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
200+累计阅读
201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
页面上输入一个年份（需验证），判断是否是闰年（能被 4 整除，却不能被 100 整除的年份;能被 400 整除的是闰年），并且在页面上显示相应提示信息。
如何通过命令提示符登入 MySQL？如何列出所有数据库？如何切换到某个数据库并在上面工作？如何列出某个数据库内所有表？如何获取表内所有 Field 对象的名称和类型？
1. mysql -u -p
2. show databases;
3. use dbname;
4. show tables;
5. describe table_name ; 一个数如果恰好等于它的因子之和，这个数就称为「完数」。例如 6=1＋2＋3.编程找出 1000 以内的所有完数。
芯片良率提升：从设计到制造的系统性工程实践
在半导体制造领域，良率是衡量生产效率和成本控制的核心指标，直接关系到芯片的经济效益与市场竞争力。其本质是合格芯片占总产出芯片的百分比，提升良率需要从设计源头到制造末端的全流程协同优化。从技术原理看，它涉及可制造性设计、工艺过程控制、缺陷检测与根因分析等多个复杂环节，尤其在先进制程下，随机缺陷和参数变异成为主要挑战。其技术价值不仅在于降低单颗芯片成本，更能加速产品上市、保障可靠性，并成为企业技术实力的关键体现。应用场景贯穿智能手机、数据中心、自动驾驶和人工智能等所有高端芯片领域。本文将结合【芯片制造】与【良率
MySQL 计算今年是否为闰年
MySQL 计算今年是否为闰年
论文阅读--射频电源在半导体领域的应用
射频电源（RF Power Supply）是能够输出300kHz~300GHz正弦波电压的高频交流电源，在半导体制造领域，射频电源的典型工作频率范围为400kHz∼60 MHz，主要由射频发生器(功率源)、匹配器及配套控制电路组件构成，其关键组件包括变压器与电抗器、晶闸管、绝缘栅双极型晶体管（IGBT）、钣金件、铜材及各种电子元器件。射频电源是刻蚀和薄膜沉积工艺的核心，主要用于产生等离子体，以增强气体反应活性，其性能直接影响反应腔体内等离子体的浓度、均匀性和稳定性。
mysql判断是否为闰年
DELIMITER $$DROP FUNCTION IF EXISTS `F_IS_LEAY_YEAR`$$CREATE FUNCTION `F_IS_LEAY_YEAR`(p_year BIGINT) RETURNS INT(11) BEGIN DECLARE v_year INT; -- 0:不是闰年,1:是闰年 DECLARE v_is_leay
TMS320F280049C 学习笔记11 控制率加速器 CLA 例程
总结了TI官方在C2000Ware_3_01_00_00版本中提供的7个与CLA相关的例程。
STM32F103+W5500网络通信
STM32F103+W5500网络通信一、W5500模块（一)W5500以太网模块介绍（二）模块排针功能表二、 modbus协议（一）modbus协议原理（二）Modbus 通讯方式1.Modbus三种通讯方式2.在Modbus网络上传输3.以太网（modbus tcp/ip）三、参考资料 一、W5500模块 （一)W5500以太网模块介绍 D-W5500 EVB以太网模块是一款基于WIZnet W5500芯片的以太网模块，且性价比高的以太网模块。W5500是一款全硬件TCP/IP嵌入式以太网控制器，为
mysql 闰年_Oracle 闰年判断
//闰年最直接的判断是:能被4整除且不能被100整除，或者能被400整除的年份 create or replace procedure pro_leap_year(year_in//闰年最直接的判断是:能被4整除且不能被100整除，或者能被400整除的年份create or replace procedure pro_leap_year(year_in in number)asv_mod1 num...
大整数因数分解工具——yafu
可以将大整数放在一个新建文件中——data.txt，最后面一定要换行，不然会出现eof;done processing batchfile，运行命令。2.点击设置——系统——系统信息——高级系统设置——环境变量——点击PATH（上下都可以）——新建 添加yafu-x64.exe 所在路径——点击确定。，如果整数数过长，会出现错误mismatched parens；1.找到yafu-x64.exe 所在的文件路径。如果出现以下图像，说明配置成功。3.打开cmd,输入。
mysql中求闰年的函数_MS SQL判断闰年的语句
我们可通过如下的两种语句来判断是否为闰年：①代码SELECT case day(dateadd(mm, 2, dateadd(ms,-3,DATEADD(yy, DATEDIFF(yy,0,getdate()), 0)))) when 28 then ''平年'' else ''闰年'' end②代码select case datediff(day,datename(year,getdate())...
