C++ 课程设计 大作业 背单词小软件

二维元胞自动机 基于二维元胞自动机的交通流模拟分析44http://xbbjb.swu.cn38St(i,j)=F[S(i,j),S,(i-1,j),S,(i+1,j),S,(i,j-1),S,(i,j+1)](1)BMI(1)BMIT=4t=341843.2/(N×N)NXN(AJAVAT=232×32,64×64,128128,256×256,512×512,T=4128×128MATLAB01100002000∷;酸(a)暂念的渐近状态(b)暂心的阻塞状态3128×128128×128C1994-2013CHinaAcademicJOurnalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net643平均速度与密度关系图1.0平均速度曲线L32×320L=64x=128×1280808=256×2560.7512x512060.6053.330H040.30.20.2w0.10152253035404500.2040.6081.012141.61820密度/时间步10T=25128×1280.34.3BMLT0.80.60.4502eBMI02515密度/6128×128BMI(T-2T-4)BMIBMI[1 NAGEL K, SCHRECKENBERG M. A Cellular Automaton Model for Freeway Traffic [J]. Journal of Physique, 19922:221-2292 HAM O, MIDDLETON AA, LEVINE D. Self-Organization and a Dynamical Transition in Traffic Flow Models [J]Physical Review E, 1992(10):6124-6127L3 FUKUI M. ISHIBASHI Y. Traffic Flow in 1D Cellular Automaton Model Including Cars Moving with High Speed LJJ996,65(6):1868-1872005,54(10):4621-4626,2010,10(2):122-128C1994-2013cHinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net46http://xbbjb.swu.cn38On Simulation and analysis on Traffic Flow Based onTwo-Dimensional Cellular automationMEI Hong, CHENG WeiZHANG Yun-sheng, YU Peng-cheng1. Faculty of Continuing Education, Kunming University of Science and Technology, Kunming 650051, China;2. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 65005 1, ChinaFaculty of Information Engineering and Automation, Kunming University cf Science and Technology, Kunming 650051. China4. Computer Center, Kunming University of Science and Technology, Kunming 650051. ChinaAbstract: BML model is a two-dimensional cellular automata model, which is especially used to simulateand analy ze the traffic system. We use Java language to implement this model. With this model, the re-ationship between the average velocity and the average density has been found by computer simulationThe phase transition and self organization have also been faund. And at last, the bml model improved intraffic light cycle changes for exploring more valuable to the research and applicationKey words: two-dimensional cellular automation; traffic flow; BML modelC1994-2013CHinaAcademicJOurnalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

基于Contourlet变换的图像去噪源代码

欢迎使用Live-DL保存直播，本工具通过FFmpeg保存直播流，CPU占用极低。支持录制所有RTMP方式推流的直播网站，如哔哩哔哩等Step1.复制直播流地址到窗口（直播流地址可以通过Chrome浏览器F12“检查”功能获得，切换到Network选项卡，刷新页面，最长的一条就是）Step2.输入保存的文件名（文件会保存为“当天日期+你输入的名字.mp4”）Step3.选择保存路径a.保存在当前文件夹 b.保存在E:ReliveSave c.自定义路径（例如C:usersyournamedesktop）按Q可停止

马尔科夫决策过程的matlab编程实现

matlab基于遗传算法实现的高效排课系统。有详细的代码

VREP与MATALB联合仿真，针对UR5机械臂进行PD加重力补偿控制。

【实例简介】四轴飞行器主控板设计原理图与PCB四轴飞行器不是儿童玩具。它太昂贵而且太危险，不适于用于玩具。不要在人的上空飞行！

C#版本的视频解码源码,使用FFMPEG解码H264视频文件,解码后的数据保存为YUV格式,可以使用yuvplayer播放.Dll目录是运行时使用的动态库.Video目录是测试用的H264视频文件.源码展示了如何用CLR封装C代码,然后在C#项目中使用.涉及CLR的数据格式转换,事件和线程。项目工程使用VS 2012创建。

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

基于华丽的JQ UI的datepicker没有对时分秒进行支持本次开发彻底解决这一问题示例中支持皮肤切换