Estimation and Prediction of the Condition of the Vehicle Engine Based on the Correlation Dimension

This paper applies the fractal dimension as a characteristic to describe the engine抯 operating condition and its developmental trend. A correlation dimension is one of the quantities that are usually used to characterize a strange attractor. With the operation of the phase space reconstruction, respective correlation dimensions of a series of vibration signals obtained under different conditions are calculated to find the intrinsic relationship between the indicator and the operating condition. The experiment result shows that the correlation dimension is sensitive to the condition evolution and convenient for the identification of abnormal operational states. In advanced prognostic algorithm based on the BP neural network is then applied on the correlation dimensions to predict the short-term running conditions in order to avoid severe faults and realize in-time maintenance. Experimental results are presented to illustrate the proposed methodology.

Global Attractor and Dimension Estimation for a 2D Generalized Anisotropy Kuramoto-Sivashinsky Equation

In this paper, firstly, some priori estimates are obtained for the existence and uniqueness of solutions of a two dimensional generalized anisotropy Kuramoto-Sivashinsky Equation. Then we prove the existence of the global attractor. Finally, we get the upper bound estimation of the Haus-dorff and fractal dimension of attractor.

Global Attractors and Dimension Estimation of the 2D Generalized MHD System with Extra Force

In this paper, firstly, some priori estimates are obtained for the existence and uniqueness of solu-tions of a two dimensional generalized magnetohydrodynamic (MHD) system. Then the existence of the global attractor is proved. Finally, the upper bound estimation of the Hausdorff and fractal dimension of attractor is got.

Global Attractor of Two-Dimensional Strong Damping KDV Equation and Its Dimension Estimation

Firstly, a priori estimates are obtained for the existence and uniqueness of solutions of two dimensional KDV equations, and prove the existence of the global attractor, finally get the upper bound estimation of the Hausdorff and fractal dimension of attractors.

The Global Attractors and Dimensions Estimation for the Higher-Order Nonlinear Kirchhoff-Type Equation with Strong Damping

The initial boundary value problems for a class of high order Kirchhoff type equations with nonlinear strongly damped terms are considered. We establish the existence and uniqueness of the global solution of the problem by using prior estimates and Galerkin’s method under proper assumptions for the rigid term. Then the compact method is used to prove the existence of a compact family of global attractors in the solution semigroup generated by the problem. Finally, the Frechet differentiability of the operator semigroup and the decay of the volume element of linearization problem are proved, and the Hausdorff dimension and Fractal dimension of the family of global attractors are obtained.

Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.

Global Attractors and Their Dimension Estimates for a Class of Generalized Kirchhoff Equations

In this paper, we studied the long-time properties of solutions of generalized Kirchhoff-type equation with strongly damped terms. Firstly, appropriate assumptions are made for the nonlinear source term g (u) and Kirchhoff stress term M (s) in the equation, and the existence and uniqueness of the solution are proved by using uniform prior estimates of time and Galerkin’s finite element method. Then, abounded absorption set B0k is obtained by prior estimation, and the Rellich-kondrachov’s compact embedding theorem is used to prove that the solution semigroup S (t) generated by the equation has a family of the global attractor Ak in the phase space . Finally, linearize the equation and verify that the semigroups are Frechet diifferentiable on Ek. Then, the upper boundary estimation of the Hausdorff dimension and Fractal dimension of a family of the global attractor Ak was obtained.

POINTWISE ESTIMATES OF SOLUTIONS FOR THE NONLINEAR VISCOUS WAVE EQUATION IN EVEN DIMENSIONS

In this article,we study the pointwise estimates of solutions to the nonlinear viscous wave equation in even dimensions(n≥4).We use the Green’s function method.Our approach is on the basis of the detailed analysis of the Green’s function of the linearized system.We show that the decay rates of the solution for the same problem are different in even dimensions and odd dimensions.It is shown that the solution exhibits a generalized Huygens principle.

POINTWISE ESTIMATE OF SOLUTIONS OF ISENTROPIC NMIER-STOKES EQUATIONS IN EVEN SPACE-DIMENSIONS

This paper is concerned with the dissipation of solutions of the isentropic Navier-Stokes equations in even and bigger than two multi-dimensions. Pointwise estimates of the time-asymptotic shape of the solutions are obtained and the generalized Huygan's principle is exhibited. The approch of the paper is based on the detailed analysis of the Green function of Iinearized system. This is used to study the coupling of nonlinear diffesion waves.

Fractal Characteristics and Fractal Dimension Measurement on Broken Surfaces of Aluminum Electric Porcelain

The characteristics of broken surfaces were r esearched by a scanning electron microscope (SEM) and a reflection microscope, a nd the fractal dimensions of broken surfaces were measured by the Slit Island me thod. The experimental results indicate that the broken surface of aluminum elec tric porcelain is a fractal body in statistics, and the fractal dimensions of br oken surfaces are different with the different amplification multiple value.In a ll of measured fractal dimensions,both of values measured in 100× under reflect ion microscope and in 500× under SEM are maximum, whereas the values measur ed in 63× under reflection microscope and in 2000× under SEM are obviously min imum. The fractal dimensions of broken surfaces are also affected by the degrees of gray comparison and the kinds of measuring methods. The relationships betwee n the fractal dimensions of broken surfaces and porcelain bend strengths are tha t they are in positive correlation on the low multiples and in negative correlat ion on the high multiples.

