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poi的版本
ECharts的版本
在做excel导出数据及ECharts图片时页面有几点要注意：1.在option中设置animation:false
2.获取url路径的Base64位编码，默认是png，所以括号后不用写图片类型，我一开始在括号中写了png页面反而报错；
在这里我将imagesBase64定义为全局变量，以便在调用下载方法时传到后端。
.
3.下载方法不能用ajax异步调用，ajax的返回值类型是json,text,html,xml类型，或者可以说ajax的接收类型只能是string字符串,不是流类型,所以无法实现文件下载。但用ajax仍然可以获得文件的内容，该文件将被保留在内存中，无法将文件保存到磁盘。这是因为JavaScript无法和磁盘进行交互，否则这会是一个严重的安全问题， js无法调用到浏览器的下载处理机制和程序，会被浏览器阻塞。所以不通过ajax,直接进路径，加上有不同的查询条件，所以这么写：
页面上的form表单
js获取echarts图表数据并实现多sheet excel导出
实现效果： 可将多个echarts图表数据下载到一个包含多sheet的excel； 上代码 <button class="btn btn-blue" onclick="exportsData()"> <span class="glyphicon glyphicon-download-alt"></span> &...
QEMU+KVM实战：5分钟搞定Ubuntu 24.04虚拟机搭建（附性能优化技巧）
本文详细介绍了在Linux宿主机上使用QEMU和KVM快速搭建Ubuntu 24.04虚拟机的完整实战流程。内容涵盖从环境准备、核心组件解析到具体的安装命令，并重点分享了针对CPU、内存、存储I/O及网络性能的深度优化技巧，旨在帮助用户构建一个响应迅速、资源隔离良好的高性能虚拟化开发与测试环境。
STM32H5 USB虚拟串口：用Classic驱动移植CDC实践
USB虚拟串口（CDC）是嵌入式系统最常用的主机通信方式之一，本质是借助USB协议模拟串口收发，无需额外驱动即可在PC上生成COM口。其实现依赖USB设备协议栈完成枚举、控制传输与批量传输。传统USBD Classic库不依赖RTOS，代码轻量、调用链短，非常适合裸机或轻量状态机架构；而USBX功能强大但需要ThreadX内核，资源开销较大。在电机控制器、传感器采集等场景中，日志输出与参数配置仅需简单的双向数据通道，Classic驱动往往更务实。本文基于STM32H5平台，详细梳理了使用Classic驱动移
基于Python大数据的智能交通分析系统
该系统能够实时监测交通状况，分析交通流量，预测交通趋势，为交通管理部门提供决策支持，帮助缓解交通拥堵，提高交通运行效率，减少交通事故发生率，改善市民出行体验。它能够为交通管理部门提供决策支持，帮助缓解交通拥堵，提高交通运行效率，减少交通事故发生率，改善市民出行体验，促进交通产业的健康发展。数据采集层：通过传感器、GPS设备、交通摄像头等数据源，实时采集交通数据，如车辆速度、位置、道路拥堵情况等。数据分析层：运用统计学方法、机器学习算法等，对处理后的数据进行深入分析，挖掘交通规律，预测交通趋势。
HA集群 执行 hdfs haadmin -transitionToActive 失败
最近把一个单NameNode集群变更为HA集群，过程中都很顺利，nn1和nn2两个NameNode都显示standby状态，但是把nn1或者nn2切换到active状态时就会报错抛异常。 查看master上namenode启动日志，如下： Incompatible clusterID for journal Storage Directory /home/hadoop-zero/bigdat...
2025-08-19利用opencv检测图片中文字及图片的坐标
该代码实现了图片中中文文本的检测与位置计算功能。主要使用CnOcr库进行OCR识别，结合OpenCV和PIL库处理图像。功能包括：1)检测图片中的中文文本及位置坐标；2)计算每个文本的像素宽度和高度；3)在原图上绘制文本边界框并标注识别结果。代码通过put_chinese_text函数解决中文显示问题，最终输出各文本内容、坐标位置及尺寸信息。适用于需要获取图片中文字精确位置的应用场景。
部署OpenClaw时设置环境变量提示export: not valid in this context的完整解决方案
OpenClaw环境变量设置错误解决方案 问题概述 在部署OpenClaw时设置环境变量出现"export: not valid in this context"错误，通常由以下原因导致： 在非交互式shell中使用export命令 在命令替换或管道中使用export 脚本文件包含Windows换行符(CRLF) 解决方案 修正脚本执行方式：确保export命令在正确的shell上下文中使用 使用source命令：通过. setup_openclaw.sh或source setup_op
自动权重化图卷积网络（Auto-Weighted GCN）算法在反洗钱识别中的应用及代码实现
该算法通过自动学习节点之间的权重信息，能够更好地捕捉AML数据中的关系和结构，从而提高反洗钱识别的准确性和效果。传统的AML识别方法通常依赖于专家定义的规则和手动提取的特征，但这些方法在应对大规模和复杂的数据时存在一定的局限性。GCN通过在图上执行卷积操作来捕捉节点之间的关系和结构信息，从而实现对图数据的学习和表示。自动权重化图卷积网络是一种基于GCN的改进算法，能够自动学习节点之间的权重信息，从而更好地捕捉图中的关系。利用自动计算的权重值执行图卷积操作。根据节点之间的相似度，计算每个节点的权重。
反应动力学仿真软件：Kintecus_（12）.高级功能与技巧
Kintecus允许用户自定义复杂的反应方程，包括非线性反应和多相反应。通过自定义反应方程，用户可以更准确地描述实际反应系统。多尺度仿真是指在同一模型中同时考虑不同时间和空间尺度的反应过程。Kintecus提供了工具来处理从微观分子动力学到宏观反应动力学的多尺度仿真。通过本节的学习，您已经掌握了Kintecus的高级功能与技巧，包括复杂反应网络的构建、参数优化与敏感性分析、自定义反应机制、多尺度仿真、并行计算与批处理，以及数据可视化与后处理。
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")
RU DOS版、EFI版32bit/64bit BIOS常用工具
DOS版、EFI版的RU工具，可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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