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在管理虚拟化环境时，使用vCenter是一种常见的方式。然而，有时候在对vCenter中的Manager设备进行删除操作后，可能会导致ESXi主机上出现一些问题，例如残留的N-VDS交换机和被占用的网卡。本文将介绍如何解决这些问题，并提供相应的代码示例。
解决残留的N-VDS交换机问题：
当在vCenter中删除Manager设备后，可能会导致ESXi主机上的N-VDS交换机残留。为了解决这个问题，我们可以通过PowerCLI（PowerShell的VMware扩展）连接到vCenter，并使用以下代码删除残留的N-VDS交换机：
# 连接到vCenter
Connect-VIServer -Server <vCenter IP> -User <用户名> -Password <密码>
# 获取ESXi主机
$esxiHosts = Get-VMHost
# 遍历每个ESXi主机，并删除残留的esxi删除分布式交换机（vcenter丢失）
如果 ESXi 主机已连接到分布式虚拟交换机 （DVS），但您无法将其从 vCenter 清单中正确移除（该清单会在移除过程中清理 VDS），则 VDS 配置将过时
PowerCLI 通过vCenter批量删除所有esxi主机或者单一台esxi上的虚拟机
代码】PowerCLI 通过vCenter批量删除指定的虚拟机。
7、vSphere分布式交换机网络配置全解析
本文详细解析了VMware vSphere环境中分布式交换机（vDS）的网络配置方法，涵盖了添加/移除ESXi主机、创建和管理dvPort组、配置LACP、VLAN/PVLAN、负载平衡与故障转移策略等核心内容。同时提供了操作流程图、常见问题解答以及最佳实践建议，帮助管理员高效配置和管理vDS网络，提升虚拟化环境的稳定性和性能。
关于ESXi的ARP缓存如何清理及清除方式如下：
特别需要说明的是，如果要想清除掉。esxi arp缓存，必须要指定特定的条目；、如果想要查看当前的。、如果想要清理掉某条。
闲置服务器之代理服务器篇
这台服务器是在阿里云免费白嫖的，天工开物计划，阿里给的300块无门槛优惠券，买了一台新加坡的境外服务器，288，刚刚抵扣完，因为是境外服务器，能够访问一些国内不能访问的网站，所以本文章仅从技术的角度来搭建代理服务器，使用时，请自觉遵守国家相关规定，如有任何违法行为，与本人无任何关系！！！。本文使用tinyproxy来搭建代理服务器。
保姆级教程：从零到飞，用QGC地面站搞定PX4无人机全流程配置（含避坑指南）
本文提供了一份详细的QGC地面站使用教程，指导用户从零开始配置PX4无人机，涵盖固件烧录、传感器校准、遥控器设置及航点规划等关键步骤，并包含实用的避坑指南，帮助新手顺利完成首飞。
10X Genomics单细胞数据分析实战：从FASTQ到UMAP可视化的完整流程
本文详细介绍了10X Genomics单细胞数据分析的完整流程，从FASTQ原始数据处理到UMAP可视化。通过Seurat工具包实现数据质量控制、标准化、降维聚类和细胞注释，特别适合生物信息学新手学习。文章包含可复现的代码示例和常见问题解决方案，帮助研究者快速掌握单细胞测序数据分析的核心技术。
告别系统迁移蓝屏！用Intel RST驱动搞定11代酷睿装Win11的硬盘识别难题
本文详细介绍了如何通过Intel RST驱动解决11代酷睿处理器在安装Win11时遇到的硬盘识别问题。针对VMD技术导致的系统迁移蓝屏错误，提供了从驱动下载、PE环境创建到驱动注入的完整解决方案，帮助用户顺利完成系统升级或迁移。
实战经验：Sensor Bringup中那些坑我都帮你踩过了（避坑指南）
本文是一份详尽的Sensor Bringup实战避坑指南，总结了从I2C通信、MCLK时钟、电源管理到图像异常等关键环节的常见问题与排查方法。文章提供了系统化的调试工作流，强调先硬件后软件的排查原则，旨在帮助硬件工程师快速定位并解决Sensor启动过程中的难题，提升开发效率。
饥荒Mod 开发(二)：Mod目录结构
饥荒中的每个Mod 都有一些固定的目录结构，各个目录的放着不同类型的文件。动画，图片，源代码等。我们最好是遵守Mod 开发中的各个目录结构，我用的是WeGame 安装的饥荒游戏，Mod 目录如下，这个目录下保存所有订阅的Mod。
基于知识图谱的时空事件关联推理能力培养
随着信息技术的飞速发展，大量的时空事件数据被产生和收集，如交通流量数据、气象观测数据、社交媒体上的地理定位信息等。这些数据蕴含着丰富的知识和规律，但要从中提取有价值的信息并非易事。基于知识图谱的时空事件关联推理旨在通过构建知识图谱，将时空事件数据进行整合和关联，从而实现对事件之间潜在关系的挖掘和推理。本文的目的是系统地介绍基于知识图谱的时空事件关联推理能力的培养方法，包括核心概念、算法原理、数学模型、实际应用等方面。
化工设计软件：AutoCAD Plant 3D二次开发_（1）.AutoCADPlant3D概述
AutoCAD Plant 3D 是一款专为化工行业设计的三维工厂设计和建模软件。它基于AutoCAD平台，提供了强大的三维建模功能和专业的化工设计工具，帮助工程师和设计师在工厂设计过程中提高效率和准确性。AutoCAD Plant 3D不仅能够创建详细的三维模型，还可以进行管道设计、设备布置、支架设计等复杂任务。通过上述案例，我们可以看出 AutoCAD Plant 3D 在化工厂设计中的强大功能和优势。该软件不仅提供了丰富的建模工具和专业的设计功能，还支持二次开发，使用户能够根据具体需求进行定制化操作。
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
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")
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
蓝牙模块HM-10手册
蓝牙模块HM-10手册，该手册对蓝牙模块HM-10进行了详细的介绍和使用方法以及一些AT指令。
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