洲综合色惰| 国产白丝娇喘喷水9色精品| 亚洲精品色激情综合| 久久久久久久精品精品| 天美传媒精品一区二区| 18禁在线无遮挡免费观看视频| 免费黄网站久久成人精品| 国产欧美亚洲国产| 亚洲国产av新网站| 成年人午夜在线观看视频| 成人午夜精彩视频在线观看| 久久精品国产亚洲av涩爱| 亚洲av成人精品一二三区| 国产免费一区二区三区四区乱码| 99热这里只有是精品50| av网站免费在线观看视频| 亚洲美女视频黄频| 国产真实伦视频高清在线观看| 最新中文字幕久久久久| videossex国产| 欧美xxxx性猛交bbbb| 国产成人午夜福利电影在线观看| 国产淫语在线视频| 亚洲四区av| 国产男女内射视频| 搡老乐熟女国产| 久久久久久久精品精品| 18禁裸乳无遮挡动漫免费视频 | 毛片女人毛片| av在线老鸭窝| 国产熟女欧美一区二区| 我的女老师完整版在线观看| 少妇猛男粗大的猛烈进出视频 | 97超碰精品成人国产| 又黄又爽又刺激的免费视频.| 久久久久久久亚洲中文字幕| 中文天堂在线官网| 亚洲自偷自拍三级| 少妇人妻精品综合一区二区| 搞女人的毛片| 搡女人真爽免费视频火全软件| 毛片女人毛片| 人人妻人人看人人澡| 成人一区二区视频在线观看| 99热全是精品| 精品少妇黑人巨大在线播放| 少妇被粗大猛烈的视频| 亚洲精品久久午夜乱码| 午夜激情福利司机影院| 看免费成人av毛片| 日韩人妻高清精品专区| 99久久精品热视频| 精品少妇久久久久久888优播| 欧美丝袜亚洲另类| 成人欧美大片| 欧美日韩精品成人综合77777| 国产老妇伦熟女老妇高清| 成年女人在线观看亚洲视频 | 国产高清国产精品国产三级 | 国产视频首页在线观看| 伊人久久精品亚洲午夜| 国内精品美女久久久久久| 亚洲精品自拍成人| 成人国产av品久久久| 99久国产av精品国产电影| 国产精品人妻久久久影院| 免费观看的影片在线观看| 成人欧美大片| 在线观看一区二区三区| 午夜福利视频1000在线观看| 成人鲁丝片一二三区免费| 免费观看av网站的网址| 激情 狠狠 欧美| 老师上课跳d突然被开到最大视频| 国产乱人视频| 日本爱情动作片www.在线观看| 99热这里只有精品一区| 亚洲av日韩在线播放| 日韩强制内射视频| 久久久久久久久久久丰满| 国产高潮美女av| 色视频在线一区二区三区| 又黄又爽又刺激的免费视频.| av在线观看视频网站免费| 国产精品久久久久久av不卡| 一级爰片在线观看| 国产精品福利在线免费观看| 丝袜美腿在线中文| 成年女人在线观看亚洲视频 | 亚洲国产色片| 亚洲最大成人中文| 少妇人妻久久综合中文| 国产精品99久久99久久久不卡 | h日本视频在线播放| 亚洲精品色激情综合| 国产69精品久久久久777片| 一个人看的www免费观看视频| 国产成人福利小说| 又爽又黄无遮挡网站| 午夜福利在线观看免费完整高清在| 听说在线观看完整版免费高清| 日日啪夜夜爽| 亚洲激情五月婷婷啪啪| 观看美女的网站| 久热久热在线精品观看| 成人亚洲欧美一区二区av| 中国国产av一级| 中文字幕人妻熟人妻熟丝袜美| 亚洲真实伦在线观看| 嫩草影院入口| 日韩在线高清观看一区二区三区| 亚洲国产精品专区欧美| 秋霞在线观看毛片| 99视频精品全部免费 在线| 男女边吃奶边做爰视频| 91精品国产九色| 中文天堂在线官网| 高清日韩中文字幕在线| 亚洲,欧美,日韩| 亚洲精品成人久久久久久| 国产淫片久久久久久久久| 九九在线视频观看精品| 国产亚洲最大av| 天堂俺去俺来也www色官网| 