潮流计算中导纳矩阵的计算

qt 使用echarts图表，利用qt的webkits 组件加载echarts图表呈现，可以进行交互。

ds18b20的verilog代码，已经通过了测试，所用的板子是21EDA

主要研究矩形零件的排样方法，遗传算法的用途在此处体现的淋漓尽致本文算子的选择是有效的为进一步验证算法,对零件数量从16~97的不同算例进行试验,每类有3个例子,其最优排放图均已知,各个算例的基本试验数据(零件数量,板材尺寸)和本文试验最好结果见表2,表2算例2的基本试验数据及本文试验结果Tab 2 Dimension of second exampleand best result given by this paper问题零件最优高度原最优板材所得最低高度()SA+文算法结果(b)SA+最低水平线算法结果种类数量mm尺寸/mmmm2020×2020图1算例1的排放图40×15Fig 1 Layout of first example2860×3032表1本文算法与最低水平线算法排样结果对比C4496060×6064Tab. 1 Difference between our algorithm and7360×90the lowest outline algorithm9712080×120129最小高度最小高度最大高度平均高度运行时间图2给出了部分算例的最好排放结果。 Hopper算法/mm出现频次/mm/mm/ms8. Turton对以上规模不同的矩形件采用BL、BLFSA+最低水平线481/5053.716算法进行排放,允许零件旋转90°,GA、NE、SA、HCSA+本文方法483/505 I48.6等算法搜索排放顺序。文中指出采用BLF排放效果优于BL算法10%~30%,采用SA+BLF算法所得4.2算例2结果最优,见表3。(a)C11(b)C41(c)C61图2算例2采用本文算法所得的排放图ig 2 Best layout of second example with our algorithm表3各类别实例的相对距离百分比1表4各实例运行时间对比表Tab3 Relative distance of best solution toTab 4 Average elapsed time foroptimum height for six cases%six cases with different algorithm问题种类BIBLF SA+BLF本文算法问题A+BLFSA+本文算法174种类ms162.824126.7C41816132120C657.5注:1)表中值表示所得最好结果U与最优值lO)pt差值的白分比C61528189447(U-Op:)/lOpt。宇航材料工艺2007年第4期17对比表2、表3知,本文算法和文献[6]中采用图3表明:矩形排放耗时10ms,经人机交互调BLF解码的综合算法结果相近,并且在零件数量较整后材料利用率为86.4%,比人工排样提高约11少(如n=16)时能获得最优解,与埋论分析一致;由8%。表4知,本文算法的运行时间大大少于BLF算法,这5结论是因为在排放R;时只需搜索当前轮廓线段,比BLF实际算例表明最低轮廓线搜索算法能有效地进算法(搜索所有空域区域)搜索空间减少,因此效率明行矩形件排放,与模拟退火算法相结合,能在较短时显提高。由于文献[6的运行环境是:处理器奔腾间内获得与BLF算法相近的排放结果,并且在零件200MHκ,RAM65M, Windows nt4.0;而本文运行数量较少时能获得最优解,是解决大规模矩形件排放环境为:CPU2.8GHz,RAM512M,其速度大约是问题的有效方法200MHz处理器的15倍,因此表4所给BLF混合算参考文献法的运行时间做了相应处理。可见采用轮廓搜索法1张丽萍,张春丽,蒋寿伟.皮料优化排样的有效方法与BLF算法可获得相近的排放效果,但前者效率明软件学报,2005;16(2):316~323显高于后者。文献[7采用启发式递归(HR)算法对2曹炬,周济,余俊.矩形件排样优化的背包算法.中国以上算例进行求解,大大提高了运行效率,但在零件机械工程,1994;5(2):11~12数量较多时其速度也明显低于本文算法。因此最低3曹炬.二维异形切割件优化排样的拟合算法.中国机轮廓搜索法可用于求解大规模矩形件的排样问题。械工程,2000;11(4):438~4414.3应用举例1 Jakobs S On genetic algorithms for the packing of针对不规则复合材料铺层,采用矩形包络法求出 polygons,Eur. of oper,Res.,1996881):165-181其包络矩形,然后采用上述算法进行排放。图3是飞5贾志欣.面向发电设备制造的下料优化排样原理与关机坐舱罩顶棚的铺层展开数据采用以上策略获得的键技术,四川大学博士学位论文,2002排放图。