• <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 ?

    Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

    2022-08-23 02:20:08GhazaliSemanIsaRamliAbidinMustamHaekAbidinandAsrokin
    Computers Materials&Continua 2022年6期

    N.B.Ghazali,F.C.Seman,,K.Isa,K.N.Ramli,Z.Z.Abidin,S.M.Mustam,M.A.Haek,A.N.Z.Abidin and A.Asrokin

    1Faculty of Electrical and Electronic Engineering,Universiti Tun Hussein Onn Malaysia,Batu Pahat,Johor

    2TM Research&Development,Cyberjaya,Malaysia

    Abstract:Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System.The network performance depends on the occurrence of cable fault along the copper cable.Currently,most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm.This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods.The DSL Access Network is emulated in the laboratory to accommodate VDSL2 Technology with various types of cable fault along the cable distance between 100 m to 1200 m.Firstly, the line operation parameters and loop line testing parameters are collected and used to analyze.Secondly,the feature transformation,a knowledge-based method,is utilized to pre-process the fault data.Then, the random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault data.Finally, the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network System.The results show that the cable fault detection has an accuracy of more than 97%, with less minimum absolute error in cable fault localization of less than 11%.The proposed algorithm may assist the telecommunication service provider to initiate automated cable faults identification and troubleshooting in the DSL Access Network System.

    Keywords:Twisted pairs;random forest machine learning;cable fault;DSL

    1 Introduction

    Worldwide fixed access technologies depend on fiber-optic and copper-wires technologies to deliver high-speed internet access to the end customers.Fiber-optic lines offer much higher data rates than copper wires that is becoming more common for bringing broadband Internet access to homes.However,with the latest DSL Technologies,internet access can offer up to 100Mbps within a specific distance.In Malaysia,the network dependencies on the fixed access technologies to Fiber to The Home(FTTH)and DSL is about 40%and 60%,respectively.The fiberisation national plan has increased to 70%,and that allows high-speed internet access using copper wires within less than 1.5 km distance to the fiber-optic backhaul,especially in the suburban area.The offered data speed using copper wires is also subjected to the cable conditions along the access network, and cable fault due to the impedance mismatch becomes the most concern.Therefore, the copper cable fault diagnosis,network monitoring,and qualification on the DSL access network systems are necessary to increase and maintain the network performance.

    The popular copper line fault detection is based on Time Domain Reflectometry (TDR).The commercial test gear is normally attached to the copper line for line fault detection.TDR is a wellknown method to measure the characteristics of the electrical lines,impedance of discontinuities as a function of time or distance,then reflection signal can be translated to determine the faults caused by a splice,cable transition,and mismatched cable connections[1].This method is reliable to detect the fault type and localization;however,test gears are needed with manual intervention to the field site.In [2], a TDR simulator is proposed to assist loop diagnostics on copper twisted pairs based on the transmission line ABCD parameter modelling, which also needs on-site simulation and verification during DSL network maintenance and troubleshooting.

    The loop diagnostics based on the scattering parameters at microwave frequencies allows the backscattering model for twisted pairs is established in[3],where local deterioration of transmission parameters, faults can be precisely localized and instantly addressed by comparing with a reference measurement.The copper cables transmission line characteristics based on the resistance,inductance,conductance, and capacitance per unit length (RLGC) circuit [4] are exploited to investigate the impact of cable bleeding of twisted pair towards the achievable bit rate.In[1],the copper line quality assessment process is proposed to assist the technical operation team and network access planner before deployment of the high-speed broadband services to the subscribers at High Speed Broadband(HSBB),Suburban Broadband(SUBB)or Rural Broadband(RBB)areas.

    Recently,the technologically advanced techniques exploiting fault diagnosis approach based on machine learning.Feature extraction and clustering analysis become the framework of fault detection,classification,and localization,which are mapped based on the electrical signals along the cables[5].In the case where the data clustering is capable of distinguishing ideal and faulty conditions,there is no need to implement an additional fault detection method.The data clustering can be performed based on unsupervised or supervised machine learning, where the algorithm interprets the input of data and discovering the group of data from the featured data.Automated analysis techniques [5,6]are demonstrated to detect,locate,and diagnose the fault using Support Vector Machine[7],Neural Network[8],and Random Forest[9].

    In[10],an intelligent fault diagnosis machine learning method using random forests algorithms for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods is developed.The slopes of two current trajectories of three-phase AC currents are adopted to train the fault diagnosis classifier based on a data-driven method (the random forest algorithm),which has the adaptive ability to different loads.In[9],a new Convolutional Neural Network(CNN)using the Random Forest (RF) classifier is proposed for hydrogen sensor fault diagnosis.The datadriven is established where the input 1-D time-domain data of fault signals are converted into 2-D gray matrix images,and later the features extraction by using CNN are processed into the random forest classifier.Analytical-driven based on the time-frequency domain reflectometry signals is proposed[11]to detect the presence and the location of a fault and further differentiates the faulty line within the multi-core control and Instrumentation(C&I)cables.Neural networks and the hierarchical clustering algorithm are used, and the algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&I cables for nuclear power plants.

    In the sequence to the fault localization, the conventional methods can be categorised into impedance focused methods and travelling wave based methods.Depending on the source of data,cable fault location methods may be further divided into single-ended technique and double-ended technique [5].In [12], the performance of different regression methods, which are linear regression and regression trees,are developed on a hybrid power system.Different faults with random distances on the transmission line were simulated and a fault database was created by recording the current and voltage signals of these faults.The same applications are deployed in intelligent fault location[13]based on the integration of Artificial Neural Network(ANN)and fuzzy expert system called Adaptive Network-Based Fuzzy Inference System (ANFIS).The fault location error is very minimum, which are ANFIS network(0.0012),followed by Gaussian process regression(0.0684)and linear regression(0.4092).

