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
100+累计阅读
134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
grep（全称Generalized Regular Expression Parser）是Linux中一款常用的文本搜索工具，用于在文件或文本流中查找指定的模式，并输出匹配的行。它可以根据正则表达式进行高级模式匹配，提供了强大的文本搜索功能。本文将详细介绍grep命令的使用方法，并提供一些实例来帮助读者更好地理解其功能。
基本语法：
grep [选项] 模式 [文件]
选项：
-i：忽略大小写进行匹配。-v：输出不匹配的行。-r：递归搜索目录中的文件。-l：仅显示包含匹配项的文件名。-n：显示匹配项所在行的行号。-E：启用扩展正则表达式（支持更多元字符和操作符）。-F：禁用正则表达式，按照字面值进行匹配。-w：仅匹配完整单词，而不是部分匹配。君正T31x metartc集成测试
metartc为国内杨大侠部分开源的一款用于嵌入式领域webrtc库，它与谷歌官方webrtc相比，编译到君正平台的可执行文件仅有5M即可实现webrtc的通信。1. 目前实现的是基于开源版v5.0-b0版本做的简单修改，官方的好几个版本都多少缺点东西，我这里给整理了一下。目前是局域网连接的webrtc，后续尝试走stun和turn试一下NAT穿越的效果。
liquibase扩展支持达梦数据库
近期项目中需要支持国产数据库，需要liquibase底层支持达梦数据库，记录下操作的详细步骤以便分享。 1.liquibase支持的数据库很多如MySQL, PostgreSQL, Oracle, Sql Server, DB2。但是达梦数据库并不支持，所以需要新建DMDatabase类来适配达梦数据库。达梦语法和Oracle很像所以只需要复制liquibase-core下的Oracle Database做些修改就可了。以下为需要修改的地方 public class DMDatabase extends A
Winform从入门到精通（1）——Label（史上最全）
本篇文章是对专栏《winform控件从入门到精通》的详细介绍
RT-DETR改进前必看 - Ultralytics调参 1：1还原RT-DETR官方版本实验环境，更方便改进
的项目文件中集成了YOLOv3v5v6v8v9v10rt-detr11以及其中不同大小的模型结构，更方便我们学习和使用。的参数配置在路径下，其中中设置了模型在训练Train验证Val/Test预测Predict可视化Visualize导出Export超参数Hyperparameters、以及跟踪Tracker的相关参数。在调参过程中主要涉及训练Train和超参数Hyperparameters。下面主要介绍一下初始超参数Hyperparameters。
NVIDIA Isaac GR00T N1 入门教程（一） 人形机器人多模态大语言模型
英伟达™ Isaac GR00T N1 是世界上首个用于通用仿人机器人推理和技能的开放式基础模型。该跨体模型采用多模态输入（包括语言和图像）在不同环境中执行操作任务。GR00T N1 在一个庞大的仿人机器人数据集上进行训练，该数据集包括真实捕获数据、使用英伟达 Isaac GR00T Blueprint 组件生成的合成数据（神经生成轨迹示例）以及互联网规模的视频数据。通过后期训练，它可以适应特定的应用、任务和环境。
http协议
HTTP 协议由起始行、头部、空行、消息体组成。
3.38 工业缺陷检测实战：物体表面缺陷检测，从图像处理到深度学习
本文介绍了工业缺陷检测的完整流程，从传统图像处理到深度学习方法。首先定义了缺陷检测任务的目标和应用场景，然后详细讲解了传统图像处理方法（边缘检测、阈值分割等）和深度学习方法（分类、检测、分割模型）。文章最后总结了核心要点，提出了思考题和实践建议，建议从简单方法开始逐步优化。下一篇将介绍工业视觉基础技术——相机标定与角点检测。
危废焚烧优化控制系统设计与实现：基于数据驱动的控制参数预测
本文提出了一种基于数据驱动的危险废物焚烧过程优化控制方法。系统利用历史运行数据和实时监测数据，通过深度学习模型预测关键控制参数，实现焚烧过程的智能化控制。系统采用Python开发，结合TensorFlow/PyTorch框架，构建了完整的数据采集、预处理、特征工程、模型训练和实时预测闭环系统。该系统能够处理多变量非线性关系，适应工况变化，从历史数据中学习最优控制策略，相比传统PID控制方法具有显著优势。论文详细介绍了系统架构、算法设计和实现细节，为危废焚烧过程的优化控制提供了有效解决方案。
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。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
3159
扫一扫
