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本文介绍了如何在C#中通过SqlSugar这个轻量级ORM工具调用SQLServer存储过程，包括配置连接、定义参数、执行过程以及处理返回结果的过程，并提到了可能的调整需求和社区资源。
SqlSugar 是一个轻量级的.NET ORM（对象关系映射器），它支持多种数据库，并提供了丰富的功能来简化数据库操作。要在C#中使用SqlSugar调用存储过程，你可以遵循以下步骤：
SqlSugarClient db = new SqlSugarClient(new ConnectionConfig()
{
ConnectionString = "你的连接字符串",
DbType = DbType.<C# ORM模式之 SqlSugar使用
3、稳定性和技术支持： 虽然不是官方ORM, 但在稳定性上也是有着数年用户积累，如果遇到问题可以在GITHUB提出来，会根据紧急度定期解决；1、高性能：不夸张的说，去掉Sql在数据库执行的时间，SqlSugar是EF数倍性能，另外在批量操作和一对多查询上也有不错的SQL优化；MySql、SqlServer、Sqlite、Oracle 、 postgresql、达梦、人大金仓。2、高扩展性 ：支持自定义拉姆达函数解析、扩展数据类型、支持自定义实体特性，外部缓存等；5、创新、持续更新 ，向下兼容。
ORM_SQLSugar
很多人都会问您这款ORM有哪些特点,有什么竞争力，我归纳出以上几点供你参考 1、高性能 ,不夸张的说，去掉Sql在数据库执行的时间，SqlSugar是EF数倍性能，另外在批量操作和一对多查询上也有不错的SQL优化 2、高扩展性 ，支持自定义拉姆达函数解析、扩展数据类型、支持自定义实体特性，外部缓存等 3、稳定性和技术支持， 虽然不是官方ORM, 但在稳定性上也是有着数年用户积累，如果遇到问题可以在GITHUB提出来，会根据紧急度定期解决 4、功能全面，虽然SqlSugar小巧可功能并不逊色于EF框架 5、创新、持续更新 ，向下兼容
矢量网络分析仪VNA的校准之SOLT方法
作者：杭州冥鸿电子科技有限公司 邮箱：huanglong@deepelec.com S参数的测量方法有两种，一种是基于扫频测量的原理（VNA）,另外一种是基于快沿阶跃响应的原理（TDR）。 下图是VNA的原理框图，主要包括以下部分: （1）激励信号源：提供感兴趣的频率范围内的入射信号； （2）信号分离装置：含功分器和定向耦合器，分离出入射，反射和传输信号； （3）接收机：...
保姆级教程：用GMT6.1绘制专业地形起伏图（从数据下载到出图避坑）
本文提供了一份详细的GMT6.1绘制地形起伏图的保姆级教程，从环境搭建到数据下载，再到核心绘图命令解析，帮助用户避开常见错误。特别适合科研人员和地理信息爱好者快速掌握专业地形图的绘制技巧，提升工作效率。
期权专题9：雪球期权（一）（BSM+蒙特卡罗定价）
python；雪球定价;蒙特卡罗
VTK学习（入门级教程，包括安装和使用）~持续更新中
说明：研究QT+VTK有段时间了，准备把学到的东西分享给大家，这篇博客以VS+QT+VTK为主进行展开学习的。
正点原子lwIP实战指南——从FreeRTOS移植到网络应用开发
本文详细介绍了如何将轻量级TCP/IP协议栈lwIP移植到FreeRTOS实时操作系统上，并基于正点原子STM32开发板进行网络应用开发。内容涵盖从使用STM32CubeMX快速搭建工程、核心概念解析（RAW/NETCONN/Socket API），到关键的系统适配、驱动实现，并通过TCP Echo服务器和云台控制两个实战案例，手把手指导读者完成从移植到应用开发的全过程，帮助嵌入式开发者快速掌握lwIP在FreeRTOS环境下的稳定部署与开发技巧。
基于matlab的阈值分割的车牌定位识别系统
车牌自动识别模块是现代社会智能交通系统（ITS）的重要组成部分，是图像处理和模式识别技术研究的热点，具有非常广泛的应用。车牌识别主要包括以下三个主要步骤：车牌区域定位、车牌字符分割、车牌字符识别。本案例通过对采集的车牌图像进行灰度变换、边缘检测、腐蚀及平滑等过程进行车牌图像预处理，并由此得到一种基于车牌颜色纹理特征的车牌定位方法，最终实现了车牌区域定位。车牌字符分割是为了方便后续对车牌字符进行匹配，从而对车牌进行识别。
告别手动查文献！用Claude Haiku 4.5一键帮你检索+插入参考文献，科研效率直接翻倍（附实操演示+AI提示词）
以下是我的论文段落内容与参考文献列表。以下是我论文段落内容【粘贴论文段落】，请根据每个段落的主题自动检索近五年内的高相关学术文献（以真实DOI或可查询来源为准），要求：1）为每个段落匹配2–3条最相关的文献；另一种是你没有现成的文献列表，需要AI能够帮你检索，筛选输出合适的参考文献列表，并将其正确插入至段落中。选择完成后，先让它检索文献，我设计了一段AI提示词，让Haiku 4.5模型围绕研究主题，给我在各大数据库搜索近五年的文献，并以表格的形式输出文献的核心信息，确保DOI号能直接跳转到原文。
49、Linux内核内存管理：MGLRU、DAMON与OOM杀手详解
本文详细解析了Linux内核内存管理中的三大核心机制：MGLRU（多代LRU）页面追踪与回收机制、DAMON数据访问监控工具及其在内存优化中的应用，以及OOM（内存不足）杀手的工作原理和触发方式。同时，文章还介绍了VM overcommit_memory策略的设置及其对内存分配的影响，帮助开发者和系统管理员更好地理解Linux内存管理机制，从而优化系统性能和稳定性。
RU DOS版、EFI版32bit/64bit BIOS常用工具
DOS版、EFI版的RU工具，可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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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