织物悬垂性能预测与评价系统的研究
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摘要
计算机技术和网络技术的飞速发展,使得繁琐的服装设计知识元素的获取变得更为简易。作为影响服装美感的重要因子,面料悬垂性能的优劣直接关系着服装的设计决策。根据测试数据建立悬垂性能指标自动预测和评价的系统,对生产与科研单位研制新型纤维及改进织物设计、提高织物悬垂性能和美感度是十分必要的。
针对现有悬垂性能预测系统与评价体系的现状与需求,本文围绕悬垂性能的预测及评价过程,结合服装纺织专业知识以及软件工程的设计思想,论述了系统的总体架构分析设计、后台数据库建立、功能算法的实现,以及系统的详细设计过程和系统实现过程等。
本文探讨了悬垂性能指标预测以及模糊聚类评价两个功能算法。利用神经网络预测算法对50组织物试样数据进行学习和预测,最终验证了算法的实用性,确定了基于有动量和自适应学习速率梯度下降方法的BP预测算法,并实现从织物规格参数到悬垂性能参数的非线性预测;在模糊聚类评价系统中,着重讨论了FCM聚类算法无法避免噪声点影响,引入基于FPCM的模糊聚类算法,抑制了各种不利因素产生的误差。实测数据证明,聚类算法的准确度较高,聚类分析结果与主观评价能够保持良好的一致性。
基于B/S(Browser / Server)结构的织物悬垂性能预测与评价系统的建立将大大简化织物悬垂性的测试与评价工序,实现了纤维、纱线及织物相关性能与织物悬垂性能指标的查询,以及从已知织物组织结构参数到悬垂性能指标的预测,最终实现对悬垂性能优劣的客观全面评价。
With the rapid development of computer and network technology, the element knowledge of fashion design has become easier to obtain. As an important impact on the aesthetic factor, the performance of fabric drape is directly related to the fashion design decision. According to the test data to establish the system which can predict and evaluate fabric drape indexes automatically, it is very necessary to develop new types of fibers, improve the fabric design , enhance fabric drape performance and aesthetic value for manufactory and scientific research units.
Based on current situation and demand of the existing fabric drape test and evaluation system, combined with garment and textiles professional knowledge and software engineering design idea, this paper which takes the process of fabric drape prediction and evaluation as center, discusses the overall architecture analysis and design of system, the establishment of background database, the implementation of function algorithms, as well as the detailed design process of system and the process of system implementation.
This article discusses both algorithms of the drape performance prediction and fuzzy clustering evaluation. The BP prediction algorithm based on additional momentum and adaptive learning rate was determined by training and predicting 50 groups of fabric sample data. And finally, the results prove the validation of the algorithms and implement the non-line parameters prediction from fabric specifications to the drape performance; In the system of fuzzy clustering evaluation, this article emphatically discusses on the noise point of the FCM fuzzy clustering algorithm, introduces FPCM fuzzy clustering algorithm to inhibit errors aroused from a variety of unfavorable factors. Measured data shows that the accuracy of prediction algorithm is higher, and cluster analysis has a good consistency with the results of subjective evaluation.
The establishment of Fabric Drape Performance Prediction and Evaluation System based on B/S(Browser / Server)architecture will simplify the process of drape testing and evaluation consumedly, realizes the query of fibers、yarn and fabric related performance, as well as the prediction from fabric organization structure parameters to fabric drape indexes and finally realizes fabric drape objectively and completely evaluated.
