CARMES五性一体化分析软件-模板
五性一体化协同设计平台五性工具与数据集成环境五性工程软件CARMES6工业和信息化部电子第五研究所数据中心协同一致的五性工作平台，全面到位的五性解决方案CARMES 6突破五性（指可靠性、维修性、保障性、测试性和安全性，即RMS ）工具集的定位，从型号五性一体化设计和全寿命、全过程、全特性管理需求出发，统一筹划，构建企业级五性协同工作环境和平台，强化五性项目、任务、流程、状态和数据的管理监控，功能更加强大实用，可有效辅助企业全方位实现型号五性工作的顶层管理、过程协同和数据共享，提供五性工程一体化解决方案。
1. 全面覆盖，全局掌控2. 综合集成，工程实用CARMES 6集成了型号五性工作所需的22个功能模块和丰富的基础数据库，覆盖30多个RMS 工作项目，形成工程实用的先进RMS 平台和解决方案。
发展历程五性工程软件CARMES自2001年推出以来，历经10多年的工程磨砺，持续创新，集科研成果和工程经验于一体，已成为集成化的五性工程领域专业软件平台。
CARMES 6是在系统总结型号五性工程发展需求和吸取广大用户多年应用经验及反馈意见基础上的一次重大升级，是CARMES发展历程的一个重要里程碑。
CARMES 6充分体现了装备RMS全寿命周期、全系统、全过程、全特性管理的需求，在寿命周期RMS任务、过程和状态集成管理，五性协同设计，功能与数据的一体化集成共享等方面取得突破，在五性系统性、集成化、规范化、自动化和易用性方面取得重要进展。
工程应用CARMES全面融合我国国防工程所需的五性技术和标准，立足国际前沿，密切结合型号工程实际，以贴身服务五性工程为出发点，在RMS工程领域得到广泛应用并取得显著实效，已成功应用于神舟飞船总体及分系统，以及卫星、导弹、核装备、飞机、电子对抗、雷达、C4I、舰船、兵器等系统的500多家用户。
CARMES项目获国防科学技术进步奖，并因成功应用于我国载人航天工程而荣获中国载人航天办公室嘉奖。
CARMES 6新特性1.五性综合管理协同设计平台CARMES 6强化了型号五性综合管理和监控功能，从五性系统工程角度对装备全寿命、全过程的RMS工作实施有效的监管。
通过增加五性管理层，实现型号寿命周期五性工作项目、任务、过程和数据的集成管理。
型号总师、项目负责人、设计师和任务委托方等不同类型用户通过这一管理层次，借助相应的五性工具，实现全局掌控、任务分配与监管、设计分析和监督考核等功能，形成高度一致的五性协同工作环境和集成平台。
2.丰富实用的五性基础数据库CARMES 6从工程应用实际出发，从多年的型号五性工作和实践中积累了大量的五性基础数据，建立起丰富实用的五性基础库。
CARMES 6的五性基础数据库覆盖了航天、航空、电子、船舶、兵器、工程物理等领域主要型号用国产和进口元器件可靠性预计参数库、模块失效率数据，以及机械、机电产品可靠性数据。
依托这些丰富的可靠性预计参数库，可快速便捷的实现可靠性预计的自动化。
CARMES 6包含大量的传感器和敏感元件的性能和可靠性参数，为测试系统的设计提供重要技术支持。
此外，还积累了大量的产品故障模式库、型号元器件适用信息库、保障资源库、危险源信息库等。
CARMES 6提供按用户型号需求定制五性参数库服务，满足用户个性需求。
3.有机融合型号元器件优选和进口元器件风险管理功能型号元器件优选、国产化和进口电子元器件风险管理是当前型号管理的重要工作，与型号五性工作存在紧密联系。
CARMES 6通过下列手段将型号元器件优选管理与可靠性工作有机融合在一起：✧针对型号元器件优选目录中的元器件清单建立通用产品库、可靠性预计参数库和故障模式库等。
✧结合元器件国产化工作，建立进口电子元器件风险信息库，实施进口元器件风险控制，依据国产化前后电子元器件的可靠性水平差异对设备或模块的可靠性进行对比分析，从使用风险、可靠性和费用等方面权衡各种国产替代方案，辅助国产化决策。
4.一体化建模CARMES 6从五性系统工程角度设计出一体化可靠性综合建模方法，一次建模即可完成系统基本可靠性、任务可靠性和可靠性评估等多个五性工作项目的模型构建、数据录入和分析评估工作。
系统树模块增加了节点逻辑关系定义功能，建立系统树时可直接定义节点间的可靠性逻辑关系（如串联、并联、表决、储备等），据此自动生成可靠性框图（RBD）和可靠性评估需要的可靠性树。
建立系统树时可录入节点的逻辑结构、分布类型、失效率、试验数据等信息，选定相应的节点后，即可生成RBD图和可靠性评估逻辑树，点击RBD计算或可靠性评估计算按钮，就可计算生成RBD的任务可靠度和可靠性评估的可靠度置信下限等计算结果报表。
同理，依据FMECA结果可自动生成相应的故障树。
CARMES 6充分利用五性工程的内在联系，高度融合集成各工作项目所需的功能和数据，形成高效的五性设计分析和评估集成环境。
✧工作状态可靠性预计/非工作状态可靠性预计/降额设计集成在一个页面表格中进行，元器件类型、结构参数、复杂度、应力等参数集中录入，一次完成，避免重复录入和不一致问题。
✧可靠性预计得到的失效率数据，在RBD、可靠性评估、FMECA、故障树分析（FTA）、以可靠性为中心的维修分析（RCMA）等模块中可直接自动引用，不需要用户额外操作按钮再获取。
✧可靠性预计模块中用户设定了预计依据、环境、温度和降额准则等应用模版的缺省值后，按[保存]按钮，依托CARMES 6强大的预计参数库，系统即可自动完成可靠性参数查找和失效率计算功能，极大提高可靠性预计效率。
✧可靠性预计模块得到的失效率数据、FMECA模块的故障模式及其频数比数据可直接用于测试性建模分析和RCMA模块的逻辑决断分析。
5.高效的五性工作导航CARMES 6用工作导航图的方式，依据顶层国军标要求给出了产品寿命周期各阶段的五性工作流程和主要项目，为用户在产品寿命周期的不同阶段开展五性相应工作项目提供导航式的指引，用户双击相关工作项目框即可进入相应的工作项目。
CARMES 6中各模块都有清晰的帮助向导，用户双击相应的流程框即可激活所需的工作窗口，快速地协助用户完成工作。
6.符合RMS标准，满足工程要求CARMES 6支持国内外RMS标准规范，符合国内工程标准和管理要求，其算法与报表完全满足国家军用标准要求。
☆GJB 450A《装备可靠性工作通用要求》☆GJB 368B《装备维修性通用大纲》☆GJB 2547 《装备测试性大纲》☆GJB 1371《装备保障性分析》☆GJB 900《系统安全性分析大纲》☆GJB/Z 299C《电子设备可靠性预计手册》☆GJB/Z 108A《电子设备非工作期可靠性预计手册》☆MIL-HDBK-217F《电子设备可靠性预计》☆MIL-HDBK-217F NOTICEⅡ☆电信、电力等行业预计方法☆GJB 1378 《装备预防性维修大纲的制订要求与方法》☆GJB/Z 57《维修性分配与预计手册》☆GJB 2961 《修理级别分析》☆GJB 4355 《备件供应规划要求》☆GJB/Z 35《元器件降额准则》☆GJB/Z 841《故障报告、分析和纠正措施系统》☆GJB/Z 1391《故障模式、影响及危害性分析指南》☆GJB/Z 768A 《故障树分析指南》☆GJB 899A 《可靠性鉴定和验收试验》☆GJB 1407 《可靠性增长试验》☆GJB/Z 20517 《武器装备寿命周期费用分析估算》☆……7.一流的技术支持服务工业和信息化部电子第五研究所作为国内唯一的可靠性专业研究机构，是国军标GJB/Z 299C《电子设备可靠性预计手册》、GJB/Z 108A《电子设备非工作状态可靠性预计手册》的编制单位，是国产军用电子元器件产品手册编制单位和信息中心，具有雄厚的技术实力，能够提供及时、到位的本地化技术和数据支持。
能够以工程和用户需求为牵引不断对软件创新和持续稳定升级。
CARMES拥有强大的软件研发和技术服务团队，提供源代码级技术支持，可及时增加新型元器件的预计模型和参数，动态跟踪国内外元器件最新产品，定期更新扩充元器件库。
CARMES可开发用户EDA电路设计软件接口，可按工程及用户要求编制报表。
惟有CARMES能够郑重承诺：根据用户提供的元器件清单完成可靠性预计参数等数据的收集、分析与整理，建立用户自己的、针对用户型号工程常用元器件的预计参数库、优选信息库和故障模式库等基础数据库。
可以根据用户的不同需要，协助用户做好可靠性预计、分析和设计，生成用户需要的可靠性设计报告。
8.高度的数据安全性CARMES 6可按系统树节点设定五性各工作项目的权限，实现任何级别产品的安全管理要求，可定义三员管理，完全符合国防军工保密要求。
并能根据系统日志实现关键数据的恢复，即使在突然宕机、退出CARMES的情况下，仍然可恢复系统树及相关五性分析的关键数据。
9.架构先进，支持跨平台，多样化呈现CARMES 6充分利用程序设计技术发展的最新成果，采用先进的三层架构，支持跨平台、跨数据库应用和分布式部署，具有良好的可扩展性和按模块独立升级能力。
CARMES 6采用WPF（Windows Presentation Foundation）用户界面框架，支持多窗口、多线程、多属性并行操作，功能和界面设计更加合理易用，呈现方式更加丰富多彩、规范明晰。
例如，系统树用树形表格呈现，用户可在系统树中获取更多的信息，诸如节点名称、型号规格、产品层次、功能等，输出形式更加多样化。
CARMES 6功能模块RMS任务及信息综合管理RMSMIS通过实施RMS工作项目管理、任务分配、流程指引、参数管理、状态监控及报表归档等综合管理手段，构造企业级五性协同工作环境，实现RMS一体化管理。
主要功能包括：✧依据GJB 450A《装备可靠性工作通用要求》、GJB 3872《装备综合保障通用要求》等五性顶层标准和五性大纲要求，结合本单位型号特点，柔性设定五性工作项目、规划和分配五性工作任务，检查五性任务和报告提交状况，监控任务进度和五性状态。
✧检查五性任务和报告提交状况，监控任务进度和五性状态。
✧依据研制合同要求，确定和集中管理工程项目各层次产品的五性参数（目标值/规定值、门限值/最低可接受值、预计值、评估值、验证值等），能直接从提交的五性报告摘要中提取五性参数值。
✧根据产品层次（系统树）对生成的各种五性设计分析报告进行集中管理，并具备报告提交、审核和归档功能。
✧具有根据五性工作流程导航功能，能够根据导航引导开展相应的五性工作。
可靠性建模RBD可靠性建模RBD能完成系统任务可靠性建模及可靠度计算功能。
具有可靠性一体化建模功能，能够在一个统一的程序界面中实现可靠性一体化建模，包括其结构和逻辑关系、分布参数、试验数据等方面的定义和数据录入，实现系统树、可靠性预计、可靠性框图和可靠性评估等工作项目一体化建模和综合分析计算。
CDMA测试参考书
CDMA测试参考书目录第1章概述 (4)第2章基础知识简介 (5)2.1 呼叫接入过程 (5)2.1.1 呼叫发起的定义 (5)2.1.2 系统接入状态定时 (6)2.1.3 呼叫发起过程概述 (6)2.2 切换流程 (7)2.2.1 切换过程 (7)2.2.2 切换的类型 (8)2.2.3 切换过程信令 (8)2.3 呼叫信令流程 (9)2.3.1 主叫呼叫流程 (9)2.3.2 被叫呼叫流程 (10)2.4 CDMA掉话机制 (10)2.4.1 移动台的掉话机制 (10)2.4.2 基站掉话机制 (11)2.5 Rake接收机 (11)第3章测试硬件连接注意事项 (12)3.1 测试硬件组成部分 (12)3.1.1 测试业务 (12)3.1.2 测试硬件及经验说明 (12)3.2 三星手机工程模式设置 (13)3.2.1 Markov呼叫 (13)3.3 手机的数据业务设置 (14)3.3.1 Modem安装 (14)3.3.2 建立新连接 (14)第4章Panorama测试和分析软件的使用 (14)4.1 软件安装和说明书 (14)4.1.1 建立工程 (14)4.1.2 基站导入 (15)4.1.3 设备配置 (15)4.1.4 开始路测 (16)4.1.5 主要窗口介绍 (16)4.1.6 数据回放 (19)4.1.7 数据统计 (19)4.2 经验总结 (20)4.2.1 设备连接 (20)4.2.2 网络指标的一些评估经验 (21)第5章测试指标解释 (21)5.1 分析指标内容 (21)5.2 测试指标定义 (22)5.3 测试数据出图和统计表 (22)第6章异常事件分析及建议 (23)6.1 掉话率高 (24)6.1.1 问题现象 (24)6.1.2 问题分析 (24)6.1.3 掉话分析建议 (25)6.2 切换失败 (25)6.2.1 问题现象： (25)6.2.2 问题分析： (25)6.2.3 解决建议： (26)6.3 呼叫失败率高 (26)6.3.1 问题表现现象 (26)6.3.2 问题分析： (26)6.3.3 解决建议 (27)6.4 语音质量差（FFER/RFER） (28)6.4.1 FFER问题表现现象 (28)6.4.2前向链路高FER问题分析 (28)6.4.3解决建议： (29)6.4.4反向链路高FER原因分析 (29)6.4.5问题表现现象 (29)6.4.6问题原因分析 (29)6.4.7 优化建议 (30)6.5 呼叫建立时延过长 (30)6.5.1 表现现象 (30)6.5.2 问题分析 (30)6.5.3 优化建议 (31)6.6 导频污染 (31)6.6.1 问题现象 (31)6.6.2 问题分析 (31)6.6.3 解决建议 (32)6.7 软切换比例高 (32)6.7.1 问题现象 (32)6.7.2 问题分析 (32)6.7.3 解决建议 (33)第1章概述编写本指导书的目的：(1) 本书主要针对中国电信最新《CDMA网络DT测试评估报告模板》中数据分析内容部分的需要而编制，结合此书中所列的其他参考书，基本可以完成测试数据的分析和评估报告的撰写。
易用描述性分析工具-easyDes V6.0说明书
Package‘easyDes’October13,2022Type PackageTitle An Easy Way to Descriptive AnalysisVersion6.0Author Zhicheng Du,Yuantao HaoMaintainer Zhicheng Du<*****************>Description Descriptive analysis is essential for publishing medical articles.This package provides an easy way to conduct the descriptive analysis.1.Both numeric and factor variables can be handled.For numeric variables,normal-ity test will be applied to choose the parametric and nonparametric test.2.Both two or more groups can be handled.For groups more than two,the post hoc test will be ap-plied,'Tukey'for the numeric variables and'FDR'for the factor variables.3.T test,ANOV A or Fisher test can be forced to apply.4.Mean and standard deviation can be forced to display.License GPL-3Imports PMCMRplus,multcomp,stats,utilsNeedsCompilation noRepository CRANDate/Publication2021-11-1910:30:02UTCR topics documented:easyDes (1)Index5 easyDes An Easy Way to Descriptive Analysis1DescriptionDescriptive analysis is essential for publishing medical articles.This package provides an easy way to conduct the descriptive analysis.1.Both numeric and factor variables can be handled.For numeric variables,normality test will be applied to choose the parametric and nonparametric test.2.Both two or more groups can be handled.For groups more than two,the post hoc test will beapplied,’Tukey’for the numeric variables and’FDR’for the factor variables.3.T test,ANOV A or Fisher test can be forced to apply.4.Mean and standard deviation can be forced to display. UsageeasyDes(nc.g,nc.n,nc.f,nc.of,dataIn,fisher,aov,t,mean,mcp.test.method,mcp.stat,mcp.t.test,mcp.t.test.method,table.margin,decimal.p,decimal.prop)Argumentsnc.g