MODULATION

The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (e.g. road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented.(The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (eg road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented.)

模拟雷达，对雷达的功能进行分析，可以根据对参数的修改方便地看到输出的图形结果(simulated radar, the radar functional analysis Based on parameters can be amended easily see the graphics output results)

A Discrete 3-Phase ProgrammableSource block is used to generate a 1 pu, 15 degrees positive sequence voltage. At t = 0.05 s the positive sequence voltage is increased to 1.5 pu. At t = 0.1 s an unbalance is introduced by adding a 0.3 pu negative sequence component with a phase of -30 degrees. The magnitude and phase of the positive-sequence component is evaluated in two diiferent ways: 1) Sequence calculation of phasors using Fourier analysis and 2) abc to dq0 transformation. Math Function and Trigonometric Function blocks are used to evaluate the magnitude and phase of the positive sequence from the d and q components: V1 = sqrt(Vd^2 + Vq^2) , Phi1 = atan(Vq/Vd))

包含两种针对风电机组的MPPT算法：经典MPPT；风速湍流作为搜索信号的MPPT(contains 2 applications corresponding to the two types of MPPT algorithm: the classical one, using the hillclimbing method, and another one, which uses extremum seeking control methods)

(有源代码)数值分析作业,本文主要包括两个部分,第一部分是常微分方程(ODE)的三个实验题,第二部分是有关的拓展讨论,包括高阶常微分的求解和边值问题的求解(BVP).文中的算法和算例都是基于Matlab计算的.ODE问题从刚性(STIFFNESS)来看分为非刚性的问题和刚性的问题,刚性问题(如大系数的VDP方程)用通常的方法如ODE45来求解,效率会很低,用ODE15S等,则效率会高多了.而通常的非刚性问题,用ODE45来求解会有很好的效果.从阶次来看可以分为高阶微分方程和一阶常微分方程,高阶的微分方程一般可以化为状态空间(STATE SPACE)的低阶微分方程来求解.从微分方程的性态看来,主要是微分方程式一阶导系数大的时候,步长应该选得响应的小些.或者如果问题的性态不是太好估计的话,用较小的步长是比较好的,此外的话Adams多步法在小步长的时候效率比R-K(RUNGE-KUTTA)方法要好些,而精度也高些,但是稳定区间要小些.从初值和边值来看,也是显著的不同的.此外对于非线性常微分方程还有打靶法,胞映射方法等.而对于微分方程稳定性的研究,则诸如相平面图等也是不可缺少的工具.值得提出的是,除了用ode系类函数外,用simulink等等模块图来求解微分方程也是一种非常不错的方法,甚至是更有优势的方法(在应用的角度来说).((Source code) numerical analysis homework, this docment includes two parts, the first is ordinary differential equations (ODE) of the three examples, the second part is about the expansion of the discussion, including the higher-order ordinary differential & boundary value solution Problems (BVP). the text of the algorithm and numerical examples are based on the Matlab. ODE from the rigid (STIFFNESS) look into the issue of non-rigid and rigid problem, rigid problems (such as large coefficient VDP equation) such as using the always method ODE45 used to solve the problems , efficiency will be low, with ODE15S the other hand, many of the high efficiency. and the usual problem of non-rigid, there will be used to solve ODE45 very good results. Judging from the order can be for high-order differential equations and first-order ordinary differential equations, higher-order differential equations can be transformed into a general state space (STATE SPACE) used to solve the low-order different)

matlab中Petri net的工具箱ToolboxPN5(matlab Petri net toolbox—ToolboxPN5)

RF APPLICATIONS AND THEORY

optimal location of capacitor in radial distribution system

针对二维矩形条带装箱问题提出了一种启发式布局算法，即底部左齐择优匹配算法（lowest—level left align bestfit，简称LLABF）．LLABF算法遵循最佳匹配优先原则，该原则综合考虑完全匹配优先、宽度匹配优先、高度匹配优先、组合宽度匹配优先及可装入优先等启发式规则．与BL（bottom-left），IBL（improved-bottom-left）与BLF（bottom—left-fill）等启发算法不同的是，LLABF能够在矩形装入过程中自动选择与可装区域匹配的下一个待装矩形．计算结果表明，LLABF结合遗传算法（genetie algorithm,简称GA）解决二维条带装箱问题更加有效．(A heuristic placement algorithm for two-dimensional rectangular strip packing problem is proposed, which is called bottom-level left align best fit algorithm (LLABF). Different from BL (bottom-left), IBL (improved-bottom-left) and BLF (bottom-left-fill) heuristic algorithms, LLABF can automatically select the next rectangle to be loaded that matches the mountable region during the rectangular loading process. Genetie algorithm (GA) is more effective to solve the two-dimensional strip packing problem.)

精通matlab科学计算中的所有实例的源代码！！！(Versed in scientific computing matlab source code for all instances! ! !)