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我们这里是docker安装，所以需要提前安装好docker
hostnamectl set-hostname master
systemctl stop firewalld
systemctl disable firewalld
sed -i 's/enforcing/disabled/' /etc/selinux/config
首先下载离线资源
install.sh：https://github.com/k3s-io/k3s/blob/v1.25.16%2Bk3s4/install.sh
k3s：Release v1.25.16+k3s4 · k3s-io/k3s · GitHub
镜像k3s-airgap-images-amd64.tar.gz：Release v1.25.16+k3s4 · k3s-io/k3s · GitHub</
轻量级Kubernetes之k3s：4:离线安装与部署
k3s安装已经非常方便，考虑到企业内网无法直接联网的情况，可以考虑使用设定INSTALL_K3S_SKIP_DOWNLOAD进行安装。使用官方脚本无需做任何改动即可进行离线安装。
K3s与Kuboard离线部署及集群管理实战
Kubernetes作为容器编排的事实标准，在混合云和边缘计算场景中面临离线环境部署的挑战。其核心原理是通过声明式配置实现应用的生命周期管理，而轻量级发行版K3s通过精简组件实现了低资源消耗。在军工、金融等安全敏感领域，掌握Kuboard可视化面板与K3s集群的离线绑定技术，能有效解决网络隔离环境下的集群管理难题。该方案涉及容器镜像离线处理、私有仓库配置等关键技术环节，特别适用于边缘计算节点和物理隔离的生产环境。通过规范的离线资源包制作和证书管理，可确保部署过程的可靠性。
部署harbor私有镜像仓库及k3s离线部署集群
harbor私有镜像仓库及k3s离线部署集群
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【K3s】第2篇 一篇文章学习实践K3s部署安装（离线安装）
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llama.cpp部署大模型
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11.3 海岛微电网与工业园区的构网应用
海岛微电网与工业园区配电网是构网技术下沉应用的两大典型场景。海岛微电网以构网型储能为核心，解决孤岛系统稳定性问题，需具备黑启动、多能源协调和故障穿越能力；工业园区则侧重电能质量治理、并离网无缝切换和经济优化运行。两类场景均需解决多机并联稳定性、故障特性适配和分层协同控制等关键技术。构网型变流器的电压源构建能力、自适应控制策略及飞轮储能等特殊应用，为构建高可靠性、高经济性的分布式能源系统提供了创新解决方案。
Oracle OCP 19c 认证1Z0-083考试大纲(Oracle官方版)
Oracle Database: Managing Multitenant Architecture Ed 1 course
Rockit VDEC解码实战：从零构建高效视频处理流水线
本文详细介绍了Rockit VDEC硬件解码模块在RK3568/RK3588平台的应用实践，涵盖开发环境配置、MPI接口使用、码流发送模式选择、内存管理优化及多路解码系统构建。通过实战案例展示如何利用VDEC实现高效视频处理流水线，显著降低CPU占用率，提升嵌入式多媒体开发效率。
爬取Unsplash高清壁纸：Python爬虫实战教程（最新技术）
在当今数字化时代，高清壁纸已经成为人们个性化设备的重要元素。Unsplash作为全球最知名的免费高质量图片网站，汇集了数百万张由全球摄影师贡献的高清图片。然而，Unsplash官网的下载方式需要手动操作，对于需要批量下载壁纸的用户来说效率较低。本文将带你深入探索如何使用Python爬虫技术高效爬取Unsplash高清壁纸，从基础到进阶，全面掌握图片爬取的核心技术。Unsplash成立于2013年，是一个完全免费的图片分享平台。高质量内容：所有图片均由专业摄影师上传，分辨率普遍达到4K以上免费商用。
SE模块理解+SE-Resnet模块pytorch实现
文章目录SE模块理解SE实现注意力机制原因SE-resnet网络pytorch实现定义residual定义SE模块 SE模块理解 SENet是Squeeze-and-Excitation Networks的简称，拿到了ImageNet2017分类比赛冠军，其效果得到了认可，其提出的SE模块思想简单，易于实现，并且很容易可以加载到现有的网络模型框架中。SENet主要是学习了channel之间的相关性，筛选出了针对通道的注意力，稍微增加了一点计算量，但是效果比较好。 SE模块广泛应用于去噪，去雨方向，利用SE模
EuROC 数据集格式及相关代码
Euroc提供ROS和zip两种数据格式。
把核心变干净的代价与回报：为什么推动 Clean Core，几乎绕不开 SAP Integration Suite 与 API Management
Clean Core的核心价值在于构建可持续升级的ERP系统架构，其关键不在于减少代码或限制扩展，而是通过解耦和治理实现系统边界的清晰划分。这一理念将推动企业从传统的"私搭乱建"式集成向平台化工程转型，而SAP Integration Suite和API Management正是实现这一转型的核心组件。 Integration Suite作为集成中枢，通过统一平台、可复用内容库和事件驱动架构，将碎片化连接转变为标准化能力；API Management则负责建立接口治理体系，通过统一入口、
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")
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
android 蓝牙通讯demo
android 下蓝牙通讯demo，实现了发现蓝牙，使能蓝牙，通过蓝牙发送消息。
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