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CNN在遥感图像配准中的实现源代码，参考论文：利用深度卷积特征进行多时相遥感图像配准《Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Feature》

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LZW压缩算法 在matlab 中对图片的处理

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基于有限元分析软件ABAQUS的Johnson-Cook材料模型以及断裂准则模拟高速切削淬硬钢锯齿状切屑形态,并讨论刀具前角和锯齿状切屑形态对切削力的影响。研究表明仿真结果和试验结果是一致的,文中介绍的有限元模拟方法可以准确地模拟并预测高速切削淬硬钢时的切屑形成过程。刀4有限元模拟及试验结果将有限元模拟仿真预测的切屑形态与试验结果进行比较,如图2、图3、图4所示。预测的切屑形态结果以积分点处等效塑性应变( equivalent plastic strain,PEEQ)的形式显示。由图中可以看出,有限元预测的切屑形态与试验结果非常接近。图中PEEQ的最大值随刀具前角从-10°改变到10而逐渐减小,说明主剪切(a)锯齿状切屑显微照片(b)锯齿状切屑形态有限元模拟结果区内的切屑变形也随刀具前角的增大而减小。刀具前(a) A micrograph of(b) fe simulation result of serrated角对切屑形态有重要影响,当使用负前角时容易形成serrated chipchip morphology锯齿状切屑。图4锯齿状切屑形态有限元模拟仿真与试验结果的对比(Fig 4 Comparison between experimentally and numerically obtained80.70.6hI号0.5H总0.4(a)锯齿状切屑显微照片(b)锯齿状切屑形态有限元模拟结果(a)a micrograph of(b)fe simulation result of serrated肥0.2●试验结果 Experimental resulserrated chipch0:→摸拟结果 Simulation result图2锯齿状切屑形态有限元模拟仿真与试验结果的对比(yo=-10)1010Fig 2 Comparison between experimentally and numerically obtained刀具前角 Tool rake angle(°)chip morphology (Yo =-10)图5不同刀具前角条件下的锯齿化程度Fig 5 Sawtooth degree under different tool rake angles4000003600320.l6000012000(a)锯齿状切屑显微照片(b)锯齿状切屑形态有限元模拟结果4000(a)A microgram(b)FE simulation result of serrated0.000.050.100.150.20serrated chchip morphology时间Time×103/s图3锯齿状切屑形态有限元模拟仿真与图6锯齿状切屑形成时的切削力波动(y6=-10)试验结果的对比(y0=0)Fig 6 Efect of tool angle on the cutting force(%o =-10Fig 3 Comparison between experimentally and numerically obtained加而逐渐降低。刀具前角对切削力也有很大影响,如图6、图7和图8所示,平均切削力F的值随着刀具刀前通常使用锯齿化程度C9表示锯齿状切屑变形和角的增加而逐渐降低。切屑形态。Gs的定义如下Gs =(H-h,)/h(4)5结论Gs的测量方法如图2所示,Gs与刀具前角之间的关系本文的目的在于预测高速切削过程中的切屑形如图5图5说明模拟结果与试验结果符合很好,当切态。使用适合高速变形条件的 Johnson-Cook材料模削速度和进给量一定时,锯齿化程度随刀具前角的增型、断裂准则和 ABAQUS有限元软件,模拟并测量高速0.80LIU Zhan Qiang, WAN Yi, AI Xing. Cutting forces in High Speed Milling[J]. China Mechanical Engineering, 2003, 14(9): 734-737( In Chix0.60[2]Kishawy H A. An experimental evaluation of cutting temperature duringhigh speed machining of hardened D2 tool steel[ J]. Machining Science0.40and Technology, 2002, 6(1): 67-79[3]刘战强,艾兴.高速切削刀具磨损表面形态研究[门摩擦学学〓0.20报,2002,22(6):468-471RLIU Zhan Qiang, Al Xing. Wear characteristics of cutting tools in high尽0speed machining[J]. Tribology, 2002, 22(6): 468-471( In Chinese)0.000.050.100.150.20[4]赵文祥,龙震海,王西彬,等.高速切削超高强度钢时次表面层时间 Time x103/s的组织特性研究[J.航空材料学报,200,25(4):2025图7锯齿状切屑形成时的切削力波动(y0=0)ZHAO Wen Xiang, LONG ZhenHai, WANG XiBin, et al. Study on theFig7 Effect of tool angle on the cutting force(yo =00)metallurgical structure characters of sub-surface layer of ultra-high strength0.80alloy steel in high speed milling condition[J]. Joumal of Aeronautical MEterials, 2005, 25(4): 20-25( In Chinese)[5] Sung H R, Soo-Ik 0. Prediction of serrated chip formation in metal cutting0.60process with new flow stress model for AISI 1045 steellJ]. Joumal of Ma-terials Processing Technology, 2006,171: 417-4220.40[6】赵军,孟辉,王素玉,等.高速切削锯齿状切屑的有限元模拟[.工具技术,2005,39(1):29-310.20ZHAO Jun, MENG Hui, WANG SuYu, et al. Finite element simulatinganalysis of serrated chip formation in high speed cutting[J]. Tool Engi-0ing,2005,39(1):29-31( In Chinese)早0.000050.100.150.20时间 Time x103/s[7]Christian H, Svendsen B. Simmlation of chip formation during high-speed图8锯齿状切屑形成时的切削力波动(=10)cutting[ J]. Joumal of Materials Processing Technology, 2007, 186: 66Fig8 Effect of tool angle on the cutting force(%o = 100)[8] Klocke F, Raedt H W, Hoppe S. 2D-deform simulation of the orthogonal切削AISI4340钢过程中不同刀具前角条件下的切屑high speed cutting process[]. Machining Science and Technology, 2001形态和切削力,讨论刀具前角和切屑形态对切削力的5(3):323-340.影响。研究结果表明,模拟结果与试验结果能很好地9)]shkH,AbeE, Sahm a. Material aspects of chip formation in HSC相符。因此,本文使用的有限元模拟方法可以准确预machining[ J]. Amals of the CIRP- Manufacturing Technology, 2001, 50(1)测高速切削淬硬钢时切屑形成过程。参考文献( References)[1]刘战强,万熠,艾兴.高速铣削中切削力的研究[J.中国机械工程,2003,14(9):734-737.

单片机控制步进电机系统实例（源程序+原理图）单片机控制步进电机系统实例（源程序+原理图）

最速梯度下降法，有详细的注释matlab程序

完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138