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今天突发奇想想用PHP接受POST表单，用python做运算，毕竟python灵活度更高，工作也需要用到，顺便用于练习了。
有几种方法：
1. 用php自带函数
exec()、 system()、 passthru() php手册中有介绍，貌似都需要注意安全性问题，因为很有可能允许用户执行系统命令。python响应有延时时，system()和passthru()也都能成功，也就是说，php会等待程序执行完成才返回结果。使用起来非常方便，但是程序执行错误不容易发现。调试非常麻烦！
期间还遇到一个问题，就是由于运行目录变化，遇到python提示文件不存在的问题，只要给sys.path加入py脚本文件所在目录，并且把运行环境目录改为该脚本所在目录即可：
curpath=os.path.normpath(os.path.join(os.getcwd(),os.path.dirname(__file__)))
sys.path.append(curpath)
os.chdir(curpath)另外编码的问题耗费了好长时间，擦泪啊，搞了好久，最终解决办法是套了原先的测试模
SRAM vs RRAM：存算一体芯片选型指南（含最新技术对比）
本文深入对比了SRAM与RRAM在存算一体芯片设计中的核心差异，为硬件工程师和架构师提供实战选型指南。文章从技术本质、性能、功耗、成本及设计风险等多维度剖析，指出SRAM基于成熟CMOS工艺，适合高精度、低延迟场景；而RRAM凭借非易失性和高密度潜力，在超低功耗、固定模型应用中优势明显。决策需综合项目预算、上市时间及技术储备。
Blender.粒子效果
Blender.粒子效果 ✦ shift+A新建一个棱角球，细分（之后要分解成许多小颗粒） ✦ 给球添加粒子系统，这时播放粒子会向下掉，力场权重☛重力☛0后，粒子会在球体周围运动 ✦ shift+A添加一个力场，调整力场强度 ✦如果粒子运动太快，我们可以在输出属性中调整时间重映射的原始值 ✦想让粒子在球周围运动而不是发散出去，在物理属性找到力场设置，调整流的大小 ✦S键将球形缩小到看不见 ✦给粒子变添加立方体的形状，在粒子属性里找到渲染选项，将“光晕”改为“物体”，在实例物体中选择新建的立方
用nTopology五分钟生成三维Voronoi泡沫：节点式建模从原理到实操
多孔结构在增材制造与轻量化设计中应用广泛，其中Voronoi泡沫因其优异的缓冲吸能和比刚度表现备受关注。传统CAD建模需处理复杂布尔运算和网格修复，而隐式建模通过数学场描述几何体，将空间距离与密度分布转化为可视化节点，大幅降低操作门槛。nTopology基于场驱动与参数化逻辑，无需编写命令，即可实时调节胞元尺寸、壁厚及随机种子，快速生成可打印的泡沫实体。这种工作流适用于缓冲垫、医疗植入体、航空支架等场景，也为梯度密度结构与力学仿真验证提供了高效路径。本文围绕三维Voronoi泡沫的生成原理与节点操作流程，详
基于SHIS凝视型短波红外高光谱相机（900-1700nm）区分不同宝石的差异性和可分性（宝石分选）
SHIS系列凝视型高光谱相机基于液晶可调谐滤波器（LCTF），通过电控LCTF进行连续细分窄波段分光，对每个波段进行高分辨率的二维面阵成像，进行图像处理后，获得完整的高光谱高清图像序列。具有低功耗、小型化、使用方便和易于扩展的优势。其工作波段覆盖400nm至1000nm，能够捕捉宝石在不同波长下的光谱特征，为宝石鉴定提供了新的工具。
【OpenFOAM】-olaFlow-算例4- irreg45degTank
不规则波浪模拟
C++11、C++14 和 C++17 标准新特性详解
特性C++11C++14C++17auto类型推导✅范围 for 循环✅Lambda 表达式✅✅✅智能指针✅移动语义✅nullptr✅统一初始化{}✅✅constexpr✅✅✅✅数字分隔符✅结构化绑定✅✅✅文件系统库✅。
Matlab 轴承信号仿真+分析
文章目录项目说明1、信号仿真1.1 仿真公式1.2 信号参数设置1.3 代码实现2、仿真信号分析2.1 小波变换2.2 快速谱峭度 项目说明 本文主要解决两个问题： 1、仿真包含周期性冲击信号、随机干扰冲击信号、调制干扰信号以及随机噪声四个成分的故障轴承信号； 2、使用小波变换以及快速谱峭度对该仿真信号进行分析。 1、信号仿真 1.1 仿真公式 这里的仿真信号公式主要参考了包文杰学者的硕士论文《加权谱峭度故障诊断方法研究与应用》，列举公式如下： 周期性冲击信号仿真 随机干扰冲击信号仿真 调制
【四足机器人项目实战】四足机器人弹簧倒立摆（SLIP）动力学模型
系列文章目录 提示：这里可以添加系列文章的所有文章的目录，目录需要自己手动添加 TODO:写完再整理 文章目录系列文章目录前言1.动力学建模构型方法2.四足机器人动力学模型（1）多体动力学模型【针对躯干+脚建模】方法一：VMC（VirtualModelController）模型方法二：SLIP模型（2）浮基单体动力学模型【针对躯干建模】【用于MPC】 前言 认知有限，望大家多多包涵，有什么问题也希望能够与大家多交流，共同成长！ 本文先对四足机器人动力学模型-VMC、SLIP和浮动机体模型做个简
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
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