• <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 ?

    Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

    2022-08-23 02:17:56ElSayedElkenawyAbdelhameedIbrahimSeyedaliMirjaliliYuDongZhangShaimaElnazerandRokaiaZaki
    Computers Materials&Continua 2022年6期

    El-Sayed M.El-kenawy,Abdelhameed Ibrahim,Seyedali Mirjalili,Yu-Dong Zhang,Shaima Elnazer and Rokaia M.Zaki

    1Department of Communications and Electronics,Delta Higher Institute of Engineering and Technology,Mansoura,35111,Egypt

    2Faculty of Artificial Intelligence,Delta University for Science and Technology,Mansoura,35712,Egypt

    3Computer Engineering and Control Systems Department,Faculty of Engineering,Mansoura University,Mansoura,35516,Egypt

    4Centre for Artificial Intelligence Research and Optimization,Torrens University Australia,Fortitude Valley,QLD 4006,Australia

    5Yonsei Frontier Lab,Yonsei University,Seoul,03722,Korea

    6School of Computing and Mathematical Sciences,University of Leicester,Leicester,LE1 7RH,UK

    7Nile Higher Institute for Engineering and Technology,Mansoura,Egypt

    8Computer and Information Technology College,Taif University,Taif,Saudi Arabia

    9Higher Institute of Engineering and Technology,Kafrelsheikh

    10Department of Electrical Engineering,Shoubra Faculty of Engineering,Benha University,Egypt

    Abstract: Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines (SVM),Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.

    Keywords:Metamaterial antenna;machine learning;ensemble model;feature selection;guided whale optimization;support vector machines

    1 Introduction

    Metamaterials are materials with special physical properties that cannot be reproduced using natural materials, and so metamaterials are popular materials in today’s world and are frequently used in many fields, such as microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, as filters, and antennas that are compact, efficient, and have a negative refractive index.One of its most important uses is the design of antennas made possible by metamaterials[1–3].

    This is due to the fact that metamaterials have unique properties,and as a result,we may construct antennas with innovative features that standard materials cannot provide.One or more layers of metamaterials may be utilized as a substrate or in addition to the antenna design in order to boost the system’s capabilities.Even if a compact antenna with low cost and high efficiency is desired,a slightly larger antenna that costs less money and has better efficiency is the best option.The metamaterial may help increase the bandwidth and gain of tiny antennas.Another advantage is that it reduces their electrical size,but the ability to direct a signal increases.In the case of smaller antennas,metamaterial antennas provide an advantage over traditional antennas since they have more bandwidth[4,5].

    Simulation software is used to estimate the metamaterial effect.The CST Microwave Studio(CST MWS) is an example of a software program that simulates electromagnetic simulations [6].Antenna characteristics like bandwidth,gain,Voltage Standing Wave Ratio(VSWR),and return loss may be calculated after the simulation.In the simulation phase, researchers may make adjustments in Metamaterial Antenna, beginning with trial and error to get the set of antenna characteristics.The amount of time it will take to finish this procedure is completely unpredictable.They are using a machine learning model to estimate antenna characteristics.Numerous studies have examined machine learning applications in antenna design.Machine learning is anticipated to speed the antenna design process while retaining high accuracy, minimizing errors, saving time, and the ability to forecast the antenna behavior, improve computing efficiency, and decrease the number of required simulations[7–9].

    Optimization is the study of finding optimal solutions to problems.Because optimization issues are complex and grow with time,we resort to improved optimization algorithms[10–13].Metaheuristic algorithms are an excellent option for tackling complex issues that are difficult to solve with conventional techniques.Algorithms start with a random population and pass on the best to the next generation.Metaheuristic algorithms are dynamic and widely looking for a solution[14–17].

    In this paper,an Antenna-derived metamaterial ensemble model is presented as a way to estimate the bandwidth and gain of the Antenna.Of the basic models, we utilise Support Vector Machines(SVM) [18,19], Random Forest [20], K-Neighbors Regressor [21,22], and Decision Tree Regressor[23]to be compared with the presented method.Ensemble model is optimized using an optimization method to identify the optimum features based on the adaptive dynamic polar rose guided whale optimization(AD-PRS-Guided WOA)[24]algorithm.A regression analysis using the suggested model indicated that it was superior to the other models,predicting antenna bandwidth and gain efficiencies.

    The structure of this work is organized as follows:Section 2 presents a literature review.Section 3 describes data preparation and the suggested ensemble model in detail.Section 4 displays results and discussion.The last section of the given study(Section 5)examines the conclusion.

    2 Literature Review

    In general, the following steps can be taken to incorporate machine learning into the antenna design problem.The electromagnetic properties of an antenna are first determined via a series of simulations.These attributes are then saved in a database and used to train a machine learning system.Finally,the algorithm determines the Antenna that produces the closest results based on the designer’s requirements.

