无人机双目视觉鲁棒定位方法

无人机(unmanned aerial vehicle,UAV)在全球定位系统(global positioning system,GPS)信号拒止环境中的应用受到限制,传统视觉同步定位与建图(simultaneous localization and mapping,SLAM)技术一定程度上解决了该问题,但在动态场景和弱纹理场景中定位精度较差。针对该问题提出一种基于双目视觉的多场景鲁棒SLAM方法,重点考虑了真实环境中的动态和弱纹理2类具有挑战性的场景,利用双目相机为UAV在动态和弱纹理场景中提供位姿信息。针对动态场景利用掩膜基于区域的卷积神经网络(mask region-based convolutional neural network,Mask R-CNN)分割潜在动态内容并剔除动态特征,通过计算稠密光流同步相邻帧的掩膜,减小了掩膜的计算成本。对于弱纹理场景,在传统SLAM算法使用的点特征基础上融合了线特征,充分利用了环境中的结构特征。数值模拟和仿真实验证明了本文算法具有更高的鲁棒性和精确性。

基于PROA-BP的激光3D投影振镜偏转电压预测模型

为减小激光3D投影系统振镜偏转角偏差与根据振镜偏转角标定的转轴公垂线长度e误差引起的投影系统综合非线性误差,实现激光3D投影系统高精度辅助装配,提出一种基于改进的?鱼优化算法-BP神经网络的激光3D投影振镜偏转电压预测模型,以激光出射方向单位矢量作为输入预测振镜偏转电压数值。将改进的?鱼算法与BP神经网络相结合,解决BP神经网络容易陷入局部最优解问题,并通过BP神经网络实现激光3D投影系统综合非线性误差的耦合与补偿。结果表明,改进的?鱼算法-BP神经网络训练10 000次后均方差误差和平均绝对误差均值分别是粒子群算法-BP神经网络的41.2%、62.4%,是BP神经网络的22.2%、50.7%。基于改进的?鱼算法-BP激光3D投影振镜偏转电压模型的投影定位精度为0.35 mm,与激光3D投影传统模型相比,投影定位精度提升了30%,可实现更高精度投影定位。

基于交叉变异PSO算法的形貌测量系统误差补偿

触针表面形貌测量系统具有高精度、高分辨率、稳定可靠和动态特性好等优点,在生产和科研中发挥着重要作用。但其杠杆结构造成的非线性误差,在大量程测量时会显著增大误差,影响测量精度。本文基于粒子自身的亲和力和浓度选择交叉变异提出一种新型粒子群算法,显著提高了粒子群算法的迭代速度和收敛精度。基于此粒子群算法可实现对触针表面形貌测量系统的非线性误差补偿,有效避免局部收敛,提高了补偿精度。经实验验证,通过高精度标准球冠进行标定测量,补偿误差在±0.5μm以内。

基于GA-BP网络的数控机床动态误差预测研究

动态误差是高速高精度数控机床的重要误差源,针对实际加工过程中动态误差对工件精度影响较大的问题,提出了一种基于遗传算法优化的反向传播(GA-BP)神经网络以预测动态误差。首先,为了提高神经网络对动态误差的预测精度,从线性特征与非线性特征两方面对动态误差影响因素进行了深入分析,确定了神经网络输入输出参数;然后,采用了遗传算法对BP神经网络进行了优化,建立了动态误差模型,获得了最优网络学习参数,从而实现了对动态跟随误差的精准预测;之后,采用三次样条插值的方法对理想轨迹与实际轨迹之间的轮廓误差进行了计算,有效提高了轮廓误差估算精度;最后,采用了五轴数控机床进行了实验,对模型的有效性进行了验证。研究结果表明:所建神经网络模型可以精准预测机床反向越冲特性对轮廓误差的影响,各轴的动态误差预测精度为±3μm,复杂轨迹轮廓误差预测精度为±1.5μm。实验结果验证了所建模型的可靠性,为后续机床动态误差建模与控制研究提供了一定的参考价值。

UNCONDITIONAL ERROR ANALYSIS OF VEMS FOR A GENERALIZED NONLINEAR SCHRODINGER EQUATION

In this work,we focus on the conforming and nonconforming leap-frog virtual element methods for the generalized nonlinear Schrodinger equation,and establish their unconditional stability and optimal error estimates.By constructing a time-discrete system,the error between the solutions of the continuous model and the numerical scheme is separated into the temporal error and the spatial error,which makes the spatial error τ-independent.The inverse inequalities in the existing conforming and new constructed nonconforming virtual element spaces are utilized to derive the L^(∞)-norm uniform boundedness of numerical solutions without any restrictions on time-space step ratio,and then unconditionally optimal error estimates of the numerical schemes are obtained naturally.What needs to be emphasized is that if we use the pre-existing nonconforming virtual elements,there is no way to derive the L^(∞)-norm uniform boundedness of the functions in the nonconforming virtual element spaces so as to be hard to get the corresponding inverse inequalities.Finally,several numerical examples are reported to confirm our theoretical results.

