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
855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 2024E卷题库疯狂收录中，刷题点这里
本专栏收录于《华为OD机试（JAVA）真题（E卷+D卷+A卷+B卷+C卷）》。
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。
求单向链表中间的节点值，如果奇数个节点取中间，偶数个取偏右边的那个值。
第一行 链表头节点地址 后续输入的节点数 n
后续输入每行表示一个节点，格式：节点地址 节点值 节点地址
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。
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。
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机考新系统真题 + 双机位C卷 + 往年真题 - 题库目录分值说明 + 考点说明+在线OJ
2026年4月1日起，华为OD机试正式切换至新系统模式！采用力扣模式，无需自己写输入！ 难度大幅度降低。正在备考华为OD机试的同学，优先刷新系统机试真题，可大幅提升通过率。 新系统考题考点与旧版一致，无需重新适应。本专栏所有算法题均配有详细思路解析，并提供C语言、JS、C++、Python、Go、Java多语言源码实现。订阅后私信即可开通专属OJ刷题权限，高效备考一次上岸！
Matcap与菲涅尔
菲尼尔效果（简化版公式）：fresnel = base + scale*((1-v*n)^power)透明队列为后前渲染，不关闭深度写入则会覆盖之前的像素，导致混合不了。y的值为0~1，根据x3返回对应y值。两色混合做个demo看看！混合为什么要关闭深度写入？
基于LORA的双机通信的智能农业系统
通过LoRa模块与主机进行通信，监测环境中的温度、湿度、土壤湿度和光照强度等参数，并根据预设的自动控制逻辑或者手动指令控制继电器开关，从而调节风扇、加热器、水泵、喷雾器和灯光等设备，为植物提供适宜的生长环境。使用的Lora是大夏龙雀的感觉这个是我使用过的Lora里面最好用的，因为不用设置频道就可以直接透明传输，使用起来得心应手。就像两个单片机直接串口通信一样，缺点是异步串行，不能同时进行接收和发送。继电器灯亮模拟打开对应的风扇、水泵、灯、喷雾、加热。1.使用温度传感器测当前环境的温湿度。
LiveReload：释放你的F5，实时预览HTML效果
一、liveReload插件开发背景Web前端开发人员在写页面时，如果修改了HTML or CSS，要想看效果通常的做法就是保存代码就得 Alt+Tab，切换到浏览器（当然，我知道很多开发人员是双屏操作不一定需要切换到浏览器），但是一定要F5刷新才能看自己刚写的代码有没有效果，没有效果就再切换到编辑器下修改，然后再切换到浏览器F5刷新看效果，还没有效果，就再继续修改，如此反复。时间久了，前端自动化...
CANdb++ Editor实战：5步搞定DBC文件信号布局与字节对齐（附常见错误排查）
本文详细介绍了使用CANdb++ Editor进行DBC文件信号布局与字节对齐的实战方法。通过五步法，深入解析Intel与Motorola字节序差异、跨字节信号布局规则，并借助Layout视图等工具，帮助工程师高效解决信号错位、数据解析错误等常见问题，确保CAN网络通信的准确性与可靠性。
17【Protues单片机仿真】基于51单片机的太阳能智能谷物翻晒机器人
基于51单片机，避障，低于50CM报警，LED灯亮起，自动翻晒用光敏电阻，光照强度大，电机转动，相当于翻晒粮食，远程控制用按键代替，按下去电机就转动，相当于翻晒粮食，然后光照强度和超声波的距离都在LCD1602显示屏显示出来。
Python 爬虫实战：淘宝直播间实时数据抓取（弹幕分析 + 流量监控）
随着电商直播的迅猛发展，淘宝直播已成为品牌推广和商品销售的重要阵地。通过爬取淘宝直播间的实时数据，包括弹幕互动和流量信息，可以帮助商家深入了解用户行为、优化直播策略，同时为市场分析和商业决策提供数据支持。本文将深入探讨如何利用 Python 爬虫技术实现对淘宝直播间实时数据的抓取，并进行弹幕分析和流量监控。
RT-DETR-R18 上下文信息融合：引入全局特征提升遮挡目标检测
"""轻量化全局特征提取器（2层Transformer编码器）"""# 特征投影层（将多尺度特征统一维度）# 轻量化Transformer编码器（分组卷积优化）# 全局特征池化（输出 [bs, hidden_dim]）# local_features: 多尺度特征列表 [C3, C4, C5]，各 [bs, c, h, w]# 统一投影至 hidden_dim 并展平为序列# Transformer编码全局上下文# 全局池化得到全局向量。
COSMO-RS理论：流体相热力学预测的核心模型与技术突破
摘要：COSMO-RS模型通过量子化学计算分子表面电荷分布，实现了从微观量子尺度到宏观化工过程的热力学预测。该模型基于介电连续介质理论，将分子表面片段化处理，通过σ-剖面描述电荷分布，并计算片段间的静电失配能和氢键能。其核心算法通过自洽化学势方程求解活度系数，克服了传统模型依赖实验参数的局限。随着技术进步，模型引入了高阶描述符和色散力修正，提升了预测精度。在药物研发、化工分离等领域广泛应用，但仍存在对长程有序结构预测不足等局限性。该模型为复杂体系的热力学性质预测提供了重要理论工具。
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
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")
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/
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
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
20万+
扫一扫