integer,the column number of the grouping variable,length of’nc.g’must be1 nc.n numeric vector,the column number of the numeric variable,length of’nc.n’can be more than1nc.f numeric vector,the column number of the factor variable,length of’nc.f’can be more than1nc.of numeric vector,the column number of the ordinal factor variable,length of ’nc.of’can be more than1dataIn data frame including variables abovefisher logic,whether to apply Fisher test by force,the default is’TRUE’aov logic,whether to apply ANOV A test by force,the default is’FALSE’t logic,whether to apply T test by force,the default is’FALSE’mean logic,whether to disply the mean and standar deviation for the numeric variables by force,the default is’FALSE’mcp.test.methodcharacter,specific for ANOV A,the method for the multiple comparisons in’multcomp’package,’Tukey’or’Dunnett’mcp.stat logic,whether to display the statistic for the multiple comparsionsmcp.t.test logic,specific for ANOV A,wether to use the pairwise t tests as the multiple comparsions instead of that in’multcomp’packagemcp.t.test.methodcharacter,specific for’mcp.t.test’==TRUE,the method for the pairwise t tests,’holm’(Holm,1979),’hochberg’(Hochberg,1988),’hommel’(Hommel,1988),’bonferroni’,’BH’(Benjamini&Hochberg,1995),’BY’(Benjamini&Yeku-tieli,2001),’fdr’,’none’table.margin1or2,which margin of the table should be calculated the proportion,1=row, 2=columndecimal.p integer,the number of decimals of the p valuedecimal.prop integer,the number of decimals of the proportions for factor variablesDetails1.Nemenyi test was used as a Kruskal-Wallis post-hoc test.2.FDR(False Discovery Rate)was used to adjust the p values after pairwise comparision of Chi-square test or Fisher test.3.Tukey test was used as a ANOV A(Analysis of Variance)post-hoc test.4.Shapiro-Wilk test was used as normality test if the sample size was between3~5,000,whileKolmogorov-Smirnov test was used if the sample size was greater than5,000.Valuetotal the descriptive statistic for all datagroup names the descriptive statistic for data of each groupmethod the method applied to test between groups,i.e.ANOV A and Tukey,Fisher and FDRstatistic the statistic of test,i.e.,the’W’to Wilcoxon test,the’chi-squared’to Kruskal-Wallis,the’t’to t test,the’F’to ANOV A testp.value the p value derived from the test between groupsstat.*_va_*the statistic derived from the post hoc test,the’t’of Tukey for ANOV A,the’q’of Nemenyi for Kruskal-Wallisp.*_va_*the p value derived from the post hoc testNotePlease feel free to contact us,if you have any advice andfind any bug!Update description:Version2.0:1.T test can be forced to apply.Version3.0:1.Fixing the wrong colnames in Chi-squre test.2.Limiting the number of the decimal digits of the statistic in Chi-squre test to three.3.The number of decimal digits of the propotion for the factor variables can be set free.Version4.0:1.Mean and standard deviation can be forced to display.2.The help document has been revised.3.Fix the problem with more than5,000samples in the normality test.Version5.0:1.Unify the number of decimal digits(i.e.,output"0.010"rather than"0.01"for p value).2.Add the’nc.of’to analyze ordinal factors.Version6.0: 1.Add the pairwise t tests for the the multiple comparsions. 2.Fix the error of "Increase workspace or consider using’simulate.p.value=TRUE’"infisher test. 3.Add the’ta-ble.margin’argument.4.Add the’decimal.p’argment.5.Fix the bugs caused by the names with specific characters in numeric variables.Author(s)Zhicheng Du<*****************>,Yuantao Hao<***************>Examplesgroup=rep(c(0,1),each=30)nx1=rnorm(60)nx2=rnorm(60)fx1=rep(c(1:3),20)fx2=rep(c(1:5),12)fx3=factor(fx2)data=data.frame(group,nx1,nx2,fx1,fx2,fx3)easyDes(nc.g=1,nc.n=2:3,nc.f=4:5,nc.of=6,dataIn=data,fisher=TRUE,aov=FALSE,t=FALSE,mean=FALSE,mcp.stat=FALSE) easyDes(nc.g=4,nc.n=2:3,nc.f=c(5,5),nc.of=6,dataIn=data,fisher=TRUE,aov=FALSE,t=FALSE,mean=FALSE,mcp.stat=FALSE) easyDes(nc.g=4,nc.n=3,nc.f=5,nc.of=6,dataIn=data,fisher=TRUE,aov=FALSE,t=FALSE,mean=FALSE,mcp.stat=TRUE)Index∗Descriptive analysiseasyDes,1easyDes,15。
RMS设计分析软件-西安盛安睿电子技术工程有限公司
RMS设计分析软件-西安盛安睿电子技术工程有限公司GARMS软件功能简介北京金网拓技术有限公司2015.10目录1 GARMS软件简介 ..................................................................... .. (1)2软件总体管理与控制 ..................................................................... ................................ 1 2.1 RMS设计过程控制与管理系统 ..................................................................... ... 1 2.2 RMS设计分析结果及状态总览 ..................................................................... .... 2 2.3 成品RMS设计分析结果管理系统 ....................................................................23 RMS要求论证与权衡软件 ..................................................................... ......................... 2 3.1 RMS要求论证软件...................................................................... ..................... 2 3.2 性能与RMS综合权衡软件...................................................................... (2)4 RMS设计分析软件...................................................................... ................................... 3 4.1 可靠性设计分析软件 ..................................................................... (3)4.1.1 可靠性建模软件 ..................................................................... .. (3)4.1.2 可靠性分配软件 ..................................................................... .. (3)4.1.3 可靠性预计软件 ..................................................................... .. (4)4.1.4 故障模式影响及危害性分析软件 (4)4.1.5 故障树分析软件 ..................................................................... .............. 5 4.2 维修性设计分析软件 ..................................................................... (5)4.2.1 维修性分配软件 ..................................................................... .. (5)4.2.2 维修性预计软件 ..................................................................... .. (6)4.2.3 维修问题核查软件...................................................................... .......... 6 4.3 测试性设计分析软件 ..................................................................... (6)4.3.1 测试性分配 ..................................................................... (6)4.3.2 测试性建模与分析软件...................................................................... ... 6 4.4 保障性设计分析软件 ..................................................................... (7)4.4.1 以可靠性为中心的维修分析软件 (7)4.4.2 修理级别分析软件...................................................................... . (7)4.4.3 修复性维修工作分析软件 .....................................................................84.4.4 使用与维修任务分析软件 .....................................................................84.4.5 备件和保障设备需求预测软件 .............................................................. 9 4.5 安全性设计分析软件 ..................................................................... (9)4.5.1 事件树分析软件 ..................................................................... .. (9)4.5.2 功能危险分析软件...................................................................... . (9)4.5.3 区域安全分析软件...................................................................... ........ 10 4.6 机械机构可靠性分析评价软件 ..................................................................... .. 105 RMS试验设计与评价软件 ............................................................................................ 10 5.1 可靠性试验设计 ..................................................................... .. (10)5.1.1 可靠性试验方案设计软件 ...................................................................10I基于参考应力的环境条件设计软件 ..................................................... 11 5.1.25.1.3 基于实测应力的环境条件设计软件 (11)5.2 可靠性试验评估 ..................................................................... .. (11)5.2.1 复杂系统可靠性综合评估软件 (11)5.2.2 加速试验分析与评估软件 ...................................................................115.2.3 可靠性试验管理软件 ..................................................................... ..... 12 6 RMS基础数据库管理系统 ..................................................................... ....................... 12 7 数字化设计环境接口...................................................................... ............................. 12 8 结束语...................................................................... .. (13)II北京可维GARMS软件功能简介 1 GARMS软件简介GARMS软件是由北京金网拓技术有限公司(以下简称金网拓)和北京航空航天大学可靠性工程研究所联合开发。