6 Hopper E, Turton B C H. An empirical investigationof meta-heuristic and heuristic algorithms for a 2D packingproblem. EurJ of Oper Res, 2001; 128(1): 34577 Zhang Defu, Kang Yan, Deng Ansheng. A new heuristicrecursive algorithm for the strip rectangular packing problemComputers &. Operations Research, 2006; 33(8): 2209-2 217图3复合材料铺层排放实例(编辑李洪泉)ig. 3 Layout for composites plys18宇航材料工艺2007年第4期矩形件优化排样的研究旧万数据WANFANG DATA文献链接作者:邓冬梅,厝米水,安鲁陵,王桂宾, Deng Dongmei, Zhou laishui, An Luling,Wang guibin作者单位:南京航空航天大学机电学院,南京,210016刊名宇航材料工艺sTc|PKU英文刊名:AEROSPACe mATERIALS technology年,卷(期):2007,37(4)被引用次数4次惨考文献(条)1.张丽萍.张春丽.蒋寿伟皮料优化排样的有效方法[期刊论文]软件学报2005(02)2.曹炬.周济.余俊矩形件排样优化的背包算法[期刊论文]中国机械工程1994(02)3.曹炬二维异形切割件优化排样的拟合算法「期刊论文]中国机械工程2000(044.Jakobs S On geretic algorithms for the packing of polygons 1996 (05.贾志欣面向发电设备制造的下料优化排样原理与关键技术[学位论文]20026. Hopper E Turton B C H An empirical investigation of meta-heuristic and heuristic algorithms for a 2Dpacking problem 2001(01)7. Zhang Defu. Kang Yan. Deng Ansheng A new heuristic recursive algorithm for the strip rectangularpacking problem 2006 (08)相似文献(1条)1.学位论文邓冬梅复合材料铺层排样抆术硏究与开发2007复合材料因其比强度高、比模量大、材料的刚度和强度可设汁等一系列优点,在航空航天领域得到广泛应用,但高昂的价咯成了复合材料应用的最大壁垒。国外的硏究和应用成果表明数字化技术是降低复合材料构件制造成本、提高构件性能的有效途径。目前国内主要还以手工没计和手工制造为主、自动化程度不高,不仅浪费人力、物力,而且产品质量难以保证,因此有必要对复合材料数字化技术进行研究。优化排样是复合材料构件数字化生产过程中的重要环节。本文在研宄各种排样算法的基础上,提岀丁新的矩形件排样算法、优化算法以及不规则样片的排样算法,并与复合材料铺层排样的特点相结合开发了复合材料铺层排样软仁。主要研究内容和创新点如下矩形件排样不仅适用于矩形样片的排放,也是不规则样片排咩的基础。本文在建立矩形件排样数学模型的基础上,介绍了各种常见的定序列矩形件排样算法并分析其特点,提出了一种新的启发式排样算法——最低轮廓线搜索算法。该算法满足“最下最左”条件,克服了其他排样算法对某些排栏图不能给出排列的缺点,实验结果表明该算法排样效果好于最低水平线算法和最下最左(BL)算法。利用该算法实现了大量不同规格图纸的集中出图,省时省力,节约氏张2050%。矩形件排样问题具有图形运算和组合优化两方面的特性,单纯的排样算法只能解决图形运算问题,样片的排放顺序对排样结果同样重要。针对较小规模(一般少于100个图形)的矩形件排样问题,本文提岀了模拟退火与最低轮廓线搜索算法相结合的综合优化算法。对于十多个图形的排样,该算法可短时间内求得最优舾:对于近百个图形的排样,在排样效果相当旳情冮下,该亥算法比其基于模拟退火的综合优化算法效率提髙百以饣。针对大规模矩形件排样问题本文提出了蚁群笪法与最低轸廓线搜索算法相结合旳综合优化算法,该算法比模拟退火与最低轮廓线算法相结合的综合优化算法效率提高十倍以上。不规则图形排栏是所有排样研究中的热点和难点。本文将不规则样片简化成多边形进行排样,提出了两种不同的解法方法:一是基于矩形的排样方法,二是直接对多边形进行排样。基于矩形求解不规则样片排样时,将图形运算、矩形件排样算法及交互调整相结合,提出了基于矩形的多边形综合排样算法。通过各种优化组合策略,对单一样片和多种样片进行组合求其最小包络矩形,从而将不规则形状样片排样转化为矩形件排样进行求解。直接冄放多边形时,重点研究两个多边形的临界多边形(NFP)的求解。首先对基于倾斜图法的NFP求解法进行了改进和优化,完善了凹、凸两多边形NFP的求解,然后提出了适用于任意两多边形N求解的边界绕行法,该方法比基于倾斜图的求解方法适用范围广,计算简单、效率高。根据复合材料构件数宇化生产的主要过程,分析总结了复合材料铺层排样的特点,并将伉化排样算法与复合材料铺层排样的特点相结合,设计丌发了复合材料构件铺层排栏软件系统。引证文献(3条)1.卢远志杨建新.文桂林.周兵.钟志华基于排样思想的工程图坐标尺寸防干涉方法[期刊论文]中南大学学报(自然科学版)2010(2)2.张伟.安鲁陵.邵挠眀.郑盈一种矩形件分层排样算法[期刊论文]宇航材料工艺2010(1)3.陈婷.许超钣金零件排样技术及其发展[期刊论文]锻压装备与制造技术2008(4)本文链接http://d.wanfangdata.comcn/periodicAlyhclgy200704005.aspx授权使用:广东工业大学图书馆( gdgydxtsg),授权号:4flc88c5-bfdd-4dec-8ebf-9ec501113fe6下载时间:2011年4月14日

