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DateTime dt = DateTime.Now;//当前时间
当天：dt.Date; //当前日期的 00:00
dt.Date.AddDays(1).AddSeconds(-1); //当前日期的23:59
本周：DateTim begintime= (dt.AddDays(1 - Convert.ToInt32(dt.DayOfWeek.ToString("d")))).Date; //得到当前日期的周一的00:00
(begintime.AddDays(6)).AddDays(1).AddSeconds(-1); //得到当前日期的周日的23:59
本月月初：DateTim startMonth= dt.AddDays(1 - dt.Day).Date; //本月月初的00:00
本月月末：startMonth.AddMonths(1).AddSeconds(-1);//本月月末的23:59
AWS入门实践-如何在AWS云上创建一个内外网隔离的生产环境
在 AWS 上建立一个内外网分离的生产环境,可以减少应用服务的暴露面，有效的保证你的应用服务器的安全。通常我们会将web应用放在外网的子网内，数据库服务器等放在内网的子网。我们将按照下图来部署动手实践环境，实现在public subnet的EC2虚拟机可以直接通过互联网可以访问，在Private subnet的EC2虚拟机不能通过互联网直接访问，但是却可以在内部来访问互联网安装应用等等。
(转)获得每日，每周，每月的0点和24点的时间戳
转自:http://chenp1111.iteye.com/blog/744259 //获得当天0点时间 public static int getTimesmorning(){ Calendar cal = Calendar.getInstance(); cal.set(Calendar.HOUR_OF_DAY, 0); cal.set(Calendar.SECOND, 0); ca
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获取时间-下周一
//获取时间 下周 Calendar cal = Calendar.getInstance(); LocalDate date = LocalDate.now(); String today = LocalDateTimeUtil.format(date, "yyyy-MM-dd"); Date todayDate = DateUtils.parseDate(today); cal.setTime(todayDate); int dayWeek = cal.get(Calendar.DAY_OF_WEEK.
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实战演练 | ABAQUS显式动力学分析在金属切削仿真中的应用
本文详细介绍了如何利用ABAQUS显式动力学分析进行金属切削过程的二维仿真。通过从零开始的完整流程，包括几何建模、材料本构与失效定义、网格划分、接触设置、边界条件施加，到结果后处理与切削力提取，为工程师和研究人员提供了实用的仿真方法，以优化刀具设计和加工工艺。
C# 输入指定日期获取当前年的第一天 、当前年的最后天、某月的第一天 、某月的最后一天...
方法 /// <summary> /// 取得当前年的第一天 /// </summary> /// <param name="datetime">要取得月份第一天的时间</param> /// <returns></returns> private DateTime FirstDayOfYear(Date...
使用MATLAB和Simulink来设计并仿真一个智能家居多传感器数据融合系统
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C#获取本周，上周，下周信息怎么获取今天的开始时间和结束时间
C#获取本周，上周，下周信息 下面的一周都是以星期天开始，星期六结束。 本周： DateTime.Now.AddDays(0 - Convert.ToInt16(DateTime.Now.DayOfWeek)) DateTime.Now.AddDays(6 - Convert.ToInt16(DateTime.Now.DayOfWeek)) 上周： DateTime.Now.AddDays(0...
PCL 均匀采样【2026最新版】
根据输入点云数据创建一个微小的三维立方体集合，然后计算每个三维立方体的重心；由于该重心点不一定是原始点云中的点，直接用该点来表示立方体内所有的点会丢失原始点云的细小特征；所以使用k最近邻搜索寻找距离重心点最近的原始点来代替重心点。
ESP32开发板使用Arduino实现变成蓝牙鼠标教程
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c#.net 获取时间日期年月日时分秒格式
DateTime dt = DateTime.Now; //当前时间 //获取当天 dtpStartDate.Text = DateTime.Now.ToLongDateString().ToString(); dtpEndDate.Text = DateTime.Now.ToLongDateString().ToString();
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
c# 根据当前时间获取，本周，本月，本季度，月初，月末，各个时间段
DateTime dt = DateTime.Now; //当前时间DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss") 24小时制DateTime.Now.ToString("yyyy-MM-dd hh:mm:ss") 12小时制DateTime startWeek = dt.AddDays(1 - Convert.ToInt32(dt.DayOf
jre9下载 jdk9
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C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
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DateTime
//大家在做报表或查询的时候都会有给用户预设一些可选的日期范围(如上图)<br /> //如本年度销售额、本季度利润、本月新增客户<br /> //C#里内置的DateTime基本上都可以实现这些功能，巧用DateTime会使你处理这些事来变轻松多了<br /> <br /> //今天<br /> DateTime.Now.Date.ToShortDateSt
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
根据当前系统日期求出本周，本月，本年，上周，上月，上年的第一天
//今天 DateTime.Now.Date.ToShortDateString(); //昨天，就是今天的日期减一 DateTime.Now.AddDays(-1).ToShortDateString(); //明天，同理，加一 DateTime.Now.AddDays(1).
C#根据当前时间确定日期范围（本周、本月、本季度、本年度）
DateTime dt = DateTime.Now; //当前时间 int dayOfWeek = Convert.ToInt32(dt.DayOfWeek.ToString("d")); DateTime startWeek = dt.AddDays(1-((dayOfWeek == 0)?7:dayOfWeek)); //本周周一DateTime endWeek = startWe...
获取本周的周一日期与本周的周日日期
#region 获取本周周一 /// <summary> /// 获取本周的周一日期 /// </summary> /// <returns></returns> public static string GetThisWeekMonday() {...
C# 根据当前时间获取 本月 上月 本周 ...
DateTime dt = DateTime.Now; //当前时间 DateTime startWeek = dt.AddDays(1 - Convert.ToInt32(dt.DayOfWeek.ToString("d"))); //本周周一 DateTime endWeek = startWeek.AddDays(6); //本周周日 DateTime startMonth...
C#获取前一天日期
C#里内置的DateTime基本上都可以实现这些功能，巧用DateTime会使你处理这些事来变轻松多了今天 DateTime.Now.Date.ToShortDateString();昨天，就是今天的日期减一 DateTime.Now.AddDays(-1).ToShortDateString(); 明天，同理，加一 DateTime.Now.AddDays(1).ToShortDateStrin...
C#获取系统时间，获取日期与格式转换
private void button1_Click(object sender, EventArgs e) { int year,month,day; System.DateTime currentTime = new System.DateTime(); // string strYMD = curr...
C#获取当前日期时间
通过调用类中的各种方法我们可以获取不同的时间：如：日期（2008-09-04）、时间（12：12：12）、日期+时间（2008-09-04 12：11：10）等。原文链接：https://blog.youkuaiyun.com/luohaitao/article/details/127326497。获取年份 // 2008。
C# 获取时间，日期大全
C# 获取时间，日期大全 //C#里内置的DateTime基本上都可以实现这些功能，巧用DateTime会使你处理这些事来变轻松多了 //今天 DateTime.Now.Date.ToShortDateString(); //昨天，就是今天的日期减一 DateTime.Now.AddDays(-1).ToShortDateStrin
得到本周一时间，本月1号时间，C#
有时遇到，默认按照周一到周日排序
C#里获取相关日期 例如做报表时某段时间(如本年度、本季度、本月、本周、上周等)
//大家在做报表或查询的时候都会有给用户预设一些可选的日期范围(如上图) //如本年度销售额、本季度利润、本月新增客户 //C#里内置的DateTime基本上都可以实现这些功能，巧用DateTime会使你处理这些事来变轻松多了 //今天 DateTime.Now.Date.ToShortDateString();
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