墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1k累计阅读
1k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
一、PICT简介
PICT工具是在微软公司内部使用的一款承兑组合的命令行生成工具，现在已经对外提供，可以在 http://download.microsoft.com/download/f/5/5/f55484df-8494-48fa-8dbd-8c6f76cc014b/pict33.msi 下载。
二、PICT作用
PICT可以有效地按照两两测试的原理，进行测试用例设计。在使用PICT时，需要输入与测试用例相关的参数，以达到全面覆盖的效果。
1、解决手动设计大量测试用例、或覆盖不全面问题，提高测试效率
2、读取excel，将生成的参数组合自动带入脚本，进行接口自动化测试
三、PICT的安装
三、PICT的使用
1、在安装目录C:\Program Files (x86)\PICT下新建case.txt文本
2、在安装目录下cmd打开命令框，输入pict case.txt回车，生成正交试验用例
正交试验法在自动化测试中的高效应用与PICT工具实战
本文深入探讨了正交试验法在自动化测试中的高效应用，重点介绍了如何利用PICT工具解决多因素组合测试的难题。通过解析因素、水平与正交表等核心概念，并结合实战案例，展示了如何从海量组合中科学筛选测试用例，大幅提升测试设计效率与覆盖率，是测试工程师应对复杂配置与兼容性测试的利器。
pict33安装包.zip
PICT33
pict33.zip
正交测试用例生成工具（PICT）可帮助您有效地设计软件系统的测试用例和测试配置。使用PICT，您可以生成比手动生成的测试更有效的测试，并且只需动手测试用例设计所需的时间即可创建它们。PICT生成一组紧凑的参数值选择，这些参数值表示您应该使用的测试用例，以获得对参数的全面组合覆盖。
300个fpga练手项目推荐
• 按键设置BPM，LED和蜂鸣器同步打拍（支持4/4、3/4拍）• 例：先实现DDR3控制器（基础），再构建视频处理流水线（集成）◦ 驱动小型e-Paper显示日期/时间（SPI接口，静态刷新）◦ 音频输入经FFT后，用LED柱状显示频率能量（8频段简化）◦ 通过HC-05模块接收手机指令，控制电机方向（前进/后退）• 随机点亮LED，用按钮响应，数码管显示反应时间（毫秒级）• 8个LED旋转闪烁，按钮停止后显示中奖编号（带减速动画）• 用4方向按键输入手势（如→→←↑），正确后点亮绿色LED。
GEE AI: 利用 Satellite Embedding 语义搜索快速识别研究区目标斑块
摘要 本文介绍了一种基于Google Earth Engine的遥感语义搜索技术，利用Satellite Embedding数据集快速识别目标地物。该方法通过将光谱信息转化为高维向量，在武汉市的案例中展示了如何：1)定义研究区和样本点；2)加载预处理的Embedding数据；3)计算向量相似度；4)结合面积过滤优化结果。相比传统监督分类，该技术具有响应快速（无需模型训练）、抗季节干扰和精准界定等优势，特别适用于森林碳储量监测和土地利用检测等应用场景。文中提供了完整的GEE代码实现流程，包括样本定义、相似度计
【C#学习记录】如何让界面控件实现自适应布局（Winform）
小伙伴们大家好，我是雷工！在软件界面设计中，客户常常要求设计的界面可以随意缩放，缩放过程中，界面中的按钮等控件也会随着窗体变大缩小自动调整显示位置和尺寸大小。在C#的Winform窗体中如何实现这个效果，下面我们一起学习下。
R语言机器学习：XGBoost线性回归模型的SHAP值
SHAP（SHapley Additive exPlanations）值是一种基于博弈论Shapley值的机器学习模型解释方法，能够解释任何模型的预测结果。其主要用途包括：1）提高模型的可解释性，帮助非技术用户理解模型预测；2）识别对模型预测最重要的特征，辅助特征选择；3）调试模型，发现预测中的异常；4）评估模型公平性，避免偏见。SHAP值的计算方法包括初始化、迭代计算、归一化和解释，常用工具如Python的shap库支持多种计算方式（如TreeSHAP、KernelSHAP等）。本文还提供了基于R语言计算
多智能体记忆架构设计：从分层模型到一致性实践
多智能体记忆架构本质上是一个有状态、分布式、支持遗忘的知识系统。从分层模型入手，结合RBAC隔离、版本向量一致性以及混合存储，可以构建出既灵活又健壮的生产级方案。可微分记忆：让记忆的读写操作嵌入端到端训练。基于大模型的记忆压缩：用LLM自动生成情景摘要，取代规则式遗忘。联邦记忆：不同组织之间的多智能体系统在不共享原始数据的情况下协同记忆。希望本文能为你的多智能体项目提供一块坚实的基石。欢迎在评论区分享你在实践中遇到的记忆架构难题。
SI好文翻译：铜箔表面纹理对损耗的影响：一个有效的模型（一）
Paul G. Huray发表在DesignCon2010的提出铜箔表面雪球模型的论文。第一部分说明之前的铜箔表面模型只适用于5GHz以内，超过5GHz后就不再准确。
【回眸】Tessy集成测试软件使用指南（一）新手使用篇
这个专栏的文章前4篇都在记录如何使用Tessy进行单元测试，集成测试需要有一定单元测试基础，且做集成测试之前，需要做好单元测试，否则将会大幅增加软件单元代码纠错的代价。集成测试所花费的时间通常远远超过单元测试。如果直接从单元测试到系统测试，结果往往是不理想的。集成测试可以检测由于单元相互交互而导致的错误，通常由一组专业的测试人员执行。
7.18 GART 与 GPUVM 对比：一颗 GPU 上两套页表的分工
本文横向对比AMD GPU上并存的GART与GPUVM两套页表。GART为内核专用，全局单一，线性单层结构，CPU同步直写；GPUVM为每进程48位地址空间，四级页表，含并发状态机，支持隔离与迁移。二者通过VMID区分，共享GMC硬件与PTE编码，分工互补。
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")
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
329
扫一扫