    2.1 Machine Learning Models

    Machine learning is a technique that relies on algorithms which can learn from data without the need of pre-programming.It can be classified into three types,named supervised,unsupervised and reinforcement learning.To attain high performance in Artificial Neural Networks(ANN),extensive interconnections of“neurons,”which are basic processing cells,are used.When complicated functions with many characteristics are discovered, neural networks provide an alternative method for doing machine learning.Multiple layers comprise neural networks: an input layer, an output layer, and hidden layers between the input and output layers[25].The SVM method is another kind of algorithm for guided learning.It is mainly used in classification and employs kernel techniques to scope with a challenging situation of non-linearly separable patterns.K-Nearest Neighbors (KNN) is considered to be one of the simplest machine learning methods available.After remembering the training set,the algorithm predicts the outcome of each new input using the outputs of its nearest neighbors in the training set.

    Machine learning algorithms have been applied in smart grid networks,where machine learning can be used to anticipate malicious events,communication technology,including antenna selection in wireless communications,wireless networks,where machine learning can be used to forecast wireless users’mobility patterns and content requests,and speech recognition.A technique for using machine learning in antenna design is to train a learning algorithm on data from prior simulations in order to improve the antenna parameters.

    Metaheuristic algorithms solve unexpected issues since they are intelligent and have prior knowledge of random search.These algorithms are either flexible, simple, or able to avoid local perfection.Exploration and exploitation are two elements of population-based heuristic algorithms.The metaheuristic algorithm here selects between Exploration and exploitation.While exploring, the technique examines the search space thoroughly.The area’s local search is currently at the exploitation stage.Several global optimization methods inspired by nature have been developed in recent decades.Population-based metaheuristics, often known as general-purpose algorithms, may be utilized in a variety of situations.Metaheuristics are split into two types:metaphor-based and non-metaphor based.In contrast, metaphors employ algorithms to represent natural phenomena or human behavior in contemporary life[26].

    2.2 Feature Selection

    All machine learning processes rely on feature engineering, which entails the extraction and selection of features, which are critical components of contemporary machine learning pipelines.Despite the fact that feature extraction and feature selection procedures overlap in certain ways,these words are often used interchangeably.Feature extraction is the process of extracting additional variables from raw data in order to make machine learning algorithms function.The feature selection method is focused on identifying the characteristics that are the most consistent,meaningful,and nonredundant.The feature selection issue is unique in that the search space is constrained to two binary values: 0 and 1.As a result, the continuous version of an optimizer should be used and updated to function correctly to address this issue.This method is considered in order to transform the suggested continuous values of AD-PRS-Guided WOA algorithm to binary values,allowing it to be utilised to solve the issue of feature selection.To transform, the Sigmoid form converts continuous values to binary values.

    3 The Proposed Ensemble Model

    Ensemble techniques are getting preferred in addressing various artificial intelligence issues.The average ensemble is among the most basic ensemble strategies that integrate base regressors’outputs and compute the mean.This method aggregates the outcome of various regressors as well as determines the mean value.In this paper,the average ensemble is employed as a reference set version to review the efficiency of the suggested ensemble model.As shown in Fig.1,the presented ensemble model is based on the stages of preprocessing, feature selection and optimized ensemble algorithm for both bandwidth and gain prediction.Ensemble model instead of selecting one ideal version from the candidates combines all the designs by assigning weight to every model.The Ensemble technique is verified as one of the significant methods in enhancing the prescient capability of conventional versions.The ensemble model typically has two stages wherein the first stage,the outcome variable of the best ensemble member,is picked to obtain the final forecast.The second stage blends the ensemble members’output variables using the mixed formula[27].

    Figure 1: The presented ensemble model based on the stages of preprocessing, feature selection and optimized ensemble algorithm for both bandwidth and gain prediction

    3.1 Data Preprocessing

    The dataset utilised in this study includes eleven Metamaterial Antenna characteristics.The dataset was obtained through Kaggle [28].There are 572 records in this collection.Each record contains the following information about the metamaterial antenna:the width and height of the split ring resonator,the distance between rings,the width of the rings,the gap between the rings,the distance between the antenna patch and the array, the number of split ring resonator cells in the array, the gain of the Antenna,the distance between split ring resonator cells in the array,the bandwidth of the Antenna,and the return.Tab.1 summarises the dataset’s characteristics.These characteristics will be utilised to estimate the Antenna’s bandwidth using a machine learning algorithm, and Fig.2 shows the distribution of bandwidth and gain feature.

    Table 1: Description of features of the dataset[28]

    Figure 2:Distribution of bandwidth and gain feature

    The first step is to format the nulls,the second step is to filter out null values,and the third step is to deal with nulls using a formula.Min-max normalisation is one of the most frequently used methods of data normalising.For each feature,the lowest value is converted to a 0,the highest value is converted to a 1,and all other values are converted to a decimal between 0 and 1.The dataset’s correlation matrix,as shown in Fig.3,Wmandtmare strongly correlated with the bandwidth.