    From the literature studies that have been discussed earlier, the fault diagnosis tools on many applications have been established by using machine learning algorithms.However,the copper cable faults detection still relies on the manual detection by using on-site commercial test gear and to date, there is no established fault diagnosis that focuses on the DSL Access Network that has been discussed.The motivation of the work also considers that most of the telecommunication providers still troubleshoot the present of the cable fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm and catered by the proposed diagnostic tools presented in here.In this paper, data-driven methods assisted with knowledge-driven methods are adopted to interpret the network parameters, which are gathered based on a single line testing in twisted pair cable for fault diagnosis.The DSL access network system is collected and used to analyze for initial observation.Later, based on the knowledge-based method, the featured data are extracted,which consists of line operation parameters(LOP)and loop line testing parameters(LLT).The processing data are trained into the random forest algorithms to determine the copper cable fault type and localization.The accuracy of the algorithm is analyzed, and the decision criteria based on the LOP and LLT are evaluated.

    2 General Background on VDSL 2 Technologies

    A typical VDSL2 network infrastructure consists of central office (CO), Multi-Service Access Network (MSAN), cabinet, distribution point (DP), and customer premises [14], as illustrated in Fig.1.In the VDSL2 copper access networks, fiber cables become the backbone infrastructure that connects the CO and MSAN.The copper cables are laid between MSAN and the customer premises through the DP.The VDSL2 network cards and Plain Old Telephone Service Line(POTS)cards are integrated at MSAN, and this is the important key factor that allows both telephone and internet services to be offered using the same copper cables infrastructure[15].This network topology is usually established at the suburban site where the capital expenditure of the telco needs to reflect the customer’s populations in that particular area.

    Figure 1:Experimental platform of DSL Access Network System

    Very-High-Speed Digital Subscriber Line 2 (VDSL2) is a family of DSL Technologies that allow fast internet connection via legacy copper lines.VDSL 2 co-exist from VDSL and falls under International Telecommunication Union(ITU-T)G.993.2[16].There are several types of parameter profiles in VSDL2 technologies.In this paper,the VDSL2 Profile 17a was exploited,using modulation method of Discrete Multi-Tone(DMT),where data is carried in the 4095 tone spacing of 4.3125 kHz within a spectrum bandwidth of 17.664 MHz.The modulation technique allows a separation between the upstream and downstream data signals.This VDSL2 network configuration can support up to 100 Mbps data rates for distance up to 1.5 km [17].However, the possible offered data rates is also affected by the nature of the copper cables itself where the attenuation degrades rapidly with distances and the presence of external electromagnetic interferences which leads to crosstalk.The most common cable faults caused by the imbalance impedance normally occur at the cable jointing and improper termination line at the customer premises.In the field site,these cable faults can be identified through manual inspection by using commercial test gears[18].

    The cables faults that may occur in the copper loop will modify LLT parameters which consist of primary elements such are resistance and capacitance,and secondary parameters such as current and voltage[19,20].In any case,where the cable faults are not severe,the internet service is still accessible by the subscriber, perhaps at lower data speed.In this case, the VDSL2 network conditions can be assessed from valid LOP parameters,which are the speed rate,attenuation,and signal to noise ratio(SNR)[21].Note that the LOP and LLT will be used as the parameter attributes for the copper fault detection and localization.To date,there are not many papers emphasize the usage of machine learning for cable fault classification in the VDSL2 copper access network.

    3 DSL Experimental Platform and Data Acquisition

    The experimental platform DSL access network system shown in Fig.2 consists of a traffic generator,MSAN,copper cable binder,and modems.The experimental platform is set up to imitate the actual deployment for the DSL access network system in the field site.Note that the cable binder contains ten unshielded twisted pairs(UTP Cat-3)which are connected to the 10 modems to represent the network termination at the customer premises.The cable faults are realized along the cable at the tag block or cable jointing.The raw data are gathered from MSAN using the command prompt Telnet script.These raw data are in text file (.txt format) and later processed into comma separated values file(.csv format)for data preparation.

    Figure 2:Experimental platform of DSL Access Network System

    3.1 Twisted Pair RC Lumped Elements

    The twisted pair can be modelled as the RLGC transmission line model.However,the transmission line model can be simplified as RC lumped components,where other available components are neglected in the electrical schematic due to insignificant changes when different types of cable faults are emulated.This is carried out when the initial data observation was conducted.The twisted pair consists of Tip(A)and Ring(A),and the resistive and capacitance components(RC)[22]as illustrated in Fig.3.Each A and B is connected to the Ground (G), which are represented by component Resistances,RA-B,RA-G,and RB-G;and Capacitances,CA-B,CA-G,and CB-G.Each A and B component has the same component of RC for ideal cable condition but may change when the cable fault occurs along the line[23].In MSAN, these components are categorized under LLT parameters.In the typical case where the internet data is accessible by the modem,the network performances are also provided by MSAN under LOP parameters.

