delta机械手工作空间matlab程序

使用指数模型的克里金插值以及对插值结果的等高线的生成。

在特征波长选取方面有一些创新，可以作为参考。在特征波长选取方面有一些创新，可以作为参考。（基于高光谱成像的蓝莓内部品质检测特征波长选择方法研究古文君1 ，田有文 1* ，张芳1 ，赖兴涛 1 ，何宽1 ，姚萍1 ，刘博林 2）586-482016620010~15mm0.8~2.3g。fone3:(InSpector V10E, Spectral InFinland)1392pix×1040pixCCDL CCD2(IGV-B141OM, IMPERX Incorporated, USA), 150W1. CCD Camera; 2.Spectrometer; 3.Shot; 4. Light source; 5. Samples(3900 Illuminatior, Illumination Tech6.Translationplatform7.Lightsourcecontroller;8.computernologies inc.,USA)、(IRCP0076-19. Translation platform controllerCOM,)、(120cm×50cmx(DELL VoStro 5560D-1528Figure 1 Schematic diagram of hyperspectral imagingcmsystem400~1000nm,4722.8nmRRGY-4(10mm)(DBR45(successive projections algorithm, SPA(stepwise multiple linear regression, SMLR)(SPA)(SMLR)SPASPASMLRSPA-SPA、SMLR_SMLR、SPA- SMLRSMLR-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5871.6BP(error back propagation)BP17(correlation coeffiient of calibration, Re)(root mean square error of calibration set, RMSEC)correlation coeffiient of pre-diction, Rp)(root mean square error of prediction set, RMSEP)ENVI 4.8(Research System Inc, ), MATLAB 2014a(The Math Works Inc)、TheUnscrambler9.7、 Excel2010(Ⅵ icrosoftdgle banddWcvef.BP models for soluble solidsThe selected characteristic wavelengthCurve of relative reflectanceExtract the region of interescontent and firmness prediction2figure 2 Flow chart of data processing280mm,68ms,28mm·s-。99%202.2600nm600nm2b2c)21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5884823(2f)BPSavitzky-Golasavitzky -golayTable 1 The effect of different spectra preprocessingCalibration setPredictioSpectrum typeRMSECRMSEPOriginal spcctrum0.933/0.9230.3510.4040.9200.9100.508/0.319MSCThe spectrum after MSC processing0.940/0.9450.56lO.3120.9190.9320.516/0.282SNThe spectrum after SNV processin0.93709340.60210.24309220.9010.6320.462Savitzky-golayThe spectrum after Savitzky-Golay processing 0.955/0.9550.3240.2410.951/0.9490.400/0.2782.5SPA-SPA SMLRSMLR SPA-SMLR SMLR-SPASPA-SPASPASavitzky-GolaySPATable 2 The results of multi-stage characteristic wavelength selection methodnmCharacteristie wavelength selection methodSPA-SPA452,455,470,482,490,785,893,912,921,942,950455,470,482,785,893.912SMLR-SMLR457,508,516,534,543,51,556,568,712,720.774,778508,534,543,712,720,774SPA-SMLR452,455,470,482,490,785,893,912,921,942,950452,470,482,490,893,912SMLR-SPA457,508,516,534,543,551,556,568,712,720,774,78534,7202.6Savilzky-gola(FS)392SPA-SPASMLR-SMLRSMLR-SMLRSMLR-SPABPBP0.001500021994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct589BPBPSPA-SPARp RMseP0.9520.391°Brix,RpRMSEP0.9530.234BrixTable 3 Detection results of soluble solid content and firmness of blueberry based on different multi-stagecharacteristic wavelength selection methodsCalibration setPrediction setCharacteristic selection method Wavelength numberRMSECRMSEP3929550.9550.324/0.2410.9510.9490.400/0.278SPA-SPA0.9590.9560.3180.1530.9520.9530.391/0.234SMLR-SMLR0.9560.9340.414/0.243912109020.559/0.349SPA SMLR0.828/0.8581.3670.58582208091.440/0.719SMLR- SPA20.958/0.9360.402/0.3359320.9280.435/0,4041387nm1229nm91.5%BPRRMSEP0.904215.163lBP3Rv0.84V0.94Rv0.83,SEV0.63。400-1000nmSavitzky-GolayBPSPA-SPASPA-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct59048[1 KADER F,ROVEL. B Fractionation and identification of the phenolic compounds of highbush blueberries(Vaccinium corymbosumLUJ].Food Chemistry, 1996,55(1): 35-40「J,2012,33(1):340-342,2017,38(2):301-305.[4 MENDOZA F, LU R, ARIANA D,et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction ofple [ruil firmness and soluble solids conlenl[J] Poslharvesl Biology and Technology, 2011, 62(2: 149-160[5 SUN M J, ZHANG D, LIU L,et al. How to predict the sugariness and hardness of melons a near-infrared [J]. Food Chemistry,2017,218(3:413-42116 SIEDLISKA A, BARANOWSKI P, MAZUREK W, ct al. Classification models of bruise and cultivar detection on the basis of hy-perspectral imaging data[J]. Computers and Electronics in Agriculture, 2014, 106: 66-74[7 LIU D, SUN D W, ZENG X N, el al. Recenl aDvances in wavelength seleclion lechniques for hyperspectral image processing inthe food industry[J]. Food Bioprocess Technol, 2014, 7: 307-323[8 ZHANG C, GUO C T, LIU F,et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector ma-chine[j] Journal of Food Engincering, 2016, 179: 11-18[9J,2016,47(5:634-6402009,29(:1611-1615201536(12)171-17612]J,2012,32(11:3093309[13] LI B C, HOU B L, ZHANG D W,et al. Pears characteristics (soluble solids content and firmness prediction, varieties) testingInethods based on visible-near infrared hyperspecTral imaging[J]. OpLik, 2016, 127: 2624-2630[14] FAN S X, ZHANG B H,LI J B, et al. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data[J. Postharvest Biology and Technology, 2016, 121: 51-61[15 RAJKUMAR P, WANG N,EIMASRY G, et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[ JIJournal of Food Engineering 2012, 108: 194-200,2015,36(16):10172015,35(8:2297-2302[18]WANG N,2007,23(2:151-155.「192008,39(5):91-9320」201536(10:70-74.[21] WU D, SUN D WAdvanced applications of hyperspectral imaging technology for food quality and safety analysis and assess-ment a review part T[J]. Innovative Food Science and Emerging Technologies, 2013, 19(4): 1-14J2014,35(8:57-61BP,2012.124」13,44(2):142-146.25],201523(6:1530-1537M011:41-48.[27,2013,24(10:1972-19762010,30(10):2729-2733?1994-2018ChinaAcadcmicJournaleLcctronicPublishingHousc.Allrightsreservedhttp://www.cnki.nct