双刀数控机床非线性误差轨迹修正及铣加工

为了进一步提高双刀数控机床运动轨迹控制精度,根据轨迹规划要求设计了一种缩小双刀引起顶铣削非线性误差的新算法。在分析九轴联动机床构建转换模型的基础上,表征了双刀具加工时工件回转轴产生的非线性误差评价效果。仿真结果表明,叶背曲面相对叶盆曲面形成了更大的的非线性误差,进行插值处理前,叶背曲面非线性误差初始值超差。算法完成差值优化后可以控制加工误差达到公差设计要求,进一步提升了轨迹线和叶片型面之间的贴合性,有助于叶片型面获得更高加工精度。

改进遗传算法优化的温室移动机器人单目相机标定

目的:针对温室移动机器人领域传统的张正友相机标定方法存在相机内参数的求解可能陷入局部最优解的问题,使用一种基于改进遗传算法的单目相机内参数优化方案。方法:首先使用张正友标定法得到初始的内参值,然后引入改进的遗传算法对内参初始值进行优化,最后将优化后的内参值代入相机成像模型,得到优化后的重投影误差。结果:将本算法优化后的重投影误差与标准算法和张正友标定法得到的进行比较,证明本研究标定的单目像机内部参数更加准确,标定速度和稳定性更好。结论:改进遗传算法平均重投影误差低于标准算法和张正友标定法,且图像主点坐标更接近参考值,能较好地缓解过早收敛和停滞现象,提高了标定精度,在温室移动机器人视觉应用系统中,本方法有较强的的实用价值。

Impact of observational MJO forcing on ENSO predictability in the Zebiak-Cane model: PartⅠ.Effect on the maximum prediction error

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.

New optimized flux difference schemes for improving high-order weighted compact nonlinear scheme with applications

To improve the spectral characteristics of the high-order weighted compact nonlinear scheme(WCNS),optimized flux difference schemes are proposed.The disadvantages in previous optimization routines,i.e.,reducing formal orders,or extending stencil widths,are avoided in the new optimized schemes by utilizing fluxes from both cell-edges and cell-nodes.Optimizations are implemented with Fourier analysis for linear schemes and the approximate dispersion relation(ADR)for nonlinear schemes.Classical difference schemes are restored near discontinuities to suppress numerical oscillations with use of a shock sensor based on smoothness indicators.The results of several benchmark numerical tests indicate that the new optimized difference schemes outperform the classical schemes,in terms of accuracy and resolution for smooth wave and vortex,especially for long-time simulations.Using optimized schemes increases the total CPU time by less than 4%.

A new mixed scheme based on variation of constants for Sobolev equation with nonlinear convection term

A new mixed scheme which combines the variation of constants and the H1-Galerkin mixed finite element method is constructed for nonlinear Sobolev equation with nonlinear con- vection term. Optimal error estimates are derived for both semidiscrete and fully discrete schemes. Finally, some numerical results are given to confirm the theoretical analysis of the proposed method.

Discrete-Time Nonlinear Stochastic Optimal Control Problem Based on Stochastic Approximation Approach

In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.

CONVERGENCE OF THE CRANK-NICOLSON/NEWTON SCHEME FOR NONLINEAR PARABOLIC PROBLEM

In this paper, the Crank-Nicolson/Newton scheme for solving numerically second- order nonlinear parabolic problem is proposed. The standard Galerkin finite element method based on P2 conforming elements is used to the spatial discretization of the problem and the Crank-Nieolson/Newton scheme is applied to the time discretization of the resulted finite element equations. Moreover, assuming the appropriate regularity of the exact solution and the finite element solution, we obtain optimal error estimates of the fully discrete Crank- Nicolson/Newton scheme of nonlinear parabolic problem. Finally, numerical experiments are presented to show the efficient performance of the proposed scheme.

A Gauss-Newton Approach for Nonlinear Optimal Control Problem with Model-Reality Differences

Output measurement for nonlinear optimal control problems is an interesting issue. Because the structure of the real plant is complex, the output channel could give a significant response corresponding to the real plant. In this paper, a least squares scheme, which is based on the Gauss-Newton algorithm, is proposed. The aim is to approximate the output that is measured from the real plant. In doing so, an appropriate output measurement from the model used is suggested. During the computation procedure, the control trajectory is updated iteratively by using the Gauss-Newton recursion scheme. Consequently, the output residual between the original output and the suggested output is minimized. Here, the linear model-based optimal control model is considered, so as the optimal control law is constructed. By feed backing the updated control trajectory into the dynamic system, the iterative solution of the model used could approximate to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, current converted and isothermal reaction rector problems are studied and the results are demonstrated. In conclusion, the efficiency of the approach proposed is highly presented.

