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一、SpringBoot框架搭建
二、Java中SpringBoot组件集成接入【MySQL和MybatisPlus】
三、SpringBoot项目中根据数据表自动生成entity\DAO\DTO\VO\QO\Convertor\service\service.impl\controller等基础CRUD代码
四、Java中SpringBoot组件集成接入【Knife4j接口文档（swagger增强）】
五、Java中SpringBoot组件集成接入【slf4j日志文档】
六、Java中SpringBoot组件集成接入【MQTT中间件
【springboot集成mqtt开发】
springboot集成mqtt
springboot集成MQTT实现消息收发，断线重连
springboot集成MQTT实现消息收发，断线重连 springboot中集成netty。我在代码里用到了lombok的@Slf4j注解输出日志日志。我们先加入mqtt的依赖 mqtt配置 配置 host 里的ip换成自己服务器的公网ip mqtt服务器如果设置了用户名和密码需要填写，没设置就不需要 spring: application: name: mqtt profiles: active: local #local：配置的是本地或测试环境 dev：配置的是生产环境
【SpringBoot集成slf4j】SpringBoot集成接入slf4j日志文档组件的详细教程
【SpringBoot集成slf4j】SpringBoot集成接入slf4j日志文档组件的详细教程
SpringBoot集成MQTT实现交互服务通信
SpringBoot集成MQTT实现交互服务通信
JAVA开发MQTT实例（附带相应jar）
【初学者】JAVA开发MQTT实例。服务部署对应“虚拟机Centos7搭建MQTT服务”（服务部署：https://download.youkuaiyun.com/download/qq_35773820/13094431）；
实战指南：基于汇川PLC与EtherNET/IP的无线以太网通信配置详解
本文提供了一份详细的实战指南，讲解如何配置汇川PLC基于EtherNET/IP协议的无线以太网通信。通过硬件选型（如达泰DTD419MB模块）、软件设置（使用AutoShop配置IP与I/O映射）及通信测试，实现PLC间稳定、灵活的数据交换，有效解决工业现场布线难题，提升部署与维护效率。
【低空经济】低空无人机公共测试场与验证基地设计方案
公共测试场与验证基地是为低空无人机相关技术的研发、测试与应用提供一个系统性的平台。在无人机产业蓬勃发展的背景下，建设一个配备完善、功能齐全的公共测试场与验证基地显得尤为重要。该基地不仅可以作为无人机的测试和验证场地，还可以为相关企业、科研机构以及政府部门提供技术支持和服务。因此，为此基地的设计方案必须综合考虑功能性、安全性、灵活性与可持续性等多方面的因素。首先，公共测试场需划分为多个专用区域，以便满足不同类型无人机的测试需求。这包括低空飞行区域、紧急着陆区、操控模拟区和监测与数据采集区等。
Kettle下载与安装教程【保姆版】
Kettle简介 Kettle最早是一个开源的ETL工具，全称为KDE Extraction, Transportation, Transformation and Loading Environment。后来Kettle重命名为Pentaho Data Integration 。 它由Java开发，支持跨平台运行，其特性包括：支持100%无编码、拖拽方式开发ETL数据管道；可对接包括传统数据库、文件、大数据平台、接口、流数据等数据源；支持ETL数据管道加入机器学习算法。 主要功能 Pentaho D
AIStarter避坑指南：Wan2.2 Animate整合包从下载到出片的完整流程（附常见错误修复）
本文提供了Wan2.2 Animate整合包从下载部署到生成高清视频的完整避坑指南。详细解析了通过AIStarter启动、环境配置、核心参数调优以及七大常见错误（如CUDA内存不足、节点缺失、视频模糊等）的根因与修复方案，帮助用户高效利用ComfyUI工作流，稳定生成720P高质量AI动画视频。
从网格划分到结果解读：一次完整的子弹侵彻LS-DYNA仿真避坑指南
本文详细解析了子弹侵彻LS-DYNA仿真中的关键技术与避坑指南，涵盖网格优化、对称边界条件设置、沙漏控制参数调整及结果可信度验证。通过HyperMesh网格划分和LS-DYNA高级参数配置，帮助工程师提升仿真精度与效率，确保计算结果物理可信。
VASP能带计算避坑指南：从CHGCAR读取失败到成功解决的全过程
本文深度解析VASP能带计算中常见的“CHGCAR读取失败”报错，并提供从静态自洽到非自洽计算的全流程避坑指南。核心解决方案包括使用CONTCAR替代POSCAR、调整SYMPREC参数以处理对称性判定问题，并推荐利用vaspkit工具自动化生成输入文件，确保计算流程稳健高效。
【异常】QClaw客户端安装失败（OpenClaw资源解压出错）问题排查与修复指南： 安装失败：OpenClaw 资源解压出错。 请重新安装或联系支持。
摘要：QClaw客户端安装失败报错"OpenClaw资源解压出错"的主要原因是资源包解压异常，常见于安装包损坏、系统权限不足、安全软件拦截、旧版本残留或系统环境问题。解决方案分三步：1)快速修复：重新下载安装包、管理员权限运行、关闭安全防护；2)深度修复：清理旧版本残留、修复系统组件；3)终极方案：收集日志联系官方支持。建议优先检查安装包完整性并使用纯英文安装路径，80%问题可通过快速修复方案解决。
Vue3 - Arco Design Vue 表格组件 table 隐藏鼠标移入时 hover 高亮背景色，el-table 组件去除鼠标悬停在表格行的 hover 高亮效果（改变鼠标移入时的背景色）
vue3，arcodesignvue，arcodesign，表格，a-table，hover，表格hover的class类名是什么，class，hover，在arcodesignvue项目中，表格组件如何去掉鼠标移入时的hover效果，vue3arco.design表格组件修改鼠标移入行的背景颜色，table表格，取消表格行row移入高亮效果，去掉鼠标移入行时的hover效果，取消a-table的hover状态，取消高亮显示，隐藏表格鼠标hover效果，去除鼠标触摸变色效果，如何取消arco表
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
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")
锂电池充电器电路设计 里面有电路
锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
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