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
600+累计阅读
681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
为其他对象提供一种代理以控制对这个对象的访问。
Provide a surrogate or placeholder for another object to control access to it.
代理方法模式的结构中包括三种角色。
+ 抽象主题（Subject）：抽象主题是一个接口，该接口是对象和它的代理所共用的接口，即是RealSubject角色和Proxy角色实例所实现的接口。
+ 实际主题（RealSubject）：实际主题是实现抽象主题接口的类。实际主题的实例是代理角色(Proxy)实例所要代理的对象。
+ 代理（Proxy）：代理是实现抽象主题接口的类(代理和实际主题实现了相同的接口)。代理含有主题接口声明的变量，该变量用来存放RealSubject角色的实例引用，这样一来，代理的实例就可以控制对它所包含的RealSubject角色的实例访问，即可以控制对它所代理对象的访问。
半导体测试（一）
探针台是一种用于在晶圆上进行配合探针卡进行电路量测的设备，在测试过程中，探针工作站会将探针与晶圆上的每个die进行接触，以进行电性测量。在这个过程中，每个die都被测试以确保它能基本满足器件的特征或设计规格书（Specification），通常包括电压、电流、时序和功能的验证。晶圆是一个圆形的硅片，在这个半导体的基础之上，建立了许多独立的单个的电路；在所有的die都被探测（Probed）之后，晶圆被切割成独立的dice，这就是常说的die saw，所有被标示为失效的die都被报废，。
手把手教你如何修改YOLO11的模型结构（以CBAM和可变性卷积为例）
YOLO11修改模型结构
Ansys -PEmag 平面变压器仿真与circuit电路模型验证
ansys 软件集成了电路、磁路、PCB参数提取，结构参数提取等功能仿真，基于内置模块的功能参数的仿真后可以提取出主要关键参数，寄生参数，频域响应参数等。该软件的优势在于能够将所有的仿真参数导出为电路模型文件，可以在自带的模块中仿真也可以导出在其他第三方软件中仿真，比如matlab-simulink文件扥。主要介绍ANSYS插件的使用，对PEmag做了详细使用说明并通过案例得到对用的变压器参数；基于变压器参数再将其磁路模型导入到电路中进行建模仿真，实现PEmag与circuit的联合仿真。
dToF直方图之美
上面视频里如果你能看懂，那么就能理解对于dToF单点的测距问题，对于大部分的描述在AMS和ST里的手册都非常的详细，有疑问的可以后台来找我探讨，今天我们单纯从上层讨论成像的问题。作为dToF算法工程师，应该是对直方图敏感的，直方图的每个Count峰都会表征出问题，因为dToF的输出只有直方图，观察直方图能够逆推出dToF的很多东西，这也是大多数公司不愿意开放直方图和Raw data的缘故。以上是一个小面阵的分辨率，第一张是直方图，后面是基于直方图寻峰出来的成像ToF图，Peak图，以及噪声Noise图。
Ubuntu 24.04 极速部署 Dify：从零到一的保姆级实践
本文详细介绍了在Ubuntu 24.04系统上快速部署Dify的完整教程，从环境准备到部署验证，再到深度优化和安全加固，提供了一套保姆级的实践指南。通过使用Docker和Docker Compose，结合国内镜像源加速，帮助用户高效完成Dify的安装和配置，确保系统稳定运行。
推荐系统[六]：混排算法简介、研究现状混排技术以及MDP-DOTA信息流第三代混排调控框架，高质量项目实战。
混排是指将多个不同的推荐结果混合在一起，以提供更多样化的选择和更全面的覆盖。混排的目的是为了避免推荐系统出现过度偏向某些内容或类型的情况，从而提高推荐的多样性和准确性。混排的实现方式通常是基于多种推荐算法和数据源，通过加权和组合的方式来生成最终的推荐结果。信息流推荐算法的核心是理解用户兴趣，捕捉用户逛买意图，并据此做到高效的人货匹配。
Go语言全面解析：从入门到精通
Go语言是由Google开发的一种静态类型、编译型编程语言，具有简洁、高效、安全的特点。它特别适合高并发服务、云原生应用和网络编程等场景。Go采用Goroutine和Channel实现轻量级并发，性能接近C/C++。与其他语言相比，Go语法更简洁但类型更严格，编译速度快且跨平台支持好。虽然Go支持多种设计模式和编程范式，但其面向对象实现方式与传统语言不同。标准库丰富且拥有Gin、GORM等企业级框架。核心语法包括包管理、变量声明、函数定义、流程控制等，适合构建高性能后端服务和系统工具。
携程机票数据爬取实战：使用 Python 获取携程网机票信息
通过本篇博客，我们详细介绍了如何使用 Python 爬取携程网的机票信息，涉及了静态网页爬取（使用 Requests 和 BeautifulSoup）和动态网页爬取（使用 Selenium）两种方法。通过获取的数据，我们不仅能够进行票价分析，还可以构建更为复杂的预测模型或趋势分析工具。爬虫技术不仅能够帮助我们收集有价值的数据，还能够为商业决策提供数据支持。在实际应用中，我们可以结合更多的分析方法，如自然语言处理、图像处理等，进一步提升数据的价值。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
IMSL CNL 7.0.0 (x86-64)Crack
IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件，这是C++版本的破解文件
STM32F407中文手册
STM32f407中文手册 本参考手册面向应用开发人员，提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列，各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性，请参见数据手册。有关 ARM Cortex™-M4F 内核的信息，请参见《Cortex™-M4F 技术参考手册》。
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。
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
8423
扫一扫
