改进的基于LMA算法的电子稳像技术

提出了一种改进的基于LMA算法的电子稳像技术.通过对LMA进行变换和改进将它运用到仿射变换模型的参数估计当中,从而获得参考帧与当前帧之间的全局运动矢量.通过变换降低了Hessian矩阵的计算量,并将多分辨率的匹配技术引入到算法当中,使得该电子稳像技术具有速度快和精度高的特点.实验表明,该技术对于视频的稳像具有良好的效果.

一种基于LMA的电子稳像技术

提出一种基于LMA的电子稳像技术,将LMA运用到仿射变换模型的参数估计中,从而获得参考帧与当前帧之间的运动矢量。该电子稳像方法具有速度快和精度高的特点。实验表明,这种稳像算法在进行视频稳像时具有很好的效果。

几何约束求解的扩展C-树分解法

为了有效分解几何约束系统,本文提出了一种基于增量LMA(ILMA)算法的扩展C-树分解法,该方法将几何约束系统分解为一棵扩展C-树。与C-树分解法相比,该方法能够保证以任意装配几何约束的方式构造的扩展C-树是几何约束系统的最大化分解。实例分解结果表明:将本文方法应用于几何约束求解是行之有效的。

摄像机运动下基于背景匹配的运动目标检测

提出一种摄像机运动下的运动目标检测技术。首先获取相邻两帧图像上的对应特征点的坐标后应用到仿射变换模型得到全局运动参量来描述背景图像的运动变化。然后根据估计出的全局运动参量通过双线性内插法完成背景匹配。最后采用背景匹配后的两帧图像差进行目标检测。实验证明本算法在提高处理的精确度和时效性的同时还可以处理摄像机发生较大幅度旋转运动情况下的目标检测。

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

内置式永磁同步电机宽转速范围最小损耗控制研究

为实现内置式永磁同步电机宽转速范围内的损耗最小控制,建立了永磁同步电机损耗模型,考虑电机动态工况,推导了损耗最小的直轴电流隐式表达式,采用数值方法求解该直轴电流;基于扩展卡尔曼观测器,估算铁损支路电流,实时计算等效铁损电阻修正损耗模型.结合矢量控制,与额定转速以下采用最大转矩电流比(Maximum Torque per Ampere,MTPA)、额定转速以上采用弱磁控制(Flux Weakening,FW)或最大转矩电压比(Maximum Torque per Voltage,MTPV)的传统控制策略进行比较研究.结果表明:在全转速范围内,所提策略均能使可控总损耗最小,动态和稳态效率均优于传统策略,且在额定转速动态响应更快,磁阻转矩利用更好.所提损耗最小控制策略实现了宽转速范围内的统一求解,具有计算过程快速简洁、动稳态工况均可适用和更高效节能的优点.