    Figure 3: Twisted pair RC, Tip (A), Ring (B), and Ground (G) component based on RC lumped component elements

    3.2 Cable Fault Realization

    Cable fault imitation in the DSL Access Network can be imitated by understanding the scenario of the physical connection.In normal cases,the networks are in ideal condition without any cable fault occurrence where the access to the internet is at the optimum condition.Tab.1 describes the scenario of the five copper cable faults that occur in the typical DSL Access Network.The cable faults are emulated at the cable jointing, which are conducted on the tag block.The standard practice in the telecommunication industry is to use commercial test gears to troubleshoot the present of the cable fault and its localization.For on-site installation and repair, the EXFO test gear is commonly used by the telco engineer to observe the DSL network performance, therefore here, the same test gear was used to verify the cable fault emulations conducted in the laboratory.This DSL copper cable test gear features the traditional copper measurements,which involve voltage,resistance,capacitance,and time domain reflectometry and also correlated with featured parameters given by MSAN.This testing is very important to make sure that the impairment emulations have the same characteristics as the actual cable fault existing in the field site.

    Table 1: Data gathering for various cable conditions

    During the fault emulation,the group of data are labelled based on the scenario.Each of the cable faults would either degrades the internet speed,but the line condition is still in the‘Activated’mode or disrupts the network,causing the line condition in an‘Activating’mode.However,Partial open fault has triggered the network condition in both activated and activating mode,and this depends on how severe the fault is,especially in a longer cable length.In Activated mode,both LOP and LLT can be used as the dataset for the machine learning algorithm;however,in the activating mode,the internet access is disrupted,and only LLT is valid.

    4 Realization of Fault Diagnosis Using Machine Learning

    The overall flow chart of the fault diagnosis using machine learning algorithm is illustrated in Fig.4.The algorithm consists of importing a dataset which is based on the featured parameters,data pre-processing,training the build algorithm,testing on the developed algorithm,and analysis of fault detection accuracy.

    Figure 4:Flow chart of RF algorithm

    4.1 Selection of Parameters and Pre-Processing

    There are many primary parameters which is categorized as LLT parameters and LOP parameters,that are captured in MSAN during network operation.However,only the most significant parameters are selected for the cable fault detection by using feature important selection.Due to abbreviate the details of the important feature selection will not be discussed here.Out of all LLT and LOP parameters,only six LOP and LLT parameters are selected for cable fault detection and localization as tabulated in Tab.2.

    Table 2: LOP and LLT parameters

    These parameters go through the data-preprocessing stage, which transforms the data into a suitable format for machine learning algorithms.The collected data are prepared, the categorical parameters and any invalid or missing data that may affect the accuracy are handled.The targeted parameters, which are the labelled fault type and localization, are determined.Depending on the presence of cable fault in the network,the line can be either in the activated or activating as explained in Section 3.2.For activating case, the LOP captured by MSAN is only historical data and is no longer valid to represent the current line condition.In some other cases as well, such as when cable fault is present such as short and partial short,the values of capacitance either A-G or B-G are not available.The same trend is applicable to the values of resistance A-G or B-G for open and partial open conditions.These parameters are replaced with a specific value beyond the actual values.The values of the parameters are also standardized within the range of features of the input data set.

    4.2 Machine Learning Algorithm

    Initially,there are several supervised machine learning classification algorithms that were explored using WEKA,which are Artificial Neural Network,Na?ve Bays,Random Forest(RF),and k-Nearest Neighbor and Decision tree.WEKA is an open source software that was developed for data mining tasks using Java languages [24].It contains tools for data preparation, classification, regression,clustering,association rules mining,and visualization which are very useful for initial machine learning exploration in many applications.At first,MSAN data were exported into WEKA software and the outcome showed that RF algorithm provided the highest accuracy which is about 80%.Therefore,RF algorithm is finalized for further development due to its better accuracy in classifying fault based on the electrical characteristics of the MSAN dataset.There are two algorithms developed for fault diagnosis.Firstly, the RF classifier is used to detect the fault type, secondly is the RF regression method,which is used to localize the location of the fault.

    The RF algorithm is an ensemble learning method based on a decision tree.The split train test method of 70:30 is utilized.In a dataset,a training set is implemented to build up the algorithm,while a testing data set is to validate the model built.Out of 2738 datasets,each tree randomly selects 70%of training samples as a sub-training set.There are six features to be selected for an optimal feature from each split.Therefore, each tree can obtain training results according to different sub-training sets.Each input sample is determined separately,and the final classification is determined according to the voting results.Then the deployed RF model is reliable to classify in large datasets.In the case of fault localization, the averaging of the results of each decision tree is made.The accuracy of the model to detect impairment and localization are determined,and for the case,if the accuracy is less than 90%,the algorithm will be re-trained.The RF model is shown in Fig.5.

    Figure 5:Flow chart of RF algorithm

    5 Data Analysis

    The proposed cable fault diagnosis is conducted based on the knowledge-driven and data-driven.The knowledge-driven of the LLT and LOP parameters trending is carried out by observing the collected data.Then the accuracy fault detection and localization algorithm is carried out based on the data-driven.

