58mm热敏打印机控制源代码（ARM920T） 实时组点阵无需缓存

基于threejs的商场楼层展示导航系统源码.zip

Ansys Workbench高斯移动热源操作步骤及代码；Ansys Workbench高斯移动热源操作步骤及代码；Ansys Workbench高斯移动热源操作步骤及代码；

这个资源是一个QAM是为十六的正交幅度调制信号的Mdl文件，请你思考清楚了再下载免得浪费时间

IEC 61850标准第二版新增加的标准，IEC61850-90-6，应用于配电自动化。是个草案，对前五章进行了详细展开。

LabVIEW振动测试分析.zip

Java技术改变了建立和部署应用程序的方法，为网络管理提供了新的手段。本设计是将 Web服务技术应用于网络管理中，提出一个通用的分布式与集中式相结合的网络管理系统模型。在实现其基本功能的同时运用Java技术建立并部署应用程序，采用封装技术提高系统的扩展性和灵活性。基于用户界面层、中间层和存储层的3层体系结构，设计并实现该网络管理系统。本设计基于简单网络管理协议(Simple Network Management Protocol, SNMP)，应用Java语言设计并实现了具有高效率高可用性的网络管理系统。

关于模糊C-均值(FCM)聚类算法的改进关于模糊C-均值(FCM)聚类算法的改进∑∑md2(x1,v,)离有变化,即(1)式中改为(,)=|P)(x;-v大连大学学报其中W为模糊加权因子,由式(5)确定。在模糊￠一均值算法中引入模糊加权因4子,使得数据空间中各个数据点对同一橐类中心所具有的特征优势不同,导致对距离的贡献也不同,更具合理性,使得聚类效果更好,分类更清晰,改进数据预处理的方法。4数据仿真结果将200个二维数据分为三类。使用了两种方法,本文提出的改进的模糊聚类算法(引入了模糊加权因子),结果见图2;经典的模糊C-均值聚类算法6,结果见图3对比聚类效果图如通过对比两种算法的效果图可以看出:图图3FCM聚类效果图2是改进后的模糊聚类算法(引入了模糊加权刈比目标函数曲线如下因子)的效果图,聚类效果比图3经典的模糊C均值聚类算法更好,数据点更集中,有若干点集中在聚类中心上。我们可以看右下角的数据点,改进后的模糊聚类算法将紫色的点和蓝色的点能清楚的分开,两个类之间的界限很明显而模糊C-均值算法分类的程度就不是很清晰,分别属于两个类的绿色的点和紫色的点几乎重合,可见类与类之问划分不清晰。图4改进算法后的目标函数图图2改进算法后的聚类效杲图喷笔签义,引入了重要参数-模糊加权因子,模糊加权因子的引入,使得数据空间中各数据点所具有的特大连大学学征优势不同,导致对距离的贡献也不同,这是两种距离定义方法的根本区别之处。并且用数据仿真验证了这种改进了的模糊聚类算法比原来的算法聚类更有效,分类更清晰,速度快。参考文献O一0年第五期[l} Timothy J.Ros.模糊逻辑及其工栏应用[M].北京:电子工业出版社,20032]鲁宇,范希鲁.模糊加权距离及其合理性讨论[J].北方交通大学学报,1990(2)[3]王士同、神经模糊系统及其应用[M].北京:北京航天航空大学出版社,1998(6)图5FCM目标函数图T 4 1 Kazutaka Umuyaharu, Saclaaki MiyarIulo and Yoshiteru图4的是改进算法后的目标函数图(引入模糊Nakamori, Formulations of Fuzzy Clustering for Categorical加权因子),图5是经典的模糊C-均值算法目Data, International Journal of Innovative ComputingInformation and Control(lICIC), vol 1, no, 1, pp 83标函数图。可以看出图4的函数曲线比图5的函94,2005(3)数曲线更加平滑,收敛速度快。[5 Hugang Han, Information System with Fuzzy Weights5结论Intermational Journal of Innovative Computing, Information本文讨论的是对模糊C-均值聚类算法的改and Control JICIC ) vol. 2, no 3, pp 553-565, 2006进,在原有的模糊C-均值算法的基础上,用一种6]吴晓莉,林哲辉.MAⅣLAB埔助模湖系统设计[M.西安:新的定义距离的方法替代欧氏空间中距离的定西安电子科技大学出版社,2002.Improvement of the Fuzzy C-Means Clustering AlgorithmWANG Ying-jie Wang, BAI Feng-bo, WANG Jin-hui(1. College of Information Engineering, Dalian University, Dalian, 116622, China2. MSPD, HiSoft Technology Intemational Ltd., Beijing, 100074, China3. Beijing Electromechanical Engineering Insitute, Beijing, 100074, ChinaAbstract: An improvement algorithm about the fuzzy c-means clustering algorithm is discussed in this paper. Basedon original fuzzy c -rneans clustering algorithm, the improvement algorithm uses a new way of defining distance todisplace the distance in Euclidean space. Experimental results show that the improvement algorithm is better thanal algurithm and the classification is clearer than original algKey words Fuzzy c-means algorithm; Fuzzy weighted distance; Fuzzy weighted factor

利用labview开发的STM32单片机USB程序，包括上位机的枚举，数字电压表，光敏电阻波形，DS18B20温度显示，热敏电阻波形显示，USB控制LED灯，波形回访等。

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

快速搭建垃圾分类模型：使用inception快速搭建的图像分类模型，目前支持1000类识别。从图像中识别出类别后，再通过textcnn模型对垃圾类别进行映射，最终输出垃圾的类别。注：垃圾类别是以上海分类标准。