In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average...In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.展开更多
Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. ...Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of node nearest neighbor average degree of China aviation netwo...In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of node nearest neighbor average degree of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the nearest neighbor average degrees of nodes in China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the nearest neighbor average degree had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network, the relationship between the node degree and the nearest neighbor average degree and its evolution trace of Chi...In order to reveal the complex network characteristics and evolution principle of China aviation network, the relationship between the node degree and the nearest neighbor average degree and its evolution trace of China aviation network in 1988, 1994, 2001, 2008 and 2015 were studied. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network. According to the statistical data, the node nearest neighbor average degree of China aviation network in 1988, 1994, 2001, 2008 and 2015 was calculated. Through regression analysis, it was found that the node degree had a negative exponential relationship with the nearest neighbor average degree, and the two parameters of the negative exponential relationship had linear evolution trace.展开更多
Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-展开更多
Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the foc...Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.展开更多
It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when th...It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.展开更多
The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional ne...The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional nearest neighbor query method in specific direction space based on Voronoi diagram is put forward.This work studies two cases,i.e.the query point is static and the query point moves with a constant velocity.Under the static condition,the corresponding pruning method and the pruning algorithm of the specified direction nearest neighbor(pruning_SDNN algorithm)are proposed by combining the plane right-angle coordinate system with the north-west direction,and then according to the smallest external rectangle of Voronoi polygon,the specific query is made and the direction nearest neighbor query based on Voronoi rectangle(VR-DNN) algorithm is given.In the case of moving with a constant velocity,first of all,the combination of plane right angle coordinate system,geographical direction and circle are used,the query range is determined and pruning methods and the pruning algorithm of the direction nearest neighbor based on decision circle(pruning_DDNN algorithm) are put forward.Then,according to the different position of motion trajectory and Voronoi diagram,a specific query through the nature of Voronoi diagram is given.At last,the direction nearest neighbor query based on Voronoi diagram and motion trajectory(VM-DNN) algorithm is put forward.The theoretical research and experiments show that the proposed algorithm can effectively deal with the problem of the nearest neighbor query for a specified geographical direction space.展开更多
Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general c...Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general consensus on the height at which tree diameter should be measured[1.3 m:diameter at breast height(DBH)],the minimum measureddiameter(MMD)often varies in different studies.In this study,we assumed that the outcomes of forest structure analysis can be influenced by MMD and,to this end,we applied g(r)function and stand spatial structural parameters(SSSPs)to investigate how different MMDs affect forest spatial structure analysis in two pine-oak mixed forests(30 and 57 years old)in southwest China and one old-growth oak forest(>120years old)from northwest China.Our results showed that 1)MMD was closely related to the distribution patterns of forest trees.Tree distribution patterns at each observational scale(r=0-20 m)tended tobecome random as the MMD increased.The older the community,the earlier this random distribution pattern appeared.2)As the MMD increased,neighboring trees became more regularly distributed around a reference tree.In most cases,however,nearest neighbors of a reference tree were randomly distributed.3)Tree species mingling decreased with increasing diameter,but it decreased slowly in older forests.4)No correlations can be found between individual tree size differentiation and MMD.We recommend that comparisons of spatial structures between communities would be more effective if using a unified MMD criterion.展开更多
Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic fire...Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.展开更多
The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth...The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.展开更多
Zipf＇s approach in linguistics is utilized to analyze the statistical features of frequency and correlation of 16 nearest neighboring nucleotides （AA, AC, AG, …, TT） in 12 human chro- mosomes （Y, 22, 21, 20, 19, ...Zipf＇s approach in linguistics is utilized to analyze the statistical features of frequency and correlation of 16 nearest neighboring nucleotides （AA, AC, AG, …, TT） in 12 human chro- mosomes （Y, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, and 12）. It is found that these statistical features of nearest neighboring nucleotides in human genome： （i） the frequency distribution is a linear function, and （ii） the correlation distribution is an inverse function. The coefficients of the linear function and inverse function depend on the GC content. It proposes the correlation distribution of nearest neighboring nucleotides for the first time and extends the descriptor about nearest neighboring nueleotides.展开更多
An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented.The frequency response function was employed as the input parameters to detect the severity and ...An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented.The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures.Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data.The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios.The accuracy of the approach was verified using measured data from an experimental test;moreover,two asymmetric 3D numerical examples were considered as the numerical study.The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network.Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data.The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise,but the nearest neighbor search method is more robust against noisy data than the artificial neural network.展开更多
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting inc...Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.展开更多
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s...As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE）...By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE） of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring （NNN） interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic.展开更多
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.
基金supported by the Taif University Researchers Supporting Project Number(TURSP-2020/79),Taif University,Taif,Saudi Arabia.
文摘Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of node nearest neighbor average degree of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the nearest neighbor average degrees of nodes in China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the nearest neighbor average degree had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network, the relationship between the node degree and the nearest neighbor average degree and its evolution trace of China aviation network in 1988, 1994, 2001, 2008 and 2015 were studied. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network. According to the statistical data, the node nearest neighbor average degree of China aviation network in 1988, 1994, 2001, 2008 and 2015 was calculated. Through regression analysis, it was found that the node degree had a negative exponential relationship with the nearest neighbor average degree, and the two parameters of the negative exponential relationship had linear evolution trace.