iHealthTM EN Wireless Body Analysis Scale (HS5) 使用
Wireless Body Analysis Scale (HS5) Balance d'analyse corporelle sans fil (HS5)Bilancia pesapersone wireless iHealth (HS5)Báscula inalámbrica de análisis corporal (HS5)Drahtlos-Körperanalysewaage (HS5)OWNER’S MANUALMODE D'EMPLOIMANUALE D’ISTRUZIONIMANUAL DEL PROPIETARIOBEDIENUNGSANLEITUNGiHealth TM Wireless Body Analysis Scale (HS5) OWNER’S MANUALTable of Contents INTRODUCTIONPACKAGE CONTENTSINTENDED USEIMPORTANT NOTE FOR USERS CONTRAINDICATIONPARTS AND DISPLAY INDICATORSSET UP REQUIREMENTSSET UP PROCEDURESSET UP THE SCALE’S WI-FI CONNECTION MEASUREMENT INSTRUCTIONS SPECIFICATIONSGENERAL SAFETY AND PRECAUTIONS TROUBLESHOOTINGCARE AND MAINTENANCE WARRANTY INFORMATION EXPLANATION OF SYMBOLS1 1 12 2 23 345 8 9 10 12 12 12INTRODUCTIONThank you for purchasing the iHealth Wireless Body Analysis Scale. You will now be able to measure, track, and share vital body composition parameters from the comfort of your home.In addition to body weight, the iHealth Wireless Body Analysis Scale measures:• BMI• Body Fat• Lean Mass• Muscle Mass• Bone Mass• Body Water• Visceral Fat Rating• Daily Calorie Intake (DCI)This manual will guide you through the set up procedures and highlight the Scale’s key features. Please keep it handy for future reference.To learn more about these body composition parameters, refer to the FAQ section of the iHealth App or visit . PACKAGE CONTENTS• iHealth Wireless Body Analysis Scale• Owner’s Manual• Quick Start Guide• 4 AA BatteriesINTENDED USEThe iHealth Wireless Body Analysis Scale is a precision electronic instrument intended for adult use. The Scale utilizes full electronic methodology and pressure sensors to non-invasively measure body composition components automatically. The measurements are displayed and stored on an iPod touch, iPhone, or iPad with a date and time stamp.IMPORTANT NOTE FOR USERS Pregnant women need to consult their healthcare provider before use. Some physical conditions could affect hydration levels that may lead to inaccurate results. Please consult your healthcare provider for more information.Always store the iHealth Wireless Body Analysis Scale in a dry place. To ensure accurate results, keep the Scale away from magnetic fields as these may adversely affect results or possibly damage the Scale.CONTRAINDICATIONNever use this product in combination with medical electronic devices such as:(1) Medical electronic implants such as pacemakers.(2) Electronic life support systems such as artificial heart/lungs.(3) Portable electronic medical devices such as electrocardiographs.This product could cause these devices to malfunction posing a considerable health risk to users of these devices.PARTS AND DISPLAY INDICATORS! ! !SET UP REQUIREMENTS The iHealth Wireless Body Analysis Scale is designed to be used with the following iPod touch, iPhone and iPad models:iPod touch (4th generation)iPhone 4S iPhone 4iPhone 3GS iPad (3rd generation)iPad2iPad The iOS version of these device should be V5.0 or higher.Prior to first use, download and install “iHealth MyVitals” App from the App Store. It is very important that every Scale user follows the on-screen instructions to register and complete a personal profile because data points such as height and age are necessary to measure body composition.SET UP PROCEDURESDownload the Free iHealth App • iOS device is compatible and is version V5.0 or higher.•Wireless Internet connection provided by a router is compatible with Wi-Fi IEEE 802.11 b/g standard and supports WEP , WPAPreparing for Set UpSTEP 1: Connect iOS device to your home Wi-Fi. (Settings->Wi-Fi->On)STEP 2: Turn Bluetooth “On” on your iOS device and it will start searching for the Scale.STEP 3: Wait until the model name, “iHealth HS5xxxxx” , and “Not Paired” appear on your device’s Bluetooth screen. Select the model name “iHealth HS5xxxxx” to pair and connect. (Note: it may take up to 30 seconds for your iOS device to detect the Bluetooth signal”)Open the battery cover on the back of the Scale and insert four “AA” batteries.batteries.Scale.be used for 3 months or more.and contact a physician.Install Batteries Select a weight unit by adjusting the switch under the battery cover.Select Weight Unit and WPA2 personal security modes.! ! ! !MEASUREM ENT INSTRU CTIONSa. Launch the iHealth App. Step on the Scale to turn it on and wa it until “0.0” ap pears on the display.The Bluetooth connection will then automatically disconnect from the Scale. Your scale is now connected to your home Wi-Fi and ready to use.STEP 4: Select “Allow” in the pop-up window, as shown below.STEP 5: The wireless signal icon on the Scale’s display will flash for a few seconds and then stabilize when Wi-Fi connection is successful. The Scale should display asshown below.b. Stand on a ll four electro des with bare feet. Your we ight data will appear on th e Scale displ ay first, follow ed by Body F at %. Remain on th e scale for a few seconds to allow the s cale to continue the measuremen t. If you have socks on, th e Scale will only display y our weight.The iHealth A pp will show detailed body composition data including We ight, BMI, Bo dy Fat, Lean Mass, Body W ater Percentage, M uscle Mass, B one Mass, Vi sceral Fat Ra ting and Daily Calorie Intake (DCI).Note: If the B ody Fat meas urement fails, only your w eight will be displayed.Taking Measurements Without iOS DeviceUp to 20 people can use the Scale. The Scale determines who theThe Scale can store up to 200 weight results for each user. When each user’s memory is full, any new measurements will overwrite the oldest ones.Offline Memory If you switch your ISP or internet router, first press the “Set” button under the battery cover on the back of the Scale to reset the Wi-Fi connection, then proceed to the “SET UP THE SCALE’S WI-FI CONNECTION” instruction steps.Visit to obtain additional product informa -tion. For Customer Service, please call +1(855) 816-7705.Instructions for switching ISP or internet router The Scale shuts down automatically after 2 minutes following completion of the measurement. If a new measurement is started or any App operation is performed during this time, such as uploading the memory contents of the Scale to an iOS device, the shutdown timer will be reset to 2 minutes.Automatic Shutdown Featureuser is by matching the new weight with weight previously recorded. If the weight of two or more users is similar, the Scale will display the user number (e.g. User # “x”). Step lightly on the lower left corner to select the appropriate user, and then step lightly on the lower right corner to confirm. When taking measure-ments without an iOS device, your current measurement data will be uploaded to the Cloud automatically.Operating InstructionsFor more detailed operating instructions, please visit or the FAQ section of the iHealth App on your iOS device.SPECIFICATIONS1. Product name: iHealth Wireless Body Analysis Scale2. Model: HS53. Classification: internally powered, type BF applied part (fourelectrodes)4. Machine dimensions: 16.2"x13.2"x1.7"(411 mm×335 mm×43 mm)5. Weight: approx. 