    5.1 Data Observation

    Even though the fault detection location and localization algorithm are developed based on machine learning,the understanding of the data behavior is very important as it reflects the electrical characteristics of the lines.Fig.6 shows samples of the features selection over the distance.These parameters are shown as it obviously illustrating the changes of LLT and LOP when the faults are emulated.Figs.6a and 6b shows the maximum attainable inversely proportional with cable distance while the signal attenuation increases proportionally with cable distance.Both parameters show a linear correlation with distance despite outliers in the few cases,especially when the cable distance is less than 300 m.The maximum attainable rate of the line reaches more than 100 Mbps at 100 m distance and reduces to 30Mbps at 1000 m.This linear correlation can be represented by y=-47.21 x+87300.In average,the attainable rate for BT condition is slightly 10%to 20%lower than the ideal condition.While for signal attenuation,the average signal attenuation is 5 dB at 100 m and gradually increases to 20 dB at 1000 m.The correlation also can be represented by y=0.0143x+5.6942.Note that both attainable rate and signal attenuation for ideal and BT cases are overlapping and need to depend on other features to differentiate.Fig.6c illustrates the RA-Gfor Ideal case, BT case, Partial Short and Short case,and the parameter is not available for Partial Open and Open case.This is due to short and Partial Short conditions introduce an extremely low impedance at the cable jointing hence reflects the overall RA-G.This significant data trend is expected and used by the machine learning during dataset training.The similar trend is observed for CA-Bwhere the data is not available for cable fault short and partial short as shown in Fig.6d.Open and partial open conditions also produce an average 20%lower CA-Bfrom Ideal and BT conditions.Fig.6e is highlighted to show the trend of the CA-G.Note that the linear correlation,which is represented by y=0.0595x–6.777 for all cable conditions may be used to determine the fault localization in the regression techniques.Note that there are six LOP and LLT parameters are involved in the development of machine learning however, only five parameter selections are shown here due to distinct noticeable data pattern that can be observed to represent each fault type.

    5.2 Performance of Cable Fault Detection Algorithm

    Random forest algorithm is implemented for cable fault classification.The classification models are conducted using the Sklearn pickle model to load the trained dataset and later detect the new testing data.Tab.3 shows the confusion matrix of the cable fault detection with Random Forest Classifier.The confusion matrix illustrates the classification results of all cable conditions in detail,including both classification and misclassification fault types.The top row of the confusion matrix represents the actual label of classification, whereas the first column represents the detected label,and the diagonal value of the confusion matrix up to the seventh column represents the matching or mismatching sample number between the actual and the detected condition.

    Figure 6:Selection of LLT and LOP parameters trend with cable faults;Ideal,Bridge Tap,ShortPartial Short,Openand Partial Open

    Tab.3 shows the classification performance of the VDSL dataset that is done using the train-test split method function in python programming.The VDSL dataset samples are also split into 67%of the training dataset and 33% of the testing dataset.The classification of VDSL fault type gives out 97% using 2738 testing samples of VDSL data which each fault location sample are separated into distances up to 1200 m.The classifications of short and partial short fault types seem to result in higher accuracies; this is due to their quite distinct differences in parameter values such as RA-Gas shown in Fig.6c compared to bridge tap and ideal VDSL condition.The lowest precision is 91%and 93%,which occurs for open conditions and partial open.Despite this,the average accuracy of the proposed method,which is 97%,indicates that the proposed method is capable of cable fault diagnosis.

    5.3 Performance of Cable Fault Localization Algorithm

    The localization of the cable fault that may occur in the DSL Access Network is conducted in a separate machine learning algorithm which is the Random Forest regression algorithm.The regression model is conducted using the Sklearn pickle model to load the trained dataset and later detect the new testing data,which is similar to the detection algorithm.Note that for ideal condition,the length of the distance represents the actual of cable distance between MSAN and cable termination before the modem.While for other fault conditions it will represent the fault location distance from the MSAN.The accuracy of the localization is assessed from the scale-dependent metrics, which are Minimum Absolute Error(MAE),Minimum Absoluter Percentage Error(MAPE),and Root Mean Square Error(RMSE).MAE is a metric used to measure the average magnitude of the absolute errors between the predicted fault location and actual location.While for MAPE, the absolute errors are normalized to the actual location to give a percentage error.RMSE measures the average magnitude of error between the predicted fault location and actual location.Thus,RMSE is the average distance measured vertically from the actual value to the corresponding predicted value on the fit line.In comparison with MAE,the RMSE has a relatively high weight for large errors because the errors are squared before averaging,as shown in Tab.4.

    Table 4: Accuracy of cable fault localization using random forest regression

    Tab.4 also shows the average deviation of location based on MAPE for all cable faults is less than 6.6%.However,for Bridge Tap and Open faults,the accuracy of the cable fault localization is about 11%.The accuracies are measured based on the magnitude of error but do not indicate the direction of error.

    5.4 Comparison with Other Diagnostic Tool on DSL Access Network

    The coppers wires are vulnerable,prone to signal leakage and interference issues,the offered data speed using coppers wires is also subjected to the cable conditions along the access network and cable fault becomes the most concern.Therefore, the copper cable fault diagnosis, network monitoring,and qualification on the DSL access network systems are necessary.To evaluate the abilities of the proposed fault diagnosis method,the proposed method was compared with other diagnosis methods on DSL Access Network as shown in Tab.5.The diagnostic tools that are based on TDR which also called Single Ended Loop Testing(SELT)provides high accuracy in the fault location identification,however it is a destructive testing where a disconnection to the existing network is required during the assessment.The frequency domain concept generally incurs Double Ended Loop Testing(DELT)where the cable tapping is needed on the starting point and the ending point of the network.Obviously,this is not practical for on-site testing.All above stated concepts also require prior domain knowledge during the assessment.The most significant difference between the previous methods and the proposed method are the utlisation on the machine learning method that (1) it can diagnose both offline and online fault cables;(2)it is nonintrusive and nondestructive method;and(3)it needs no prior domain knowledge.

    Table 5: Comparison with other fault diagnosis methods

    Table 5:Continued

    6 Conclusion

    In this paper, the copper cable faults classification and localization with Random Forest were carried out using Python programming.The results showed that the proposed machine learning algorithm able to provide accuracies up to 97% in the fault classification and average 6% difference in fault localization.Based on the existing literature, the accuracy is considered excellent for fault diagnosis.The proposed solution allows the telecommunication service’s provider to troubleshoot the present of the cable fault online instead of relying on the commercial test gear on-site.In the future,the cable fault diagnosis may be extended by considering the possibilities of having multiple faults in the same cable.

