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visio 图库
【实例简介】目前为最新的H3C Visio图库，没有H3C的标签，可应用到各种项目文档拓扑绘制中。内容包含交换路由等网络设备、机柜等机房设施、楼宇、信号等，为PPT版，可通过复制粘贴到Visio中
- 2021-10-29 00:34:59下载
- 积分：1
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安川MP2000系列编程手册0606(内部资料)
安川MP2000系列编程手册0606(内部资料),不可多的,学习安川运动控制器的最佳资料,有详细样例和注解!涵盖安川全系列运动控制器!本手册的使用方法本手册的使用方法前言机器控制器MP900/WP2000系列是指,采用最新的电子信息技术,将迄今为止以机构为主体的机器实现杋电一休化,大幅度提高其性能、功能,冋时集成了机器的控制技术及使用技巧的控绱器以往的机器是使用通用的电机作为动力源,由电机驱动齿轮、连杆、凸轮等产生各种各样的运动来进行加工和装配等。安川电机凭借数百年的发展历史和技术积累,各种各样的机器正不断面世,并且其性能、功能显著提正因为如此,迎来了个日诸多机器的辉煌但是,由于这些杋器也是由称作机构的硬件构成的,因此缺乏高灵活性。另外,事实表明:由于受机器本身的限制,无法期待其性能、功能有更进一步的提高。另一方面,材料的革新性开发和提高,特是半导体领域的芧命性的性能提高和小型化,对电机以及电机控制技术带米的影响是无可估量的。其影响之一就是变频器、伺服电机及其驱动技术得到了惊人的发展。另外,即使是在驱动伺服电机的运动控制技术领域,基」微电脑的,融合了已普及的以数值控制(NC)为代表的运动控制器(MC)和可编程控制器(PC)的新型控制器正在不断面世采用这些伺服电机和新型控尙器,使以往的以机构为主休的机器实现机电一休化,进一步提高其性能、功能,以及在搭载前所未有的新功能等方面都在进行不断的积极尝试。然而,由于这些新技术采用的配套方法还没有得以充分普及,许多机槭技术人员对机电一体化还是很犹豫,有时甚至敬而远之,这是不可否认的亨实。因此,本手册作为用户指南,主要介绍采用安川电机的伺服驱动器∑系列和其机器控制器Ⅶ2000系列如何来实现机电一体化以及如何实现控制的方法。机器品种繁多,不可能一一介绍但是,由」作为基础的基本控制技术对哪个札器鄱是通用的,通过对其的介绍,无论将其应用」哪个机器上都能仗过去的机器实现机电一体化。木手册正是为此目的编写的参考手册,它与控制器和伺服驱动器的使用说明书不同,主要说明如何能实现各种搾制以及具体的编稈方法本手〗按以卜项目分门别类列出了苫丨梯形图或运动程序的编写示例。同步控制速度控制位置控制转矩控制位置控制以轨迹为主的运动控制各种机器的运动,如果从电机运转角度来看,都可以还原为上述控制。因此,在进行某种机器的控制时,就要弄清楚感应电机和伺服电机需要何种控制,如果知道其控制的具休控制内容(梯形图和运动程序),就可将该控制组合起来。有村一种控制并不够,必须同时使用个同的控制模式。木手册就是木着这样的观点列出了实际的栏序。如果以这些程序作为指导编程的方针,任何人都能编写出某种机器的基本控制程序。使用本手册时的推荐设定本手册示例的梯形图是使用安川电机的MP900/MP2000系列程序编辑软件MPE720Ver.5.22编写的。使用该梯形图时,请使用上述软件的Ver.5.22或史高的版木。另外,请在勾选“使用新的梯形图编辑器”后再使用梯形图编辑器。/7yPLc欄重動作環境棵能指定工P礻一?2一212-步管理7配送他1辛么[列=于F-工使用了新一老使用主寸1作成大几2工使用主OK也卟另外,作为OS,可选择 Windows95/98/T4.0/2000/XP中的任何一个。以MP2000系列控制器为主体的程序实例已发行的本手册2004年8月版本中,针对MP00系列给出了程序的基本框架巾最近面世的MP2000系列可构成新的机器控制虽然两机型的基本使用方法都相同,但在地址和运动命令的使用方法上存在一些差别。MP2000系列全面地强化并改善了MP900系列的功能。籍此札会,决定发行以M2000系列控制器为主体的编程手册另外,对于MP900系列,已发行版(2004年8月版)仍然可以使用,请加以利用。目录本手册的使用方法第1章计算机与机器控制器的连接1.1利用机器控制器进行设计1.2计算机和MP2000系列的通信参数的设定1.3设定MPE720的使用环境---1.4新建文件夹1-91.5进行M2200构成设定1-23第2章设定参数2.1设定伺服驱动器或变频器的规格2-42.2设定编码器的规格2-62.3设定机器的相关参数2-102.4设定控制结构202.5设定设定参数2-222.6设定伺服参数2-23第3章连接硬件3.1连接伺服驱动器(∑-Ⅱ、∑-I系列)3.2连接带 MECHATROL