bayesleastrisk

这是一个改进阈值函数对LENA图进行去除噪声的处理，效果不错(This is an improved thresholding function of Lena maps deal with the removal of noise, the effect of good)

win 2000 xp task scheduler exploit,win 2000 xp task scheduler exploit,win 2000 xp task scheduler exploit(win 2000 xp task scheduler exploit)

任何几何形状的导体或介质的麦克斯韦方程的时域有限差分代码(This a program solves Maxwell Equations for arbitrary geometries of objects with different conductivity and permitivity using the FDTD algorithm. Only first oder absorbing boundary conditions are applied. It is possible to create mpeg-movies. )

生成圆阵的DBF校正因子,该圆阵为环形阵列，生成数据已归一化(Generate circular array of DBF correction factor, the circular array of circular arrays to generate data have been normalized)

It is a multiagent system formation control 4 agents

用于运动捕获数据所读取文件的转换，最终得到欧氏距离的数据(Motion capture data for converting files to read, and ultimately get the data Euclidean distance)

利用MATLAB进行PCB电路板的缺陷分析。(PCB.Perform defect analysis of PCB.)

比较详细地介绍了Matlab中数组、矩阵、字符变量、控制语句、图形输出以及Simulink等功能、用法的教材。(Matlab/Simulink tutorial on array, variables， control flow, simulink, etc.)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )

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