Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien...Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.展开更多
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo...K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.展开更多
A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the a...A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.展开更多
The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the ...The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.展开更多
针对目前指纹室内定位系统指纹库管理效率低、实时性差和定位精度低的问题,提出了一种新的基于核化K-means和SVM分类回归的无线定位算法。首先利用核化K-means算法将输入的预处理后的RSS(Received Signal Strength)信号进行无监督聚类,...针对目前指纹室内定位系统指纹库管理效率低、实时性差和定位精度低的问题,提出了一种新的基于核化K-means和SVM分类回归的无线定位算法。首先利用核化K-means算法将输入的预处理后的RSS(Received Signal Strength)信号进行无监督聚类,将聚类后的数据信息存入指纹特征数据库,然后通过SVM回归的机器学习算法对特征数据库的数据进行训练,得到一种最优的拟合位置函数的数学模型。并且采用粒子群算法对参数进行寻优,进行实验仿真。实验结果表明,该算法有效地提升了定位精度,优于KNN、WKNN、SVR等室内定位算法。展开更多
Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle com...Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.展开更多
A decentralized network made up of mobile nodes is termed the Mobile Ad-hoc Network(MANET).Mobility and a finite battery lifespan are the two main problems with MANETs.Advanced methods are essential for enhancing MANE...A decentralized network made up of mobile nodes is termed the Mobile Ad-hoc Network(MANET).Mobility and a finite battery lifespan are the two main problems with MANETs.Advanced methods are essential for enhancing MANET security,network longevity,and energy efficiency.Hence,selecting an appropriate cluster.The cluster’s head further boosts the network’s energy effectiveness.As a result,a Hybrid Swallow Swarm Optimisation-Memetic Algorithm(SSO-MA)is suggested to develop the energy efficiency&of the MANET network.Then,to secure the network Abnormality Detection System(ADS)is proposed.The MATLAB-2021a platform is used to implement the suggested technique and conduct the analysis.In terms of network performance,the suggested model outperforms the current Genetic Algorithm,Optimised Link State Routing protocol,and Particle Swarm Optimisation techniques.The performance of the model has a minimum delay in the range of 0.82 seconds and a Packet Delivery Ratio(PDR)of 99.82%.Hence,the validation shows that the Hybrid SSO-MA strategy is superior to the other approaches in terms of efficiency.展开更多
文摘Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.
文摘K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
基金supported by the National Natural Science Foundation of China(Nos.61174180,U1433125)the Jiangsu Province Science Foundation (No.BK20141413)the Chinese Postdoctoral Science Foundation (No.2014M550291)
文摘A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.
文摘The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.
文摘针对目前指纹室内定位系统指纹库管理效率低、实时性差和定位精度低的问题,提出了一种新的基于核化K-means和SVM分类回归的无线定位算法。首先利用核化K-means算法将输入的预处理后的RSS(Received Signal Strength)信号进行无监督聚类,将聚类后的数据信息存入指纹特征数据库,然后通过SVM回归的机器学习算法对特征数据库的数据进行训练,得到一种最优的拟合位置函数的数学模型。并且采用粒子群算法对参数进行寻优,进行实验仿真。实验结果表明,该算法有效地提升了定位精度,优于KNN、WKNN、SVR等室内定位算法。
基金The National Defence Foundation of China (No.NEWL51435Qt220401)
文摘Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.
文摘A decentralized network made up of mobile nodes is termed the Mobile Ad-hoc Network(MANET).Mobility and a finite battery lifespan are the two main problems with MANETs.Advanced methods are essential for enhancing MANET security,network longevity,and energy efficiency.Hence,selecting an appropriate cluster.The cluster’s head further boosts the network’s energy effectiveness.As a result,a Hybrid Swallow Swarm Optimisation-Memetic Algorithm(SSO-MA)is suggested to develop the energy efficiency&of the MANET network.Then,to secure the network Abnormality Detection System(ADS)is proposed.The MATLAB-2021a platform is used to implement the suggested technique and conduct the analysis.In terms of network performance,the suggested model outperforms the current Genetic Algorithm,Optimised Link State Routing protocol,and Particle Swarm Optimisation techniques.The performance of the model has a minimum delay in the range of 0.82 seconds and a Packet Delivery Ratio(PDR)of 99.82%.Hence,the validation shows that the Hybrid SSO-MA strategy is superior to the other approaches in terms of efficiency.
