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MySQL 是一个流行的关系型数据库管理系统，它支持多种表引擎。两种最常用的表引擎是 MyISAM 和 InnoDB。本文将详细解析这两种表引擎的特点和使用方式，并提供相应的源代码示例。
下面是一个创建 MyISAM 表的示例：
CREATE TABLE myisam_table (
id INT PRIMARY<MySQL表类型选择：MyISAM vs InnoDB（基于PHP）
适用场景：MyISAM适合用于读密集型的应用，例如博客、新闻网站等，其中对数据的读取操作远远超过写入操作。锁定机制：MyISAM使用表级锁定机制，在进行写操作时会锁定整个表，这就意味着并发写操作的性能较差。锁定机制：InnoDB使用行级锁定机制，这意味着在进行写操作时只锁定涉及的行，而不是整个表。如果你的应用需要进行大量的写入操作、事务支持和外键约束，以保证数据的完整性和一致性，那么选择InnoDB是更好的选择。如果你的应用主要是读取操作，并且对全文本搜索有较高的需求，那么选择MyISAM是合适的。
获取上传至DMS服务器上文件的URL
如果需要失效时间延长会设置URL永久有效， 可以去修改函数SCMS_URL_GENERATE的参数DURATION 或EXPIRATION ，DURATION最大时间是999999，若需要永久有效，则需要设置EXPIRATION的时间，这时系统会忽略DURATION的参数，生成URL的时候 SCMS_URL_GENERATE会调用SCMS_URL_SIGN函数这是其中默认这是了URL的生效时间为两小时。
wsl2上运行带有Isaac Gym的程序报错: GPU Pipeline: disabled
显示GPU Pipeline:disabled,查找相关文档发现是WSL2目前不支持Isaac Gym,时,想要在wsl2上运行,最后报错如下。
避坑指南：大疆智图集群建模中5个90%新手会踩的雷（附错误日志分析）
本文针对大疆智图集群建模功能，总结了新手在部署和运行时极易遇到的五个常见问题，包括共享目录权限与路径、主从节点版本一致性、临时存储路径设置、网络与防火墙配置以及错误日志分析。文章结合具体错误日志，提供了详细的排查步骤和解决方案，旨在帮助用户快速定位并解决问题，充分发挥集群建模的效率优势。
【ARM Trace32(劳特巴赫) 高级篇 20 -- SNOOPer 使用介绍】
在 Lauterbach TRACE32 调试系统中，Snooper是一种特殊的监视工具，它能够在不中断处理器执行的情况下监视和记录特定内存地址的数据变化。这对于系统级调试、性能分析、数据流监视以及实时系统的问题追踪特别有用，尤其是在无法使用中断的情况下。当你设置了一个或多个Snooper时，TRACE32 硬件将不断地监视指定的内存地址，一旦发生了读写操作，就会将相关信息记录下来。这样你就可以看到某个地址的数据是什么时候以及如何被修改的，而不会影响目标系统的运行。
15、导频辅助信道估计：挑战与解决方案
本文探讨了导频辅助信道估计在双选择性信道中的挑战，分析了传统方法的局限性，并提出了一种新的导频子载波结构。该结构综合考虑相干频率带宽与相干时间，通过动态移动导频位置并结合时间与频率分集，提升了高速移动环境下的信道估计性能。文章还讨论了其硬件实现、系统开销及兼容性等实际问题，并展望了优化策略、降低开销及与其他技术融合的未来研究方向。
某联达OA任意用户登录漏洞复现
由于`/WebService/Lk6SyncService/DirectToOthers/GetSSOStamp.asm`j接口未作限制，导致可以通过该接口调用`admin`的`cookie`值，通过对`LoginCredence`和`LoginTimestamp`的参数修改，进而实现任意登录进去系统.
手撕Lua字节码：从黑盒到透明
lua 字节码 怎么‘手撕’一个 chunk 看看里面啥样？我手里有一个.luac或出来的二进制字节码，想知道里面到底是什么结构、哪些字段、有哪些指令，怎么一步一步“拆箱”，从一个黑盒变成有迹可循的东西？也就是：不满足于只会loadpcall而是想亲眼看看一个 chunk 内部长啥样，甚至写点工具把它解析出来。这篇就用带你从“拿到一坨.luac不搞高深玄学，尽量让你看完就能自己动手。注：以 Lua 5.3/5.4 为主，细节各版本略有不同，但大体套路是一致的。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
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