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在SQLServer中使用该sql语句可以生成GUID：select cast(NEWID() as varchar(36)) as uuid
通过一下语句将GUID中的'-'字符去掉：
select SUBSTRING(uuid,1,8)+SUBSTRING(uuid,10,4)+SUBSTRING(uuid,15,4)+
SUBSTRING(uuid,20,4)+SUBSTRING(uuid,25,12) from (select cast(NEWID() as varchar(36)) as uuid ) s
在oracle中使用select sys_guid() from dual;可以获得guid。
深入解析音频信号处理的核心算法——傅里叶变换、语音增强、回声消除与主动降噪
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SQL语句中生成UUID方法
第一种：直接输出的uuid不带横杠的 INSERT INTO 表名(字段名) VALUES ((SELECT REPLACE(UUID(),"-","") AS UUID)； 第二种：直接uuid批量插入 INSERT INTO 表名(字段名) VALUES (SELECT UUID())；
(赛事-2022)[re:Invent 2018]-002:通过分析log多次迭代优化奖励函数后- 最快7.6s / 圈
Deepracer training 流程
postgresql使用UUID函数
postgresql有两种生成uuid的方法。 可以先通过sql查看是否已安装扩展函数，和可以安装的扩展函数 查看已有的扩展函数 select * from pg_extension; 2. 查看可以安装的扩展函数 select * from pg_available_extensions; 从中可以看到两种可扩展uuid的函数，一个为1.1版本的，另一个为1.3版本的，可以根据不同的版本选择不同的函数扩展 1，安装 pgcrypto 扩展函数 安装扩展函数 create extension
Python办公自动化：用python-docx打造专业级Word表格边框（附完整代码）
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PostgreSQL中UUID的完整指南
目录 理解SQL中的键 自然键与代理键 使用PostgreSQL创建主键的语法 创建复合主键 通用唯一ID？UUID介绍 UUID_v4与UUID_V1 使用uuid-osp创建UUID主键-PostgreSQL示例 安装SQL客户端 安装uuid-osp 将UUID添加到表——语法 UUID的替代方案——自动递增主键 UUID与自动递增主键 但是UUID真的是独一无二的吗？ 结论 更改大型关系数据库的架构每年会花费公司数百万美元。关系数据库高度交织，因此一个小的更改可能会
HFSS仿真PCB射频通路间的隔离度
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sql如何自动生成uuid_SQL_定义变量，自动生成uuid和时间
1 ，定义变量set @name='test';2，常用函数,自动生成uuidSELECT REPLACE ( uuid( ), '-', '' )自动生成时间NOW()示例set @sub_net_id = '9e0b276318084ee8b9d19ee12016fb86';set @name ='测试ip池';set @plane_id = '507b6d20c0bc4d3bb2806c77...
数字滤波器应用介绍
线性滤波器FIR ，会引起恒定延迟，通过补零的方式补偿非线性滤波器IIR，会引起频率相关的延迟，通过零相位延迟filtfilt补偿从信号中去除不需要的频谱时，通过滤波和补偿实现。如果需要去除的频率与采样率相差甚远，比如4kHz 与100Hz, 可通过 低通滤波->补偿->降采样–> 滤波 补偿 -->上采样还原的方法。*降采样前一定要先低通滤波为了防止混叠。
QGis二次开发 —— 1、win10/win11搭建Vs2017-QGis3.40.12环境（附Vs2017环境效果，附全部问题解决方法）（附：Qt助手加入QGis接口说明文档）
QGis二次开发 —— 1、Windows10搭建Vs2017-QGis环境（附Vs2017环境效果）（附：Qt助手加入QGisA接口说明文档）
sql生成随机uuid
sql生成随机uuid select replace(uuid(),'-','')
【Python机器学习】决策树与随机森林：解释性与鲁棒性的平衡
本文深入探讨了决策树和随机森林在解释性与鲁棒性之间的权衡。决策树以其完美的可解释性、快速的推理速度和无需特征标准化等优势，在许多场景中仍然是首选算法。一个经过合理剪枝的决策树，其每个决策路径都可以转化为业务规则，这在金融风控、医疗诊断等对透明度有严格要求的领域无可替代。然而，单棵决策树的高方差和容易过拟合的特性限制了其泛化能力。随机森林通过集成学习的思想，巧妙地解决了这个矛盾。它维持了相对较好的解释性（通过特征重要性、OOB估计等方式），同时通过多棵树的投票或平均，大幅降低了模型的方差。
MySQL SQL语句 生成32位 UUID
SQL语句： SELECT UUID(); 在Mybatis中的sql应用： 将随机生成的UUID用作主键，随着其他字段同时存入表中 <insert id="addRole" parameterType="com.xxx.dto.Role" useGeneratedKeys="true" keyProperty="fid"> <selectKey result...
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")
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
SQL Server 2008 R2生成UUID
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sql生成uuid,获得时间
昨天在写一个sql的时候，遇到了两个问题，一个是查询的时候自动生成uuid，一个是获得当前日期。我只记得有两个函数，但具体是哪两个忘记了记录一下。
sql 指定位数生成 UUID
UPDATE user_base SET idcard = LEFT((SELECT REPLACE(UUID(),'-','')),18); UPDATE user_base_lms SET idcard = LEFT((SELECT REPLACE(UUID(),'-','')),18);
postgres 在sql语句中自动生成UUID
> create extension "uuid-ossp"; CREATE EXTENSION > select uuid_generate_v4(); uuid_generate_v4 -------------------------------------- a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11
java /SQL / WEB随机生成UUID
java import java.util.UUID; public String getUUID(){ UUID uuid=UUID.randomUUID(); String str = uuid.toString(); String uuidStr=str.replace("-", ""); return uuidStr; } SQL SELECT LOWER( REPLACE( newid(), '-', '' )) AS U
Microsoft SQL Server 生成随机数字、字符串、日期、验证码以及 UUID
本文介绍了在 Microsoft SQL Server 中生成随机数据的方法，包括随机数字、验证码、随机字符串以及随机日期和时间等，同时还介绍了如何生成 UUID，以及如何从表中返回随机记录。
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