    Acknowledgement:The authors wish to acknowledge the funding received from Ministry of Science,Technology and Innovation Malaysia, and Research Management Centre (RMC) of Universiti Tun Hussein Onn Malaysia (UTHM), the research collaborator of the grant which is Telekom Malaysia Research and Development (R&D) Sdn Bhd.Communication of this research is made possible through monetary assistance by Universiti Tun Hussein Onn Malaysia and the UTHM Publisher’s Office via Publication Fund E15216.

    Funding Statement:The authors received the funding from Smart Challenge Fund (SR0218I100)and GPPS Grant VOT H404, from Ministry of Science, Technology and Innovation Malaysia, and Research Management Centre(RMC)of Universiti Tun Hussein Onn Malaysia(UTHM)

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    波多野结衣av一区二区av| 成人三级黄色视频| av中文乱码字幕在线| 欧美激情 高清一区二区三区| 欧美黑人欧美精品刺激| 丝袜美腿诱惑在线| 欧美日韩亚洲综合一区二区三区_| 美女高潮喷水抽搐中文字幕| 99国产精品一区二区蜜桃av| 国产极品粉嫩免费观看在线| 亚洲avbb在线观看| 三级毛片av免费| 亚洲第一欧美日韩一区二区三区| 久久国产精品人妻蜜桃| 超碰成人久久| 老司机亚洲免费影院| 天堂俺去俺来也www色官网| 欧美日韩黄片免| 久久久久久免费高清国产稀缺| 国产av在哪里看| 欧美激情极品国产一区二区三区| 久久久久国内视频| 亚洲欧美精品综合久久99| 精品久久久久久久久久免费视频 | 久久人人97超碰香蕉20202| 国产成人系列免费观看| 亚洲中文av在线| 亚洲欧美一区二区三区黑人| 精品无人区乱码1区二区| svipshipincom国产片| 欧美久久黑人一区二区| 久久精品亚洲熟妇少妇任你| 午夜福利欧美成人| 91成年电影在线观看| 国产成人精品久久二区二区91| 国产精品成人在线| 国产亚洲欧美98| 久久影院123| 日韩欧美在线二视频| 人妻久久中文字幕网| 亚洲精品久久午夜乱码| 日韩国内少妇激情av| 最好的美女福利视频网| 欧美日韩中文字幕国产精品一区二区三区 | 欧美成人午夜精品| 国产成人系列免费观看| 黄色视频不卡| 欧美人与性动交α欧美精品济南到| 久久草成人影院| 我的亚洲天堂| 亚洲五月色婷婷综合| 免费看a级黄色片| 99精品欧美一区二区三区四区| 国产欧美日韩一区二区三| 久久精品国产清高在天天线| 亚洲人成77777在线视频| 日韩高清综合在线| aaaaa片日本免费| 美女扒开内裤让男人捅视频| 国产精品乱码一区二三区的特点 | 久久久国产欧美日韩av| 欧美日本中文国产一区发布| 桃红色精品国产亚洲av| av片东京热男人的天堂| av国产精品久久久久影院| 美女国产高潮福利片在线看| 久久国产精品男人的天堂亚洲| 亚洲熟妇中文字幕五十中出 | 少妇 在线观看| 伊人久久大香线蕉亚洲五| 国产精品国产av在线观看| 极品教师在线免费播放| 色婷婷av一区二区三区视频| 亚洲精品av麻豆狂野| 免费在线观看视频国产中文字幕亚洲| 欧美精品亚洲一区二区| 搡老岳熟女国产| 久久精品人人爽人人爽视色| 大香蕉久久成人网| 精品久久蜜臀av无| 男男h啪啪无遮挡| 又黄又爽又免费观看的视频| 亚洲全国av大片| 中文字幕人妻丝袜一区二区| 久久久久国内视频| 久久午夜综合久久蜜桃| 变态另类成人亚洲欧美熟女 | 欧美一区二区精品小视频在线| 日韩欧美三级三区| 国产色视频综合| 欧美一级毛片孕妇| 黑人操中国人逼视频| 国产无遮挡羞羞视频在线观看| avwww免费| 欧美日韩亚洲综合一区二区三区_| 少妇裸体淫交视频免费看高清 | 亚洲avbb在线观看| 日韩精品中文字幕看吧| 搡老岳熟女国产| 天天添夜夜摸| 日韩一卡2卡3卡4卡2021年| 精品一区二区三区av网在线观看| 