1、支持双向收发的组播，仅供参看，若有错误，请指出；2、带一个网卡接口的PC1跑send_recv, 带两个网口的PC跑recv_send程序，PC1发送两个组播数据分别给PC2的两个网卡收，PC2的第一个网卡发送组播数据给PC1收；

系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！Revision Historypril 1988First printingJuly 1991Second printingMay1995Third printingNovember 2000 Fourth printingRevised for Version 5.0(Release 12)pril 2001Fifth printingJuly 2002Online onlyRevised for Version 5.0.2 Release 13)June 2004Sixth printingRevised for Version 6.0.1(Release 14)March 2005Online onlyRevised for Version 6.1.1Release 14SP2)September 2005 Seventh printingRevised for Version 6.1.2(Release 14SP3)March 2006Online onlyRevised for Version 6.1.3(Release 2006a)September 2006 Online onlyRevised for Version 6.2 Release 2006b)March 2007Online onlyRevised for Version 7.0 ( Release 2007a)September 2007 Online onlyRevised for Version 7.1 (Release 2007bMarch 2008Online onlyRevised for Version 7.2(Release 2008a)October 2008Online onlyRevised for Version 7.2.1 Release 2008b)March 2009Online onlyRevised for Version 7.3(Release 2009a)September 2009 Online onlyRevised for Version 7.3.1(Release 2009b)March 2010Online onlyRevised for Version 7. 4 (Release 2010a)eptember2010 Online onlyRevised for Version 7.4.1(Release 2010b)pril 2011Online onlRevised for Version 7.4.2(Release 2011a)September 2011 Online onlyRevised for Version 7.4.3(Release 2011b)March 2012Online onlyRevised for Version 8.0( Release 2012aabout the DevelopersAbout the Developersystem Identification Toolbox software is developed in association with thefollowing leading researchers in the system identification fieldLennart Ljung. Professor Lennart Ljung is with the department ofElectrical Engineering at Linkoping University in Sweden. He is a recognizedleader in system identification and has published numerous papers and booksin this areaQinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut Nationalde recherche en Informatique et en Automatique(INria) and at Institut deRecherche en Informatique et systemes Aleatoires (Irisa), both in rennesFrance. He conducts research in the areas of nonlinear system identificationfault diagnosis, and signal processing with applications in the fields of energyautomotive, and biomedical systemsPeter Lindskog. Dr. Peter Lindskog is employed by nira dynamiAB, Sweden. He conducts research in the areas of system identificationsignal processing, and automatic control with a focus on vehicle industryapplicationsAnatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire JeanKuntzmann at the Universite Joseph Fourier, Grenoble, france. He conductsresearch in the areas of nonparametric statistics, system identification, andstochastic optimizationAbout the developersContentsChoosing Your System Identification ApproachLinear model structures1-2What Are Model objects?Model objects represent linear systemsAbout model data1-5Types of Model objectsDynamic System Models1-9Numeric Models1-11umeric Linear Time Invariant (LTD Models1-11Identified LTI modelsIdentified Nonlinear models1-12Nonlinear model structures1-13Recommended Model Estimation Sequence1-14Supported Models for Time- and Frequency-DomainData,,,,,,,1-16Supported Models for Time-Domain Data1-16Supported Models for Frequency-Domain Data1-17See also1-18Supported Continuous-and Discrete-Time Models1-19Model estimation commands1-21Creating Model Structures at the command Line ... 1-22about system Identification Toolbox Model Objects ... 1-22When to Construct a Model Structure Independently ofEstimation1-23Commands for Constructing Model Structures1-24Model Properties1-25See als1-27Modeling Multiple-Output Systems ......... 