OpenCV实战：5分钟搞定图像拼接（附Python代码）
本文通过OpenCV实战教程，详细讲解如何使用Python在5分钟内完成图像拼接。重点介绍`findHomography`函数和单应性矩阵的应用，提供完整的代码示例和常见问题解决方案，帮助开发者快速掌握图像拼接技术。
RK3588部署rtmpose模型
摘要： 本文详细介绍了在RK3588开发板上部署RTMPose模型的全流程。首先在Ubuntu环境中搭建MMCV框架，通过MMPose工具将PTH模型转换为ONNX格式。随后使用RKNN-Toolkit2将ONNX模型转换为RKNN格式，并完成开发板部署。关键步骤包括模型转换（PTH→ONNX→RKNN）、环境配置和推理验证，最终实现人体姿态估计功能。整个过程涉及MMPose、RKNN-Toolkit等工具链的使用，为边缘计算场景下的实时姿态分析提供了可行方案。
FPGA FIR滤波器设计实战：从MATLAB到Vivado IP核的完整流程
数字信号处理中，有限脉冲响应(FIR)滤波器以线性相位和稳定特性被广泛采用。在FPGA工程实现中，需将浮点设计转换为定点系数，并通过Xilinx Vivado平台的FIR Compiler IP核高效部署。本文从滤波器基本概念出发，剖析MATLAB浮点设计、系数定点化、IP核参数配置、多相滤波结构及AXI4-Stream接口时序等核心环节，并探讨多通道复用下的DSP资源优化策略。结合典型通信基带场景，给出从仿真验证到板上调试的完整实践路径，帮助工程师解决系数导入、输出异常、时序收敛等常见问题，快速构建可靠的
【Android逆向工程】第18章：Ollvm 混淆与对抗
本章介绍了Ollvm混淆技术及其对抗方法。Ollvm是基于LLVM的开源代码混淆工具，主要实现控制流平坦化、虚假控制流和指令替换三种混淆技术。控制流平坦化通过状态变量和switch-case结构隐藏真实控制流，将原本清晰的逻辑拆分为多个状态块。混淆后的代码在静态分析中难以理解，但可通过动态追踪执行路径、符号执行等方法来还原原始逻辑。本章还提供了混淆代码示例和对抗策略，帮助理解Ollvm的工作原理及其防御方法。
20180313交换排序-冒泡
2019独角兽企业重金招聘Python工程师标准>>> ...
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")
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
20180313-task
https://www.cnblogs.com/linkworld/p/7819270.html
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
20180313前端笔试
1. JS基本数据类型：Undefined，Null，Boolean，Number，String 2. 1 function test() { 2 var n =123; 3 function add() { 4 n++; 5 console.log(n...
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
signature=8dfeb54e036883a518e97630c0013eed,unit3.lfm · BLumia/BLumiaTimidityShell - Gitee.com
object Form3: TForm3Left = 357Height = 211Top = 100Width = 578BorderStyle = bsDialogCaption = 'About..'ClientHeight = 211ClientWidth = 578Color = clWindowOnShow = FormShowPosition = poScreenCenterLCLV...
20180313风格迁移调试
今天主要还是根据这篇文章——https://www.leiphone.com/news/201701/tGlVRXWShwe7ffHW.html?viewType=weixin 进行 use trained model 的调试，在昨天download了model的基础上进行操作：python eval.py --model_file <your path to wave.ckpt-do...
北京集训:20180313
考试对于蒟蒻而言简直就是灾难......T1: 又是一道组合数学神题。显然这张图会由n个环组成，我们考虑在每个环内分别选>=1个点，最后点总方案的乘积就是在这些环中选k个点的答案。怎么计算呢?我们可以f[i][j]表示前i个环，选j个点的方案数。转移就是f[i][j]=f[i-1][j-p]*C(siz[i],p)(0<=p<=siz[i])。答案就是f[len][k]了。...
20180313 java 反射基础 class对象
反射是java非常重要的知识点之一 通过反射 程序可以动态调用类 简单来说就是 在什么时间点调用什么类可以不用再重载了 直接用反射载入一个对象 这个对象是动态生成的 可以是范围内的任何类 关于获取class类的三种方式 from--http://blog.youkuaiyun.com/sinat_38259539/article/details/71799078 /** * 获取C...
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
632
扫一扫
