Fan_Conjugate_gradient

获取计算机硬件信息的动态链接库，CPU序列号，硬盘序列号，MAC地址等信息 例程： void gethardinfo() { QString str1=""; char buff[1024]; char buff2[1024]; char temp; typedef int (WINAPI ICEPUB_MACHINEGETINFO)(int typeFlag,char *strInfo); ICEPUB_MACHINEGETINFO *icePub_machineGetInfo = 0; HINSTANCE hDLLDrv = LoadLibrary("icePubDll.dll"); if(hDLLDrv) { icePub_machineGetInfo = (ICEPUB_MACHINEGETINFO *)GetProcAddress(hDLLDrv, "icePub_machineGetInfo"); } icePub_machineGetInfo(3,buff); str1=buff; int i=0; int n=0; n=str1.length(); qDebug()<<n; for(i=0;i<n;i+=2) { temp=buff[i]; buff2[i+1]=temp; buff2[i]=buff[i+1]; } QString str2=buff2; qDebug()<<buff2; qDebug()<<str2; }(Access to the computer hardware information dynamic link library, CPU serial number, hard disk serial number, MAC address and other information)

The Swift Programming Language swift 4.epub

学生成绩录入及管理系统，用于录入、对比、查询、修改学生的成绩(Student Achievement System)

说明：  各种cfd的fortran小程序，有sod激波管精确解、近似解，shuosher激波管近似解，包含WENO差分格式，牛顿法；还有网格生成书籍。(Various CFD FORTRAN programs, including SOD shock tube exact solution, approximate solution, shuosher shock tube approximate solution, including WENO difference scheme, Newton method, and grid generation books.)

说明：  windows 7 下 OPC 的网络配置(windows 7 and windows 7 with sp1 OPC net setting)

几篇关于graph cut的论文,GRABCUT彩色图像分割算法的研究,基于GRAPH CUT的快速纹理合成算法(Several papers on graph cut paper, GRABCUT color image segmentation algorithm, based on GRAPH CUT Fast Texture Synthesis Algorithm)

说明：  为了提升配网供电可靠性的预测精度!提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠 性预测模型$ 从多方面分析影响供电可靠性的指标!利用主成分分析得到综合变量!实现对数据的降维$ 在此基 础上!构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值!完成对训练供电可靠性预测模型 的训练$ 以某大型电网的 ?L 个供电局样本 !% 种影响供电可靠性因素为例进行仿真分析!并将 E S R C E FQ C 4 G D算 法与 ! 种回归拟合算法对比!验证了该方法的有效性(It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can p)

利用R软件建立混合效应模型，对于不同随机效应参数的混合模型进行对比(develop the mixed-effects models and compare the goodness-of-fit)

Lavxabhahab sjiius s d dwosisis dvvd

freeswitch 文档说明，百问freeswitch第三版(Freeswitch Document Description, Baiqu Freeswitch 3rd Edition)