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
2.8k累计阅读
2.9k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
AI摘要
本文介绍了如何使用ArcGIS Pro绘制常见的分析图中的扇形箭头。通过组合半圆形、矩形和三角形，利用‘合并形状’功能创建箭头，并通过调整颜色和透明度来实现所需效果。
小伙伴们，
在各种分析图、空间结构图中，
是不是经常看到这种“扇形”的箭头，
同时伴随着一些“引绿入城”等分析语言。
于是，你有没有好奇，
这种箭头是怎么做出来的？
强大的GIS是否可以绘制出这种箭头？
还是那句话——
别问，问就是能！
其实ArcMap 和 ArcGIS Pro都可以实现，
用ArcGIS Pro会更方便一些，
本文就用Pro来演示一下，
ArcMap的方法类似。
第一步—制作箭头的尾部
对于这种特殊的图形，可以将其想象为由多个不同的图形组成的。
要绘制这个箭头，你就可以线绘制出两个半圆形，一个矩形，还有一个三角形。
在ArcGIS Pro中，首先新建好一个布局视图，单击菜单栏的“插入”，在“图形和文本”选项中选择两个半圆形，并将其旋转镜像放置，你可以自由调整半圆形大小、圆弧大小、以及这两个半圆形的距离，这些将最终决定箭头“扇尾”的姿态。
别再看枯燥的菜单了！用ArcMap 10.x从零开始，手把手教你做一张完整的区位分析图
本文详细介绍了如何使用ArcMap 10.x从零开始制作专业级区位分析图，涵盖项目准备、数据导入与预处理、地图可视化设计、专业布局与输出等关键步骤。通过实战指南和效率提升技巧，帮助城市规划学生和职场新人快速掌握GIS技能，提升工作效率。
ArcGIS Pro尺寸注记实战：让GIS数据直接说工程语言
在工程制图与GIS数据融合的场景中，尺寸注记是连接空间数据与图纸表达的关键技术。它不同于普通文本注记，而是基于要素几何的测量结果可视化，能够随源要素变化自动更新，并将数值与空间参考紧密关联。通过要素数据集统一坐标系，利用线性、对齐、角度、半径等尺寸类型，配合可复用的标注样式，用户可以在ArcGIS Pro中生成接近CAD工程图标准的尺寸标注。这项能力不仅提升了规划地块、道路工程等出图场景的规范性，也为PDF导出和CAD数据交换提供了可靠的度量基础。掌握尺寸注记的单位精度设置与样式配置，有助于让GIS成果直接
arcgis箭头绘制
arcgis 箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制、箭头绘制
Arcgis实现路线等线方向箭头表示
Arcgis实现路线等线方向箭头表示
FastApi教程
HTTP协议是Hyper Text Transfer Protocol（超文本传输协议）的缩写,是用于万维网（WWW:World Wide Web ）服务器与本地浏览器之间传输超文本的传送协议。HTTP是一个属于应用层的面向对象的协议，由于其简捷、快速的方式，适用于分布式超媒体信息系统。它于1990年提出，经过几年的使用与发展，得到不断地完善和扩展。HTTP协议工作于客户端-服务端架构为上。浏览器作为HTTP客户端通过URL向HTTP服务端即WEB服务器发送所有请求。
STM32F103ZE精英板驱动ESP8266与DHT11构建物联网网关，实现乐联网温湿度数据实时监控
本文详细介绍了如何使用STM32F103ZE精英板驱动ESP8266与DHT11构建物联网网关，实现乐联网温湿度数据实时监控。从硬件选型、连接调试到乐联网平台配置和STM32程序开发，提供了完整的实现步骤和常见问题解决方案，适合物联网入门开发者参考。
自托管AI邮件助手imap-mcp：安全连接Claude与个人邮箱的完整指南
在AI助手日益普及的今天，如何安全地让AI访问个人邮箱数据成为关键挑战。传统方法如直接暴露IMAP/SMTP凭证存在严重安全隐患，而第三方SaaS服务则涉及隐私泄露风险。Model Context Protocol (MCP) 作为一种新兴的标准化协议，为AI工具安全接入提供了解决方案。其核心价值在于实现数据源与AI助手间的安全、标准化交互，特别适用于处理敏感信息的场景。imap-mcp正是基于MCP协议构建的自托管邮件网关，它通过多层安全机制（包括主密钥加密和OAuth令牌哈希化）保护用户凭证，让用户能在
Mac用户必看：Parallels Desktop 18.1.1最新破解版安装与优化全攻略（附资源）
本文为Mac用户提供Parallels Desktop 18.1.1的详细安装与优化指南，涵盖虚拟化技术选型、专业级配置流程及性能调优技巧。重点解析Parallels Desktop在图形加速、系统资源调度方面的核心优势，并分享生产力增强技巧与典型应用场景实战方案，帮助用户最大化利用这一高效虚拟化工具。
Win 11 Fastboot驱动的详细安装教程
Fastboot驱动的详细安装教程
【华为OD机试真题 2025双机位A卷】958、 高效的任务规划 | 机试真题+思路参考+代码解析（C++、Java、Py、C语言、JS）
【华为OD机试真题 2025双机位A卷】958、 高效的任务规划 | 机试真题+思路参考+代码解析（C++、Java、Py、C语言、JS） 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2025e卷，od机试，2025E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，2025华为od，
SAP Fiori Elements 扩展实战：UI 层扩展、OData 服务扩展、CDS 视图增强及全流程案例
UI 层扩展主要关注在不修改标准生成界面的基础上，自定义用户界面元素和交互逻辑。典型需求包括：在现有 Fiori Elements 界面上添加新的字段（如表格列或表单项）、增加自定义按钮并绑定操作、以及通过控制器扩展加入事件处理等。无论在 RAP 还是传统 BOPF 架构下，前端 UI 扩展通常采用 SAPUI5 提供的**扩展点（Extension Points）和控制器扩展（Controller Extension）**机制来安全地插入自定义代码。下面分别说明两种后端架构下 UI 扩展的实现方式。
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")
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
超级会员免费看
订阅专栏 解锁全文
422
扫一扫
