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
879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
免授权采集海天注塑机、伊之密注塑机和住友注塑机参数的.NET应用
在注塑机行业中，实时监控和采集设备参数对于生产过程的控制和优化至关重要。为了实现这一目标，我们可以使用.NET框架开发一个免授权的应用程序，用于采集海天注塑机、伊之密注塑机和住友注塑机的参数数据。
首先，我们需要使用.NET的网络编程功能来与注塑机进行通信。在.NET中，我们可以使用Socket类来建立与注塑机的TCP连接。以下是一个简单的示例代码，展示了如何与注塑机建立连接并发送指令来获取参数数据：
using System;
using System.Net;
using System.Net.Sockets注塑机数据采集网关，注塑协议标准化
例如，发那科的数据采集软件起价为10万元，住友的软件起价高达30万元，而东芝、恩格尔等品牌的授权费用同样不菲，给行业带来了一定的障碍。面对注塑机行业联网的这一挑战，我们凭借多年的控制系统开发经验和与各种方案的对接经历，提出了自己的解决方案：设计了一个适用于90%情况的注塑行业协议标准化网关。这个注塑机数据采集网关简化了协议的对接过程，无论是弘讯、科强、KEBA、长飞亚还是东芝的数据，都能通过标准化的MQTT协议传输，变量命名也得到了统一，从而让上层MES系统无需关心数据来源的差异。
海天注塑机弘讯Tech/AK系列采集方案
海天注塑机数据采集
避开这些坑！海天MA1600注塑机数据采集项目经验分享
本文分享了海天MA1600注塑机数据采集项目的实战经验。针对KEBA控制器接口不开放的难题，项目采用专用网关进行协议转换，并详细剖析了网络部署、ModbusTCP配置、数据准确性保障及现场运维中遇到的典型陷阱与解决方案，为同类工业物联网项目提供宝贵参考。
Cadence HDL原理图库避坑指南：从Excel导入引脚到解决‘V- V+’报错（附FPGA实例）
本文详细介绍了Cadence HDL原理图库的高效构建与排错方法，重点讲解如何从Excel智能导入引脚数据并优化FPGA封装设计。通过数据预处理、智能导入和深度排错三个关键阶段，帮助工程师避免常见错误如‘V- V+’报错，提升设计效率和准确性。
从STM32转战华大HC32F4A0：我的踩坑实录与10个USART的灵活配置心得
本文分享了从STM32迁移至华大HC32F4A0的实战经验，重点解析了10个USART的灵活配置技巧及常见问题解决方案。通过对比STM32F407，详细介绍了HC32F4A0在外设灵活性、IO功能组设计及多串口应用中的优势，帮助开发者快速掌握国产芯片的开发要点。
告别调参玄学：用 DINOv2 (ViT-S/ViT-G) 和 PCA 可视化，直观理解自监督特征
本文深入解析了DINOv2（ViT-S/ViT-G）模型在自监督学习中的特征提取机制，通过PCA降维技术实现特征空间的可视化。文章对比了不同规模模型的特征表达差异，并提供了PCA阈值调优和深度分析的实用技巧，帮助读者直观理解自监督学习的视觉密码。
Java全栈AI视频生成开发实战：从DeepSeek接入到剪映自动化（附完整源码）
【代码】Java全栈AI视频生成开发实战：从DeepSeek接入到剪映自动化（附完整源码）
docker-compose Install OrangeHRM
OrangeHRM是一个全面的人力资源管理HRM) 系统，它包含任何企业所需的所有基本功能。OrangeHRM旨在支持任何规模的团队，包括初创企业、中小企业以及大型跨国组织。
64.企业级案例（上）-金融行业DDD落地全流程支付系统-附架构演进图
本文以金融支付系统为例，完整展示了DDD在企业级项目中的落地流程。首先通过事件风暴识别核心业务领域，包括交易、渠道、账务等限界上下文。在战略设计中划分上下文边界，确定交易上下文为核心域。战术设计聚焦交易上下文，详细设计了支付单聚合、值对象和支付路由服务。架构实现采用CQRS+EDA模式，通过命令端处理交易，查询端异步更新读模型，并利用防腐层隔离外部渠道。案例展示了DDD从业务探索到架构实现的全过程，体现了其在复杂业务系统中的价值。
基于FPGA的数字信号处理（11）--定点数的舍入模式（2）向最临近值取整nearest
基于FPGA的数字信号处理（11）--定点数的舍入模式（2）向最临近值取整nearest
KingSCADA实现按钮点击效果
哈喽，你好啊，我是雷工！在做SCADA项目的时候，按钮是不可缺少的功能，但软件自带的按钮太丑，已经无法满足现如今客户对界面美观度的要求。这时候就需要UI小姐姐设计美观大气的SCADA界面，但UI设计的按钮切图放到程序中只是图片素材，当添加了按钮功能时，虽然能实现按钮功能，但没有任何点击效果；当遇到有需要连锁条件才能点击的按钮时，操作员无法区分是有连锁点击了没反应，还是系统界面卡死了导致的没点击成功，这时候有点击效果就能很好的区分这种问题。本篇介绍一种实现按钮点击效果的方法，以下为操作过程。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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.
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
2851
扫一扫
