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在Java开发中，处理日期和时间是一个常见的任务。Jackson是一个流行的Java库，提供了强大的对象映射功能，可以帮助我们在Java对象和JSON之间进行转换。当我们需要处理多个日期格式时，Jackson提供了灵活的方式来实现这一目标。
下面是使用Jackson映射多个日期格式的步骤：
添加Jackson库的依赖
首先，我们需要在项目的构建文件中添加Jackson库的依赖
com.fasterxml.jackson.core
jackson-databind
2.12.3
创建Java对象
假设我们有一个包含日期字段的Java对象，如下所示：
public class MyObject {
private String name;
@JsonFormatJackson ObjectMapper深度解析：Java JSON序列化核心原理与实战配置
JSON序列化与反序列化是现代Java后端开发中的基础技术，尤其在微服务与RESTful API架构中，它负责实现数据对象与网络传输格式之间的高效转换。其核心原理在于通过反射机制分析对象结构，并依据预定义的映射规则生成或解析JSON文本。掌握这项技术对于构建高性能、可维护的服务至关重要，能够显著提升接口响应效率并确保数据一致性。在实际工程中，开发者常面临日期格式化、空值处理、多态类型支持等复杂场景，而Jackson库的ObjectMapper组件正是解决这些问题的关键工具。通过合理配置日期格式、空值忽略策略
JSON转换为Java对象及日期格式转换处理
本文介绍了如何使用Jackson库将JSON数据转换为Java对象，并且处理了日期格式转换的情况。通过以上的代码示例，我们可以轻松地实现JSON与Java对象之间的转换，并且灵活处理日期格式的转换。在Java编程中，我们经常需要将JSON数据转换为Java对象，并且在处理日期时，也需要进行格式转换。现在，我们已经成功地将JSON数据转换为Java对象，并且日期字段也完成了格式转换。在处理日期时，JSON数据中的日期格式通常是字符串形式，而Java中的日期通常使用。因此，我们需要进行日期格式的转换。
LAP-D协议概述
Q.921协议由称为LAPD（链路访问协议-D通道），与HDLC的关系密切，它是在ISDN D通道上使用的数据链路协议。 注：http://www.freesoft.org/CIE/Topics/125.htm LAP-D的帧格式 标志、位填充技术和FCS校验计算与HDLC一致。 16位的地址包括C/R字段、SAPI(服务访问点标识)和TEI(端点标识)。TEI用于区分使用相同ISDN...
Backtrader 文档学习- 整体架构功能分析理解
backtrader是一个用于开发和执行交易策略的Python框架。它提供了一套完整的工具和功能，使得用户可以方便地进行策略回测、实盘交易以及数据分析。backtrader的入口为Cerebro类，该类将所有输入(Data Feeds)、策略 (Strategy)、观察者(Observers)、策略评估(Analyzers) 、经纪人（Broker）、订单（Order）、交易（Trade）和日志记录(Writers)整合起来，实现回测以及交易，并返回结果和图表。
2 海康视觉平台VisionMaster 上手系列：大纲及准备
海康视觉平台VisionMaster 大纲，课前准备 快速入门 工业机器视觉 机器视觉
芒果YOLO11改进176：2025最新顶会论文｜尺度动态损失函数Scale-based Dynamic Loss，即插即用｜以减少标签不准确对损失函数稳定性的影响
芒果YOLO11改进186：2025最新顶会论文｜尺度动态损失函数Scale-based Dynamic Loss，即插即用｜以减少标签不准确对损失函数稳定性的影响
Python爬虫教程：抓取区块链交易信息及加密货币市场数据
随着区块链技术和加密货币的迅猛发展，区块链交易和加密货币市场的数据逐渐成为金融、技术、经济研究等领域的热点。对于开发者和研究者而言，实时获取区块链交易数据和加密货币市场行情，对于投资分析、市场预测、技术研究等具有重要的参考价值。本文将通过Python爬虫技术，介绍如何抓取区块链交易信息及加密货币市场数据，详细阐述数据获取的原理、技术方案、实现方法以及抓取到的数据的存储与分析。我们将依托最新的爬虫技术，如requestsSeleniumScrapy，以及相关的数据处理库（如pandasmatplotlib。
十、Teigha.Net示例的源代码下载链接，以及英文的chm说明文件和相关案例
目录官方案例demo下载Teigha.net 英文版手册下载Teigha.net Entity实体中文翻译Teigha4.0 解决闪烁问题demo下载Teigha4.0 收录demo案例类似CAD看图王软件无AutoCAD环境下操作显示CAD文件的功能源码，整个示例模块源码 官方案例demo下载 在之前找到的官方案例3_03版本中包含32位与64位dll 其中的chm的说明文件内容也不完全，后面有更为详细的英文版的chm； 示例项目中，主要包括一个winfrom程序与控制台程序，OdWriteExMgd
clock min pulse width检查
高电平脉宽 = clock fall edge到达FF/CK的时间 - clock rise edge到达FF/CK的时间；低电平脉宽 = 下一T clock rise edge到达FF/CK的时间 - clock fall edge到达FF/CK的时间。而required pulse width是根据FF/CK处的transition，根据查找表计算得到的。为什么clock transition越大，会导致required pulse width越大呢？5、 降低clock上的串扰。
Python数据分析师必备：OpenClaw开启Pandas清洗脚本自动生成新时代
本文介绍了OpenClaw这一AI智能体框架如何帮助Python数据分析师高效完成数据清洗工作。OpenClaw通过自然语言交互理解用户需求，自动生成Pandas清洗脚本，显著提升了数据清洗效率，降低了技术门槛。文章详细阐述了OpenClaw的工作原理、实战应用步骤及其在电商、金融、医疗等行业中的成功案例。相比传统手动编写脚本方式，OpenClaw能快速生成规范准确的清洗代码，使数据分析师能更专注于业务洞察。该工具为数据驱动决策提供了可靠支持，是数据分析领域的重要创新。
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
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
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")
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
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