1-28About Modeling multiple-Output Systems1-28Modeling Multiple Outputs Directly1-29Modeling multiple outputs as a Combination ofSingle-Output Models.......1-29Improving Multiple-Output Estimation Results byWeighing Outputs During Estimation ....... 1-30Identified linear Time-Invariant models1-32IDLTI Models1-32Configuration of the Structure of Measured and Noise oRepresentation of the Measured and noise Components foVarious model Types1-33Components ....1-35Imposing Constraints on the Values of ModeParameters1-37Estimation of Linear models1-8Data Import and Processing2「Supported Data ...2-3Ways to Obtain Identification DataWays to Prepare Data for System Identification ... 2-6Requirements on Data SamplingRepresenting Data in MATLAB Workspace·····Time-Domain Data Representation2-9Time-Series Data Representation2-10ContentsFrequency-Domain Data Representation ....... 2-11Importing Data into the Gui2-17Types of Data You Can import into the GUi2-17Importing time-Domain Data into the GUI2-18Importing Frequency-Domain Data into the GUI2-22Importing Data Objects into the GUI ......... 2-30Specifying the data sampling interval2-34Specifying estimation and validation Data2-35Preping data Using Quick StartCreating Data Sets from a Subset of Signal Channelo2-362-37Creating multiexperiment Data Sets in the gUi2-39Managing data in the gui ............. 2-46Representing Time- and Frequency-Domain Data Usingiddata object2-55iddata constructor2-55iddata Properties.........2-58Creating Multiexperiment Data at the Command Line .. 2-61Select Data Channels, I/O Data and Experiments in iddataObjects2-63Increasing Number of Channels or Data Points of iddataObjects2-67Managing iddata Objects2-69Representing Frequency-Response Data Using idfrdObiec2-76idfrd Constructor2-76idfrd Properties2-77Select I/o Channels and Data in idfrd Objects ..... 2-79Adding Input or Output Channels in idfrd Objects2-80Managing idfrd Objects2-83Operations That Create idfrd Objects2-83Analyzing Data quality2-85Is your data ready for modeling?2-85Plotting Data in the guI Versus at the command line2-86How to plot data in the gui2-86How to plot data at the command line2-92How to Analyze Data Using the advice Command2-94Selecting Subsets of Data2-96IXWhy Select Subsets of Data?2-96Extract Subsets of Data Using the GUI2-97Extract Subsets of data at the Command Line2-99Handling Missing Data and outliers2-100Handling missing data2-100Handling outliers2-101Extract and Model Specific Data Segments2-102See also2-103Handling offsets and Trends in Data2-104When to detrend data2-104Alternatives for Detrending Data in GUi or at theCommand-Line2-105Next Steps After detrending2-107How to Detrend Data Using the Gui2-108How to detrend data at the Command line2-109Detrending Steady-State Dat109cending transient Dat2-109See also2-110Resampling Data2-111What Is resampling?...,,.,,,,,,,,,,,.2-111Resampling data without Aliasing Effects2-112See also2-116Resampling data Using the GUi.,,,,2-117Resampling Data at the Command line2-118Filtering Data2-120Supported Filters2-120Choosing to Prefilter Your Data2-120See also2-121How to Filter Data Using the gui2-122Filtering Time-Domain Data in the GuI........ 2-122Content

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开关电源设计

Cmos Data Converters for Communications，ADC大牛写的，系统介绍ADC设计的书

基于LoRa终端平台（STM8L151C8T6和SX1278）和Contiki系统，开发了点对点通信系统：通过评估软件，LoRa终端从PC接收数据，可以远程发送给另外的LoRa终端。

本文介绍了数字喷泉码原理，详细讨论了随机线性喷泉码、LT码和RAPTOR码编译码原理及实现方法，针对LT码仿真验证了数字喷泉码在删除信道的性能。