国产有黄有色有爽视频| 18禁在线播放成人免费| 久久久久久久亚洲中文字幕| av免费观看日本| 下体分泌物呈黄色| 欧美激情久久久久久爽电影| 亚洲av福利一区| 蜜桃亚洲精品一区二区三区| 人妻夜夜爽99麻豆av| 亚洲欧美日韩卡通动漫| 国产欧美日韩精品一区二区| av国产免费在线观看| 热99国产精品久久久久久7| 99re6热这里在线精品视频| 国产成人精品婷婷| 亚洲人与动物交配视频| 欧美区成人在线视频| 久久影院123| 国产视频内射| 欧美三级亚洲精品| 久久99热这里只频精品6学生| 亚洲av成人精品一区久久| 亚洲av中文av极速乱| 久久韩国三级中文字幕| 国产伦精品一区二区三区四那| 国产精品久久久久久精品电影| 日本一二三区视频观看| 亚洲在久久综合| 久久久久久国产a免费观看| 亚洲图色成人| 国产男女超爽视频在线观看| 深爱激情五月婷婷| 真实男女啪啪啪动态图| 日本色播在线视频| 熟女人妻精品中文字幕| 全区人妻精品视频| 黄色怎么调成土黄色| 波多野结衣巨乳人妻| av又黄又爽大尺度在线免费看| 久久久久久久久久久丰满| 简卡轻食公司| 看免费成人av毛片| 亚洲最大成人av| videos熟女内射| 免费观看a级毛片全部| av女优亚洲男人天堂| av在线老鸭窝| 中文字幕免费在线视频6| 在线观看人妻少妇| 久久精品国产自在天天线| 日本一二三区视频观看| 免费大片黄手机在线观看| 少妇的逼好多水| 国产精品伦人一区二区| 国产伦理片在线播放av一区| 在线观看一区二区三区| 热re99久久精品国产66热6| 免费观看av网站的网址| 欧美日韩视频高清一区二区三区二| 国产精品久久久久久精品古装| 日韩人妻高清精品专区| 久久国内精品自在自线图片| 日本熟妇午夜| 亚洲av免费在线观看| 国产精品国产三级国产专区5o| 亚洲欧美精品自产自拍| 18禁在线无遮挡免费观看视频| 国产免费又黄又爽又色| 交换朋友夫妻互换小说| 久久精品久久精品一区二区三区| 99热这里只有是精品50| 亚洲av成人精品一二三区| 久久韩国三级中文字幕| 欧美人与善性xxx| 久久ye,这里只有精品| 国产日韩欧美在线精品| 国内揄拍国产精品人妻在线| 午夜激情福利司机影院| 国产女主播在线喷水免费视频网站| 国产一区二区亚洲精品在线观看| 亚洲欧美日韩另类电影网站 | 少妇的逼好多水| 亚洲在线观看片| 精品国产乱码久久久久久小说| 成年免费大片在线观看| 午夜福利在线在线| 99久久精品国产国产毛片| 国产毛片a区久久久久| 国产精品精品国产色婷婷| 中文字幕久久专区| 国产一区亚洲一区在线观看| 新久久久久国产一级毛片| 亚洲精品,欧美精品| 国产人妻一区二区三区在| av天堂中文字幕网| 偷拍熟女少妇极品色| 免费人成在线观看视频色| 免费电影在线观看免费观看| 晚上一个人看的免费电影| 国产亚洲5aaaaa淫片| 乱系列少妇在线播放| 亚洲第一区二区三区不卡| 黄色一级大片看看| www.色视频.com| 伊人久久精品亚洲午夜| 日本与韩国留学比较| 熟女人妻精品中文字幕| 亚洲成人av在线免费| av国产久精品久网站免费入址| 看免费成人av毛片| 欧美激情在线99| 久久久久久久久久久免费av| 欧美日韩视频高清一区二区三区二| 人妻一区二区av| 国产精品爽爽va在线观看网站| 国产成人午夜福利电影在线观看| 夫妻午夜视频| 欧美变态另类bdsm刘玥| 一级片'在线观看视频| 又黄又爽又刺激的免费视频.