城轨列车运行曲线计算仿真研究

列车运行曲线是分析列车运动状态、能耗指标等性能的基础,也可作为驾驶员导引系统或列车自动控制系统的输入。首先分析了列车的动力学原理,根据列车受到的牵引力、制动力以及运行阻力建立了列车受力模型。选择最小时分和固定时分两种运行策略进行列车运行曲线计算,分析最小时分和固定时分运行策略包含的工况序列,结合给定线路的条件信息划分限速区间,对相邻工况区间的限速情况进行判断,以此确定各子区间的所含工况情况。最终以北京某地铁线路数据为例,输入至编写好的程序中,得到列车在最小时分和固定时分策略下的运行曲线。

Least-squares reverse time migration method using the factorization of the Hessian matrix

Least-squares reverse time migration(LSRTM)can eliminate imaging artifacts in an iterative way based on the concept of inversion,and it can restore imaging amplitude step by step.LSRTM can provide a high-resolution migration section and can be applied to irregular and poor-quality seismic data and achieve good results.Steeply dipping refl ectors and complex faults are imaged by using wavefi eld extrapolation based on a two-way wave equation.However,the high computational cost limits the method’s application in practice.A fast approach to realize LSRTM in the imaging domain is provided in this paper to reduce the computational cost signifi cantly and enhance its computational effi ciency.The method uses the Kronecker decomposition algorithm to estimate the Hessian matrix.A low-rank matrix can be used to calculate the Kronecker factor,which involves the calculation of Green’s function at the source and receiver point.The approach also avoids the direct construction of the whole Hessian matrix.Factorization-based LSRTM calculates the production of low-rank matrices instead of repeatedly calculating migration and demigration.Unlike traditional LSRTM,factorization-based LSRTM can reduce calculation costs considerably while maintaining comparable imaging quality.While having the same imaging eff ect,factorization-based LSRTM consumes half the running time of conventional LSRTM.In this regard,the application of factorization-based LSRTM has a promising advantage in reducing the computational cost.Ambient noise caused by this method can be removed by applying a commonly used fi ltering method without signifi cantly degrading the imaging quality.

Numerical Minimum Time Control to a Class of Singular Integro-Differential Equations

This study presents a numerical method for determining the minimum time required for the states of one class of integro-differential equations of the first kind to reach its attainable region by assuming the forcing terms of the equations as controls. These equations consist of integro-differential parts containing weakly singular kernels. The feasibility of the numerical method is demonstrated by comparing the minimum time and corresponding possible time by using extreme controls to reach the attainable region under different initial conditions.

Pure q P-wave least-squares reverse time migration invertically transverse isotropic media and its application to field data

The anisotropic properties of subsurface media cause waveform distortions in seismic wave propagation,resulting in a negative infl uence on seismic imaging.In addition,wavefields simulated by the conventional coupled pseudo-acoustic equation are not only aff ected by SV-wave artifacts but are also limited by anisotropic parameters.We propose a least-squares reverse time migration(LSRTM)method based on the pure q P-wave equation in vertically transverse isotropic media.A fi nite diff erence and fast Fourier transform method,which can improve the effi ciency of the numerical simulation compared to a pseudo-spectral method,is used to solve the pure q P-wave equation.We derive the corresponding demigration operator,migration operator,and gradient updating formula to implement the LSRTM.Numerical tests on the Hess model and field data confirm that the proposed method has a good correction eff ect for the travel time deviation caused by underground anisotropic media.Further,it signifi cantly suppresses the migration noise,balances the imaging amplitude,and improves the imaging resolution.

Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.