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报错：
error Error: certificate has expired
at TLSSocket.onConnectSecure (_tls_wrap.js:1497:34)
at TLSSocket.emit (events.js:315:20)
at TLSSocket._finishInit (_tls_wrap.js:932:8)
at TLSWrap.ssl.onhandshakedone (_tls_wrap.js:706:12)
info Visit https://yarnpkg.com/en/docs/cli/install for documentation about this command.
报错如图：
解决：
前端：JavaScript、Vue、Node、3D可视化等
解决yarn安装依赖报错：certificate has expired at TLSSocket.onConnectSecure
在使用npm或yarn等包管理器安装依赖时，可能会遇到一些常见错误。其中之一是 SSL 证书验证问题。
yarn安装依赖时报错certificate has expired at TLSSocket.onConnectSecure
安装依赖时，使用npm或者yarn等包管理器从远程仓库下载包，这些操作通常是通过HTTPS协议进行的，而HTTPS协议是基于SSL/TLS协议的。在这个过程中，您的计算机（客户端）会与远程服务器进行安全通信，这时就需要进行SSL证书验证。原文链接：https://blog.csdn.net/Z_Joker_J/article/details/136028544。这个错误表明Node Sass不支持您当前的环境：Windows 64位，运行时版本为93。可以尝试禁用SSL证书验证。
EtherCAT主站视角：如何正确配置一个支持动态PDO的从站（以倍福TwinCAT为例）
本文详细解析了在TwinCAT环境下配置支持动态PDO的EtherCAT从站的全过程。通过MDP（Modular Device Profile）实现模块化配置，涵盖从站工作原理、TwinCAT工程配置、调试技巧到高级应用实践，帮助工程师快速掌握动态PDO配置技术，提升工业现场设备扩展与维护效率。
NPM install报错certificate has expired
npm certificate has expired
LM Studio中文版零基础入门：从安装到模型下载的保姆级教程（附国内镜像源配置）
本文提供LM Studio中文版的零基础入门教程，涵盖从软件安装、中文界面切换到国内镜像源配置的全流程。特别针对模型下载速度慢的问题，详细介绍了如何通过修改配置文件使用国内镜像站，并推荐适合新手的入门模型及硬件配置建议，帮助用户快速上手本地大模型运行。
python实现RsaWithSHA256签名以及国密Sm3WithSm2签名
【代码】python实现RsaWithSHA256签名以及国密Sm3WithSm2签名。
WebStrom 报错：error Error: certificate has expired at TLSSocket.onConnectSecure (node:_tls_wrap:15
StackOver同样有这样的问题。②重新设置npm镜像源（二选一）
UA MATH567 高维统计I 概率不等式1 Hoeffding不等式与Chernoff不等式
UA MATH567 高维统计I 概率不等式1 Hoeffding不等式与Chernoff不等式 这个系列介绍高维统计，一些需要知道的结论可以参考UA MATH 564与566两个系列，分为两大部分，高维的概率论相关结果、高维的统计模型。概率论相关结论分六个部分介绍：概率不等式、高维随机向量、随机矩阵、Concentration without Independence、二次型、随机过程。 ...
error Error: certificate has expired at TLSSocket.onConnectSecure (node:_tls_wrap:1536:34)
解决方法：取消ssl验证。出现下图说明设置成功。
2019年数学建模美赛经验总结
北京时间2019年1月29号上午9时，数模美赛结束。美赛结束已经10多天了，我在这次竞赛中负责建模和编程，趁现在记忆还比较清晰，写下这篇博客记录第一次参加数学建模美赛的经历。 补更：2019年4月20日公布成绩，笔者最终获得了美赛一等奖 目录 1、基本情况 2、选题 3、分工合作 4、论文写作 5、建模 6、编程 7、入门资源 ...