针对现有悬垂性能预测系统与评价体系的现状与需求,本文围绕悬垂性能的预测及评价过程,结合服装纺织专业知识以及软件工程的设计思想,论述了系统的总体架构分析设计、后台数据库建立、功能算法的实现,以及系统的详细设计过程和系统实现过程等。
本文探讨了悬垂性能指标预测以及模糊聚类评价两个功能算法。利用神经网络预测算法对50组织物试样数据进行学习和预测,最终验证了算法的实用性,确定了基于有动量和自适应学习速率梯度下降方法的BP预测算法,并实现从织物规格参数到悬垂性能参数的非线性预测;在模糊聚类评价系统中,着重讨论了FCM聚类算法无法避免噪声点影响,引入基于FPCM的模糊聚类算法,抑制了各种不利因素产生的误差。实测数据证明,聚类算法的准确度较高,聚类分析结果与主观评价能够保持良好的一致性。
基于B/S(Browser / Server)结构的织物悬垂性能预测与评价系统的建立将大大简化织物悬垂性的测试与评价工序,实现了纤维、纱线及织物相关性能与织物悬垂性能指标的查询,以及从已知织物组织结构参数到悬垂性能指标的预测,最终实现对悬垂性能优劣的客观全面评价。
With the rapid development of computer and network technology, the element knowledge of fashion design has become easier to obtain. As an important impact on the aesthetic factor, the performance of fabric drape is directly related to the fashion design decision. According to the test data to establish the system which can predict and evaluate fabric drape indexes automatically, it is very necessary to develop new types of fibers, improve the fabric design , enhance fabric drape performance and aesthetic value for manufactory and scientific research units.
Based on current situation and demand of the existing fabric drape test and evaluation system, combined with garment and textiles professional knowledge and software engineering design idea, this paper which takes the process of fabric drape prediction and evaluation as center, discusses the overall architecture analysis and design of system, the establishment of background database, the implementation of function algorithms, as well as the detailed design process of system and the process of system implementation.
This article discusses both algorithms of the drape performance prediction and fuzzy clustering evaluation. The BP prediction algorithm based on additional momentum and adaptive learning rate was determined by training and predicting 50 groups of fabric sample data. And finally, the results prove the validation of the algorithms and implement the non-line parameters prediction from fabric specifications to the drape performance; In the system of fuzzy clustering evaluation, this article emphatically discusses on the noise point of the FCM fuzzy clustering algorithm, introduces FPCM fuzzy clustering algorithm to inhibit errors aroused from a variety of unfavorable factors. Measured data shows that the accuracy of prediction algorithm is higher, and cluster analysis has a good consistency with the results of subjective evaluation.
The establishment of Fabric Drape Performance Prediction and Evaluation System based on B/S(Browser / Server)architecture will simplify the process of drape testing and evaluation consumedly, realizes the query of fibers、yarn and fabric related performance, as well as the prediction from fabric organization structure parameters to fabric drape indexes and finally realizes fabric drape objectively and completely evaluated.
引文
[1]于伟东.纺织材料学[M].北京:中国纺织出版社,2006:321-323.
[2]冯毅力.纺织品悬垂性的计算机仿真[J].纺织学报,2001,22(1):54-55.
[3]郭永平、李长龙、李汝勤.织物悬垂性理论及测试方法研究综述[J].中国纺织大学学报,1999,25(3) :94-97.
[4]郭永平.织物动静态悬垂评价方法研究[D].上海:东华大学,2000.
[5]张顺兵.基于ASP.NET的中职学校校园信息管理系统设计与实现[D].武汉:华中师范大学,2007.
[6]刘晓华.SQL Server 2000数据库应用开发[M].电子工业出版社,2001.
[7]SQL Server 2000数据处理技术[M].飞思科技产品研发中心.北京:电子工业出版社,2001.
[8]张跃廷、许文武、王小科.C#数据库系统开发完全手册[M],北京:人民邮电出版社,2006:287-288.
[9]李亚伟.基于神经网络的智能预测系统研究与开发[D],北京,北方工业大学,2006.
[10]Simon Haykin.神经网络原理[M].叶世伟,史忠植.北京:机械工业出版社,2004.
[11]陈祥光、裴旭东.人工神经网络技术及应用[M].北京:中国电力出版社,2003.
[12]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社.2007.
[13]谭显胜,周铁军.BP算法改进方法的研究发展[J].怀柔学院学报,2006,25(2):126-129.
[14]曹建达.BP神经网络预测棉织物悬垂性能[J].上海纺织科技,2003,31(4):59-60.
[15]王健、诸声伟.棉织物悬垂性能影响因素的定量分析[J].纺织科技进展,2006,2:75-77.
[16]刘晋钢、李华玲.BP神经网络改进算法的应用[J].华北工学院学报,2002,23(6):449-451.
[17]董长虹.MATLAB神经网络与应用[M].北京:国防工业出版社,2007.
