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使用.NET Core封装的分布式事件总线与RabbitMQ
分布式系统中，事件总线是一种常用的模式，用于在不同的服务之间进行消息传递和通信。RabbitMQ是一个流行的开源消息队列系统，它提供了可靠的消息传递机制，适用于构建分布式应用程序。在本文中，我们将探讨如何使用.NET Core封装RabbitMQ，以实现一个简单的分布式事件总线。
首先，我们需要安装RabbitMQ的客户端库。可以使用NuGet包管理器来安装RabbitMQ.Client库。在Visual Studio中，右键单击项目，选择“管理NuGet程序包”，然后搜索并安装RabbitMQ.Client。
安装完成后，我们可以开始编写代码。首先，我们需要创建一个用于连接RabbitMQ的帮助类。以下是一个简单的实现示例：
using RabbitMQ.Client;
public class RabbitMQHelper
{
c# RabbitMQ封装类，连接发送接收解析都支持
c# RabbitMQ封装类，连接发送接收解析都支持
服务器卡死排查实战：从现象分类到根因定位的完整手册
服务器卡死是运维中最令人头疼的故障之一，从游戏圈吐槽的“土豆服务器”到生产环境中的Linux系统无响应、rsync复制挂起，本质都是系统或进程失去正常响应能力。其原理通常涉及CPU、内存、磁盘IO或网络资源耗尽，以及内核和驱动异常。掌握系统化的排查方法，能够帮助工程师快速定位根因、缩短故障时间，避免盲目重启。本文从资源、层级、场景等多维度分类，结合top、dmesg、strace等工具，梳理一套从现象确认、指标收集、日志分析到进程定位的实战排查流程，并覆盖常见高频场景与预防治理建议。无论你是运维新手还是老手
别再纠结高边低边了！用TI BQ76952设计两轮车BMS，手把手教你选对架构（附电路图）
本文深度解析基于TI BQ76952芯片的电动两轮车BMS设计策略，涵盖高边/低边驱动选择、四种基础架构的电流路径分析及选型决策矩阵。通过实战案例分享，帮助工程师优化BMS架构，提升系统可靠性和成本效益，特别适合电摩和共享单车应用场景。
从标准到实战：详解IEC 61000-4-2下的ESD电路防护设计
本文深入解析IEC 61000-4-2标准下的ESD电路防护设计，从静电防护的基础原理到实战应用。详细介绍了接触放电与空气放电的区别、防护器件选型要点（如TVS管和压敏电阻）以及PCB布局的黄金法则，帮助工程师有效提升电子产品的抗静电能力，确保设备在严苛环境下的稳定运行。
为什么你的ESXI无法识别移动硬盘？一步步教你解决USB存储挂载问题
本文深入解析了ESXi无法识别移动硬盘的根本原因，并提供了从诊断到解决的完整方案。核心在于理解ESXi的USB Arbitrator服务机制，通过检查服务状态、内核设备识别及分区表，逐步定位问题。文章详细指导了如何禁用USB仲裁服务以在主机挂载USB存储，或将其直通给虚拟机，并针对大容量硬盘、USB 3.0兼容性等进阶问题给出了实用建议。
PCAN-Explorer5使用教程
在PCAN中新建工程，导入DBC文件（右键apply），导入采集的数据选择对应的CAN通道点击红框中的标记可选择想要查看的信号在plot界面跳出属性，可调整信号显示的样式和最大最小值调出cursor,选择需要观测的变量，选中右键style——Period 可显示间隔时间增加cursor，可观测多个变量。
PX4从放弃到精通（十九）：串口驱动
文章目录前言一、创建进程二、编写驱动 前言 硬件: PIXHAWK4 USB转串口模块 软件： Ubuntu18.04 PX4 1.11.3固件 QGC地面站 串口助手 一、创建进程 参考：https://mbot1.blog.csdn.net/article/details/122587617 二、编写驱动 .........
电力负荷预测 | 电力系统负荷预测模型（Python线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）
电力负荷预测 | 电力系统负荷预测模型（Python线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）
024、反激变换器的短路保护设计
本文详细探讨了反激变换器短路保护的设计要点与实践经验。作者通过实际案例分析了短路时反激拓扑的电流特性，指出初级峰值电流限制是最基础的保护手段，但需配合打嗝模式使用。文章重点剖析了打嗝模式设计的三个关键陷阱：频率设定、退出条件和软启动衔接。同时强调次级侧辅助保护、变压器参数优化、环路响应协调的重要性，并比较了自恢复与锁死方案的适用场景。最后提出实测验证的四个关键点和设计经验：不盲信芯片手册、保护电路独立设计、充分留有余量、进行破坏性测试。全文以工程实践为导向，为反激电源的可靠短路保护提供了系统性解决方案。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
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。
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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