4 lbs (1800 g)6. Measuring method: automatic full electronic measurement7. Power: 4×1.5V AA batteries8. Measurement range:Body Weight: 11 lbs-330 lbs/5 kg-150 kgBody Fat: 5.0%-65.0%Body Water: 20.0%-85.0%Visceral FatRating: 1-599. Accuracy:Body Weight: ±1.1 lb/0.5 kg(5 kg-40 kg / 11-88 lbs);±1%+ 0.2 lbs / 0.1 kg(40 kg-150 kg / 88-330 lbs) Body Fat: ±1%Body Water: ±1%Body MuscleMass: ±(1% + 0.2 lbs / 0.1 kg)BonesMass: ±0.66 lbs / ±0.3 kgVisceral FatRating: ±2DCI (Daily Calorie Intake): ±200 kcal10. Operating temperature: 10℃ - 35℃(50°F-95°F)11. Operating humidity: 20 - 85%RH12. Environmental pressure for operation: 86-106 kPa13. Storage and transport temperature: -20℃ - 60℃(-4°F-140°F)14. Storage and transport humidity: 10 - 95%RH15. Environmental pressure for storage: 50-106 kPa16. Battery life: approx. 3 months with daily usagepossible until the results appear on the display.3. Do not stand on the edge of the Scale as you may fall or receive inaccurate measurements.4. Do not use the Scale on a tile or wet floor as this may result in a fall.5. Ensure that the surface of the Scale is clean and dry before you step onto it as it may become slippery when wet.6. Treat your Scale with care. Do not drop it or jump on it. The Scale is designed to be stood on; misuse or abuse may render the electronic sensors inoperative, cause you to fall, or adversely affect the accuracy of measurements.7. Never immerse the Scale in water. Clean the surface with a damp cloth.8. Do not use the Scale on an uneven floor, a soft surface or acarpet as doing so may result in unreliable data.9. To avoid damage as a result of battery leakage, remove thebatteries if the Scale is not going to be used for more than 3 months.10. This Wireless Body Analysis Scale is designed for adults.Infants or young children or any person who cannot stand still without assistance, should not use it.11. It may not be safe for people with pacemakers to use this Scale.12. Avoid using this Scale near strong magnetic fields, such asmicrowave ovens, etc.13. The Scale may not perform accurately if it is stored or usedoutside the specified temperature and humidity ranges cited under Specifications.14. This device complies with part 15 of the FCC Rules. Itsoperation is subject to the following two conditions: (1) this device may not cause harmful interference, and(2) this device must accept any interference received, includingGENERAL SAFETY AND PRECAUTIONS1. Read all of the information in the Owner’s Manual and otherincluded product information in the packaging before operating this product.2. Please stand on the Scale with bare feet, keeping as still as ! ! ! ! ! !interference that may cause undesired operation.15. Changes or modifications not expressly approved by iHealthLab Inc. invalidate the user’s warranty for this equipment.16. This equipment has been tested and found to comply with thelimits for a Class B digital device, pursuant to part 15 of the FCC Rules. These limits are designed to providereasonable protection against harmful interference in aresidential installation. This equipment generates, uses and can radiate radio frequency energy and, if not installed and used in accordance with the instructions, may cause harmful interference to radio communications. However, there is no guarantee that interference will not occur in a particularinstallation. If this equipment does cause harmful interference to radio or television reception, which can be determined by turning the equipment off and on, the user is encouraged to try to correct the interference by one or more of the following measures:—Reorient or relocate the receiving antenna.—Increase the separation between the equipment and receiver. —Connect the equipment into an outlet on a circuit different fromthat to which the receiver is connected.—Consult the dealer or an experienced radio/TV technician forhelp.17. This device complies with Industry Canada license-exempt RSS standard(s). Operation is subject to the following two conditions:(1) this device may not cause interference, and(2) this device must accept any interference, including interference that may cause undesired operation of the device.TROUBLESHOOTING! PROBLEMS e t U p P r o b l e m sScale model name is not listed on the Bluetooth Menu of the iOS deviceMake sure Scale is on “Set”status by pressing the “Set” button on the back of the Scale.SOLUTIONCARE AND M AINTENANC E1. Avoid high temperature s and directly sunlight. Do not immerse the Scale in w ater, as this w ill damage th e Scale.2. If the Scale is stored in n ear freezing o r freezing tem peratures, allow it to acc limatize to ro om temperat ure before us e.3. Do not atte mpt to disass emble the Sc ale.4. Remove th e batteries if the Scale is n ot going to b e used for more than 3 m onths.5. Clean the S cale with a so ft damp cloth if dirty. Do no t use abrasive or s olvent-based cleaners.6. The Scale is essentially maintenance -free and req uires no user intervention.7. The Scale will maintain its safety and performance features for at least 10,000 measureme nts or two ye ars of use.WARRANTY INFORMATIO NThe iHealth W ireless Body Analysis Sca le is warrante d to be free from defects in materials a nd workmans hip appearing within 1 year from the date of purc hase, when u sed in accord ance with the instructions p rovided. The above warran ties extend o nly to the original retail purchaser. W e will, at our o ption, repair o r replace without charg e any produc t covered by the above wa rranties. Repair or rep lacement is o ur only respo nsibility and y our only remedy unde r the above w arranties.EXPLANATION OF SYMBOLSMake sure Scale is connected to a router.Make sure router is connected to internet. The Scale failed to detect bodyimpedance. Make sure you step on all four electrodes with bare feet, and try again.The Scale displays “Er 5”The Scale displays “- - - -”Symbol for “CAUTION”!Symbol for “THE OWNER’S MANUAL MUST BE READ”12Symbol for “MANUFACTURER”Symbol for“ENVIRONMENT PROTECTION – Wasteelectrical products should not be disposed of with house-hold waste. Please recycle where facilities exist. Checkwith your local Authority or retailer for recycling advice”.Symbol for “KEEP DRY”Symbol for “TYPE BF APPLIED PARTS”Symbol for “Year of Manufacture”Manufactured for iHealth Lab Inc.Mountain View, CA 94043, USA +iHealth is a trademark of iHealth Lab Inc.Bluetooth ® associated logos are registered trademarks owned by Bluetooth SIG, Inc. and any use of such marks by iHealth Lab Inc. is permitted under license.“Made for iPod”, “Made for iPhone”, and “Made for iPad” mean that an electronic accessory has been designed to connect specifically to iPod, iPhone, or iPad, respectively, and has been certified by the developer to meet Apple performance standards. Apple is not responsible for the operation of this device or itscompliance with safety and regulatory standards. Please note that the use of this accessory with iPod, iPhone, or iPad may affectwireless performance. iPad, iPhone, and iPod touch are trademarks of Apple Inc., registered in the U.S. and other countries.Other trademarks and trade names are those of their respective owners.Symbol for“COMPILES WITH RTTE 99/5/EC REQUIREMENTS”ANDON HEALTH CO., LTD.No. 3 Jinping Street, YaAn Road, Nankai District,Tianjin 300190, China. Tel: 86-22-60526161。