[18]Antony Lam、Amar Raheja、Muthu Govindaraj.Neural Network Models for Fabric Drape Prediction[J]. 0-7803-8359-1(4) ,2004:2925-2929.
[19]易帆.神经网络预测研究[D].西安:西南交通大学,2005.
[20]徐军、姚穆.织物悬垂性客观评价的研究[J].纺织学报,1999,20(4):207-210.
[21]左桐林、李汝勤.织物静态悬垂性能评价指标提取的研究[J].东华大学学报(自然科学版),2004,30(5):87-90.
[22]王夕源,赵文贤,关燕等.织物动态悬垂风格自动测试方法研究[J].纺织学报,1992,13(11):515-516.
[23]王晓东.YG811型织物悬垂性测试仪的理论分析与应用[J].纺织学报,1987,8(4):11-15.
[24]闫兆振.自适应模糊C-均值聚类算法研究[D].山东:山东科技大学,2006.
[25]郑鹏程、陈培根.模糊聚类分析在织物变形舒适性能评价中的运用[J],2006,25(6):30-32.
[26]严骏.模糊聚类算法应用研究[D].浙江:浙江大学,2006.
[27]崔文迪、蔡佳佳.基于K-means算法和FCM算法的聚类研究[J].现代计算机,2007:269.
[28]Nikhil R Pal, Kuhu Pal, James C Bezdek, A mixed c-means clustering model[C]. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, vol.1, 1-5, 1997:11-21.
[29]Krishnapuram, R. and Keller J, A Possibilistic Approach to Clustering[J], IEEE Trans on Fuzzy Systems, 1(2), 1993:98-110.
[30]喻璐芝.基于Web的科研管理信息系统设计与实现[D].武汉:华中科技大学,2006.
[31]张跃廷、王小科、张宏宇. C#程序开发范例宝典[M].北京:人民邮电出版社,2007.
[32]张跃廷、韩阳、张宏宇.C#数据库系统开发案例精选[M].北京:人民邮电出版社,2007.
[33]郭宇鹏、余序芬.织物悬垂性能研究综述[J].现代纺织技术,2001,99(3) :49-51.
[34]李振然,贾旭彩.基于人工神经网络的短期负荷预测[J].广西电力.2004(4):7-10.
[35]Stylios G K and Zhu R. The characterisation of the static and dynamic drape of fabrics. J Text Inst,1997,88(4):465-475.
[36]蒋艳凤.织物的悬垂性能比较[D].浙江纺织职业技术学院,浙江宁波,2001.
[37]郭宇鹏,织物悬垂性能研究[D],上海:中国纺织大学,2001.
[38]Postle J.R,Postle R.Fabric Drape[J].Textile Asia,2000,31(1):30-32.
[39]Chen Bejian and Govindaraj Muthn.A phynically hased model of fabric drape using flexible sbell theory[J].Textile Res J,1995,65(6):324-330.
[40]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006:24-39.
[41]Perice F T.The handle of cloth as a measurable quantity[J].Text Inst. 1930,21:377-416.
[42]Olofsson B.A general model of a fabric as a geometricmechanical structure[J], Text Inst,1964,55(11):541-557.
[43]韩力群.人工神经网络理论、设计及应用[M].北京:化学工业出版社,2007.
[2]冯毅力.纺织品悬垂性的计算机仿真[J].纺织学报,2001,22(1):54-55.
[3]郭永平、李长龙、李汝勤.织物悬垂性理论及测试方法研究综述[J].中国纺织大学学报,1999,25(3) :94-97.
[4]郭永平.织物动静态悬垂评价方法研究[D].上海:东华大学,2000.
[5]张顺兵.基于ASP.NET的中职学校校园信息管理系统设计与实现[D].武汉:华中师范大学,2007.
[6]刘晓华.SQL Server 2000数据库应用开发[M].电子工业出版社,2001.
[7]SQL Server 2000数据处理技术[M].飞思科技产品研发中心.北京:电子工业出版社,2001.
[8]张跃廷、许文武、王小科.C#数据库系统开发完全手册[M],北京:人民邮电出版社,2006:287-288.
