墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
800+累计阅读
871平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
SQL Server 提供了专用管理员链接（DAC）。DAC允许管理员访问运行的服务器以执行诊断函数或Transact—SQL语句，或对服务器上的问题进行故障排除，即使服务器以锁定或在非正常状态下运行。
DAC默认情况下只有服务器上可以使用DAC，但是你可以通过 SQL Server 2005 外围应用配置器设置允许远程计算机上的客户端应用程序使用 DAC，如下图所示
你也可以通过下面的SP_CONFIGURE命令更改数据库服务器配置
--0 - 指明仅允许本地连接使用 DAC
如何建立到SQL Server的专用管理员连接（DAC）
使用DAC连接到SQL Server实例的一些技巧
SQL Server 2005中专用管理员连接(DAC)使用技巧
SQL Server 2005 为管理员提供了一种特殊的诊断连接，以供在无法与服务器建立标准连接时使用。
基于大疆TSDK与Pix4D的热红外数据工程化处理：从RJPG到温度正射影像的完整工作流
本文详细介绍了基于大疆TSDK与Pix4D的热红外数据工程化处理完整工作流。通过Python脚本自动化解析RJPG格式的热红外图像，提取并转换其中的原始辐射数据为包含真实温度信息的TIFF文件，再结合Pix4Dmapper进行批量拼接，最终生成可直接用于GIS分析的温度正射影像图。该流程解决了手动处理效率低下、参数校准复杂等难题，实现了从原始数据到定量分析成果的高效、准确、可重复生产。
基于Python批量获取过去某地某时的天气数据
此段代码为经Visual Weather API官网样例后修改。原代码返回数据格式为json，经测试json风速字段不可用，后改为返回数据格式为csv。若修改weather_api_endpoint，还可获取该地点的预测数据，但返回数据字段可能发生变化，请以官网文档为准。获取到的字段包括：平均温度、露点、湿度、风速、降雨量、能见度、云量、天气类型等。目的：已知某一地址，获取过去某一时间该地的天气数据，并能够批量运行。这一步也会有获取不到的时间和地点，因此为空白。此段代码为经百度地图api官网样例后修改。
量化交易新手必看：如何用Python快速接入XTP极速交易系统（附避坑指南）
本文为量化交易新手提供了使用Python快速接入中泰证券XTP极速交易系统的完整指南。文章详细介绍了XTP系统的微秒级性能优势、与CTP系统的核心区别，并逐步讲解了环境配置、API连接、资金查询、下单撤单等关键步骤，同时分享了网络维护、订单处理等实战避坑技巧，帮助开发者高效对接专业交易柜台。
小白学电路之cadance搭建buffer电路
Buffer（缓冲器）是一种放大器或驱动电路，主要用于隔离输入和输出，以防止负载对前级电路造成干扰或影响。本文作者在电路方面是小白水平，目前正在学习，有不足之处欢迎指正。3.创建新的cellview，对此buffer进行仿真验证。2.创建buffer的symbol。电路中的V2的详细参数。
Cursor Pro版保姆级开通教程：绕过7天试用，支付宝一步搞定（附地区报错解决方案）
本文详细介绍了Cursor Pro版的订阅流程，包括支付宝支付步骤和解决区域报错的实用方案。针对开发者常见的网络问题和云服务器配置需求，提供了从本地环境调整到云端部署的完整指南，帮助用户高效解锁Pro版的代码补全、私有代码库等核心功能。
Android Framework WMS面试题及参考答案
什么是 WindowManagerService（WMS）？它的作用是什么？ WindowManagerService（WMS）是 Android 系统中非常重要的一个系统服务。它运行在系统进程（system_server 进程）中。 从本质上来说，WMS 是 Android 窗口管理系统的核心。它负责管理系统中的所有窗口，包括应用程序窗口、系统窗口（如状态栏、导航栏）等。 它的主要作用有很多。首先是窗口的创建和销毁。当一个应用请求创建一个窗口时，WMS 会协调各个模块完成窗口的创建工
【置顶】ARMv8/ARMv9架构入门到精通-[目录]
周贺贺，baron，代码改变世界，coding_the_world，Arm精选，arm_2023，安全启动，加密启动 optee、ATF、TF-A、Trustzone、optee3.14、MMU、VMSA、cache、TLB、arm、armv8、armv9、TEE、安全、内存管理、页表，Non-cacheable,Cacheable, non-shareable,inner-shareable,outer-shareable, optee、ATF、TF-A、Trustzone、optee3.14、MMU、V
ArcGIS Pro 制作这种地图海报
ArcMap 版本与 ArcGIS Pro 版本的制图功能相差太多，无论从美观上还是制图效率都远远优于 ArcMap，用了一段时间的 ArcGIS Pro ，真心推荐有条件的大家赶紧试一试！一个软件也能搞定一张不错的海报哦！----吐槽：好的设计真的是门花时间的活，这个教程大概花了我7小时左右，我都忘了我自己教程中截图多少张了！（可能是我速度太慢了）测试数据：阿里云盘分享-----北极海冰数据.https://www.aliyundrive.com/s/SUhsnqfkmXh。
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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")
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。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
377
扫一扫