    Figure 3:Correlation of metamaterial antenna

    3.2 The AD-PRS-Guided WOA Algorithm

    The AD-PRS-Guided WOA algorithm was first proposed in [24].A binary version of the ADPRS-Guided WOA algorithm is used to select the ideal attributes from the datasets to offer an optimal ensemble design for predicting the bandwidth and gain of the Metamaterial Antenna.The algorithm can check out the search space successfully to improve exploration efficiency.The algorithm also uses three arbitrary solutions as it makes use of significant change to transform between exploration and exploitation processes.According to the most effective remedy,it also calculates a listing of generated walks in a diffusion process as a polar increased feature.The AD-PRS-Guided WOA algorithm is shown in Algorithm 1.

    The updating positions mechanism of the algorithm of AD-PRS-Guided WOA is modified to follow three random solutions ofXo1,Xo2andXo3.These solutions are updated every iteration to enhance the algorithm performance and get the optimal solution.

    whereX(t+1)is the updated solution in iterationt+1 andX(t)is the current solution at iterationt.Qis the optimal solution.w1,w2andw3are random values in[0,0.5],[0,1],and[0,1],respectively.zis updated asz=fortiteration andtmas maximum iterations.

    The algorithm gets the best solution related to the calculated best fitness value.Then, the individuals are split into exploration groups and exploitation groups.Individuals in the exploitation group are moving to the leaders, and individuals in the exploration group are searching for leaders.Individuals in the sub-groups are changed dynamically.For balancing purposes,the algorithm divides the population into(50/50)for the two groups.

    In the algorithm,the polar rose function is used to search the leaders’purpose to find other good solutions.Based on different values of the main parameters of this function namedaandb, Fig.4 shows the output of the polar rose function.The polar rose function is calculated as follows to search around the best solution.

    whereX(t+1)is the updated solution in iterationt+1.Theaandbparameters are within[-10,10]and 0 ≤θ≤12π.kis calculated as

    Figure 4:Changing the values of a and b to generate different polar rose function outputs

    um iterations itersmax.5:Set Q=best agent position 6:while t ≤itersmax do 7: for(i=1:i ≤n)do 8: Select three random solutions Xo1,Xo2,and Xo3 9: Set z=1-images/BZ_804_607_2563_638_2609.png t itersmaximages/BZ_804_779_2563_810_2609.png2(Continued)

    Algorithm 1:Continued 10: Update position of current search agent as X(t+1)=w1*Xo1+z*w2*(Xo2-Xo3)+(1-z)*w3*(Q-X(t))11: end for 12: Update Solutions in exploration group(n1)and exploitation group(n2)13: if(Best Fn is same for three iterations)then 14: Increase solutions of exploration group(n1)15: Decrease solutions of exploitation group(n2)16: end if 17: for(i=1:i ≤n1)do(exploration group update)18: update three random solutions Xo1,Xo2,Xo3,and Q(The best solutions were elitism)19: if(Q <Any of the best solutions)then 20: Mutate the solution by X(t+1)=k+images/BZ_805_803_1106_834_1152.png∑Xo1+Xo2+Xo3 ezkimages/BZ_805_1192_1106_1223_1152.png, k=2- 2×t2(itersmax)2 21: else 22: Update agent position by X(t+1)=w1*Xo1+z*w2*(Xo2-Xo3)+(1–z)*w3*(Q-X(t))23: end if 24: end for 25: for(i=1:i ≤n2)do(exploitation group update)26: update three random solutions Xo1,Xo2,Xo3,and Q(The best solutions were elitism)27: if(Q <Any of the best solutions)then 28: Move towards the best solution by X(t+1)=w1*Xo1+z*w2*(Xo2-Xo3)+(1-z)*w3*-(Q-X(t))29: else 30: Search around the best solution X(t+1)=k sinimages/BZ_805_913_1905_938_1951.pngaimages/BZ_805_996_1905_1021_1951.pngbθ 31: end if 32: end for 33: Amend solutions 34: Update fitness 35: end while 36: Return best agent Q

    3.3 The Binary AD-PRS-Guided WOA Algorithm

    The output solution is updated to a binary solution using (0 or 1) in case of a feature selection problem.The sigmoid function is used in this paper to update the continuous solutions of the optimizer’s output into binary solutions,as shown in Algorithm 2.

    Algorithm 2:Binary AD-PRS-Guided WOA Algorithm 1: Set AD-PRS-Guided WOA population,parameters,configuration.2: Convert solutions to binary[0,1]3: Calculate objective function and select best solutions 4: Train k-NN and calculate error 5: while t ≤itersmax do 6: Apply AD-PRS-Guided WOA algorithm 7: Convert updated solution to binary 8: Calculate fitness 9: Update parameters 10: end while 11: Return best solution

    4 Results and Discussion

    The results in this section are explained as follows.The results, based on the Decision Tree,Multilayer Perceptron(MLP),KNN,Support Vector Regression(SVR),Random Forest,regressors in addition to the Average Ensemble and the proposed Ensemble model based on Random Forest regressor, before applying the feature selection technique are discussed.Then the results are shown after using feature selection to deliver the performance of the proposed model.Tab.2 shows the configuaration of the AD-PRS-Guided WOA algorithm.