基于Power_World_Simulator的汉中电网建模与仿真
基于Power World Simulator的汉中电网建模与仿真田银刚【摘要】：了解可视化电力系统设计的基本思想及发展情况，介绍了Power world Simulator的基本功能和使用方法，以汉中电网的简化模型为例,搭建可视化电网模型，实现单机版的可视化电力系统设计；根据汉中电网的模型完成了系统参数的设置，并进行系统潮流计算的可视化分析，对电网的各种故障进行系统潮流的可视化的运行分析。
通过汉中电网实例的分析及运行，验证了该软件的合理性与有效性。
【关键字】：Power world Simulator、建模，仿真，潮流计算、汉中电网、可视化、短路故障[abstracts]: Power world Simulator 13 version of the basic functions to simplify the model grid Han zhong framework through Power world Simulator 13, visualization grid model, to achieve a single version of the visual design of the power system, according to the grid Han zhong model completion of the set of system parameters for the calculation of flow visualization, and the failure to carry out a variety of Power Flow visualization analysis of the operation.[Key word ]: Power world Simulator, a computer calculation of the trend, Han zhong grid, visualization, short-circuit fault目录1 Power world simulator的软件介绍 (2)1.1电力系统可视化技术 (2)1.2Power World软件介绍 (3)1.3 电力系统建模 (4)1.3.1电力系统单线图 (4)1.3.2仿真环境和参数设置 (7)1.4软件主要功能模块 (8)1.4.1潮流计算(PowerFlow) (8)1.4.2故障分析(Fault Analysis) (9)1.4.3电压稳定性分析(V oltage Stability) (10)1.4.4最优潮流(OPF/SCOPF) (10)1.4.5事故分析(Contingency Analysis) (11)1.4.6线性分析(Linear Analysis) (11)1.4.7可用传输容量分析(A TC) (12)2。
华喜PIMExpress-PDM系统技术白皮书
华喜PIMExpress-PDM系统技术白皮书华喜 PIMExpress-PDM 系统技术白皮书制作日期：修订版本号报告形式：2007 年 5 月 22 日 1.0发布制作单位：广州华喜信息科技有限公司/doc/f4b35464783e0912a2162a9e.html 广州市天河区建中路 51-53 号新太科技大厦 513 号本工作说明书作为广州华喜信息科技有限公司的知识财产未经广州华喜信息科技有限公司同意，其内容不得泄露给第三方。
地址：广州市天河区建中路 51-53 号新太科技大厦 513 号，邮编：510665第 1 页共 53 页目录1. 2. 前言........................................................................................................................... ........4 企业信息化与PDM .........................................................................................................4 2.1. PDM 对企业及其信息化的贡献 .....................................................................4 2.2. PDM 与 ERP 的区别及实施策略....................................................................5 2.3. PDM 项目实施成败的几个重要因素 .............................................................6 2.4. 企业如何选择及实施PDM .............................................................................7 2.4.1. 企业如何确定需求及范围....................................................................7 2.4.2. 企业如何选择 PDM 系统 .....................................................................7 2.4.3. 企业如何实施PDM 系统 (8)PDM 关注的需求领域及工作范围.................................................................................8 3.1. PIMExpress-PLM 产品战略.............................................................................9 3.2. PIMExpress-PLM 战略的实施价值...............................................................10 3.3. 企业导入 PDM\PLM 的实施路径建议.........................................................12 3.4. 项目工作范围－案例.....................................................................................12 广州华喜信息科技有限公司介绍.................................................................................14 4.1. 公司简介 (14)4.2. 华喜PDM\PLM 整体解决方案.....................................................................14 4.3. 公司的资历.. (16)PIMExpress 技术方案及特色功能介绍........................................................................17 5.1. ＣＡＤ集成模块.............................................................................................19 5.1.1. 二维CAD 集成 .........................................................................................19 5.1.1.1. 集成插件菜单...................................................................................19 5.1.1.2. 打开、查找、检出图纸...................................................................20 5.1.1.3. 检入图纸...........................................................................................20 5.1.1.4. 图纸标题、明细信息填写...............................................................21 5.1.1.5. 图纸信息管理...................................................................................22 5.1.1.6. 图纸初设功能...................................................................................22 5.1.2. 三维CAD 集成 .. (22)5.1.2.1. 集成插件菜单 (22)5.1.2.2. 打开、查找、检出模型...................................................................23 5.1.2.3. 模型属性定义及填写.......................................................................23 5.1.2.4. 检入单个模型及整个产品模型 .......................................................24 5.1.2.5. 模型信息管理...................................................................................24 5.2. 产品数据管理模块.........................................................................................24 5.2.1. 产品数据的目录管理................................................................................24 5.2.2. 图文档的管理.. (25)5.2.2.1.文档管理.............................................................................................25 5.2.2.2.文档浏览.............................................................................................25 5.2.2.3.图文档的分类管理.............................................................................26 5.2.3. 产品结构的管理. (26)5.2.3.1.产品结构管理、查看.........................................................................26 5.2.3.2.零部件查询.........................................................................................27 5.2.3.2.产品报表生成. (27)第 2 页共 53 页3.4.5.地址：广州市天河区建中路 51-53 号新太科技大厦 513 号，邮编：5106656.7. 8.5.2.4. 产品参数化管理........................................................................................27 5.2.5. 产品数据规范分析....................................................................................28 5.2.5.1.完整性分析.........................................................................................28 5.2.5.2.一致性分析.. (29)5.2.5.3.借用性分析.........................................................................................29 5.2.6. 产品数据批量入库、下载........................................................................29 5.2.6.1.图文档入库检查.................................................................................29 5.2.6.2.图文档批量入库 (30)5.2.6.3.文档批量入库.....................................................................................31 5.2.6.4.文档批量下载.....................................................................................32 5.2.7. 编码系统管理............................................................................................32 5.2.8. 研发物料管理............................................................................................33 5.3. 产品配置管理模块.. (33)5.3.1. 产品配置任务管理....................................................................................34 5.3.2. 产品配置设计管理....................................................................................34 5.3.3. 多种产品配置工具....................................................................................35 5.3.4. 产品配置变更管理 (36)5.3.5. 产品配置明细汇总、打印........................................................................37 5.3.6. 产品配置方案管理....................................................................................37 5.4. 产品设计项目管理.........................................................................................38 5.4.1. 项目任务分类管理....................................................................................38 5.4.2. 项目工作数据管理 (39)5.4.3. 项目评审流程管理....................................................................................39 5.5. 产品设计变更管理.........................................................................................40 5.5.1. 工程变更单管理........................................................................................40 5.5.2. 工程变更影响面管理................................................................................40 5.6. 系统架构及对象管理. (41)5.6.1. 