Large Time Behavior of Solutions for General Navier-Stokes Systems in Multi-Dimension

Through the estimates for Greens function of linearized system, we obtain L p estimates of the asymptotic behavior of solutions of the Cauchy problem for the Navier Stokes systems of compressible flow in several space dimensions.

Parameter estimation for rigid body after micro-Doppler removal based on L-statistics in the radar analysis

In traditional inverse synthetic aperture radar （ISAR） imaging of moving targets with rotational parts, the micro-Doppler （m-D） effects caused by the rotational parts influence the quality of the radar images. Recently, L. Stankovic proposed an m-D removal method based on L-statistics, which has been proved effective and simple. The algorithm can extract the m-D effects according to different behaviors of signals induced by rotational parts and rigid bodies in time-frequency （T-F） domain. However, by removing m-D effects, some useful short time Fourier transform （STFT） samples of rigid bodies are also extracted, which induces the side lobe problem of rigid bodies. A parameter estimation method for rigid bodies after m-D removal is proposed, which can accurately re- cover rigid bodies and avoid the side lobe problem by only using m-D removal. Simulations are given to validate the effectiveness of the proposed method.

SELECTION OF PROPER EMBEDDING DIMENSION IN PHASE SPACE RECONSTRUCTION OF SPEECH SIGNALS

In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.

THE NONPARAMETRIC ESTIMATION OF THE NEXT FAILURE TIME

The nonparametric estimation of the next failure time is considered in this paper. The estimator given in the paper has a.s. convergence under some proper conditions. The asymptotic normality of the estimator is also discussed.

ESTIMATION OF THE VARIANCE FOR STRONGLY MIXING SEQUENCES

Let {X\-n,n≥1} be a stationary strongly mixing random sequence satisfying E X\-1=μ, E X\+2\-1<∞ and (Var S\-n)/n→σ\+2 as n→∞ . In this paper a class of estimators of Var S\-n is studied. The weak consistency and asymptotic normality as well as the central limit theorem are presented.

一种改进的M-Estimators基础矩阵鲁棒估计法

针对原M-Estimators算法完全依赖由线性最小二乘法估计得到的矩阵初始值,精度较低稳定性差的缺点,提出了一种改进的M-Estimators算法。通过考虑匹配点与对应极线的距离,计算求得较原M-Estimators算法更加精确的矩阵初始值,再利用此初始值剔除掉原匹配点集中的错误匹配点及坏点,最后运用Torr-M-Estimators法对新的匹配点集进行非线性优化计算,得到了真正的匹配点对,精确恢复了对极几何关系。以大量的模拟数据和真实图像进行了实验,给出了该算法与其他鲁棒性算法的比较结果,实验结果表明,该算法在误匹配以及高斯噪声存在的情况下,提高了基础矩阵的估计精度,并且同时具有很好的鲁棒性。

基于M-estimator与可变遗忘因子的在线贯序超限学习机

该文针对时变离群值环境下的在线学习问题,提出一种基于M-estimator与可变遗忘因子的在线贯序超限学习机算法(VFF-M-OSELM)。VFF-M-OSELM以在线贯序超限学习机模型为基础,通过引入一种更加鲁棒的M-estimator代价函数来替代传统的最小二乘代价函数,以提高模型对于离群值的在线处理能力和鲁棒性。同时VFF-M-OSELM通过融合使用一种新的可变遗忘因子方法进一步增强了其在时变环境下的动态跟踪能力和自适应性。仿真实例验证了所提算法的有效性。

GRADIENT ESTIMATES AND ENTROPY FORMULAE FOR WEIGHTED p-HEAT EQUATIONS ON SMOOTH METRIC MEASURE SPACES

Let （M,g, e^-fdv） be a smooth metric measure space. In this paper, we con- sider two nonlinear weighted p-heat equations. Firstly, we derive a Li-Yau type gradient estimates for the positive solutions to the following nonlinear weighted p-heat equationand f is a smooth function on M under the assumptionthat the m-dimensional nonnegative Bakry-Emery Ricci curvature. Secondly, we show an entropy monotonicity formula with nonnegative m-dimensional Bakry-Emery Ricci curva- ture which is a generalization to the results of Kotschwar and Ni [9], Li [7].

Coefficient Estimates for a Class of m-fold Symmetric Bi-univalent Function Defined by Subordination

In this paper, we investigate the coefficient estimates of a class of m-fold bi-univalent function de?ned by subordination. The results presented in this paper improve or generalize the recent works of other authors.

Strong Consistency of M Estimator in Linear Model for φ-mixing Samples

The strong consistency of M estimator of regression parameter in linear model for φ-mixing samples is discussed by using the classic Rosenthal type inequality. We get the strong consistency of M estimator under lower moment condition, which generalizes and improves the corresponding ones for independent sequences.