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下面是两个weblogic10.3版本的下载地址：
Microsoft Windows x86: 756 MB http://download-llnw.oracle.com/otn/nt/middleware/11g/wls/oepe11_wls1031.exe
Linux x86: 773 MB http://download.oracle.com/otn/linux/middleware/11g/wls/oepe11_wls1031_linux32.bin
下载方法：
①复制网址；
②打开迅雷；
③点击新建；
这样就可以了额。
Weblogic10.3.6（wls1036_generic.jar）安装包linux,windows,mac操作系统通用百度网盘下载
Weblogic10.3.6（wls1036_generic.jar）安装包linux,windows,mac操作系统通用百度网盘下载
WebLogic10.3下载地址
Oracle WebLogic Server 10.3.1 + OEPE - Package Installer 单语言版带OEPE Microsoft Windows x86: 756 MB http://download-llnw.oracle.com/otn/nt/
多视图学习 (Multi-View Learning)
多视角/多视图学习（Multi-view learning）是陶大成提出的一个研究方向。我们都知道，在机器学习中样本可以用不同的特征（如图片可以用LBP、SIFT等特征）来表示，或者从不同的角度进行观察（如从前后左右观察一个对象），甚至是采用不同的传感器来观测（如RGB摄像头，Depth摄像头），这些不同的观测手段、角度或特征在多视角学习中称为“视角”。多视角学习通过对不同的视角进行统一分析和研究，希望能够得到更好的分类或聚类等等的效果。
WebLogic的下载地址http://www.oracle.com/technetwork/middleware/weblogic/downloads/wls-main-097127.html
http://www.oracle.com/technetwork/middleware/weblogic/downloads/wls-main-097127.html
Julia Ann老司机管理系统
https://content.badgirlsusa.com/Julia_Ann-Sexy_Julia_Ann_Fucks_A_Lil_Elf's_Big_Cock_/Julia-Ann-Naughty-America-%E6%80%A7%E6%84%9F%E7%9A%84%E8%A3%98%E8%8E%89%E5%A9%AD%E5%AE%89%E4%BA%82%E6%90%9E-lil-%E7%B2%BE%E9%9D%88%E7%9A%84%E5%A4%A7%E5%85%AC%E9%9B%9E-2020
深入解析MSPM0系列Keil开发中‘ti_msp_dl_config.h‘缺失问题的根源与修复
本文深入解析TI MSPM0系列Keil开发中'ti_msp_dl_config.h'文件缺失问题的根源与修复方法。该错误常见于编译时提示file not found，主要因SysConfig工具未正确生成或工程路径配置错误导致。文章提供了从环境检查、手动生成文件到工程配置调整的系统化解决方案，帮助开发者快速解决这一常见KEIL开发难题。
WebLogic10 3下载地址
WebLogic10 3下载地址
GPU运维基础知识
常见的GPU型号如NVIDIA Tesla系列（V100、A100）、GeForce RTX系列（3090、4090）和AMD Instinct系列（MI250X）。硬件监控工具包括NVIDIA的nvidia-smi和AMD的rocm-smi，用于实时监控GPU状态、温度、功耗和显存使用情况。NVIDIA和AMD是主流GPU供应商，NVIDIA的CUDA架构和AMD的ROCm生态是核心。GPU包含流处理器、显存、显存带宽等关键组件，不同型号的GPU在计算能力、显存容量和功耗上存在差异。
WebLogic 10.3.1 下载地址
WebLogic 10.3.1 下载地址： Oracle WebLogic Server 10.3.1 + OEPE - Package Installer 单语言版带OEPE Microsoft Windows x86: 75
应用软件——Altium Designer 19 Gerber/坐标文件
如何输出Gerber文件 步骤如下： 打开需要输出的PCB文件 2. 设置原点坐标 3. 打开Gerber Files 选项，File–>Fabrication Opuputs–>Gerber Files。 4. General参数设置。 用于指定输出Gerber文件中使用的单位（Units）和格式（Format）。单位可以是公制（Millimeters）和英制（Inches）；格式栏中2：3，2:4，2:5代表文件中使用的不同数据精度，其中2：3表示数据含2位整数3位小数；相应的，另外两个分
【华为OD】华为性格测试（综合测试）高分策略
1、性格测试非常重要，是一次否决制，18 个月有效期，所以态度一定要认真对待，不要随便乱选;2、做题原则:正向原则，积极乐观向上3、时间为 25-30 分钟，会频繁出现重复选项重复出现的题目注意一致性。
weblogic10.3下载地址
<br />Oracle WebLogic Server 10.3.1 + OEPE - Package Installer 单语言版带OEPE <br /><br />Microsoft Windows x86: 756 MB http://download-llnw.oracle.com/otn/nt/middleware/11g/wls/oepe11_wls1031.exe <br /><br /> Linux x86: 773 MB
21、时间触发以太网：通信、诊断、故障隔离与参数化详解
本文详细解析了时间触发以太网（TTEthernet）的核心机制，包括团检测算法、诊断服务、故障隔离策略、配置服务以及协议参数化等内容。TTEthernet是一种专为超高可靠性系统设计的网络技术，广泛应用于航空航天、工业自动化和汽车电子等领域。文章还探讨了其未来发展趋势，如技术创新与生态建设，为开发者、企业和研究机构提供了应用与发展的方向。
weblogic 10.3.0.0 for linux下载地址,weblogic10.3下载地址
Oracle WebLogic Server 10.3.1 + OEPE - Package Installer 单语言版带OEPEMicrosoft Windows x86: 756 MB http://download-llnw.oracle.com/otn/nt/middleware/11g/wls/oepe11_wls1031.exeLinux x86: 773 M...
基于STM32L4XX、HAL库的MCP4561数字电位器驱动程序设计
/ HAL库需要左移一位。* @param address: 设备地址(0x2C-0x2F)* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hmcp: MCP4561句柄。* @param hi2c: I2C句柄。
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
Windows Server 2008R2下Weblogic 10.3.6 下载地址及安装步骤
本文简单介绍Weblogic 10.3.6在Windows Server 2008 R2下的安装配置，包含64位软件的下载地址，安装步骤等
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
Weblogic10.3及以上版本的下载地址
http://www.oracle.com/technetwork/middleware/weblogic/downloads/wls-main-097127.html
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")
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
<转>weblogic 10.3多系统版本下载地址,以及linux下安装部署weblogic
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【亲测免费】 WebLogic 10.3.6 最新补丁下载
WebLogic 10.3.6 最新补丁下载 去发现同类优质开源项目:https://gitcode.com/ 资源文件介绍 本仓库提供了一个重要的资源文件下载，即 WebLogic 10.3.6 最新补丁，该补丁于 2022年1月18日 发布。此补丁旨在修复WebLogic 10.3.6版本中的关键漏洞和安全问题，确保您的系统能够安全、稳定地运行。 补丁详细描述 WebLogic 10.3.6 ...
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