[9]李亚伟.基于神经网络的智能预测系统研究与开发[D],北京,北方工业大学,2006.
[10]Simon Haykin.神经网络原理[M].叶世伟,史忠植.北京:机械工业出版社,2004.
[11]陈祥光、裴旭东.人工神经网络技术及应用[M].北京:中国电力出版社,2003.
[12]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社.2007.
[13]谭显胜,周铁军.BP算法改进方法的研究发展[J].怀柔学院学报,2006,25(2):126-129.
[14]曹建达.BP神经网络预测棉织物悬垂性能[J].上海纺织科技,2003,31(4):59-60.
[15]王健、诸声伟.棉织物悬垂性能影响因素的定量分析[J].纺织科技进展,2006,2:75-77.
[16]刘晋钢、李华玲.BP神经网络改进算法的应用[J].华北工学院学报,2002,23(6):449-451.
[17]董长虹.MATLAB神经网络与应用[M].北京:国防工业出版社,2007.
[18]Antony Lam、Amar Raheja、Muthu Govindaraj.Neural Network Models for Fabric Drape Prediction[J]. 0-7803-8359-1(4) ,2004:2925-2929.
[19]易帆.神经网络预测研究[D].西安:西南交通大学,2005.
[20]徐军、姚穆.织物悬垂性客观评价的研究[J].纺织学报,1999,20(4):207-210.
[21]左桐林、李汝勤.织物静态悬垂性能评价指标提取的研究[J].东华大学学报(自然科学版),2004,30(5):87-90.
[22]王夕源,赵文贤,关燕等.织物动态悬垂风格自动测试方法研究[J].纺织学报,1992,13(11):515-516.
[23]王晓东.YG811型织物悬垂性测试仪的理论分析与应用[J].纺织学报,1987,8(4):11-15.
[24]闫兆振.自适应模糊C-均值聚类算法研究[D].山东:山东科技大学,2006.
[25]郑鹏程、陈培根.模糊聚类分析在织物变形舒适性能评价中的运用[J],2006,25(6):30-32.
[26]严骏.模糊聚类算法应用研究[D].浙江:浙江大学,2006.
[27]崔文迪、蔡佳佳.基于K-means算法和FCM算法的聚类研究[J].现代计算机,2007:269.
[28]Nikhil R Pal, Kuhu Pal, James C Bezdek, A mixed c-means clustering model[C]. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, vol.1, 1-5, 1997:11-21.
[29]Krishnapuram, R. and Keller J, A Possibilistic Approach to Clustering[J], IEEE Trans on Fuzzy Systems, 1(2), 1993:98-110.
[30]喻璐芝.基于Web的科研管理信息系统设计与实现[D].武汉:华中科技大学,2006.
[31]张跃廷、王小科、张宏宇. C#程序开发范例宝典[M].北京:人民邮电出版社,2007.
[32]张跃廷、韩阳、张宏宇.C#数据库系统开发案例精选[M].北京:人民邮电出版社,2007.
[33]郭宇鹏、余序芬.织物悬垂性能研究综述[J].现代纺织技术,2001,99(3) :49-51.
[34]李振然,贾旭彩.基于人工神经网络的短期负荷预测[J].广西电力.2004(4):7-10.
[35]Stylios G K and Zhu R. The characterisation of the static and dynamic drape of fabrics. J Text Inst,1997,88(4):465-475.
[36]蒋艳凤.织物的悬垂性能比较[D].浙江纺织职业技术学院,浙江宁波,2001.
[37]郭宇鹏,织物悬垂性能研究[D],上海:中国纺织大学,2001.
[38]Postle J.R,Postle R.Fabric Drape[J].Textile Asia,2000,31(1):30-32.
[39]Chen Bejian and Govindaraj Muthn.A phynically hased model of fabric drape using flexible sbell theory[J].Textile Res J,1995,65(6):324-330.
[40]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006:24-39.
[41]Perice F T.The handle of cloth as a measurable quantity[J].Text Inst. 1930,21:377-416.
[42]Olofsson B.A general model of a fabric as a geometricmechanical structure[J], Text Inst,1964,55(11):541-557.
[43]韩力群.人工神经网络理论、设计及应用[M].北京:化学工业出版社,2007.