基金This project was supported by Shanghai Shu Guang Project.
文摘Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
基金the Natural Science Foundation of China (No. 61802404, 61602470)the Strategic Priority Research Program (C) of the Chinese Academy of Sciences (No. XDC02040100)+3 种基金the Fundamental Research Funds for the Central Universities of the China University of Labor Relations (No. 20ZYJS017, 20XYJS003)the Key Research Program of the Beijing Municipal Science & Technology Commission (No. D181100000618003)partially the Key Laboratory of Network Assessment Technology,the Chinese Academy of Sciencesthe Beijing Key Laboratory of Network Security and Protection Technology
文摘Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.
文摘It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.
基金Supported by the National Natural Science Foundation of China(No.61872105,62072136)the Natural Science Foundation of Heilongjiang Province(No.LH2020F047)+1 种基金the Scientific Research Foundation for Returned Scholars Abroad of Heilongjiang Province of China(No.LC2018030)the National Key R&D Program of China(No.2020YFB1710200)。
文摘The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional nearest neighbor query method in specific direction space based on Voronoi diagram is put forward.This work studies two cases,i.e.the query point is static and the query point moves with a constant velocity.Under the static condition,the corresponding pruning method and the pruning algorithm of the specified direction nearest neighbor(pruning_SDNN algorithm)are proposed by combining the plane right-angle coordinate system with the north-west direction,and then according to the smallest external rectangle of Voronoi polygon,the specific query is made and the direction nearest neighbor query based on Voronoi rectangle(VR-DNN) algorithm is given.In the case of moving with a constant velocity,first of all,the combination of plane right angle coordinate system,geographical direction and circle are used,the query range is determined and pruning methods and the pruning algorithm of the direction nearest neighbor based on decision circle(pruning_DDNN algorithm) are put forward.Then,according to the different position of motion trajectory and Voronoi diagram,a specific query through the nature of Voronoi diagram is given.At last,the direction nearest neighbor query based on Voronoi diagram and motion trajectory(VM-DNN) algorithm is put forward.The theoretical research and experiments show that the proposed algorithm can effectively deal with the problem of the nearest neighbor query for a specified geographical direction space.
基金financially supported by the National Science Foundation of China (grant no. 31400542 31460196)+1 种基金Guangxi Natural Science Foundation (grant 2016GXNSFBA380233)Guangxi special fund project for innovation-driven development (AA 17204087-8)
文摘Forest structure analysis is important for understanding the properties and development of a forest community,and its outcomes can be influenced by how trees are measured in sampled plots.Although there is a general consensus on the height at which tree diameter should be measured[1.3 m:diameter at breast height(DBH)],the minimum measureddiameter(MMD)often varies in different studies.In this study,we assumed that the outcomes of forest structure analysis can be influenced by MMD and,to this end,we applied g(r)function and stand spatial structural parameters(SSSPs)to investigate how different MMDs affect forest spatial structure analysis in two pine-oak mixed forests(30 and 57 years old)in southwest China and one old-growth oak forest(>120years old)from northwest China.Our results showed that 1)MMD was closely related to the distribution patterns of forest trees.Tree distribution patterns at each observational scale(r=0-20 m)tended tobecome random as the MMD increased.The older the community,the earlier this random distribution pattern appeared.2)As the MMD increased,neighboring trees became more regularly distributed around a reference tree.In most cases,however,nearest neighbors of a reference tree were randomly distributed.3)Tree species mingling decreased with increasing diameter,but it decreased slowly in older forests.4)No correlations can be found between individual tree size differentiation and MMD.We recommend that comparisons of spatial structures between communities would be more effective if using a unified MMD criterion.
文摘Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.
基金Project supported by the National Natural Science Foundation of China(Grant No.11002086)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.
基金ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China （No.20173023 and No.90203012） and the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘Zipf＇s approach in linguistics is utilized to analyze the statistical features of frequency and correlation of 16 nearest neighboring nucleotides （AA, AC, AG, …, TT） in 12 human chro- mosomes （Y, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, and 12）. It is found that these statistical features of nearest neighboring nucleotides in human genome： （i） the frequency distribution is a linear function, and （ii） the correlation distribution is an inverse function. The coefficients of the linear function and inverse function depend on the GC content. It proposes the correlation distribution of nearest neighboring nucleotides for the first time and extends the descriptor about nearest neighboring nueleotides.
文摘An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented.The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures.Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data.The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios.The accuracy of the approach was verified using measured data from an experimental test;moreover,two asymmetric 3D numerical examples were considered as the numerical study.The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network.Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data.The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise,but the nearest neighbor search method is more robust against noisy data than the artificial neural network.
文摘Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.
文摘As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
基金Project supported by the Key Higher Education Program of Hubei Province, China (Grant No Z20052201)Natural Science Foundation of Hubei Province, China (Grant No 2006ABA055)Postgraduate Program of Hubei Normal University of China(Grant No 2007D20)
文摘By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE） of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring （NNN） interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic.