    Table 2: Configuration of the AD-PRS-Guided WOA algorithm

    4.1 Performance Metrics

    The performance metrics used in this work are Root Mean Squared Error (RMSE), Mean Absolute Error(MAE),Mean Absolute Error(MBE),and the correlation coefficient(r)[22].Tab.3 shows the different performance metrics whereHp,iindicates a predicted value,Hirepresents the observed value,andnis the total number of observations.andindicate the average predicted and observed values,respectively.

    Table 3: Performance metrics for classification[22]

    4.2 Results Before Applying Feature Selection

    The results based on the bandwidth features of the tested dataset before applying the feature selection technique are shown in Tab.4.Tab.4 shows that the proposed Ensemble model using Random Forest results based on the bandwidth features of RMSE of (0.0320), MAE of (0.0231),MBE of (-0.0069), and r of (0.9752) are better than other compared models.The results using the gain features of the dataset before applying the feature selection are shown in Tab.5.

    Table 4: Results based on the bandwidth features of the dataset before applying feature selection

    Table 5: Results based on the gain features of the dataset before applying feature selection

    Tab.5 shows that the proposed Ensemble model using Random Forest results based on the gain features of RMSE of(0.0982),MAE of(0.0231),MBE of(-0.0152),and r of(0.9165)are better than other compared models.Fig.5 shows the actual and the predicted values for the bandwidth prediction from the tested dataset based on the AD-PRS-Guided WOA algorithm before applying the feature selection process.While Fig.6 shows the actual and predicted values by the AD-PRS-Guided WOA algorithm for the gain prediction before applying the method of feature selection.

    Figure 5:The actual values,in green color,and predicted values,in red color,by the proposed ensemble algorithm for the bandwidth before applying the feature selection

    Figure 6:The actual values,in green color,and predicted values,in red color,by the proposed ensemble algorithm for the gain before applying the feature selection

    4.3 Results After Applying Feature Selection

    After applying the feature selection technique,the results of the bandwidth features from the tested dataset are shown in Tab.6.Tab.6 shows that the proposed Ensemble model using Random Forest results of RMSE of(0.0102),MAE of(0.0344),MBE of(-0.0032),and r of(0.9932)are much better than other compared models.The results of the gain features from the dataset after applying the feature selection are shown in Tab.7.

    Table 6: Results based on the bandwidth features of the dataset after applying feature selection

    Tab.7 shows that the proposed Ensemble model using Random Forest results of the gain features as RMSE of(0.0891),MAE of(0.0234),MBE of(-0.0161),and r of(0.9443)which are much better than other compared models.Fig.7 shows the actual values and predicted values by the AD-PRSGuided WOA algorithm for the bandwidth after applying the feature selection.While Fig.8 shows the actual and predicted values by the AD-PRS-Guided WOA algorithm for the gain after applying the feature selection.

    Table 7: Results based on the gain features of the dataset after applying feature selection

    Figure 7:The actual values,in green color,and predicted values,in red color,by the proposed ensemble algorithm for the bandwidth after applying the feature selection

    Figure 8:The actual values,in green color,and predicted values,in red color,by the proposed ensemble algorithm for the bandwidth after applying the feature selection

    5 Conclusion

    Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.This paper uses the AD-PRS-Guided WOA method to pick the optimal features from the metamaterial antenna dataset.Metamaterial antennas can overcome the bandwidth and gain constraints associated with tiny antennas.Machine learning is receiving much interest in optimizing solutions in a variety of areas.The optimal ensemble model achieved good results for predicting the bandwidth and gain of the metamaterial antenna.The basic models have investigated SVM,Random Forest,K-Neighbors Regressor,and Decision Tree Regressor.The AD-PRS-Guided WOA algorithm was utilized to pick the optimal features from the datasets.The suggested model was compared to models based on five variables and to the average ensemble model.The findings indicated that the suggested AD-PRS-Guided WOA algorithm-based model is superior to others and can accurately predict antenna bandwidth and gain.The presented algorithm will be compared with CST software in future work.

    Funding Statement:The authors received no specific funding for this study.