INK-Ⅱ的伺服单元的输入输出(∑-Ⅲ系列)第4章运动控制设计4.1各种控制的基本构成-4-34.2将伺服设定为0N4-64.3自由切换动作模式4.4设计主干梯形图4.5J0G(点动)运行4-134.6STEP(步进)运行4-154.7给出平滑的运行指令4-17第5章速度控制5.1速度控制(基本一直线加减速—)5-25.2设定直线加减速5-95.3速度控制(S形加减速)第6章转矩控制6.1转矩控制(基本:无反馈)6.2转矩控制(有反馈)6-66.3由运动程序改变转矩限制值6-14第7章位置控制7.1位置控制(基础)7-27.2基于运动程序的位置控制7-107.3通过运动命令、用外部信号定位7-217.4用手动脉冲发生器移动--7-257.5滚珠丝杠节距误差的补偿7-327.6用直线伺服驱动器定位7-337.7使用绝对值编码器7-38第8章编写运动程序8.1进行定位8.2通过外部信号进行定位(运动程序)8-583使其直线移动(直线加工)-84使其以圆弧的方式移动(圆弧加工)8-88.5平滑地加工自由曲线(直线移动命令)86坐标变更8-1087施加额定速度比88进行无限长定位-8-1289剪裁加工中保证刀具角度垂直8-138.10由运动程序控制外部设备-8-148.11从外部赋予运动程序中的设定值8-158.12暂时从工件坐标移到机器坐标-8-168.13使数个运动程序同时动作-8-178.14单方向定位回转工作台8-198.15以最小移动量进行回转工作台定位-8-20第9章相位控制9.1相位控制的基本内容9.2控制薄膜生产线(相位控制实例)9-10第10章使用电子凸轮控制机器10.1使用双向型电子凸轮控制机器10-410.2使用单向型电子凸轮控制机器10-1910.3电子凸轮加超前角控制10-3610.4编写凸轮曲线10-40第11章进行原点复归11.1进行原点复归(基本)-11-211.2使用运动程序进行原点复归11-9第12章掌握了梯形图将会带来很大便利12.1设定图中使用的D寄存器的数量-12-212.2全部清除继电器和作业寄存器----12-312.3检测各轴的警报,显示警报12-512.4防止失控(位置控制、相位控制)一12-812.5掌握运动程序的警报内容12912.6通过运动程序进行顺序控制12-11第13章进行调试13.1故障检修的流程13-213.2数据示踪13-613.3更加便利地示踪数据13-1413.4调试运动程序-13-27第14章便利的功能14.1对程序加注释14-214.2打印程序14-414.3检查程序存储器的剩余量14-5V11附录附录1MPE720简明手册附录-2附录2指令一览附录-23附录3梯形图转换作业步骤附录-30附录4 Expression的表示方法附录-31第章第1章计算机与机器控制器的连接1.1利用机器控制器进行设计1-21.2计算机和MP2000系列的通信参数的设定1.3设定MPE720的使用环境1.4新建文件夹1.5进行MP2200构成设定1-23第1章计算机与机器控制器的连接1.1利用机器控制器进行设计本节介绍利用机器控制器MP2000系列设计运动控制系统的步骤。设计分硬件设计和软件设计,这里主要介绍以软件设计为中心的设计流程。设计流程软件设计流程如下所示选择、订购所使用的机器。绘制主电路·控制电路展开连接图。通过MPE720新建文件夹。①模块构成② MECHATR0L|NK构成③输入、输出分配(伺服单元和1/0单元的设定)脱机操作④组定义编写梯形图编写运动程序至CPU的统一传送和伺服单元的设定调试联机操作结束
- 2020-12-05下载
- 积分：1
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手势识别图像数据，三种手势每种100张图片
人手做出的“剪刀石头布”三种手势，每种手势都有100张图片，图片的大小为58*58，是经过后期处理的图片，内含有所有图片路径的txt文件。
- 2020-12-12下载
- 积分：1
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基于SSH框架的网上书店系统
基于SSH的网上书店管理系统，大三期末大实验，包含买家和买家两类用户，包含订单管理，图书增删改查，用户增改查，登陆注册页面，模拟真实的网上购物流程。。。。
- 2020-11-02下载
- 积分：1
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matlab中用FFT实现线性卷积循环卷积
用FFT实现线性卷积循环卷积，在matlab中还使用了conv、cconv函数与FFT实现相比较
- 2020-11-28下载
- 积分：1
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形状匹配find_scaled_shape_model
使用opencv实现halcon中算子find_scaled_shape_model的功能，具体功能参见博客https://blog.csdn.net/sillykog/article/details/83116793
- 2020-11-29下载
- 积分：1
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基于小波算法的分形压缩程序
该方法实现了基于小波算法的分形图像压缩方法，用MATLAB实现
- 2020-12-07下载
- 积分：1
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凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
- 2020-12-10下载
- 积分：1
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Echarts-4.0.4官方文档-配置项手册
最新版的Echarts-4.0.4配置项手册，PDF格式，官网不提供下载。
- 2020-12-05下载
- 积分：1
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C# 实现对指定文件夹压缩
程序实现压缩与解压缩 zip 程序实现压缩与解压缩 压缩
- 2020-12-08下载
- 积分：1