老司机在亚洲福利影院| 精品人妻在线不人妻| 99国产综合亚洲精品| 99精品久久久久人妻精品| 成人影院久久| 岛国在线观看网站| av在线播放免费不卡| 日韩视频一区二区在线观看| 99在线视频只有这里精品首页| 欧美最黄视频在线播放免费 | 搡老岳熟女国产| 欧美日韩中文字幕国产精品一区二区三区 | 精品国产超薄肉色丝袜足j| 丁香欧美五月| 1024香蕉在线观看| 青草久久国产| 午夜免费鲁丝| 中文亚洲av片在线观看爽| 热re99久久精品国产66热6| 亚洲第一青青草原| 久久精品亚洲av国产电影网| 黄色丝袜av网址大全| 精品第一国产精品| 两性午夜刺激爽爽歪歪视频在线观看 | www国产在线视频色| av国产精品久久久久影院| 妹子高潮喷水视频| 日本黄色日本黄色录像| 99精品久久久久人妻精品| av网站在线播放免费| 两性午夜刺激爽爽歪歪视频在线观看 | 91九色精品人成在线观看| 老汉色∧v一级毛片| 国产91精品成人一区二区三区| 免费搜索国产男女视频| 国产单亲对白刺激| 又黄又爽又免费观看的视频| 国产精品香港三级国产av潘金莲| 老鸭窝网址在线观看| 视频区图区小说| 日日爽夜夜爽网站| 亚洲专区国产一区二区| 国产主播在线观看一区二区| 久久精品成人免费网站| 成熟少妇高潮喷水视频| 午夜两性在线视频| 午夜福利一区二区在线看| 在线看a的网站| 欧美黄色淫秽网站| 9热在线视频观看99| 国产欧美日韩一区二区三| 又大又爽又粗| 我的亚洲天堂| 高清在线国产一区| 伊人久久大香线蕉亚洲五| 亚洲伊人色综图| 91成年电影在线观看| 国产av又大| 18禁黄网站禁片午夜丰满| 成年女人毛片免费观看观看9| 淫妇啪啪啪对白视频| 欧美精品亚洲一区二区| 18禁国产床啪视频网站| 亚洲成a人片在线一区二区| 一区二区三区国产精品乱码| 涩涩av久久男人的天堂| 亚洲七黄色美女视频| 国产精品亚洲av一区麻豆| 女人爽到高潮嗷嗷叫在线视频| 乱人伦中国视频| 精品一品国产午夜福利视频| 免费看十八禁软件| 欧美一级毛片孕妇| 在线观看www视频免费| 免费在线观看完整版高清| 久久精品91无色码中文字幕| 久久久久久久久免费视频了| 日韩精品青青久久久久久| 88av欧美| 一进一出抽搐动态| 好看av亚洲va欧美ⅴa在| 日韩精品青青久久久久久| 国产一区二区三区在线臀色熟女 | 亚洲精品久久午夜乱码| 免费看a级黄色片| 免费女性裸体啪啪无遮挡网站| 成人国语在线视频| 亚洲五月色婷婷综合| 午夜免费观看网址| www.www免费av| www.熟女人妻精品国产| 在线观看www视频免费| 亚洲精品一二三| 视频在线观看一区二区三区| 伦理电影免费视频| 热re99久久精品国产66热6| 午夜福利,免费看| 亚洲五月天丁香| 国产单亲对白刺激| 久久精品国产综合久久久| 97碰自拍视频| a级片在线免费高清观看视频| 国产亚洲欧美在线一区二区| 色综合站精品国产| 女警被强在线播放| 美女福利国产在线| 丝袜在线中文字幕| 久久久久久久午夜电影 | 我的亚洲天堂| 欧美精品亚洲一区二区| 一区福利在线观看| 母亲3免费完整高清在线观看| 高清av免费在线| 一边摸一边抽搐一进一出视频| 日本免费一区二区三区高清不卡 | 国产精品国产高清国产av| 欧美日本亚洲视频在线播放| 男女之事视频高清在线观看| 欧美中文综合在线视频| 在线观看www视频免费| 亚洲熟妇熟女久久| e午夜精品久久久久久久| 亚洲专区字幕在线| 亚洲精品久久成人aⅴ小说| 国产精品 国内视频| 在线播放国产精品三级| 国产乱人伦免费视频| 精品国产美女av久久久久小说| 99riav亚洲国产免费| 高潮久久久久久久久久久不卡| 精品一区二区三区视频在线观看免费 | 国产又色又爽无遮挡免费看| 视频在线观看一区二区三区| 久久热在线av| 天天躁夜夜躁狠狠躁躁| 亚洲成人国产一区在线观看| 99香蕉大伊视频| 看黄色毛片网站| 久久国产精品影院| 午夜福利,免费看| 一区在线观看完整版| 丁香六月欧美| 热re99久久精品国产66热6| 五月开心婷婷网| 99久久国产精品久久久| 国产精品久久久久成人av| 男人舔女人下体高潮全视频| 免费高清在线观看日韩| 久久人妻av系列| 日韩国内少妇激情av| 无限看片的www在线观看| 一区福利在线观看| 中文字幕人妻熟女乱码| 久久久国产一区二区| 欧美日韩精品网址| 午夜影院日韩av| 精品久久久久久久毛片微露脸| 桃色一区二区三区在线观看| 亚洲午夜精品一区,二区,三区| 丁香欧美五月| 高清毛片免费观看视频网站 | 欧美久久黑人一区二区| 亚洲 国产 在线| 国产在线观看jvid| 18禁裸乳无遮挡免费网站照片 | 久热爱精品视频在线9| 国产91精品成人一区二区三区| 日韩有码中文字幕| 两性午夜刺激爽爽歪歪视频在线观看 | 人妻久久中文字幕网| 日韩精品青青久久久久久| 一区二区三区精品91| 不卡av一区二区三区| 