基于对象的管理架构................................................................................41 5.6.2. 系统组织及用户管理................................................................................42 5.6.3. 多数据集成模式的管理............................................................................42 5.7. ERP 系统集成模块 ........................................................................................42 5.7.1. 物料信息选取.. (42)5.7.2. 生产主计划读取........................................................................................43 5.7.3. 产品信息集成.. (44)PIMExpress 系统实施案例 ............................................................................................45 6.1. 企业需求及设想.............................................................................................45 6.2. 总体规划及实施范围.....................................................................................45 6.3. 应用路线及解决方案. (46)6.4. 实施过程组织.................................................................................................51 6.5. 应用实施效果.................................................................................................52 6.6. 项目实施总结. (52)PDM 实施效益综合分析...............................................................................................53 结束语........................................................................................................................... ..53地址：广州市天河区建中路 51-53 号新太科技大厦 513 号，邮编：510665第 3 页共 53 页1. 前言产品数据管理（Product Data Management，以下简称 PDM）作为一个理论是在1980 年代提出并逐步发展起来的，从图档管理、PDM 到PLM，其发展的路线一直沿着帮助企业管理好与产品相关的数据和流程，其实施工作范围可以从设计部门的数据管理，延伸到工程部门的数据管理及研发部门、服务部门等的数据管理领域，覆盖的领域越多，就形成了越大的PLM 系统。
炼化一体化MPIMS模型的建设及应用
檵檵檵檵檵檵檵檵檵檵檵檵檵檵檵檵檵檵殝殝殝殝工业化应用炼化一体化ＭＰＩＭＳ模型的建设及应用危 拓（中海油惠州石化有限公司，广东惠州５１６０８６）摘 要： 炼化一体化多厂过程工业模拟系统（ＭＰＩＭＳ）模型集成了两个以上的炼油或化工单厂过程工业模拟系统（ＰＩＭＳ）模型，通过线性规划、分布递归、Ｂａｓｅ＋Ｄｅｌｔａ等技术，将生产过程中的非线性问题进行线性化处理，寻找炼油厂与化工厂联合运行的最优解，实现一体化价值提升。
在建设炼化一体化ＭＰＩＭＳ模型时，除按照常规的ＰＩＭＳ规则进行模型建设外，代码标准化、互供物料搭建、客户化报表等的建设将有利于提高模型的运行质量，提升结果的解读效率。
炼化一体化ＭＰＩＭＳ模型以一体化效益最大化为目标，可从原油选择优化、物料流向优化等方面提升一体化的整体效益。
关键词： 炼化一体化 过程工业 模拟系统 模型 线性规划 优化文章编号： １６７４－１０９９ （２０２０）０５－００４５－０４ 中图分类号：ＴＰ３１９ 文献标志码： Ａ收稿日期：２０２０－０５－１５。
作者简介：危拓，男，１９８８年出生，２０１１年毕业于天津大学化学工程与工艺专业，目前从事炼油化工一体化计划优化工作。
ＰＩＭＳ（ＰｒｏｃｅｓｓＩｎｄｕｓｔｒｙＭｏｄｅｌｉｎｇＳｙｓｔｅｍ）是由美国ＡｓｐｅｎＴｅｃｈ公司开发的过程工业模拟系统，基于ＰＩＭＳ系统建设的数学模型，可用来模拟企业的生产过程，在炼化企业生产计划优化的各个方面得到了广泛的应用［１］。
炼化一体化ＭＰＩＭＳ（Ｍｕｌｔｉ－ＰＩＭＳ）模型是指将两个以上的炼油厂ＰＩＭＳ模型和化工厂ＰＩＭＳ模型集成起来运行的多厂模型。
它能够综合考虑炼油厂与化工厂的约束条件，以一体化效益最大化作为目标函数，寻找最佳的原油结构、互供物料，最佳的装置生产方案，最佳的炼油、化工产品结构等，并实现三者之间最科学合理的结构配合，从而取得最佳的经济效益［２］。
１ ＭＰＩＭＳ模型的建设１ １ 核心原理ＭＰＩＭＳ的基础是ＰＩＭＳ，ＰＩＭＳ的核心技术包括线性规划、分布递归、Ｄｅｌｔａ＋Ｂａｓｅ等技术［３］。
lamme软件用户手册说明书
Package‘lamme’October13,2022Title Log-Analytic Methods for Multiplicative EffectsVersion0.0.1Description Log-analytic methods intended for testing multiplicative effects.Depends R(>=3.4.0)License GPL-3Encoding UTF-8LazyData trueRoxygenNote6.1.0.9000Suggests knitr,rmarkdownVignetteBuilder knitrNeedsCompilation noAuthor Qimin Liu[aut,cre]Maintainer Qimin Liu<************>Repository CRANDate/Publication2018-10-0623:00:06UTCR topics documented:abc (2)boot.es (2)lamme (4)lancova (4)lanova (5)ncova (6)nova (6)schoene (7)ncova (8)nova (9)Index101abc the ABC procedure for model selectionDescriptionthe AIC comparison with Modified Box-Cox Transformation(ABC)is a diagnostic procedure to help select among various additive and multiplicative modelsUsageabc(y,g,x=0)Argumentsy the raw posttest scores of a continuous outcome variable.g the categorical variable that denotes the group membership.x(optional)the raw pretest scores of a continuous outcome variable.DetailsWhen only‘y‘and‘g‘are specified,the ABC procedure compares LANOV A and ANOV A models.When‘x‘is also specified,the ABC procedure compares LANCOV A,ANCOV A,ANCOHET,and ANCOV A with log-transformed y.ValueAIC results of different models.The model with smallest AIC is preferred.Examplesdata("schoene")attach(schoene)abc(post_HRT,group,pre_HRT)abc(post_HRT,group)boot.es Boostrapped CI for Effect Size measuresDescriptionCompute the bias-corrected and expanded percentile boostrapped confidence intervals for effect size estimates zetas and the overall signal-to-noise ratio.Additionally,if pretest scores are provided, boostrapped CI on beta is also given.Usageboot.es(y,g,x=0,nrep=1000,alpha=0.05)Argumentsy the raw posttest scores of a continuous outcome variable.g the categorical variable that denotes the group membership.x(optional)the raw pretest scores of a continuous outcome variable.nrep the number of boostrapped samples.(default=1000)alpha the significance level(default=.05)Valuea table of lower and upper limit from bias-corrected and accelerated and expanded percentile boos-trapped confidence interval.Thefirst row is on the geometric mean of the control group(default group of comparison).After that,zeta estimates are given of the each respective group versus the control group(default group of comparison).Then,if pretest scores are given,CI on the beta estimate is stly,CI on the signal-to-noise ratio,an overall effect size measure,is provided.BCa LL the lower limit of the Bias-Corrected and accelerated boostrapped Confidence IntervalBCa UL the upper limit of the Bias-Corrected and accelerated boostrapped Confidence Intervalexp LL the lower limit of the expanded percentile boostrapped Confidence Interval exp UL the upper limit of the expanded percentile boostrapped Confidence IntervalReferencesEfron,B.(1987)."Better Bootstrap Confidence Intervals".Journal of the American Statistical Association.Journal of the American Statistical Association,V ol.82,No.397.82(397):171–185.doi:10.2307/2289144.JSTOR2289144.Examplesdata("schoene")attach(schoene)boot.es(post_HRT,group,pre_HRT,1000,.05)4lancova lamme lammeDescriptionLog-Analytic Methods for Multiplicative EffectsDetailsThe lamme package is designed to test and estimate multiplicative effects via log-analytic methods. UsageTo access this package’s tutorial,type the following line into the console:vignette("lamme-vignette")lancova Logged ANCOVADescriptionMathematically,LANCOV A is the ANCOV A form of a log-log model where both the dependent variable and the covariate is NCOV A can test and estimate multiplicative ef-fects.Usagelancova(y,g,x,plot=F)Argumentsy the raw posttest scores of a continuous outcome variable.g the categorical variable that denotes the group membershipx the raw pretest scores of a continuous outcome variable.plot a TRUE/FALSE variable that denotes if diagnostic plots are desired.(default=F) ValueAn summary object of the LANCOV A results.In residuals,the summary statistics are of sample multiplicative errors.In the coefficients table,the estimate of the intercept is the(control group) geometric mean estimate.The estimate for the pretest scores is the power parameter beta’s estimate.Other coefficient estimates are effect size measure zeta’s estimates.The standard error is on the logged scale.The confidence intervals are of significance level=.05for the control group geometric mean and for the zeta estimates,respectively,of the intercept and other coefficients The residual standard error is that of the logged scale residuals.Both R-squared and Adjusted R-squared are computed on the logged model.If‘plot=TRUE‘,diagnostic plots are provided.lanova5Examplesdata("schoene")attach(schoene)lancova(post_HRT,group,pre_HRT)lanova Logged ANOVADescriptionMathematically,LANOV A is the ANOV A form of a log-log model where the dependent variable is NOV A can test and estimate multiplicative effects.Usagelanova(y,g,plot=F)Argumentsy the raw scores of a continuous outcome variable.g a categorical variable that denotes the group membership.plot a TRUE/FALSE variable that denotes if diagnostic plots are desired.