| 18禁裸乳无遮挡动漫免费视频 | 免费高清在线观看视频在线观看| 国产人妻一区二区三区在| 欧美精品一区二区大全| 少妇猛男粗大的猛烈进出视频 | 91在线精品国自产拍蜜月| 久久久欧美国产精品| 国产亚洲91精品色在线| 亚洲精品乱码久久久v下载方式| 99热国产这里只有精品6| 欧美3d第一页| 国产极品天堂在线| 国产 精品1| 永久免费av网站大全| av女优亚洲男人天堂| 岛国毛片在线播放| 亚州av有码| 国产综合懂色| 一区二区三区精品91| 最近2019中文字幕mv第一页| 纵有疾风起免费观看全集完整版| 免费观看的影片在线观看| 天堂中文最新版在线下载 | 青春草国产在线视频| 久久久亚洲精品成人影院| 亚洲,一卡二卡三卡| 日韩国内少妇激情av| 校园人妻丝袜中文字幕| 日韩国内少妇激情av| 啦啦啦中文免费视频观看日本| 亚洲最大成人中文| 一二三四中文在线观看免费高清| 在线精品无人区一区二区三 | av卡一久久| 亚洲国产日韩一区二区| 在线观看免费高清a一片| .国产精品久久| 性插视频无遮挡在线免费观看| 国产成人精品福利久久| 久久午夜福利片| 成人亚洲欧美一区二区av| 一级毛片我不卡| 高清午夜精品一区二区三区| 麻豆乱淫一区二区| 亚洲国产精品专区欧美| 在线播放无遮挡| 一区二区三区乱码不卡18| 亚洲精品日韩在线中文字幕| 亚洲人成网站在线播| 亚洲国产欧美人成| 九草在线视频观看| 水蜜桃什么品种好| 日韩制服骚丝袜av| 国产伦精品一区二区三区四那| 久久韩国三级中文字幕| 午夜日本视频在线| 新久久久久国产一级毛片| av在线天堂中文字幕| 大陆偷拍与自拍| 亚洲欧美日韩另类电影网站 | 97在线视频观看| 少妇高潮的动态图| 久久久久国产精品人妻一区二区| 精品一区二区免费观看| 久久久欧美国产精品| 午夜老司机福利剧场| 2021天堂中文幕一二区在线观| 极品少妇高潮喷水抽搐| 国产精品女同一区二区软件| 人体艺术视频欧美日本| 少妇 在线观看| 国产精品一区www在线观看| 成人国产麻豆网| 免费看不卡的av| 欧美成人一区二区免费高清观看| 2021天堂中文幕一二区在线观| 日本一本二区三区精品| 超碰av人人做人人爽久久| 寂寞人妻少妇视频99o| 女人被狂操c到高潮| 亚洲精品乱久久久久久| 亚洲国产日韩一区二区| 日韩 亚洲 欧美在线| 国产精品秋霞免费鲁丝片| 91精品一卡2卡3卡4卡| 禁无遮挡网站| 国产一区二区在线观看日韩| 午夜视频国产福利| 一本久久精品| 国产精品无大码| 国产成人a区在线观看| 国精品久久久久久国模美| 中文字幕av成人在线电影| 精品人妻一区二区三区麻豆| 午夜亚洲福利在线播放| 午夜福利高清视频| 免费黄网站久久成人精品| 欧美少妇被猛烈插入视频| 波野结衣二区三区在线| 啦啦啦啦在线视频资源| 777米奇影视久久| 看十八女毛片水多多多| 午夜激情福利司机影院| 各种免费的搞黄视频| 亚洲在久久综合| 国产精品99久久99久久久不卡 | 精品久久久久久久人妻蜜臀av| 最后的刺客免费高清国语| 日本与韩国留学比较| 国产淫语在线视频| 97热精品久久久久久| 欧美日韩视频精品一区| 午夜精品一区二区三区免费看| 亚洲欧美日韩卡通动漫| 久久久午夜欧美精品| 老女人水多毛片| 国产精品女同一区二区软件| 交换朋友夫妻互换小说| av在线app专区| 视频中文字幕在线观看| 国产乱人视频| 真实男女啪啪啪动态图| 一二三四中文在线观看免费高清| 欧美亚洲 丝袜 人妻 在线| 亚洲三级黄色毛片| 欧美日韩国产mv在线观看视频 | 日日摸夜夜添夜夜爱| 欧美亚洲 丝袜 人妻 在线|