手把手教你学Simulink——四开关 Buck‑Boost（FSBB）变换器的无缝模式切换仿真
本文详细介绍了四开关Buck-Boost（FSBB）变换器的Simulink仿真方法，重点解决模式切换时的直通风险和输出电压波动问题。通过统一电压PI控制、迟滞比较模式仲裁（Buck/Boost/Pass-Through）和200ns死区设计，实现了Vin从26V到22V变化时Vout稳定在12V±0.15V。关键步骤包括：搭建功率级电路、Stateflow模式判断、PI控制器输出占空比映射（Buck模式D≈Vo/Vin，Boost模式D≈1-Vo/Vin），以及带死区的PWM信号分配。仿真验证了切换过程中
nuxt 项目启动报错 certificate has expired
nuxt 项目启动报错 certificate has expired 正确的解决办法，
⑩电子产品拆解分析-家用无线遥控开关433Mhz
①免布线随意贴，装上就能使用解决单线开关烦恼；②遥控配对简单，无线通讯距离长，信号可穿墙；
error Error: certificate has expired at TLSSocket.onConnectSecure (node:_tls_wrap:1539:34) a
报错如下：和无语啊，借鉴这两片文章解决的：
FAA机载电子硬件审查指南详解（评审和审查必备）
本咨询通告旨在协助认证机构、授权代表(包括组织授权)和申请人执行机载电子硬件评审(以下简称"硬件评审")。硬件评审的目的是评估机载电子硬件是否符合适航要求和建议的符合性方法(即RTCA/DO-254)。本工作辅助文件应作为评审过程中的参考工具使用。它不应被用作检查表，也不包含所有可能需要评审的情况。同样，本工作辅助文件也不是用来替代DO-254，而应与DO-254一起使用。此外，本工作辅助文件可能包含一些不适用于特定项目评估的问题。
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
npm,cnpm install报：Error: certificate has expired at TLSSocket.onConnectSecure
npm install时，出现certificate has expired. npm淘宝镜像源证书过期。
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
yarn安装依赖时报错，显示证书过期
更新了yarn，node，换了源，仍旧不行，只能禁用证书验证。
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")
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
error Error: certificate has expired
摘要：安装yarn时出现"certificate has expired"错误，原因是HTTPS证书验证失败。解决方法：1）执行yarn config list查看strict-ssl是否为true；2）通过yarn config set strict-ssl false命令关闭SSL验证；3）重新执行yarn-install即可正常安装。此方法通过禁用严格SSL验证解决了证书过期导致的安装问题。
cnpm install安装依赖报错：Error:Error: certificate has expired at TLSSocket.onConnectSecure
3、解决方法二：使用淘宝新域名镜像。执行：npm config set registry https://registry.npmmirror.com。今天用cnpm淘宝镜像安装项目依赖包，突然崩了，看了报错信息显示证书失效了。3、解决方法一：使用官方npm源，放弃使用镜像，当然这种方法比起镜像安装会延长安装依赖的时间。在2024年1月22日，淘宝原镜像域名(registry.（或手动找到.npm下cache文件夹，删除文件夹下所有缓存文件）.taobao.org)的 HTTPS证书正式到期。
yarn 安装包时Resolving packages... 报“certificate has expired”
安装包时，报错 Error: certificate has expired
创建vue项目时SSL证书过期
如果证书已过期，则会出现此错误[1][5]。切换镜像源：如果是因为某个特定的镜像源的 SSL 证书过期，可以考虑切换到其他的镜像源。例如，将使用的地址从 HTTPS 改为 HTTP 访问，虽然这样做可能会降低安全性，但是在证书过期的情况下，可以暂时作为一种解决方案[4][5]。跳过 SSL 证书验证：可以通过将 yarn 的配置项 strict-ssl 设置为 false，来跳过 SSL 证书验证。更新证书：如果可能，联系远程仓库的维护者，更新过期的证书，以便恢复正常的 HTTPS 验证。
yarn install报错certificate has expired at TLSSocket.onConnectSecure
出现这种情况的原因是因为证书过期的原因造成的。yarn安装时报错如下图。用于禁用ssl证书验证。之后就可以继续安装了。
转载：yarn安装依赖时报错certificate has expired at TLSSocket.onConnectSecure
安装依赖时，使用npmQ或者yamn等包管理器从远程仓库下载包，这些操作通常是通过HTTPS协议进行的，而HTTPS协议是基于。原贴链接：https://blog.csdn.net/candy0521/article/details/136228745。作者：B站和尚哥 https://www.bilibili.com/read/cv33661870/ 出处：bilibili。SSL/TLS协议的，在这个过程中，您的计算机(客户端)会与远程服务器进行安全通信，这时就需要进行SSL证书验证。
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