(default=F)ValueAn summary object of the LANOV A results.In residuals,the summary statistics are of sample multiplicative errors.In the coefficients table,the estimate of the intercept is the default group (control group)geometric mean estimate.Other coefficient estimates are effect size measure zeta’s estimates.The standard error is on the logged scale.The confidence intervals are of significance level=.05for the control group geometric mean and for the zeta estimates,respectively,of the intercept and other coefficients The residual standard error is that of the logged scale residuals.Both R-squared and Adjusted R-squared are computed on the logged model.If‘plot=TRUE‘,diagnostic plots are provided.Examples#generate datay1=rnorm(1000,5,1)+rnorm(1000)y2=rnorm(1000,5.5,1)+rnorm(1000)y3=rnorm(1000,6,1)+rnorm(1000)y1=exp(y1)y2=exp(y2)y3=exp(y3)dep=c(y1,y2,y3)tc=rep(c(0,1,2),each=1000)#applying lanova with the generated datalanova(dep,tc)nova ncova Power Calculation for LANCOVADescriptionCompute the statistical power of the LANCOV A test.Usagencova(k,n,r_sqrd,rho_sqrd,alpha=0.05)Argumentsk the number of groups.n the number of observations per group.r_sqrd the expected explained variance(on the logged scale)rho_sqrd the pretest-posttest correlationalpha the significance level(default=.05)Valuepower the statistical power of testReferencesCohen,J.(1988).Statistical power analysis for the behavioral sciences(2nd ed.).Hillsdale,NJ: Lawrence Erlbaum.Examplesncova(3,40,.1,.4,.05)nova Power Calculation for LANOVADescriptionCompute the statistical power of the LANOV A test.Usagenova(k,n,r_sqrd,alpha=0.05)schoene7Argumentsk the number of groups.n the number of observations per group.r_sqrd the expected explained variance(on the logged scale)alpha the significance level(default=.05)Valuepower the statistical power of testReferencesCohen,J.(1988).Statistical power analysis for the behavioral sciences(2nd ed.).Hillsdale,NJ: Lawrence Erlbaum.Examplesnova(3,40,.4,.05)schoene Data on Interactive Cognitive-Motor Step TrainingDescriptionData from a randomized controlled trial on Interactive cognitive-motor step training.81observa-tions are included.The outcome variable included is the hand reaction time.The data come from a randomzied pretest-posttest design with control and treatment groups.Usagedata(schoene)FormatA dataframe with81rows and3variables:group treatment or control group from experimental manipulationpre_HRT prettest hand reaction timepost_HRT posttest hand reaction timeReferencesSchoene D,Valenzuela T,Toson B,Delbaere K,Severino C,Garcia J,et al.(2015)Interactive Cognitive-Motor Step Training Improves Cognitive Risk Factors of Falling in Older Adults–A Randomized Controlled Trial.PLoS ONE10(12):e0145161.Examplesdata(schoene)head(schoene)table(schoene$group)ncova Sample Size Planning for LANCOVADescriptionCompute the required per-group sample size for the LANCOV A test.Usagencova(k,rho_sqrd,r_sqrd,power=0.8,alpha=0.05)Argumentsk the number of groups.rho_sqrd the pretest-posttest correlationr_sqrd the expected explained variance by the model(on the logged scale)power the desired statistical power(default=.8)alpha the significance level(default=.05)Valuen the per-group sample size requirementReferencesCohen,J.(1988).Statistical power analysis for the behavioral sciences(2nd ed.).Hillsdale,NJ: Lawrence Erlbaum.Examplesncova(3,.5,.01,.14,.05)nova Sample Size Planning for LANOVADescriptionCompute the required per-group sample size for the LANOV A test.Usagenova(k,r_sqrd,power=0.8,alpha=0.05)Argumentsk the number of groups.r_sqrd the expected explained variance(on the logged scale)power the desired statistical power(default=.8)alpha the significance level(default=.05)Valuen the per-group sample size requirementReferencesCohen,J.(1988).Statistical power analysis for the behavioral sciences(2nd ed.).Hillsdale,NJ: Lawrence Erlbaum.Examplesnova(3,.01,.14,.05)Index∗datasetsschoene,7abc,2boot.es,2lamme,4lamme-package(lamme),4lancova,4lanova,5ncova,6nova,6schoene,7ncova,8nova,910。
EMMIXgene 0.1.3 软件说明说明书
Package‘EMMIXgene’October12,2022Type PackageVersion0.1.3Title A Mixture Model-Based Approach to the Clustering of Microarray Expression DataDescription Provides unsupervised selection and clustering of microarray data using mixture models.Following the methods described in McLachlan,Bean and Peel(2002)<doi:10.1093/bioinformatics/18.3.413>a subset of genes are selected based one the likelihood ratio statistic for the test of one versus twocomponents whenfitting mixtures of t-distributions to the expression datafor each gene.The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers,allowing the tissue samples to beclustered byfitting mixtures of normal distributions.Encoding UTF-8Author Andrew Thomas JonesMaintainer Andrew Thomas Jones<***************************>License GPL(>=3)LazyData TRUESuggests R.rspLinkingTo Rcpp,RcppArmadillo,BHDepends R(>=3.3.0)Imports Rcpp(>=0.12.5),stats,mclust,reshape,ggplot2,scales,toolsSystemRequirements C++11RoxygenNote6.1.1VignetteBuilder R.rspNeedsCompilation yesRepository CRANDate/Publication2020-03-2315:50:14UTC12all_cluster_tissues R topics documented:all_cluster_tissues (2)alon_data (3)cluster_genes (3)cluster_tissues (4)EMMIXgene (5)golub_data (5)heat_maps (6)plot_single_gene (6)select_genes (7)top_genes_cluster_tissues (9)Index10 all_cluster_tissues Clusters tissues using all group meansDescriptionClusters tissues using all group meansUsageall_cluster_tissues(gen,clusters,q=6,G=2)Argumentsgen EMMIXgene objectclusters mclust objectq number of factors if using mfaG number of components if using mfaValuea clustering for each sample(columns)by each group(rows)Examplesexample<-plot_single_gene(alon_data,1)#only run on first100genes for speedalon_sel<-select_genes(alon_data[seq_len(100),])alon_clust<-cluster_genes(alon_sel,2)alon_tissue_all<-all_cluster_tissues(alon_sel,alon_clust,q=1,G=2)alon_data3 alon_data Normalized gene expression values from Alon et al.(1999).DescriptionA dataset containing centred and normalized values of the logged expression values of a subset of2000genes taken from Alon,Uri,et al."Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays."Proceedings of the National Academy of Sciences96.12(1999):6745-6750.The method of subset selection was de-scribed in G.J.McLachlan,R.W.Bean,D.Peel;A mixture model-based approach to the clustering of microarray expression data,Bioinformatics,V olume18,Issue3,1March2002,Pages413–422. Usagedata(alon_data)FormatA data frame with2000rows(genes)and62variables(samples).Examplesdim(alon_data)cluster_genes Clusters genes using mixtures of normal distributionsDescriptionSorts genes into clusters using mixtures of normal distributions with covariance matrices restricted to be multiples of the identity matrix.Usagecluster_genes(gen,g=NULL)Argumentsgen an EMMIXgene object produced by select_genes().g The desired number of gene clusters.If not specified will be selected automati-cally on the basis of BIC.ValueAn array containing the clustering.4cluster_tissues Examples#only run on first100genes for speedalon_sel<-select_genes(alon_data[seq_len(100),])alon_clust<-cluster_genes(alon_sel,2)cluster_tissues Clusters tissuesDescriptionClusters tissuesUsagecluster_tissues(gen,clusters,method="t",q=6,G=2)Argumentsgen EMMIXgene objectclusters mclust objectmethod Method for separating tissue classes.Can be either’t’for a univariate mixture of t-distributions on gene cluster means,or’mfa’for a mixture of factor analyzers.q number of factors if using mfaG number of components if using mfaValuea clustering for each sample(columns)by each group(rows)Examples#only run on first100genes for speedalon_sel<-select_genes(alon_data[seq_len(100),])alon_clust<-cluster_genes(alon_sel,2)alon_tissue_t<-cluster_tissues(alon_sel,alon_clust,method= t )alon_tissue_mfa<-cluster_tissues(alon_sel,alon_clust,method= mfa ,q=2,G=2)EMMIXgene5 EMMIXgene EMMIXgene:DescriptionSelects genes using the EMMIXgene algorithm,following the methodology of G.J.McLachlan,R.W.Bean,D.Peel;A mixture model-based approach to the clustering of microarray expression data,Bioinformatics,V olume18,Issue3,1March2002,Pages413–422,https:///10.1093/bioinformatics/18.3.413Functionsselect_genes:Selects the most differentially expressed genes.cluster_genes:Clusters the genes using a mixture model approach.cluster_tissues:Clusters the tissues based on the differences between the tissue samples amongthe gene groups.See vignette( The-EMMIXgene-Workflow )for more details.golub_data Normalized gene expression values from Golub et al.