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1、进入tomcat的conf目录下
cd /opt/apache-tomcat-cms/conf/
2、执行命令生成证书
keytool -genkey -alias tomcat -keyalg RSA -keystore /opt/apache-tomcat-cms/conf/.keystore -validity 3650| 参数说明 | |
| keytool -genkey | 自动使用默认的算法生成公钥和私钥 |
| -alias | 给证书取个别名，注意不要出现重复的名字 |
| -keyalg | 指定密钥的算法（如：RSA、DSA） 可通过keysize参数指定密钥长度，默认密钥长度为1024位 如果不指定密钥算法默认采用DSA，DSA算法时，密钥长度必须在512到1024之间，并且是64的整数倍 |
| -keystore | 指定密钥库存放位置和名称 密钥库就是存放密钥和证书文件，密钥库对应的文件如果不存在则会自动创建 |
tomcat 快速实现https访问(Linux)
NULL 博文链接：https://yhjhoo.iteye.com/blog/685522
OpenCV图像去畸变实战指南：从原理到参数调优（附完整代码与效果对比）
本文详细解析了OpenCV图像去畸变的原理与实战方法，对比了三种核心函数（标准、鱼眼、全景模型）的适用场景与效果。重点提供了完整的代码示例，并深入探讨了alpha、焦距、中心点等关键参数的调优技巧，以帮助开发者有效矫正镜头畸变，提升计算机视觉任务精度。
沐数科技数据开发岗笔试题2025
与数据库中的主键（Primary Key）有所不同，业务主键（Business Key）是业务层面的唯一标识，通常由业务规则决定，例如，订单号、订单明细号，身份证号等。用户授信表（table_credit）：包含用户ID（uid）、授信申请时间（credit_submit_time_local）、授信状态（credit_status）。：在正态分布中，大约68%的数据值落在距离均值一个标准差的范围内，约95%的数据值落在两个标准差的范围内，而约99.7%的数据值落在三个标准差的范围内。
使用.NET 免费开源包ViewFaceCore做一个人脸识别并注册的功能，没有比我更全的
本文介绍了一个基于WinForm的免费人脸识别系统实现方案。系统使用AForge和ViewFaceCore库，支持从USB或电脑自带摄像头采集图像，具备人脸注册和快速识别功能。硬件方面仅需普通摄像头，软件依赖多个NuGet包（如AForge.Video、ViewFaceCore等）。界面包含视频显示、图像预览和功能按钮控件，后台采用SQLServer存储用户面部特征数据。核心代码展示了人脸检测、特征提取和多线程处理逻辑，通过ViewFaceCore实现高效识别。系统具有识别速度快、准确率高的特点，适合Win
Fluent Meshing边界层设置保姆级教程：从y+理论到实战避坑，看完就会
本文详细解析了Fluent Meshing边界层设置的全过程，从y+理论基础到实战避坑技巧，帮助用户精准计算第一层高度并优化参数配置。通过案例演示和常见问题诊断，指导读者掌握边界层网格生成的核心技术，提升CFD模拟精度。特别针对不同湍流模型的y+要求提供具体参数建议。
抖音X-Medusa风控逆向：unidbg环境重建与魔改AES还原
X-Medusa并非传统加密算法，而是深度耦合运行时环境的动态混淆系统，其核心是硬件指纹感知、线程上下文绑定与非标AES变异。理解其原理需跳出‘找密钥’思维，转向对CPU型号、系统属性、微秒级时间及内存布局的综合建模。技术价值在于将不可控的客户端风控，转化为可复现、可配置、可运维的服务化能力；典型应用场景包括合规数据采集、API协议适配与移动安全研究。本文聚焦Android native层逆向实践，基于unidbg实现高保真环境模拟，并完整还原S-Box动态生成、MixColumns矩阵变异与轮密钥XOR扰
Oracle MCP Server实战：自然语言如何重塑数据库交互体验
本文深入探讨了Oracle MCP Server如何通过自然语言交互技术革新数据库操作体验。作为Oracle数据库的革命性工具，MCP Server集成了先进的Text-to-SQL技术，允许用户使用自然语言直接查询数据库，大幅降低使用门槛。文章详细介绍了从环境搭建、实战案例到企业级部署的全流程，特别强调了中文自然语言处理能力和安全最佳实践，为DBA和开发者提供了从传统SQL到智能交互的转型指南。
请举手回答，LDO和DC-DC有什么不同？
文章目录1 LDO是什么2 LDO典型电路3 DC-DC是什么4 DC-DC典型电路5 LDO和DC-DC区别10.2.6 小结一下 大家好，我是记得诚。 DC-DC和LDO都是电源芯片，两者差异很大，用法也不同，这篇博客讲述LDO和DC-DC的一些差异，帮助更好的认识LDO和DC-DC并进行选型。 1 LDO是什么 LDO是low dropout regulator的简称，即低压差线性稳压器，这是相对于传统的线性稳压器来说的，传统的稳压器，输入比输出要高出很多，否则无法工作，LDO可能输入比输出高1~2V
从零开始跑通3DGS教程：(四)修改(缩放、空间变换)colmap生成的sfm结果
通过colmap进行的sfm的普通方式会丢失场景的物理尺度信息，并且并不在符合一般认知的坐标系下，本文将读取colmap生成的点云和相机pose，将其进行空间变换和缩放之后，重新保存，新保存的结果仍然可以被colmap读取以及进行后续的稠密重建以及3dgs等工作
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
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")
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
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