As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becomin...As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.展开更多
A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theo...A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theory,and then the manifold learning algorithm MVU is used to perform nonlinear dimensionality reduction on the data of phase space in order to separate low-dimensional manifold representing the attractor from noise subspace.Finally,the noise-reduced signal is obtained through reconstructing the low-dimensional manifold.The simulation results of Lorenz system show that the proposed MVU-based noise reduction method outperforms the KPCA-based method and has the advantages of simple parameter estimation and low parameter sensitivity.The proposed method is applied to fault detection of a vibration signal from rotor-stator of aero engine with slight rubbing fault.The denoised results show that the slight rubbing features overwhelmed by noise can be effectively extracted by the proposed noise reduction method.展开更多
Maximum Variance Unfolding(MVU)是一种基于流形学习的非线性降维方法。该算法中近邻点的选取对MVU的降维效果影响很大。利用样本点聚类后的类别信息构造密度系数,提出了一种MVU改进的算法。所提出算法根据近邻点分布的不同,挖掘数据...Maximum Variance Unfolding(MVU)是一种基于流形学习的非线性降维方法。该算法中近邻点的选取对MVU的降维效果影响很大。利用样本点聚类后的类别信息构造密度系数,提出了一种MVU改进的算法。所提出算法根据近邻点分布的不同,挖掘数据局部的密度信息,有效的保持了高维数据中的流形结构。人脸表情和图像检索实验证实了所提出方法的有效性。展开更多
文摘As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.
文摘A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theory,and then the manifold learning algorithm MVU is used to perform nonlinear dimensionality reduction on the data of phase space in order to separate low-dimensional manifold representing the attractor from noise subspace.Finally,the noise-reduced signal is obtained through reconstructing the low-dimensional manifold.The simulation results of Lorenz system show that the proposed MVU-based noise reduction method outperforms the KPCA-based method and has the advantages of simple parameter estimation and low parameter sensitivity.The proposed method is applied to fault detection of a vibration signal from rotor-stator of aero engine with slight rubbing fault.The denoised results show that the slight rubbing features overwhelmed by noise can be effectively extracted by the proposed noise reduction method.
