cross-correlation

pq control of pv to grid

MATLAB中插值与拟和小函数，便于大家节省敲打键盘的时间 你们只要能够看懂它 并能够在合适的地方调用这些思想 并且有能力进行适当的变化 就已经到家了(MATLAB interpolation and fitting in small function, the keyboard is easy to save you time as long as you can understand it and be able to call these ideas at the right place and have the ability to make the appropriate changes has been the home of the)

Mysmo.mdl 永磁同步电机的主程序，可以实现有速度传感器启动运行，和无速度传感器运行。 SMOmodel.m 改进的滑模观测器模型，实现永磁同步电机反电动势的估算。 PFCDCM0hm5 用的是0.5hm的电感，实现220V交流电变换成400V的直流电，电流断续。 由于是仿真，如果要硬件实现的话，除了需要电压采样外，还需要一个输入电压过零检测 PFCCM320percent98k50 用的是5hm的电感，实现220V交流电变换成320V的直流电，电流连续。功率因数可以达到0.98，用的开关频率为50K PFCCM400percent97k50.mdl 用的是5hm的电感，实现220V交流电变换成400V的直流电，电流连续。功率因数可以达到0.97，用的开关频率为50K (Permanent magnet synchronous motor Mysmo.mdl main program, you can achieve the speed sensor up and running, and speed sensors run. SMOmodel.m improved sliding mode observer model permanent magnet synchronous motor back-EMF estimates. PFCDCM0hm5 with is 0.5hm inductor to achieve 220V AC transform into a 400V DC current is interrupted. As the simulation, hardware implementation, then, in addition to the voltage sampling an input voltage over zero detection PFCCM320percent98k50 the is 5hm inductance to achieve transformation 220V AC 320V DC continuous current. The power factor can reach 0.98, the switching frequency for 50K PFCCM400percent97k50.mdl with is 5hm inductance 220V AC transform 400V DC continuous current. The power factor can reach 0.97, with the switching frequency is 50K)

Matlab Image Processing Part 2

拉格朗日插值法。当给出一系列xi以及相应的yi，可以用此法求出一个二元一次方程f，使得f（xi）=yi。(Lagrange interpolation. When given a series of xi and the corresponding yi, a binary one can find the equation of this method f, so f (xi) = yi.)

说明：  详细说明Matlab常见图像处理函数，方便Matlab的学习(Details Matlab common image processing functions, to facilitate the learning Matlab)

video De noising - recent ieee papers 2009 papers zebros

SUPPORTING FILE FOR CONVERT GREYSCALE INTO BINARY STERAM

31个城市的VRP进行蚁群算法求解的matlab源码(31 cities, ant colony algorithm for VRP to the matlab source code)

This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.