国产午夜精品久久久久久| 久久伊人香网站| av天堂在线播放| 欧美精品一区二区免费开放| 麻豆成人av在线观看| 久久人妻福利社区极品人妻图片| 久久久国产成人精品二区 | 色综合欧美亚洲国产小说| 日韩免费av在线播放| 午夜免费激情av| 老熟妇乱子伦视频在线观看| 国产欧美日韩一区二区三| 免费在线观看黄色视频的| 精品国内亚洲2022精品成人| 亚洲精品国产区一区二| 精品免费久久久久久久清纯| 成人18禁高潮啪啪吃奶动态图| 亚洲精品成人av观看孕妇| 日本黄色日本黄色录像| 精品午夜福利视频在线观看一区| 亚洲三区欧美一区| 中文字幕人妻丝袜一区二区| 国产成人av教育| 男女高潮啪啪啪动态图| 999久久久国产精品视频| 在线看a的网站| 亚洲aⅴ乱码一区二区在线播放 | 操出白浆在线播放| 午夜91福利影院| 亚洲精品在线美女| 日韩欧美免费精品| 国产成人啪精品午夜网站| 亚洲国产欧美日韩在线播放| 国产精品av久久久久免费| 亚洲国产中文字幕在线视频| 欧美日韩亚洲高清精品| 免费在线观看完整版高清| www国产在线视频色| 中文亚洲av片在线观看爽| 国产精品爽爽va在线观看网站 | 午夜91福利影院| 在线观看一区二区三区| 国产av又大| 亚洲专区字幕在线| 亚洲男人天堂网一区| 亚洲性夜色夜夜综合| 91麻豆av在线| 久久伊人香网站| 无限看片的www在线观看| 亚洲国产毛片av蜜桃av| 最近最新中文字幕大全免费视频| 夫妻午夜视频| 国产视频一区二区在线看| 国产午夜精品久久久久久| 人妻久久中文字幕网| 99久久久亚洲精品蜜臀av| 欧美激情高清一区二区三区| 91老司机精品| 成人av一区二区三区在线看| 一级片免费观看大全| 亚洲色图综合在线观看| 在线视频色国产色| 成人手机av| 成人永久免费在线观看视频| 日韩成人在线观看一区二区三区| 欧美色视频一区免费| 欧美 亚洲 国产 日韩一| 99热只有精品国产| 在线av久久热| 国产成人欧美| 90打野战视频偷拍视频| 十分钟在线观看高清视频www| 激情在线观看视频在线高清| 国产精品免费一区二区三区在线| 久久久精品国产亚洲av高清涩受| 好看av亚洲va欧美ⅴa在| 婷婷六月久久综合丁香| 亚洲人成电影观看| 怎么达到女性高潮| 亚洲全国av大片| 精品免费久久久久久久清纯| 成人18禁在线播放| 国产精品综合久久久久久久免费 | 91成人精品电影| 99精品欧美一区二区三区四区| 国产极品粉嫩免费观看在线| 亚洲在线自拍视频| 国产av一区二区精品久久| 久99久视频精品免费| 中文字幕精品免费在线观看视频| 精品久久久久久久毛片微露脸| 国产精品电影一区二区三区| 1024香蕉在线观看| 欧美中文日本在线观看视频| 国产熟女xx| 99在线人妻在线中文字幕| 久久伊人香网站| 91成人精品电影| 69精品国产乱码久久久| 久久久久国产精品人妻aⅴ院| 91麻豆精品激情在线观看国产 | 一本大道久久a久久精品| 他把我摸到了高潮在线观看| 成熟少妇高潮喷水视频| 久久久国产欧美日韩av| 真人一进一出gif抽搐免费| 欧美不卡视频在线免费观看 | 国产精华一区二区三区| 757午夜福利合集在线观看| a级片在线免费高清观看视频| 99香蕉大伊视频| 亚洲欧美日韩另类电影网站| 欧美老熟妇乱子伦牲交| 最新美女视频免费是黄的| 欧美在线黄色| 亚洲欧美激情综合另类| 久久精品91蜜桃| 长腿黑丝高跟| 久久久久久久久免费视频了| 免费av毛片视频| 大码成人一级视频| 国产男靠女视频免费网站| 欧美日韩一级在线毛片| 精品少妇一区二区三区视频日本电影| 91成年电影在线观看| 亚洲精品国产色婷婷电影| 女人高潮潮喷娇喘18禁视频| 亚洲色图 男人天堂 中文字幕| 欧美日韩瑟瑟在线播放| 丝袜美腿诱惑在线| 夜夜躁狠狠躁天天躁| 日本欧美视频一区| 在线观看免费午夜福利视频| 久热爱精品视频在线9| 国产有黄有色有爽视频| 成人精品一区二区免费| 一进一出好大好爽视频| 大码成人一级视频| 91成人精品电影| 别揉我奶头~嗯~啊~动态视频| 一边摸一边做爽爽视频免费| 欧美成人性av电影在线观看| 国产熟女xx| 日韩三级视频一区二区三区| 伦理电影免费视频| 欧美日韩亚洲高清精品| 99精国产麻豆久久婷婷| 男男h啪啪无遮挡| 亚洲精品美女久久久久99蜜臀| 亚洲欧洲精品一区二区精品久久久| 午夜影院日韩av| 欧美人与性动交α欧美精品济南到| 亚洲人成网站在线播放欧美日韩| 久久人妻熟女aⅴ| 亚洲中文日韩欧美视频| 精品国产一区二区三区四区第35| 国产区一区二久久| 久久久久久人人人人人| 亚洲中文av在线| 久久人人爽av亚洲精品天堂| a级片在线免费高清观看视频| av片东京热男人的天堂| 国产精品综合久久久久久久免费 | 女人高潮潮喷娇喘18禁视频| 色综合站精品国产| 极品人妻少妇av视频| 久久久国产欧美日韩av| 