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谷歌地球引擎（Google Earth Engine）是一个功能强大的云计算平台，用于分析和可视化地球观测数据。全球安全网（Global Safety Net）是一个旨在保护地球上生物多样性和生态系统的倡议。结合谷歌地球引擎的强大功能，我们可以利用地球观测数据来支持全球安全网的实施和监测。
在这篇文章中，我们将介绍如何使用谷歌地球引擎应用程序来分析全球安全网的相关数据，并展示相应的源代码。
首先，我们需要在谷歌地球引擎中创建一个新的应用程序。在应用程序中，我们将加载全球安全网的数据集，并进行可视化和分析。
// 创建地图对象
var map = ee.Map();
// 加载全球安全网数据集
var globalSafetyNet = eeGoogle Earth Engine App（GEE）——Global Safety Net
GEE在2018年推出了地球引擎应用程序，以帮助你通过与更多人分享你的分析来扩大影响。从那时起，已经有超过50,000个地球引擎应用程序被创建来分享地球引擎的分析结果。今 我想分享几个由地球引擎社区制作的应用程序的例子，这些例子展示了地球引擎应用程序所带来的决策和教育影响。今天先展示一个： Global Safety Net(App) 简介： 经过两年多的发展，全球安全网（App）是第一个对生物多样性和气候复原力至关重要的陆地区域的全面全球规模分析和空间上的明确蓝图，总面积占地球土地的50.4%。该.
Python 上位机开发
首先，什么是上位机？上位机通常是指可以与下位机（如单片机、传感器等硬件设备）进行通信和交互，实现数据采集、控制指令发送以及数据处理和展示的计算机程序。 Python 作为一种高级编程语言，在开发上位机方面具有诸多优势。它拥有丰富的第三方库，使得开发过程变得相对简单和高效。
利用SPSS实现逻辑回归，树模型，以及广义线性模型
本案例是IBM SPSS数据分析与挖掘实战案例精粹----第九章的学习记录 案例背景：分析出导致急救后迟发性颅脑损伤的主要影响因素，共有201项案例： 分析思路：先使用logistic回归模型进行影响因素筛选，然后利用分类树探索自变量中是否存在交互作用，最后利用广义线性模型过程拟合带交互项的Logistic回归模型 字段：性别，年龄，收缩压，舒张压，血小板，脑挫伤，中线位移，脑肿胀，入院意识，是否手术急救，其余急救措施，是否出现迟发性脑损伤； ...
本地AI语音生成与配音工作台Pandrator：从TTS到RVC的完整实践指南
语音合成（TTS）技术通过深度学习模型将文本转换为自然语音，其核心原理涉及声学建模与波形生成。这项技术不仅提升了人机交互体验，更在内容创作领域展现出巨大价值，广泛应用于有声书制作、视频配音、虚拟助手等场景。本文聚焦于一个集成了XTTS-v2、RVC等先进模型的本地化AI语音工作台——Pandrator，它通过模块化架构将语音合成、声音克隆、音频转录与翻译等流程无缝整合，为用户提供了一个图形化、可定制的完整解决方案。文章深入解析了其基于TTS与RVC（Retrieval-based Voice Convers
STM32F103内部晶振配置实战：从代码修改到频率验证（附工程模板）
本文详细介绍了STM32F103内部晶振(HSI)的配置实战，从代码修改到频率验证的全流程，并附赠可直接使用的工程模板。通过对比HSI与外部晶振的优缺点，帮助开发者在成本敏感和空间受限的应用中实现稳定时钟配置，同时提供频率校准和常见问题排查方法。
利用ArcGIS快速生成等高线边界，完成地形图拼图
地形图边界一般在CAD中手动绘制，然而，一幅边缘复杂的地形图边界的绘制需要花费大量时间和精力，为了减少重复性工作，提高效率，用ArcGIS软件可以自动生成实测地形图的边界
Tiktok/抖音旋转验证码
主要记录一下旋转验证码的逆向思路
Win10 系统下VisualStudio2022 配置Open3D-0.18.0（C++）
Win10 系统下VisualStudio2022 配置Open3D-0.18.0
华为的C语言编程规范.pdf
华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
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