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

    亚洲不卡免费看| 国内精品宾馆在线| 人妻少妇偷人精品九色| 一级爰片在线观看| 精品国产露脸久久av麻豆| 美女主播在线视频| 日日摸夜夜添夜夜爱| 少妇 在线观看| 成人综合一区亚洲| 熟女电影av网| 99久久中文字幕三级久久日本| 免费观看性生交大片5| 少妇人妻精品综合一区二区| 午夜免费男女啪啪视频观看| 男人舔奶头视频| 国产一区亚洲一区在线观看| 99久久精品国产国产毛片| 丝瓜视频免费看黄片| 亚洲性久久影院| 午夜福利网站1000一区二区三区| xxx大片免费视频| 国模一区二区三区四区视频| 亚洲av在线观看美女高潮| 久久精品国产亚洲av涩爱| 国产乱人视频| 激情 狠狠 欧美| 午夜爱爱视频在线播放| 久热这里只有精品99| 午夜福利在线观看免费完整高清在| 3wmmmm亚洲av在线观看| 综合色丁香网| 97精品久久久久久久久久精品| 精品一区二区三卡| 亚洲av日韩在线播放| 亚洲综合色惰| 99久国产av精品国产电影| 99久久人妻综合| 亚洲最大成人中文| 欧美日韩精品成人综合77777| 久久影院123| 狂野欧美白嫩少妇大欣赏| 欧美97在线视频| 国产熟女欧美一区二区| 草草在线视频免费看| 久久6这里有精品| 热re99久久精品国产66热6| 国产欧美日韩精品一区二区| av.在线天堂| 久久久成人免费电影| 国产免费视频播放在线视频| 91精品国产九色| 国产毛片在线视频| 日韩av在线免费看完整版不卡| 五月玫瑰六月丁香| 美女cb高潮喷水在线观看| av在线app专区| 亚洲av二区三区四区| 国产一区二区在线观看日韩| 欧美性感艳星| 免费黄色在线免费观看| 美女被艹到高潮喷水动态| 国内精品美女久久久久久| 成人国产av品久久久| 美女高潮的动态| 小蜜桃在线观看免费完整版高清| 日韩人妻高清精品专区| 国国产精品蜜臀av免费| 国产人妻一区二区三区在| 亚洲在线观看片| 国产午夜福利久久久久久| 自拍欧美九色日韩亚洲蝌蚪91 | 精品少妇久久久久久888优播| 男人狂女人下面高潮的视频| 国产精品99久久久久久久久| 亚洲成人精品中文字幕电影| 精品少妇久久久久久888优播| 特大巨黑吊av在线直播| 亚洲最大成人中文| 少妇被粗大猛烈的视频| 91在线精品国自产拍蜜月| 极品教师在线视频| www.av在线官网国产| eeuss影院久久| 欧美zozozo另类| 免费黄网站久久成人精品| 小蜜桃在线观看免费完整版高清| 国产精品一区二区性色av| 在线观看一区二区三区| 亚洲av男天堂| 国产免费又黄又爽又色| 尾随美女入室| 亚洲第一区二区三区不卡| 精华霜和精华液先用哪个| 麻豆国产97在线/欧美| a级毛片免费高清观看在线播放| 嫩草影院入口| 亚洲精品成人av观看孕妇| 国产成人a区在线观看| 亚洲欧美日韩东京热| 欧美精品一区二区大全| 夜夜看夜夜爽夜夜摸| av福利片在线观看| 日本午夜av视频| 国产精品人妻久久久影院| 精品少妇久久久久久888优播| 国产成人aa在线观看| 天堂网av新在线| 久久精品国产亚洲av涩爱| 国产成人精品福利久久| 国产毛片a区久久久久| 日本-黄色视频高清免费观看| 国产一区二区三区av在线| 少妇人妻久久综合中文| 成人特级av手机在线观看| 毛片一级片免费看久久久久| 狂野欧美激情性bbbbbb| 久久久久久久大尺度免费视频| 精品视频人人做人人爽| 成年版毛片免费区| 中文字幕av成人在线电影| 亚洲av.av天堂| 色视频在线一区二区三区| 国产老妇伦熟女老妇高清| 国产精品蜜桃在线观看| 99re6热这里在线精品视频| 精华霜和精华液先用哪个| 91精品伊人久久大香线蕉| 97在线人人人人妻| 亚洲第一区二区三区不卡| 一边亲一边摸免费视频| 99热全是精品| 国产成人午夜福利电影在线观看| 亚洲精品亚洲一区二区| 欧美一区二区亚洲| 狠狠精品人妻久久久久久综合| 精品国产一区二区三区久久久樱花 | 精品亚洲乱码少妇综合久久| 高清午夜精品一区二区三区| 大片电影免费在线观看免费| 狂野欧美白嫩少妇大欣赏| 国产伦理片在线播放av一区| 国产一区二区在线观看日韩| 午夜老司机福利剧场| 最近最新中文字幕大全电影3| a级一级毛片免费在线观看| 精品一区在线观看国产| 有码 亚洲区| 国产黄色免费在线视频| 99久久精品热视频| 大话2 