rgbled

jit精益生产的论文，很经典的。毕业答辩的优的哈(jit lean production of paper, very classic. Ha excellent graduate reply)

一种基于弹性网络的混合预测的视频压缩感知方法。(A hybrid prediction based video compression elastic network sensing methods.)

single layer perceptron algorithm

High Density Impulse Noise Removal

说明：  计算HURST指数，自己编写，这是经过修正之后通过变成得出来的结果，网上一般很少有这类文件。(HURST index calculation, write your own, which is revised after the results come through into a very, very few of these files online usually.)

it is for microwave and microstrip line taht is good for all persen

IEEE 14 bus data

曲柄滑块机构的运动学仿真，研究曲轴运动的物理规律，通过建模和仿真，其运动规律可以实时动画展现，各项数据实时输出，大大增加了直观性(Slider-crank mechanism kinematics simulation, the laws of physics research crankshaft movement, through modeling and simulation, which show the movement of the animation in real-time, real-time output of the data, greatly increasing the intuitiveness)

适当选择辅助变量，使之满足相应条件，参数估计值就可以是无偏一致。估计辅助变量法的计算量与最小二乘法相当，但辨识效果却比最小二乘法好的多。尤其当噪声是有色的，而噪声的模型结构又不好确定时，增广最小二乘法和广义最小二乘法一般都不好直接应用，因为他们需要选用特定的模型结构，而辅助变量法不需要确定噪声的模型结构，因此辅助变量法就显得更为灵活，但辅助变量法不能同时获得噪声模型的参数估计。(Choose appropriate secondary variables, meet the relevant conditions and parameter estimate can be unbiased consistent. Estimated auxiliary variable method calculation and least square method is quite, but the identification effect is much better than the least square method. Especially when the noise is colored, and noise model structure and not sure, augmented the least squares and the generalized least squares method is generally not used directly, because they need to choose specific model structure, and auxiliary variable method does not need to make sure that noise model structure, so the auxiliary variable method is more agile, but not at the same time auxiliary variable method for noise model parameter estimation.)

golf 90hp stock engine remapped ecu