用MATLAB语言设计一个带通滤波器。给定抽样频率，通带起始频率，截止频率。阻带起始频率，截止频率。

神经网络 大作业bp算法bp算法 神经网络 C语言 大连理工

CAN总线简介，CAN物理层数据交换原理，CAN报文格式分析，J1939协议介绍，PDU格式，报文实例分析，PGN报文实例分析，J1939的传输协议-连接管理和多包传输，Intel与Motorola格式的区别

本密度聚类算法dbscan是基于周志华老师《机器学习》介绍编程的，经检验效率较高

【实例简介】关于卡尔曼滤波对飞机姿态进行解算以及采用自抗扰控制对无人机进行姿态控制的仿真模型

微信小程序蓝牙搜、连接、数据接收、显示，自己测试可用才上传的，如有需要远程调试和咨询，可加微信，完事后发个红包就行呵呵。

这里实现了四种SVM工具箱的分类与回归算法 1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合

【实例简介】本科毕业论文 基于B/S的ERP人力资源管理子系统的设计与开发 目 录 摘要 III 关键词 III Abstract III Key Words III 前言 1 1 相关介绍 1 1.1框架介绍 1 1.2 Struts框架介绍 2 1.3 Hibernate框架介绍 2 1.4 Spring框架介绍 3 1.5开发工具及环境介绍 3 1.5.1 MyEclipse介绍 3 1.5.2 Tomcat介绍 4 1.6 Oracle数据库介绍 4 2 系统架构设计 5 2.1 Struts构建表示层 5 2.2 Spring构建业务逻辑层 5 2.3 Hibernate构建持久化层 5 2.4 系统架构设计 6 3 需求分析与总体设计 8 3.1 系统概述 8 3.2 需求分析 8 3.3 系统的功能结构 9 3.3.1 部门管理模块 9 3.3.2 员工管理模块 9 3.3.3 考勤管理模块 9 3.3.4 招聘管理模块 9 3.3.5 培训管理模块 9 3.3.6 绩效考评模块 9 3.3.7 合同管理模块 9 3.3.8 薪酬福利模块 9 3.3.9 辅助决策模块 10 3.3.10 系统管理模块 10 3.4 系统用户权限分类及描述 10 3.4.1 普通用户 10 3.4.2 管理员 10 3.4.3 超级管理员 10 3.5 系统业务流程图 11 3.6 系统主要用例描述 11 3.6.1 系统登录 11 3.6.2 员工管理 11 3.7 数据库设计 13 3.7.1 数据库表及表关系 13 3.7.2 数据表逻辑结构 14 4 系统实现 19 4.1 搭建SSH框架 19 4.2 目录及包介绍 21 4.3 表示层实现 22 4.4 业务逻辑层实现 24 4.5 持久层及域对象层实现 25 4.6 其他实现 26 4.7 主要实现界面 29 4.7.1 登陆界面 29 4.7.2 首页面 29 4.7.3 部门信息列表界面 29 4.7.4 登记考勤界面 31 4.7.5 更新员工信息界面 31 5 系统测试与评价 32 5.1 测试环境 32 5.2 测试结果 32 5.2.1 功能测试 32 5.2.2 安全测试 32 总结 33 参考文献 34 致谢 35 基于B/S的ERP人力资源管理子系统的设计与开发 摘 要 随着社会经济的发展，人们逐渐认识到，人力是一种资源，是社会经济财富创造必须具备的条件。人力资源是企业最重要的资源之一，对于企业来说,人力资源的管理关系到企业的生存与发展。现代企业管理迫切需求设计一个能够适应和满足企业发展规模的人力资源管理信息系统。本文设计并实现了基于Struts，Spring和Hibernate的ERP人力资源管理子系统。该子系统使用Struts实现MVC，Spring负责架构的结合，Hibernate进行数据的持久化，基本实现了部门管理、员工管理、招聘管理、考勤管理、培训管理、绩效考评管理、薪资管理、合同管理、辅助决策、系统管理等模块。论文对系统的设计和实现作出了详细的描述，并对系统进行了功能测试和安全测试。