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
100+累计阅读
57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
华为OD机试 新系统 题库疯狂收录中，刷题点这里
本专栏收录于《华为OD机试（JAVA）真题》。
刷的越多，抽中的概率越大，私信哪吒，备注华为OD，加入华为OD刷题交流群，每一题都有详细的答题思路、详细的代码注释、3个测试用例、为什么这道题采用XX算法、XX算法的适用场景，发现新题目，随时更新，全天优快云在线答疑。
开发一个 FLASH 坏块监测系统，能够监测 FLASH 中坏块的数量。FLASH 介质以一个大小为 m×n 的二维二进制矩阵表示，其中：0 表示正常，1 表示异常。最初，FLASH 介质中的所有单元格都是正常（即，所有单元格都是 0）。系统运行过程中，FLASH 坏块不断产生：随着系统持续运行，某
2026华为OD机试（JAVA&Go）真题【E卷+A卷+B卷+C卷+D卷】目录+在线OJ
华为OD机试真题题库【E卷+A卷+B卷+C卷+D卷】目录，Java实现。
华为OD机试 新系统 双机位C卷 往年真题 机考题库（持续收录中）以及算法考点详解
每一题都有详细的答题思路、详细的代码注释、样例测试，发现新题目，随时更新，全天优快云在线答疑。
2026华为OD机试（Python）真题【E卷+A卷+B卷+C卷+D卷】目录+在线OJ
华为OD机试真题题库【E卷+A卷+B卷+C卷+D卷】目录，Python实现。
2026华为OD机试 新系统真题题库目录｜机考题库 + 算法考点详解
华为OD机试 新系统真题题库目录｜机考题库 + 算法考点详解华为od机试，华为od机试真题，华为od机试E卷，华为odE卷，华为od机试真题E卷，华为OD，华为OD机试，华为OD机试真题，华为od机考，华为od机试D卷，华为od机试真题D卷，华为od手撕代码，华为od，华为od机试C卷，华为od e卷，华为2026e卷，od机试，2026E卷，华为OD机试E卷，什么是华为od，华为od介绍，全面了解华为od，2026华为od，如何备战华为od，华为od算法，华为od手撕代码，华为OD机试2024年最新题库，
2026华为OD机试（JavaScript）真题【E卷+A卷+B卷+C卷+D卷】目录+在线OJ
华为OD机试真题题库【E卷+A卷+B卷+C卷+D卷】目录，JavaScript实现。
华为OD机试 新系统 双机位C卷 往年真题 机考题库（持续收录中）以及算法考点详解（Python/JS/C/C++）
刷的越多，抽中的概率越大，每一题都有详细的答题思路、详细的代码注释发现新题目，随时更新。
2026华为OD机试（C语言）真题【E卷+A卷+B卷+C卷+D卷】目录+在线OJ
华为OD机试真题题库【E卷+A卷+B卷+C卷+D卷】目录，C语言实现。
2026华为OD机试（C++）真题【E卷+A卷+B卷+C卷+D卷】目录+在线OJ
华为OD机试真题题库【E卷+A卷+B卷+C卷+D卷】目录，C++实现。
TBOX（Telematics Box）在智能网联汽车中的核心功能与应用场景解析
TBOX（Telematics Box）是智能网联汽车的“中枢神经”，基于Android或Linux操作系统，并通过CAN总线与车辆深度交互。它实现了远程控制、车辆状态监控、OTA升级及紧急呼叫（E-CALL）等核心功能，是提升用车安全、便利性与实现车辆持续进化的关键技术组件。
最新CobaltStrike4.8/CobaltStrike4.9、CS4.9/CS4.8、 Windows/Linux/Mac版下载
Cobalt Strike是一款基于java的渗透测试神器，常被业界人称为CS神器。自3.0以后已经不在使用Metasploit框架而作为一个独立的平台使用，分为客户端与服务端，服务端是一个，客户端可以有多个，非常适合团队协同作战，多个攻击者可以同时连接到一个团队服务器上，共享攻击资源与目标信息和sessions，可模拟APT做模拟对抗，进行内网渗透。 Cobalt Strike集成了端口转发、服务扫描，自动化溢出，多模式端口监听，win exe木马生成，win dll木马生成，java木马生成，
利用gprMax与Matlab实现SFCW雷达信号的高效仿真与优化
本文详细介绍了如何结合gprMax与Matlab实现SFCW雷达信号的高效仿真与优化。通过利用gprMax获取地下场景的冲激响应，并在Matlab中运用卷积与信号处理技术，可大幅提升SFCW雷达系统仿真的效率与灵活性，为探地雷达研究与设计提供强大工具。
FPGA实战 —— 基于UART的多设备串口通信系统设计
本文深入探讨了基于FPGA的多设备UART串口通信系统设计。针对工业控制与物联网应用中一对多通信的需求，文章对比了共享总线式、多路独立式等硬件连接方案，并详细阐述了可配置UART IP核、波特率自适应算法以及多路数据仲裁与分发逻辑等核心设计要点。最后，通过一个四传感器采集节点的实战案例，展示了从硬件连接到Verilog代码实现的完整系统构建过程。
QTableView为例：Qt模型视图委托（MVD）（Model-View-Delegate）
QT中的MVD（模型-视图-委托）模式是一种类似于MVC的设计模式，将数据、界面和交互逻辑分离。模型负责数据管理，视图负责显示界面，而委托则嵌入在视图中处理数据的显示和编辑。QT提供了多种视图组件（如QListView、QTreeView、QTableView）和模型类（如QStandardItemModel、QAbstractItemModel），支持数据的多样化呈现。委托（Delegate）通过定制显示和编辑行为增强了视图的灵活性。该模式适用于各类数据结构展示（列表、表格、树形），能够高效处理数据与界面
车载LIN总线避坑指南：主从节点通信失败的5个常见原因及解决方案
本文深度解析车载LIN总线主从节点通信失败的五大常见原因，涵盖物理层信号异常、调度表冲突、LDF配置错误、节点状态机缺陷及环境干扰。重点剖析了LDF文件配置不一致、波特率不匹配等关键问题，并提供了从示波器波形分析到软件配置检查的完整实战排查路径，帮助工程师高效定位并解决LIN通信故障。
【Labview与SQL Server互联（labview可以引用数据库）】
Labview与SQL Server互联
mmdetection 实战：基于Mask R-CNN与自定义COCO数据集的目标检测全流程解析
本文详细解析了如何使用mmdetection框架和Mask R-CNN模型，基于自定义COCO数据集完成目标检测任务的全流程。内容涵盖环境搭建、数据格式转换、配置文件定制化、模型训练与评估等核心步骤，并提供了针对工业缺陷检测场景的实用调优思路与避坑指南，帮助开发者高效构建高性能检测模型。
pscad IEEE标准系统搭建说明
pscad IEEE标准系统搭建说明，包含典型的IEEE 9节点、14节点、30节点、39节点和118节点系统。内含标准测试系统的原始数据和潮流结果。
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")
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
廖雪峰Python教程[完整版]
廖雪峰的python教程整理的，已经排好版了，而且还是完整的，适合小白
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
34万+
扫一扫