(1999).DescriptionA dataset containing the centred and normalized values of the logged expression values of a subsetof3731genes taken from Golub,Todd R.,et al."Molecular classification of cancer:class discoveryand class prediction by gene expression monitoring."Science286.5439(1999):531-537.Themethod of subset selection was described in G.J.McLachlan,R.W.Bean,D.Peel;A mixturemodel-based approach to the clustering of microarray expression data,Bioinformatics,V olume18,Issue3,1March2002,Pages413–422.Usagedata(golub_data)FormatA data frame with3731rows(genes)and72variables(samples).#’@examples dim(golub_data)6plot_single_gene heat_maps Heat mapsDescriptionPlot heat maps of gene expression data.Optionally sort the x-axis according to a predetermined clustering.Usageheat_maps(dat,clustering=NULL,y_lab=NULL)Argumentsdat matrix of gene expression data.clustering a vector of sample classifications.Must be same length as the number of columns in dat.y_lab optional label for y-axis.ValueA ggplot2heat map.Examplesexample<-heat_maps(alon_data[seq_len(100),])plot_single_gene Plot a single gene expression histogram with bestfitted mixture of t-distributions.DescriptionPlot a single gene expression histogram with bestfitted mixture of t-distributions according to the EMMIX-gene algorithm.Usageplot_single_gene(dat,gene_id,g=NULL,random_starts=8,max_it=100,ll_thresh=8,min_clust_size=8,tol=1e-04,start_method="both",three=TRUE,min=-4,max=2)Argumentsdat matrix of gene expression data.gene_id row number of gene to be plotted.g force number of components,default=NULLrandom_starts The number of random initializations used per gene whenfitting mixtures of t-distributions.Initialization uses k-means by default.max_it The maximum number of iterations per mixturefit.Default value is100.ll_thresh The difference in-2log lambda used as a threshold for selecting between g=1 and g=2for each gene.Default value is8,which was chosen arbitrarily in theoriginal paper.min_clust_size The minimum number of observations per cluster used whenfitting mixtures of t-distributions for each gene.Default value is8.tol Tolerance value used for detecting convergence of EMMIXfits.start_method Default value is"both".Can also choose"random"for purely random starts.three Also test g=2vs g=3where appropriate.Defaults to TRUE.min,max Minimum and maximum x-axis values for the plot window.ValueA ggplot2histogram withfitted t-distributions overlayed.Examplesexample<-plot_single_gene(alon_data,1)#plot(example)select_genes Selects genes using the EMMIXgene algorithm.DescriptionFollows the gene selection methodology of G.J.McLachlan,R.W.Bean,D.Peel;A mixture model-based approach to the clustering of microarray expression data,Bioinformatics,V olume18,Issue 3,1March2002,Pages413–422,https:///10.1093/bioinformatics/18.3.413Usageselect_genes(dat,filename,random_starts=4,max_it=100,ll_thresh=8,min_clust_size=8,tol=1e-04,start_method="both",three=FALSE)Argumentsdat A matrix or dataframe containing gene expression data.Rows are genes and columns are samples.Must supply one offilename and dat.filename Name offile containing gene data.Can be either.csv or space separated.dat.Rows are genes and columns are samples.Must supply one offilename and dat.random_starts The number of random initializations used per gene whenfitting mixtures of t-distributions.Initialization uses k-means by default.max_it The maximum number of iterations per mixturefit.Default value is100.ll_thresh The difference in-2log lambda used as a threshold for selecting between g=1 and g=2for each gene.Default value is8,which was chosen arbitrarily in theoriginal paper.min_clust_size The minimum number of observations per cluster used whenfitting mixtures of t-distributions for each gene.Default value is8.tol Tolerance value used for detecting convergence of EMMIXfits.start_method Default value is"both".Can also choose"random"for purely random starts.three Also test g=2vs g=3where appropriate.Defaults to FALSE.ValueAn EMMIXgene object containing:stat The difference in log-likelihood for g=1and g=2for each gene(or for g=2and g=3where relevant).g The selected number of components for each gene.it The number of iterations for each genes selectedfit.selected An indicator for each genes selected statusranks selected gene ids ranked by statgenes A dataframe of selected genes.all_genes Returns dat or contents offilename.Examples#only run on first100genes for speedalon_sel<-select_genes(alon_data[seq_len(100),])top_genes_cluster_tissues9 top_genes_cluster_tissuesCluster tissuesDescriptionCluster tissuesUsagetop_genes_cluster_tissues(gen,n_top=100,method="mfa",q=2,g=2)Argumentsgen An EMMIXgene object produced by select_genes().n_top number of top genes(as ranked by likelihood)to be selectedmethod Method for separating tissue classes.Can be either’t’for a univariate mixture of t-distributions on gene cluster means,or’mfa’for a mixture of factor analysers.q number of factors if using mfag number of components if using mfaValueAn EMMIXgene object containing:stat A matrix containing clustering(0or1)for each sample(columns)by each group(rows).top_gene The row numbers of the top genes.fit Thefit object used to determine the clustering.Examplesalon_sel<-select_genes(alon_data[seq_len(100),])alon_top_10<-top_genes_cluster_tissues(alon_sel,10,method= mfa ,q=3,g=2)Index∗datasetsalon_data,3golub_data,5all_cluster_tissues,2alon_data,3cluster_genes,3,5cluster_tissues,4,5EMMIXgene,5EMMIXgene-package(EMMIXgene),5golub_data,5heat_maps,6plot_single_gene,6select_genes,5,7top_genes_cluster_tissues,910。
气动分析软件集锦
翼型气动特性分析与设计软件Airfoil余雄庆在Univ. of Notre Dame 工作期间，利用工作之余时间编写的翼型气动特性分析程序。
该程序是在原NASA 的多段翼型分析程序MCARFA 基础上开发的, 适用于亚声速翼型气动特性的分析。
MCARF是根据位流理论与附面层理论相结合的方法，用Fortran 语言编写的。
Airfoi l 简化了原MCAF输入文件的格式，并用Matlab对计算结果进行后处理，可直观显示翼型外形和压力分布。
可下载Airfoil 的EXE 文件、用于演示计算结果的Matlab 文件及使用说明书（英文）。
Pablo （ P otential flow around A irfoil with B oundary L ayer coupled O ne-way ）该软件是由瑞典皇家理工学院Rizzi 教授和他的学生Christian Wauquiez 开发的。
他们应用面元法（Panel Method ）和附面层理论，用Matlab 语言编写了这个翼型分析软件。
Pablo 具有良好的用户界面，使用方便，适用于亚声速翼型气动特性的分析。
可免费下载Pablo 软件Matlab的源代码。
Airfoil Optimizer由美国DaVinci Tchnologies 公司开发，其目的是帮助设计人员选择合适的翼型。
AirfoilOptimizer 有较丰富的翼型数据库，并通过内嵌XFOIL 软件来优化翼型。
（南京航大飞机系已购买，若在作业中要使用该软件，请与余老师联系：************.cn）翼面布局气动特性分析与设计软件VLM 为了某飞机多学科设计优化研究的需要，余雄庆开发了一个机翼和翼面系统气动特性分析程序。
该程序是在NASA勺Lamar等人开发的涡格法（Vortex Lattice Method ）程序基础上开发的，适用于亚声速飞机翼面气动特性的分析。
VLM简化了原涡格法程序的的输入文件的格式，并增加了若干新功Tornado该软件是由瑞典皇家理工学院Tomas Melin 在攻读硕士学位时开发的亚声速机翼和翼面系统气动特性分析程序。