男鬼变身卡| 精品酒店卫生间| 欧美老熟妇乱子伦牲交| 特大巨黑吊av在线直播| 毛片一级片免费看久久久久| 97在线视频观看| 国产一区二区亚洲精品在线观看| 日日摸夜夜添夜夜爱| 久久精品久久久久久久性| 亚洲三级黄色毛片| 日本-黄色视频高清免费观看| 亚洲美女视频黄频| 校园人妻丝袜中文字幕| 久久人人爽人人片av| 一级毛片aaaaaa免费看小| 熟女电影av网| 国内揄拍国产精品人妻在线| 亚洲av一区综合| 99热这里只有是精品在线观看| 18+在线观看网站| 亚洲一区二区三区欧美精品 | 国产精品一二三区在线看| 观看免费一级毛片| 国产成人午夜福利电影在线观看| 涩涩av久久男人的天堂| 欧美潮喷喷水| 99热全是精品| 好男人在线观看高清免费视频| 亚洲精品久久午夜乱码| 九色成人免费人妻av| 舔av片在线| 久久精品久久精品一区二区三区| 一级毛片我不卡| 成人二区视频| 国产精品国产av在线观看| 国产精品一区二区性色av| 成人国产av品久久久| 久久这里有精品视频免费| 欧美精品一区二区大全| 各种免费的搞黄视频| 少妇猛男粗大的猛烈进出视频 | av免费观看日本| 亚洲av免费在线观看| 国产黄频视频在线观看| 成人免费观看视频高清| 又黄又爽又刺激的免费视频.| 天堂俺去俺来也www色官网| 亚洲国产最新在线播放| 亚洲av免费高清在线观看| 51国产日韩欧美| 亚洲精品乱码久久久v下载方式| 亚洲欧美日韩东京热| 亚洲天堂av无毛| 欧美性感艳星| 男人狂女人下面高潮的视频| 人妻一区二区av| 亚洲精品日韩在线中文字幕| 亚洲欧洲日产国产| 国产淫片久久久久久久久| 男人添女人高潮全过程视频| 国产有黄有色有爽视频| 久久久成人免费电影| 一区二区三区免费毛片| 欧美极品一区二区三区四区| 日韩一区二区视频免费看| 91精品国产九色| 欧美变态另类bdsm刘玥| 只有这里有精品99| 亚洲aⅴ乱码一区二区在线播放| 久久久久久久国产电影| 夫妻性生交免费视频一级片| 久久久久久伊人网av| 欧美激情在线99| 在线免费观看不下载黄p国产| 一级毛片aaaaaa免费看小| 人妻夜夜爽99麻豆av| 国产成年人精品一区二区| 久久国内精品自在自线图片| 国产免费又黄又爽又色| 男的添女的下面高潮视频| 国产成人精品婷婷| 久久久久网色| 成人午夜精彩视频在线观看| 久久久亚洲精品成人影院| 日韩强制内射视频| 国产精品伦人一区二区| 黄色配什么色好看| 搡女人真爽免费视频火全软件| 亚洲va在线va天堂va国产| 欧美人与善性xxx| 五月玫瑰六月丁香| 中文字幕制服av| 成人毛片a级毛片在线播放| 91久久精品国产一区二区成人| 精品久久久久久久人妻蜜臀av| www.色视频.com| 韩国高清视频一区二区三区| 欧美日韩一区二区视频在线观看视频在线 | 噜噜噜噜噜久久久久久91| 老女人水多毛片| 国产精品无大码| 久久久色成人| 国产精品.久久久| 嫩草影院精品99| 亚洲人成网站高清观看| 日韩中字成人| 51国产日韩欧美| 在线天堂最新版资源| 日韩成人av中文字幕在线观看| 国产淫语在线视频| 久久久久精品久久久久真实原创| 亚洲欧美一区二区三区国产| 国产男女超爽视频在线观看| 偷拍熟女少妇极品色| 国产探花在线观看一区二区| 国内揄拍国产精品人妻在线| 国产精品国产三级国产专区5o| 国产免费福利视频在线观看| 欧美成人a在线观看| 日日摸夜夜添夜夜添av毛片| 五月玫瑰六月丁香| 亚洲精品乱码久久久v下载方式| av在线观看视频网站免费| 毛片女人毛片| 精品酒店卫生间| 欧美性猛交╳xxx乱大交人| 精品久久久噜噜| 视频区图区小说| 建设人人有责人人尽责人人享有的 | 亚洲欧美日韩东京热| 男女国产视频网站| 欧美一级a爱片免费观看看| 最新中文字幕久久久久| 久久久久性生活片| 我的老师免费观看完整版| 天天躁日日操中文字幕| 精品少妇黑人巨大在线播放| 精品一区在线观看国产| 国产免费又黄又爽又色| 国产成人91sexporn| 欧美 日韩 精品 国产| 国产黄色免费在线视频| 国产淫片久久久久久久久| 欧美区成人在线视频| 亚洲精品久久午夜乱码| 在线播放无遮挡| 国产男人的电影天堂91| 日日啪夜夜爽| av国产免费在线观看| 少妇人妻精品综合一区二区| 国产免费一级a男人的天堂| 亚洲国产最新在线播放| 大香蕉久久网| 在线观看美女被高潮喷水网站| 成人午夜精彩视频在线观看| 亚洲av福利一区| 