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这是运用Fortran语言编写的地震波AVO正演的程序源码，可用来对地震波的AVO特性进行分析-This is the use of Fortran language AVO seismic source forward procedure that can be used to on the AVO seismic analysis of the characteristics of

EDEM-FLUENT耦合的UDF文件 ，DEM-CFD(EDEM-FLUENT coupled UDF files)

开源的PHP框架,我收藏了这个版本,需要的可以下载下来看看(Open source PHP framework)

In this paper, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) is improved by hybridizing it with the harmony search (HS) algorithm and the resulting algorithm is abbreviated as DMS-PSO-HS. We present a novel approach to merge the HS algorithm into each sub-swarm of the DMS-PSO. Combining the exploration capabilities of the DMS-PSO and the stochastic exploitation of the HS, the DMSPSO-HS is developed. The whole DMS-PSO population is divided into a large number of small and dynamic sub-swarms which are also individual HS populations. These sub-swarms are regrouped frequently and information is exchanged among the particles in the whole swarm. The DMS-PSO-HS demonstrates improved on multimodal and composition test problems when compared with the DMS-PSO and the HS.

局域网查看工具，可以自己设定IP来对本地局域网共享的文件作很好的搜索！功能强大！-LAN View tool, it set itself on the local IP LAN to share a document search for good! Powerful!

说明：  python 自定义生成多核svm进行分类(python Custom Generated Multi-core svm)

15个类 近200个方法 附带10个例程 源码(11111111111111)

说明：  使用深度学习方法进行信道估计,LDAMP_BASED(channel estimation based on deep learning method)

说明：  平台基于visual studio 2015，使用opencv实现霍夫变换检测直线(Implementing Hough Transform)

协同区间偏最小二乘 siPLS算法是 N rgaard等对其提出 的 iPLS方法的改进, 其基本算法步骤如下:(1)对原始谱图进行 预处理;(2)在全谱范围内建立全局偏最小二乘模型, 即上节的 模型;(3)在整个光谱区间采用 iPLS建立多个等窗口宽度的子 区间, 假设为 n个;(4)在每个子区间上建立偏最小二乘法模型, 即可得到 n个局部模型;(5)以交叉验证时的均方根误差 RMSE 值为各模型的精度衡量标准, 比较全光谱模型和各局部模型的 精度;(6)组合精度最高的局部子区间为新的回归区间;再在新 的回归区间建立 siPLS模型(Cooperative interval partial least squares (siPLS) algorithm is proposed by N rgaard et al. The improvements of the iPLS method are as follows: (1) the original spectrogram is performed. Preprocessing; (2) establishing a global partial least squares model in the full spectrum, that is, the upper part of the model. Model; (3) use iPLS to establish multiple equal window widths in the whole spectral interval. The interval is assumed to be n; (4) a partial least squares model is established on each sub interval. N local models can be obtained; (5) the root mean square error of cross validation is RMSE The values are the accuracy criteria of each model, and the comparison between the full spectral model and the local models is made. (6) the local interval with the highest combined precision is a new regression interval; and then new. The establishment of siPLS model in the regression interval)