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在Oracle数据库中，SELECT语句用于从一个或多个表中检索数据。它是SQL语言中最常用和最重要的语句之一。在SELECT语句中，我们可以选择指定要检索的列，也可以使用通配符"*"选择所有列。虽然这两种语法都可以实现相同的功能，但它们之间存在一些重要的区别。
SELECT语句：
SELECT语句允许我们选择要从表中检索的特定列。通过明确列出所需的列，我们可以有效地控制返回的结果集，并减少不必要的数据传输。这对于提高查询性能和减少网络开销非常重要。以下是一个示例：
SELECT column1, column2, column3
FROM table_name;
```
在这个例子中，我们只选择了列"column1"、"column2"ORACLE INSERT INTO SELECT *FROM 分批提交例子
ORACLE INSERT INTO SELECT *FROM 分批提交例子
虚拟机扩容——非LVM分区
虚拟机扩容——非LVM分区
ubuntu20.04配置UR机械臂的仿真环境
gazebo和rviz中的机械臂的位姿是一样的，要实现同步控制，可以拖动rviz中的球移动到指定位置，点击plan观察路径规划，点击execute执行该路径规划，可以在gazebo和rviz中同步看到刚刚的路径规划运动，实现了简单的机械臂仿真环境搭建。这是因为catkin找的python版本为anaconda下面的版本，所以需要改为指定采用下面的命令，参考之前的。之后要source一下，才能读取到工作空间里的文件，以后每次打开终端进入工作空间都要执行该命令。时，一直不成功，解决办法可以看我之前写的。
IPD的分析工具与模型（十六）CBB公用基础模块管理实现资源共享，缩短开发周期
CBB流程的开发环节，基于CBB规划中所识别和设计的CBB，进行CBB的详细设计（包括参数设计和接口设计），并最终实现CBB的落地开发。作为特殊类型的技术构件，CBB的开发流程，可沿用技术开发中的构件开发流程，具体包括概念、计划、开发和迁移等步骤，具体可参见前文的相关介绍。有了蓝图与架构设计，落地实施是关键攻坚。具体工作内容包括：具体工作内容包括：a. 以相似性、聚类、相对独行性、可组合、可扩展性、可维护性等原则为指导，按照产品的功能特征和业务类型，聚焦零散的功能点，形成功能模块，实现产品功能模块的划分；
《QGIS快速入门与应用基础》230：添加图例工具
本文系统讲解QGIS图例制作的核心方法与优化技巧。首先强调图例与地图框的绑定逻辑，提出三种绑定状态及适用场景。详细拆解三种图例添加方法：布局工具栏一键添加法（新手首选）、菜单栏添加法和右键快捷创建法，并给出单地图框和多地图框两种典型场景的规范技巧。重点解决新手常见的图例杂乱、样式不规范、内容脱节等问题，帮助用户从基础图例制作进阶到符合行业标准的专业图例设计。通过分步操作图示和实用技巧，使读者能够快速掌握图例创建的核心流程与优化方法。
【云原生技术】sonar的官方web接口，获取质量阀结果的接口
以下是一个示例思路，说明如何调用 SonarQube 的质量阀查询接口并在前端展示“哪些条件没过”。假设前端使用 AJAX 或 fetch 从后端接口拿到 SonarQube 返回的 JSON（或直接前端请求 SonarQube API），前端再把解析结果展示给用户。测试覆盖率 (Coverage) 当前仅有 60%，未满足最低 80% 的要求，需要补充测试或优化以提升覆盖率。接口），就会返回相应的质量阀状态信息；里使用它来查询当前质量阀（质量门）的状态。，你用它即可获取该次分析的质量阀状态。
Nvidia Jetson 编解码开发（3）解决H265解码报错“PPS id out of range”
基于之前的开发程序通过Jetson Xavier NX 硬编码的H265发出后，上位机断点播放发出来的H265码流，会报“PPS id out of range” 错误。
Google antigravity 不支持你所在地区？手把手教你把账号改成支持地区
Google antigravity功能火爆，但国内老账号根本用不了🤬本文分享几种有效的账号国家地区切换方法，最快几分钟就能让antigravity直接亮起🔥
51单片机 驱动有源/无源蜂鸣器+Proteus仿真
51单片机 驱动有源/无源蜂鸣器+Proteus仿真 相关篇《51单片机 Proteus仿真有源蜂鸣器始终不响原因及解决方法》 电路搭建 采用NPN管驱动有源蜂鸣器电路 采用PNP管驱动有源蜂鸣器电路 采用PNP管驱动无源蜂鸣器 采用NPN管驱动无源蜂鸣器 Proteus元器件列表 实例代码 /************************************************************************************* * 实验现象：下载程序
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。
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
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
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")
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