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转载 来源于 http://www.ruanyifeng.com/blog/2008/10/anti-frameset_javascript_codes.html 一个博客大大写的
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top.location.href =window.location.href;
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ABAP调用启信宝API
方法参数定义如下 源代码如下 method qxb_http_get. data:lv_url type string. "===============初始化=============================================== "获取http实例 cl_http_client=>create_by_destination( exporting destination = space importing
防止网页被嵌入框架
注：此文总结摘自阮一峰老师http://www.ruanyifeng.com/blog/2008/10/anti-frameset_javascript_codes.html ####防止自己的网页被嵌入别人的框架中，只需要在原网页的代码前加入以下代码即可： if (window!=top) {//判断当前的window对象是否是顶层对象 top.history.href=window.
从冰棍到铸钢：手把手用Fluent融化凝固模型模拟3个不同场景（含案例设置对比）
本文深入探讨了Fluent融化凝固模型在冰棍融化、巧克力涂层凝固和钢铁连铸三个不同场景中的应用。通过详细的参数设置和案例对比，帮助工程师掌握相变模拟的核心技巧，提升工业级CFD分析的精度和效率。
防止被人frame
if (top.location != self.location)top.location=self.location;
IC版图设计之OASIS文件格式详解
OASIS（Open Artwork System Interchange Standard）是继GDSII之后新一代的集成电路版图数据交换标准，由SEMI在2004年正式发布（SEMI标准号：SEMI P39）。高压缩率：相比GDSII可减少3-10倍文件体积扩展性强：支持纳米级工艺的复杂图形描述跨平台兼容：独立于EDA工具和操作系统安全增强：内置校验和与加密支持。
【49】Camunda8-Self-Managed部署之Docker方式
围绕camunda8 self-managed，介绍了基于Docker部署相关服务的过程。
js:限制页面必须在框架内·完美版：保证父框架启动的后，自动打开原来页面！
asp.net支持母板页了，这是一个相当强大而实用的功能，不过iframe框架页面还有它的生存空间。比如说网站的后台管理界面，比如说应用软件。 使用框架页有一个事情需要注意，那就是打开子页面的时候需要保存子页面始终被嵌套在父页面的里面。解决这个问题有一个简单的办法，就是在子页面里面加上一段js: if (top.location == self.location) {
从OpenClaw到Hermes Agent：AI Agent实战部署与自动化工作流构建
AI Agent（智能体）作为大语言模型（LLM）的能力延伸，其核心原理在于通过工具调用（Tool Calling）与环境交互，使模型从“对话”走向“执行”。这一技术价值在于将自然语言指令转化为具体操作，实现业务流程自动化，从而提升开发与运维效率。在应用场景上，AI Agent广泛适用于自动化运维、智能客服、数据报告生成等重复性任务处理。本文聚焦于两大热门开源框架OpenClaw与Hermes Agent，深入探讨其部署配置、技能扩展及实战联调。通过结合OpenClaw的模块化扩展能力与Hermes Age
防止网页被嵌入框架的代码（续）
说明 本文转载自：阮一峰网络日志 两年前，我写过一段代码，防止网页被嵌入框架（Frame）。 <script type="text/javascript"> if (window!=top) // 判断当前的window对象是否是top对象 top.location.href = window.location.href; // 如果不是，将top对象的网址自动导向被嵌入网页的网址 </script> 这段代码是有效的。但是，有一个问题：使用后，任何人都无法再把你的网
告别GPS，用Sentinel-1数据+Python实战SBAS-InSAR监测城市地面沉降（保姆级教程）
本文详细介绍了如何利用Sentinel-1数据和Python实现SBAS-InSAR技术监测城市地面沉降。通过保姆级教程，从环境配置、数据获取到干涉处理、时序分析和形变反演，逐步解析全流程技术细节，帮助读者掌握毫米级精度的形变监测方法，并以上海陆家嘴区域为例展示实际应用效果。
【Flink】FlinkCDC获取mysql数据时间类型差8小时时区解决方案
1、背景： 在我们使用FlinkCDC采集mysql数据的时候，日期类型是我们很常见的类型，但是FlinkCDC读取出来会和数据库的日期时间不一致，情况如下 FlinkCDC获取的数据中create_time字段1694597238000转换为时间戳2023-09-13 17:27:18，而数据库中原始数据如下，并没有到下午5点，这就导致了FlinkCDC读出来的时间和数据库中实际的时间不一致的情况，与数据库对比可以发现,这里的时间戳与数据库时间刚好相差了 8 个小时,在实际生产中这种情况是不
防止网页被嵌入框架的js代码
相信大家网上也见过很多类似的js代码<script type="text/javascript"> if (window!=top) // 判断当前的window对象是否是top对象 top.location.href = window.location.href; // 如果不是，将top对象的网址自动导向被嵌入网页的网址</script>这段代码...
innovus：给channel加soft blockage
dbGet [dbGet top.fplan.pBlkgs.name finishfp_place_blkg_* -p].boxes就可以获得channel坐标。意思是给30um以下的channel加soft blk，至于channel的坐标，通过抓取该soft blk去获取，比较容易。工具自己加入的placement blockage命名上叫finishfp_place_blkg_*ICC2中有给channel自动加soft blockage的命令，相应的innovus也有类似的命令，我们可以使用。
防止网页被嵌入框架的JS代码
两年前，我写过一段代码，防止网页被嵌入框架（Frame）。 if (window!=top) // 判断当前的window对象是否是top对象 top.location.href = window.location.href; // 如果不是，将top对象的网址自动导向被嵌入网页的网址 这段代码是有效的。但是，有一个问题：使用后，任何人都无法再把你的网页嵌入框架了，包括你自己在
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")
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
JS 防止网页被嵌入框架的代码
例如： <script type="text/javascript"> if (window!=top) // 判断当前的window对象是否是top对象 top.location.href = window.location.href; // 如果不是，将top对象的网址自动导向被嵌入网页的网址 </script> 这段代码是有效的。但是，有一个问...
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
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