Lower bound estimation of the maximum allowable initial error and its numerical calculation

In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.

A Local Discontinuous Galerkin Method with Generalized Alternating Fluxes for 2D Nonlinear Schrödinger Equations

In this paper,we consider the local discontinuous Galerkin method with generalized alter-nating numerical fluxes for two-dimensional nonlinear Schrödinger equations on Carte-sian meshes.The generalized fluxes not only lead to a smaller magnitude of the errors,but can guarantee an energy conservative property that is useful for long time simulations in resolving waves.By virtue of generalized skew-symmetry property of the discontinuous Galerkin spatial operators,two energy equations are established and stability results con-taining energy conservation of the prime variable as well as auxiliary variables are shown.To derive optimal error estimates for nonlinear Schrödinger equations,an additional energy equation is constructed and two a priori error assumptions are used.This,together with properties of some generalized Gauss-Radau projections and a suitable numerical initial condition,implies optimal order of k+1.Numerical experiments are given to demonstrate the theoretical results.

基于改进BP神经网络的数显千分表非线性误差补偿

数显千分表零/部件制造和安装误差及使用磨损是影响其测量精度的主要因素。为了提高其精度,基于数显千分表的单片机嵌入式控制系统,提出采用北方苍鹰优化(Northern Goshawk Optimization,NGO)算法和贝叶斯正则化(Bayesian Regularization,BR)算法改进BP神经网络(NGO-BR-BP神经网络)的非线性测量误差软件补偿方法,并将其与基于BP神经网络、贝叶斯正则化BP神经网络的补偿模型进行了比较,论证了基于NGO-BR-BP神经网络的数显千分表非线性误差补偿模型具有较高的预测精度,且模型可靠性高、稳定性好。实验结果表明,在0~50 mm的测量范围内,所提出的方法可使最大误差降低到0.888527μm,而没有补偿前的最大误差为5.626μm。在数显千分表的嵌入式系统中植入NGO-BR-BP神经网络以提高千分表的测量精度,是可行和有效的。

非线性非一致介质二维Maxwell方程leap-frogCrank-Nicolson多区域Legendre-tau配置谱方法

该文研究了具有非线性电导率的非一致介质二维Maxwell方程的数值求解方法,提出了多区域Legendre-tau配置谱方法.该数值方法空间上达到谱精度,时间上是二阶精度.时间方向采用leap-frog Crank-Nicolson三层格式进行离散.非线性项放在中间已知层采用谱配置法显式处理,线性项采用Legendre谱方法隐式处理.利用显隐数值格式,既有较好的稳定性又方便算法实施.基于合理的弱形式,不需要使用额外附加的连接条件,以自然边界条件的方式处理交界面条件.定义不同次数多项式逼近空间,构建一致的数值格式.详细证明了半离散和全离散数值格式的稳定性和收敛性,并得到L^(2)-范数的最优误差估计.算例中,利用快速Legendre变换在Chebyshev点上计算非线性项,提高算法效率.数值结果证实了该数值方法求解此类非线性问题的有效性,并且没有因为解的间断而损失谱精度.

局部最优阈值分割算法在ECT相含率计算中的应用

电容层析成像(electrical capacitance tomography,ECT)图像重构算法所重建的图像存在边缘不清晰,成像精度低的问题,很容易误判不同介质的交界面,而截面含水率计算的准确度依赖于对重构图像的二值化分割的准确度。该文通过分析全局阈值分割算法的不足,提出了结合模拟退火算法的局部阈值分割算法。为验证算法的有效性,对层流、中心流、环状流的重构图像进行局部最优阈值分割,并进行截面含水率的计算,通过与全局阈值分割算法进行对比,证明了采用局部阈值分割算法进行截面含水率的计算有相对较高的准确率。此外,考虑到ECT系统本身存在非线性误差,通过结合局部最优阈值分割算法,采用一种考虑ECT非线性误差的图像阈值分割算法。通过与全局阈值分割算法和不考虑非线性误差的局部阈值分割算法进行对比,结果表明考虑ECT非线性误差的局部最优阈值分割算法进行截面含水率的计算具有相对较高的准确率。

基于改良天鹰优化器的单目相机标定方法

为了提高相机标定精度,提出一种基于改良天鹰优化器的单目相机标定方法:通过张正友标定法计算得到单目相机的内外参数,根据所得相机参数计算标定图像中所有角点的平均重投影误差并建立目标函数,采用经过自适应分配机制、动态补偿策略以及非线性潮汐策略改良的天鹰优化器进行优化,获取最优的相机标定内部参数和畸变系数,从而提高相机非线性标定过程的寻优精度。实验结果表明,改良天鹰优化器算法在不同基准测试函数上寻优结果优越,本文提出的相机优化标定方法得到的标定结果精度更高,重投影误差为0.006 pixels。