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汽车悬架运动学仿真/双横臂悬架/可以自由下载(Kinematics Simulation of automotive suspension/double wishbone suspension/free download)

7On the Number of Rectangular Tilings.pdf

利用matlab软件得到二维的星形曲线，画出其图像(Using matlab software is a two-dimensional star graph, draw its image)

基于Matlab编写的卡尔曼滤波器，对直线运动的小车位置进行跟踪(Kalman Filter implemented using Matlab)

对数据进行EMD分析时，如果数据不是纯的白噪声,则分析过程中一些尺度将会丢失,出现模态混叠现象。EEMD通过附加均匀分布 在整个时频空间的白噪声，可有效抑制这种现象的发生。(EEMD is insufficient for the EMD method, we propose a noise-assisted data analysis methods. EEMD decomposition principle is: when the additional white noise uniformly distributed throughout the time-frequency space, the time-frequency space is carved up by the filter component of the composition at different scales.-)

Long extra high voltage (EHV) ac lines cannot be loaded to their thermal limits in order to keep sufficient margin against transient instability. With the scheme proposed in this project, it is possible to load these lines very close to their

混合模糊聚类算法,2012年有希腊学者提出，有一个参数theta。当theta为0是该算法为k均值算法，当theta为1时，该算法为模糊聚类算法；当介于0到1时，该算法性能在两者之间(hybrid fuzzy clustering method)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

14 bus model using matlab and psat for load flow

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