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
400+累计阅读
493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
JSP+JavaScript二级级联下拉菜单
在使用JSP JavaScript过程中有很多的技巧与实现，那么如何构建二级级联下拉菜单呢，本文将会向你做一个源码上的介绍。
jsp+js实现的二级联动菜单
原javascript代码: var subcat = new Array(); subcat[0] = new Array('no','大类','12') subcat[1] = new Array('pinp','楼宇包装','louy') subcat[2] = new Array('pinp','会场布置','meet') subcat[3] =
java/jsp 实现二级级联菜单
java/jsp 实现二级级联菜单 可以 从数据库里取出数据进行级联
JSP二级联动菜单
JSP页面： function changeDepart(){ var zoneNumber=$("#zoneSelect").val(); var url="${pageContext.request.contextPath}/basic/dustbin/dustbin_selectDepartByZone.action";
告别零碎资料！手把手教你搞定ASTER L1T数据的预处理全流程（附ENVI实操）
本文详细介绍了ASTER L1T数据的预处理全流程，包括数据获取、质量检查、ENVI实操步骤（辐射定标、大气校正、几何精校正）以及热红外波段深度处理。特别适合地信领域初学者和研究人员，帮助快速掌握ASTER L1T数据处理技巧，提升定量反演研究效率。
ScanNet数据集详解
ScanNet 是一个 RGB-D 视频数据集，在超过 1500 次扫描中包含了 250 万个视图，并标注了 3D 相机位姿、表面重建和实例级的语义分割。为了收集这些数据，设计了一个易于使用且具有扩展性的 RGB-D 捕获系统，包括自动化的表面重建和众包的语义标注。如果您想下载 ScanNet 数据，请使用您的机构邮箱地址填写一份 ScanNet 使用条款协议，并发送至 scannet@googlegroups.com。压缩包是将3D的标注信息“拍扁”成2D图片的结果，对应原始视频的每一帧。
js/jsp实现两级级联下拉
页面代码 <div class="form-group row"> <label class="col-sm-2 control-label text-right">一层次原因：</label> <div class="col-sm-8"> <select id="yccyy" name="yccyy" class="form-control select2"> <op
【各种轮子2】基于Icepak的PCB散热仿真比较
随着电子产品的集成度越来越高，PCB板的尺寸越来越小，板级芯片散热的问题越来越成为电子工程师的一个重要挑战。对于板级芯片散热，主要依靠工程师对PCB自身的设计进行优化，同时要兼顾系统的尺寸和成本。本文分别从芯片角度和PCB角度进行建模，芯片模型选用QFN封装，PCB模型采用走线导入模型，探讨了芯片结构，PCB铜厚，PCB叠层厚度对芯片散热的影响。
js怎么实现二级菜单
其中，HTML 文件是页面的基础结构，CSS 文件是用来美化页面的样式，JavaScript 文件则是用来实现菜单的交互效果的。在 HTML 文件中，需要创建一个菜单容器元素，在该元素下创建一级菜单项元素，每个一级菜单项元素下面再添加二级菜单项元素。在 JavaScript 文件中，则需要实现菜单的交互效果，包括鼠标悬停/离开时的效果等。的样式设置了二级菜单项内部文本的样式，以及背景颜色、文字颜色等样式设置。在 CSS 文件中，通过给菜单项元素设置合适的样式来美化菜单的外观。
halcon 深度学习标注_Halcon deep learning之目标检测笔记（一）
设置输入输出的路径输入路径有两个，一个是图像路径HalconImageDir，一个是Json文件路径PillBagJsonFile。这两个参数用于后续在图像路径中读取coco文件。输出路径首先定义一个新的文件夹路径，用于存放样本和模型文件等数据。设置基于深度学习的目标检测模型的路径，该模型文件以.hdl结尾。设置样本数据文件夹ExampleDataDir：'detect_pills_data'数据...
YOLOv11改进策略【Neck】| 适用关键点检测：PSFM，深层语义融合模块 引入深层与浅层交叉注意力机制，动态建模不同层级的全局语义依赖关系
本文记录的是利用PSFM 模块改进 YOLOv11 的颈部融合部分，专门用于关键点检测。PSFM模块（Profound Semantic Fusion Module，深层语义融合模块） 通过在特征提取网络的深层引入跨模态交叉注意力机制，动态建模红外与可见光特征的全局语义依赖关系。本文将其应用到的颈部部分，融合深层与浅层特征，捕捉长距离语义关联，增强融合特征的判别性与场景理解能力，为检测头提供包含全局上下文的高层语义表示，从而提升模型在复杂场景下的目标检测准确率与语义推理鲁棒性。Rethinking the
JSP实现2级下拉菜单实例
city.jsp city.jsp New Document cities = new Object();cities[北京市]=new Array(北京市区, 北京市辖区);cities[上海市]=new Array(上海市区, 上海市辖区);cities[天津市]=new Array(天津市区, 天津市辖区);cities[
MATLAB - 利用非线性模型预测控制（Nonlinear MPC）来控制四旋翼飞行器
本示例展示了如何利用非线性模型预测控制（MPC）为四旋翼飞行器设计一个跟踪轨迹的控制器。
Jsp+javascript打造二级级联下拉菜单
Jsp+javascript打造二级级联下拉菜单 ［ 作者： 转贴自：webjx.com整理 更新时间：2006-6-17 文章录入： ］
Unity小游戏接入抖音敏感词检测
摘要： 本文介绍了Unity小游戏接入抖音敏感词检测的方法。抖音官方提供了两种API：SensitiveWordCheck用于检测敏感词，ReplaceSensitiveWords用于替换敏感词。此外，若使用键盘输入事件，可通过tt.onKeyboardComplete监听回调，平台会自动屏蔽敏感词（替换为*）。接入官方字库可有效避免审核驳回，适用于小游戏中的用户输入功能（如起名系统）。附官方文档链接及参数说明。
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
java js 二级联动下拉列表_Jsp+javascript打造二级级联下拉菜单
数据库需求分析：class(一级栏目信息)：classId(自动编号)，className(栏目名称)Nclass(二级栏目信息)：NclassId(自动编号)，NclassName(栏目名称)，parentId(一级栏目id，与class表中的classId关联)级联菜单ResultSet rs=stmt.executeQuery(sql);%>var onecount;onecount=...
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")
用JSP+JavaScript打造二级级联下拉菜单
JSP+JavaScript打造二级级联下拉菜单： class(一级栏目信息）：classId(自动编号），className（栏目名称）, Nclass（二级栏目信息）, NclassId（自动编号），NclassName(栏目名称），parentId（一级栏目id，与class表中的classId关联） contentType=text/html;charset=GB2312 l
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。
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
用JSP+javascript打造二级级联下拉菜单
JSP+javascript打造二级级联下拉菜单： class(一级栏目信息）：classId(自动编号），className（栏目名称）, Nclass（二级栏目信息）, NclassId（自动编号），NclassName(栏目名称），parentId（一级栏目id，与class表中的classId关联）<%@ page contentType=text/html; charset=GB...
JSP+JavaScript打造二级级联下拉菜单
JSP+JavaScript打造二级级联下拉菜单： (个人博客：www.duduct.com)class(一级栏目信息）：classId(自动编号），className（栏目名称）, Nclass（二级栏目信息）, NclassId（自动编号），NclassName(栏目名称），parentId（一级栏目id，与class表中的classId关联）
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
1万+
扫一扫
