平均路径长度&聚类系数计算

机器人MATLAB m语言，实现关节机器人的运动学正解运算。算(Robot MATLAB M language, realize kinematic joint robot forward kinematics, inverse operation.)

说明：  abaqus纯镁切削算例文件，可直接计算(ABAQUS pure magnesium cutting example file can be directly calculated)

说明：  用Fortran语言编写的abaqus，Dflux热源子程序，路径为环形路径(Abaqus, Dflux heat source subroutine written in Fortran language, with a circular path)

逆流套管式换热器仿真小程序，工质物性调用refprop,所以需要自行嵌套refprop(Counter flow sleeve heat exchanger simulation small program, physical properties call RefProp, so need to self nest RefProp)

说明：  用牛顿-拉夫逊法进行电力系统潮流分布计算(Calculation of Power Flow Distribution in Power System by Newton-Raphson Method)

直流配网模型，非常基础的直流配网模型，便于对直流配网的基本掌握和学习(DC distribution network model, very basic DC distribution network model, easy to grasp and learn the basic DC distribution network)

对地面固定目标设计了一种仅依赖测距传感器的制导律，不再需要传统的视线角信号以及目标和无人机自身的定位信息，再将该制导律推广到对地面匀速和变速移动目标的跟踪制导。相比于现有制导律，本课题提出的制导律结构更为简洁，仅有一个设计参数，并且制导策略更为合理。仿真结果表明所提出的制导律能够实现无人机对地面固定目标的稳定跟踪。(A guidance law that only depends on the ranging sensor is designed for the ground fixed target. The traditional line-of-sight signal and the target and the positioning information of the UAV are no longer needed. The guidance law is extended to the uniform and variable speed moving target Tracking guidance. Compared with the existing guidance law, the guidance law structure proposed by this subject is more concise, with only one design parameter, and the guidance strategy is more reasonable. The simulation results show that the proposed guidance law can achieve stable tracking of UAV fixed targets on the ground.)

大坝变形的精确预测对其安全评价方面发挥着重要作用。为了进一步提高大坝变形预测精度，提出了一种改进的灰狼算法优化支持向量机的预测模型。通过引入非线性收敛因子和采用动态加权策略，提升灰狼算法优化支持向量机惩罚因子和核函数参数的能力，并以优化的参数来建立支持向量机大坝变形预测模型。选取实例数据，在与布谷鸟算法、差分进化算法、粒子群算法、基本灰狼算法优化的支持向量机预测比较中，实验结果表明，改进的灰狼算法对支持向量机参数的优化是有效的，可以提高大坝变形预测精度。(Accurate prediction of dam deformation plays an important role in its safety evaluation. In order to further improve the prediction accuracy of dam deformation, an improved grey Wolf algorithm is proposed to optimize the prediction model of support vector machine. By introducing the nonlinear convergence factor and using the dynamic weighting strategy, the gray wolf algorithm is improved to optimize the capacity of the support vector machine penalty factor and the kernel parameter, and the support vector machine dam deformation prediction model is established with the optimized parameters. The experimental results show that the Improved Grey Wolf algorithm is effective for the optimization of support vector machine parameters, and it can improve the accuracy of the deformation prediction of the dam.)

说明：  无人机模型，GUI界面，模拟飞机飞行过程(UAV model GUI interface Simulation of aircraft flight process)

对数据融合算法中经典的交互式多模型法进行仿真，便于入门级进行学习(Simulation of classical method which is interactive multiple model method and it is easy for entry-level learning)