Gnuplot 5.2 Manual

说明：  梁可视为平面应力状态，先按图示尺寸划分为均匀的三角形网格，共有8×10＝80个单元，5×ll＝55个节点，坐标轴以及单元与节点的编号如图。将均布载荷分配到各相应节点上，把有约束的节点5l、52、53、54、55视作固定铰链，建立如图所示的离散化计算模型。(The beam can be regarded as a plane stress state. Firstly, the beam is divided into uniform triangular meshes according to the graphic size. There are 8*10=80 elements, 5*ll=55 nodes. The coordinate axis and the numbering of elements and nodes are shown in the figure. The uniform load is distributed to the corresponding nodes, and the constrained nodes 5l, 52, 53, 54 and 55 are regarded as fixed hinges. The discrete calculation model shown in the figure is established.)

一份C++的参考文档，.CHM格式，很好用的一份资料-A C++ Reference documents,. CHM format, good use of an information

学习用matlab语言编写的分子动力学的程序。(Learn the molecular dynamics program written in MATLAB language.)

说明：  The datatypes MPI_BYTE and MPI_PACKED do not correspond to a Fortran or C datatype. A value of type MPI_BYTE consists of a byte (8 binary digits). A byte is uninterpreted and is different from a character. Different machines may have different representations for characters

说明：  使用卷积神经网络进行高光谱遥感数据分类，使用的数据源为Pavia University高光谱数据 文件夹log--日志文件夹，存放TensorBorad日志、网络参数文件、混淆矩阵图 文件夹Patch--存放数据处理的切片结果 文件夹PaviaU--高光谱数据下载存放位置 文件夹predicted--CNN对原始影像的分类结果 data.py--对原始高光谱影像进行数据处理，生成切片 net.py--神经网络模型 train.py--训练神经网络 utils.py--需要用到的函数 show.py--使用训练好的神经网络模型对原始数据进行分类(Using convolution neural network to classify hyperspectral remote sensing data, the data source is Pavia University hyperspectral data. Folder log -- Log folder, store TensorBorad logs, network parameter files, obfuscation matrix diagrams Folder Patch -- Stores slice results of data processing Folder PaviaU -- Place for Hyperspectral Data Download and Storage Folder predicted -- CNN classification results of original images Data.py -- Data processing of original hyperspectral images to generate slices Net.py--Neural Network Model Train.py--Training Neural Network Utils. py -- Functions to be used Show.py -- Classification of raw data using trained neural network model)

PKU ACM 1007 的源码,求逆序的算法时间复杂度控制在O(n)-PKU ACM 1007 source code, and reverse time complexity of the algorithm control in O (n)

潮流计算，电力系统分析使用，用于分析潮流，完成各个节点计算(power flow calculation)

说明：  （1）心电信号预处理 心电信号是一种低频且含有众多噪声干扰的信号。针对心电信号存在的 噪声干扰问题，本文采用了平稳小波变换结合双变量阈值的方法对其进行去 噪处理。通过对心电信号进行八层平稳小波变换，得到不同的小波系数，采 用双变量阈值函数表达式对其进行处理得到新的小波系数，最后进行逆平稳 小波变换实现小波重构，完成心电信号去噪。Matlab 仿真结果显示，本文算 法的准确率较高，信噪比达到 84.5934dB。 （2）心电信号波形识别 反映心电信号的特征部分往往是信号的突变点，因此需要对心电信号的 突变点进行识别检测。本文通过采用二次 B 样条小波对去噪后的心电信号 进行四层平稳小波变换，在第四尺度上对心电信号的 R 波进行波形检测。 在第二尺度上以正确检测 R 波为基础，实现 Q 波和 S 波的波峰、起始点及 终止点检测。为了保证检测精度，本文设置了误检漏检机制。最后在第五尺 度实现了 P 波和 T 波波峰、起始点及终止点检测。仿真结果表明本文的算 法检测精度较高，准确率达到 99.81%。(This paper proposes an efficient method of ECG signal denoising using the adaptive dualthreshold filter (ADTF) and the discrete wavelet transform (DWT). The aim of this method isto bring together the advantages ofthese methods in order to improve the filtering ofthe ECGsignal. The aim of the proposed method is to deal with the EMG noises, the power lineinterferences and the high frequency noises that could perturb the ECG signal. This algorithm is based on three steps of denoising,)

add CFO (carrier frequency offset) to a given signal

download and try it ...thanks