法律面前人人平等表现在哪些方面| 黄色女人牲交| 高潮久久久久久久久久久不卡| videosex国产| 欧美激情久久久久久爽电影 | 成人国语在线视频| 日韩欧美一区二区三区在线观看| 免费观看精品视频网站| 香蕉丝袜av| 国产精品乱码一区二三区的特点 | 国产aⅴ精品一区二区三区波| 国产精品二区激情视频| 一区二区三区国产精品乱码| 满18在线观看网站| 久久香蕉精品热| 亚洲人成电影免费在线| 国产成人系列免费观看| 91大片在线观看| av有码第一页| 国产精品乱码一区二三区的特点 | 9色porny在线观看| 亚洲人成77777在线视频| 欧美日韩瑟瑟在线播放| 国产精华一区二区三区| av天堂在线播放| 亚洲精品国产一区二区精华液| 欧美黄色片欧美黄色片| 看黄色毛片网站| 国产成人精品久久二区二区91| 亚洲人成网站在线播放欧美日韩| 精品国产一区二区久久| 美女午夜性视频免费| 久久影院123| 色综合站精品国产| 手机成人av网站| 久久午夜亚洲精品久久| 午夜影院日韩av| svipshipincom国产片| 国产欧美日韩精品亚洲av| 女人爽到高潮嗷嗷叫在线视频| 电影成人av| 少妇粗大呻吟视频| 亚洲第一欧美日韩一区二区三区| 免费在线观看完整版高清| 国产成人精品久久二区二区免费| av网站免费在线观看视频| 老汉色∧v一级毛片| 国产亚洲欧美98| 午夜福利一区二区在线看| 窝窝影院91人妻| 男女之事视频高清在线观看| 久久精品91蜜桃| 99久久精品国产亚洲精品| 久久精品国产亚洲av香蕉五月| 国产99久久九九免费精品| 免费女性裸体啪啪无遮挡网站| 亚洲人成网站在线播放欧美日韩| 很黄的视频免费| 一a级毛片在线观看| 人人妻人人添人人爽欧美一区卜| 成在线人永久免费视频| 在线观看免费视频日本深夜| 亚洲一卡2卡3卡4卡5卡精品中文| 日本欧美视频一区| 免费观看精品视频网站| 久久精品国产亚洲av高清一级| 精品国产亚洲在线| 首页视频小说图片口味搜索| 精品国产美女av久久久久小说| 99国产精品99久久久久| 老鸭窝网址在线观看| 日本免费一区二区三区高清不卡 | 国产乱人伦免费视频| 别揉我奶头~嗯~啊~动态视频| 搡老岳熟女国产| 我的亚洲天堂| 18禁美女被吸乳视频| 妹子高潮喷水视频| 久久草成人影院| 久久中文看片网| 国产熟女xx| 十八禁人妻一区二区| 97碰自拍视频| 亚洲精品国产色婷婷电影| 国产成人精品在线电影| 亚洲欧美日韩另类电影网站| 日韩一卡2卡3卡4卡2021年| 丰满的人妻完整版| 国产成人精品久久二区二区免费| 1024视频免费在线观看| www.自偷自拍.com| 多毛熟女@视频| 久久久久久大精品| 麻豆成人av在线观看| 很黄的视频免费| 午夜免费观看网址| av电影中文网址| 十八禁人妻一区二区| 国产三级在线视频| 欧美大码av| 大陆偷拍与自拍| 夜夜看夜夜爽夜夜摸 | 欧美日韩亚洲高清精品| 亚洲avbb在线观看| 久久久国产精品麻豆| 在线观看66精品国产| 热99re8久久精品国产| 国产主播在线观看一区二区| 一级毛片精品| cao死你这个sao货| 97碰自拍视频| 制服人妻中文乱码| 黄频高清免费视频| 欧美日韩亚洲高清精品| 日本三级黄在线观看| 国产精品爽爽va在线观看网站 | 我的亚洲天堂| 国产成人精品无人区| 99久久久亚洲精品蜜臀av| 精品久久久久久成人av| 成人亚洲精品一区在线观看| 国产精品免费视频内射| 久久这里只有精品19| 亚洲精品一二三| 高清欧美精品videossex| 欧美精品一区二区免费开放| 99re在线观看精品视频| 可以在线观看毛片的网站| 99国产精品一区二区蜜桃av| 久久人人97超碰香蕉20202| 国产精品 欧美亚洲| 国产精品自产拍在线观看55亚洲| 国产亚洲av高清不卡| 欧美中文日本在线观看视频| 在线天堂中文资源库| av免费在线观看网站| 亚洲成a人片在线一区二区| 99精品欧美一区二区三区四区| 亚洲国产精品合色在线| 三级毛片av免费| 久久中文看片网| 国产精品乱码一区二三区的特点 | 亚洲国产欧美网| 国产黄色免费在线视频| 国产三级在线视频| 一级毛片女人18水好多| cao死你这个sao货| 亚洲伊人色综图| 我的亚洲天堂| 国产精品 国内视频| 午夜影院日韩av| 亚洲 欧美一区二区三区| 夜夜爽天天搞| 乱人伦中国视频| 黄色a级毛片大全视频| 高清黄色对白视频在线免费看| 好男人电影高清在线观看| 伦理电影免费视频| 天天添夜夜摸| 黄色 视频免费看| 国内久久婷婷六月综合欲色啪| 成在线人永久免费视频| 欧美亚洲日本最大视频资源| 亚洲精品国产一区二区精华液| 日韩视频一区二区在线观看| 欧美日韩瑟瑟在线播放| 一级作爱视频免费观看| 久久精品国产亚洲av香蕉五月| 一区二区三区国产精品乱码| 日韩高清综合在线| 日韩 欧美 亚洲 中文字幕| 亚洲午夜理论影院| 亚洲 欧美一区二区三区|