亚洲国产精品国产精品| 日本午夜av视频| 天堂俺去俺来也www色官网| 在线观看av片永久免费下载| 日本欧美国产在线视频| 成人亚洲欧美一区二区av| 久久精品久久精品一区二区三区| 中国美白少妇内射xxxbb| 少妇的逼好多水| 国产 一区 欧美 日韩| 男女下面进入的视频免费午夜| 欧美3d第一页| 久久精品人妻少妇| 在线免费十八禁| 你懂的网址亚洲精品在线观看| 亚洲婷婷狠狠爱综合网| 色综合色国产| 日韩成人av中文字幕在线观看| 日韩在线高清观看一区二区三区| 国国产精品蜜臀av免费| 精品久久久噜噜| 亚洲一级一片aⅴ在线观看| 精品99又大又爽又粗少妇毛片| 亚洲欧美日韩无卡精品| 麻豆精品久久久久久蜜桃| 日韩av不卡免费在线播放| 国产片特级美女逼逼视频| 欧美三级亚洲精品| 一区二区三区免费毛片| 人人妻人人澡人人爽人人夜夜| 久久99热这里只频精品6学生| 91狼人影院| 嫩草影院精品99| 少妇的逼水好多| 国产男女内射视频| 日韩视频在线欧美| 国产精品人妻久久久影院| 老师上课跳d突然被开到最大视频| 国产精品av视频在线免费观看| 中文字幕制服av| 亚洲成人中文字幕在线播放| 成人二区视频| 亚洲精品aⅴ在线观看| 国产亚洲精品久久久com| 九九在线视频观看精品| 久久午夜福利片| 五月开心婷婷网| 69人妻影院| 国产成人freesex在线| 深爱激情五月婷婷| 尾随美女入室| 亚洲最大成人av| 赤兔流量卡办理| 激情五月婷婷亚洲| 成人欧美大片| 久久久久久国产a免费观看| 欧美97在线视频| 在线观看三级黄色| 天堂网av新在线| 亚洲av福利一区| 午夜精品一区二区三区免费看| 日韩大片免费观看网站| 久久久a久久爽久久v久久| 成人国产av品久久久| 最近中文字幕2019免费版| 亚洲自偷自拍三级| 欧美日韩精品成人综合77777| 一区二区三区四区激情视频| av在线老鸭窝| 国产精品福利在线免费观看| 美女cb高潮喷水在线观看| 日韩国内少妇激情av| 国产av不卡久久| 日韩一区二区三区影片| 肉色欧美久久久久久久蜜桃 | 熟女人妻精品中文字幕| 久久精品国产亚洲av涩爱| 大码成人一级视频| 18禁动态无遮挡网站| 国产高潮美女av| 女人十人毛片免费观看3o分钟| 秋霞伦理黄片| 久久99热这里只有精品18| 少妇人妻精品综合一区二区| 插逼视频在线观看| 黄色怎么调成土黄色| 在线观看一区二区三区激情| 黄片wwwwww| 亚洲人与动物交配视频| 人体艺术视频欧美日本| 欧美性感艳星| 中文字幕久久专区| www.色视频.com| 国产精品久久久久久精品古装| 永久免费av网站大全| 欧美日韩国产mv在线观看视频 | 欧美日韩在线观看h| 性色av一级| 亚洲成人中文字幕在线播放| 精品99又大又爽又粗少妇毛片| 视频区图区小说| 国产乱人偷精品视频| 97超视频在线观看视频| 天堂网av新在线| 日本猛色少妇xxxxx猛交久久| 少妇的逼水好多| 18禁在线播放成人免费| 久久久久国产网址| 国模一区二区三区四区视频| 国产午夜福利久久久久久| 亚洲国产精品成人久久小说| 男人舔奶头视频| 日本黄色片子视频| 欧美bdsm另类| 久久精品国产a三级三级三级| 久久久久精品久久久久真实原创| av黄色大香蕉| 精品午夜福利在线看| 成年女人在线观看亚洲视频 | 一个人看的www免费观看视频| 能在线免费看毛片的网站| 午夜福利在线观看免费完整高清在| 男人爽女人下面视频在线观看| 久久精品人妻少妇| 女人被狂操c到高潮| 国产 一区精品| 99热这里只有精品一区| 日韩大片免费观看网站| 成人欧美大片| av在线天堂中文字幕| 亚洲国产日韩一区二区| 欧美成人一区二区免费高清观看| 身体一侧抽搐| 亚洲人与动物交配视频| 亚洲熟女精品中文字幕| 直男gayav资源| av又黄又爽大尺度在线免费看| 国产欧美日韩精品一区二区| 99九九线精品视频在线观看视频| 精品酒店卫生间| 国语对白做爰xxxⅹ性视频网站| 人妻夜夜爽99麻豆av| 99热6这里只有精品| 国产 一区精品| 丝瓜视频免费看黄片| 亚洲精品色激情综合| 成年女人在线观看亚洲视频 | 最近中文字幕2019免费版| 亚洲精品日本国产第一区| 婷婷色麻豆天堂久久| 欧美 日韩 精品 国产| 亚洲av.av天堂| 一级毛片aaaaaa免费看小| av在线天堂中文字幕| 白带黄色成豆腐渣| 国产精品熟女久久久久浪| 亚洲精品视频女| 国产v大片淫在线免费观看| 亚洲综合色惰| 秋霞伦理黄片| 日本爱情动作片www.在线观看| 国产高清不卡午夜福利| 老女人水多毛片| 国产男人的电影天堂91| 久久久欧美国产精品| av在线播放精品| 全区人妻精品视频| 秋霞在线观看毛片| 亚洲美女视频黄频| 成年女人在线观看亚洲视频 | 插逼视频在线观看| 午夜激情福利司机影院| 免费播放大片免费观看视频在线观看| 亚洲av成人精品一区久久| 国产视频首页在线观看| 国产精品一区二区三区四区免费观看| 男插女下体视频免费在线播放| 亚洲精品乱码久久久久久按摩| 如何舔出高潮| 亚洲av免费在线观看| 日韩在线高清观看一区二区三区| 天天躁日日操中文字幕| 国内少妇人妻偷人精品xxx网站| 国产精品久久久久久精品古装| www.色视频.com| 99久久精品一区二区三区| 久久99热这里只频精品6学生| 熟女电影av网| 国产精品国产三级国产专区5o| 亚洲欧美成人综合另类久久久| 国产成人精品一,二区| 18禁在线播放成人免费| 美女脱内裤让男人舔精品视频| 日韩 亚洲 欧美在线| 精品久久久久久久久亚洲| 老师上课跳d突然被开到最大视频| 久久久精品94久久精品| 久久久久久久久久成人| 免费观看的影片在线观看| 婷婷色麻豆天堂久久| 欧美高清性xxxxhd video| 大片电影免费在线观看免费| 日韩,欧美,国产一区二区三区| 国产在视频线精品| 婷婷色麻豆天堂久久| 高清毛片免费看| 国产亚洲午夜精品一区二区久久 | 亚洲四区av| 日产精品乱码卡一卡2卡三| 99久久人妻综合| 久久久久久久午夜电影| 一个人看的www免费观看视频| 午夜爱爱视频在线播放| 26uuu在线亚洲综合色| 国产日韩欧美在线精品| 免费大片18禁| 日本-黄色视频高清免费观看| 色视频www国产| 亚洲国产精品专区欧美| 亚洲精品一二三| 午夜福利视频精品| 免费人成在线观看视频色| 综合色丁香网| 国内精品美女久久久久久| 美女视频免费永久观看网站| 黑人高潮一二区| 亚洲在久久综合| 一本一本综合久久| 亚洲精品成人久久久久久| 日韩视频在线欧美| 亚洲色图综合在线观看| 麻豆久久精品国产亚洲av| 免费看日本二区| 自拍欧美九色日韩亚洲蝌蚪91 | a级一级毛片免费在线观看| 国产免费视频播放在线视频| 精品人妻偷拍中文字幕| 亚洲精品乱码久久久v下载方式| 久久精品综合一区二区三区| 亚洲av二区三区四区| 51国产日韩欧美| 久久99热这里只有精品18| 国产精品人妻久久久久久| 国模一区二区三区四区视频| av在线观看视频网站免费| 亚洲久久久久久中文字幕| 精品久久国产蜜桃| .国产精品久久| 99热全是精品| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 亚洲美女视频黄频| 免费av观看视频| 一本久久精品| 欧美日韩视频精品一区| 日韩视频在线欧美| 少妇猛男粗大的猛烈进出视频 | 午夜精品国产一区二区电影 | 国产一区有黄有色的免费视频| 午夜免费鲁丝| 欧美精品一区二区大全| 99久久人妻综合| 国产视频首页在线观看| 你懂的网址亚洲精品在线观看| 国产精品久久久久久精品古装| 国产男人的电影天堂91| 女人久久www免费人成看片| 中文乱码字字幕精品一区二区三区| 99热国产这里只有精品6| 可以在线观看毛片的网站| 国产成人a∨麻豆精品| 午夜福利在线在线| 老师上课跳d突然被开到最大视频| 在线观看美女被高潮喷水网站| av在线蜜桃| 高清毛片免费看| 欧美性猛交╳xxx乱大交人| 欧美xxⅹ黑人| 亚洲久久久久久中文字幕| 国产高清国产精品国产三级 | 亚洲av福利一区| 国产精品一区二区三区四区免费观看| 少妇 在线观看| 97超视频在线观看视频| 岛国毛片在线播放| 亚洲欧美一区二区三区国产| 色吧在线观看| 亚洲国产日韩一区二区| 午夜激情福利司机影院| 日韩三级伦理在线观看| 少妇高潮的动态图| 国产视频内射| 亚洲av福利一区| 久久女婷五月综合色啪小说 | 亚洲精品aⅴ在线观看| 亚洲精品一区蜜桃| 深爱激情五月婷婷| 亚洲精品aⅴ在线观看| 久久精品国产鲁丝片午夜精品| 青青草视频在线视频观看| 欧美精品一区二区大全| 极品少妇高潮喷水抽搐| 啦啦啦中文免费视频观看日本| 亚洲精品视频女| 天堂网av新在线| 美女内射精品一级片tv| 亚洲自拍偷在线| 国产伦精品一区二区三区四那| 久久久久九九精品影院| 亚洲成人中文字幕在线播放| 少妇的逼水好多|