纺织学报 ›› 2019, Vol. 40 ›› Issue (05): 113-118.doi: 10.13475/j.fzxb.20180608606

• 服装工程 • 上一篇    下一篇

短裤特征截面廓形分析及间隙量预测模型构建

李涛1, 杜磊1,2, 孙洁1, 张益洁1, 邹奉元1,2()   

  1. 1.浙江理工大学 服装学院, 浙江 杭州 310018
    2.浙江理工大学 浙江省服装工程技术研究中心, 浙江 杭州 310018
  • 收稿日期:2018-06-29 修回日期:2018-11-21 出版日期:2019-05-15 发布日期:2019-05-21
  • 通讯作者: 邹奉元
  • 作者简介:李涛(1993—),男,博士生。主要研究方向为人体工程与服装数字化技术。
  • 基金资助:
    国家自然科学基金面上项目(11671009);“浙江省服装工程技术研究中心”重点实验室开放基金项目(2018FZFK13)

Typical cross section silhouette analysis and interval prediction model construction of shorts

LI Tao1, DU Lei1,2, SUN Jie1, ZHANG Yijie1, ZOU Fengyuan1,2()   

  1. 1. Fashion College, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Clothing Engineering Research Center of Zhejiang Province, Zhejiang Sci-Tech University,Hangzhou, Zhejiang 310018, China
  • Received:2018-06-29 Revised:2018-11-21 Online:2019-05-15 Published:2019-05-21
  • Contact: ZOU Fengyuan

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摘要：

为探究短裤松量变化对截面廓形的影响,选取与裤装造型密切相关的腰围、臀围等特征截面进行研究。通过[TC] 2三维扫描仪获取截面点云数据,利用小脑神经网络进行曲线拟合,提取最小外接矩形面积、厚宽比及间隙量表征截面廓形变化,通过回归分析构建短裤间隙量与松量的预测模型。结果表明:短裤松量与臀围、裆围及裤口围截面厚宽比呈负相关,臀围、裆围处间隙量主要向截面两侧及后中部位累积,截面形状趋于宽扁;经两配对样本T检验,间隙量的预测值与实际测量结果之间无显著差异,间隙量预测模型的拟合优度较好。研究结果可为建立人体、服装截面廓形及三维仿真之间的关系提供参考。

关键词: 短裤, 松量, 廓形, 小脑神经网络, 间隙量

Abstract:

In order to explore the influence of ease allowance on the shorts silhouette, the waist, hip and other typical cross sections which closely related to the shorts shape were selected as the research object. [TC] 2 3-D scanner was adopted to collect point cloud data of typical section, and the curve was fitted by cerebellar model articulation controller (CMAC) neural network. The minimum enclosing rectangle, thickness to width ratio and the interval were used to characterize the cross section silhouette. The prediction model of interval and ease allowance was established by regression analysis. The results show that negative correlation exists between the ease allowances and the thickness to width ratio of the hip, thigh and leg opening. The interval of hip and thigh accumulates mainly on both sides and back middle part, resulting in the shape of the cross section is gradually flattening. Upon two paired samples T examination, no significant difference exists between the predicted values and actual measured results. The interval prediction model has a good fit, which can provide reference for establishing the relationship between human body data, cross section silhouette and 3-D simulation.

Key words: shorts, ease allowance, silhouette, cerebellar model articulation controller neural network, interval

中图分类号: 

  • TS941.17

图1

样裤款式图"

表1

样裤尺寸规格表"

样本 裤长 立裆 腰围 臀围 裤口围 腰头
人台 68 90 49
样裤1 36 24.5 70 96 51 3
样裤2 36 24.5 71 98 52 3
样裤3 36 24.5 72 100 53 3
样裤4 36 24.5 73 102 54 3

图2

小脑神经网络结构"

图3

不同松量下截面廓形形态图"

表2

截面廓形与松量相关性分析"

特征
截面		 廓形面积与松量 廓形形状与松量
Pearson
相关系数		 显著性
(双侧)		 Pearson
相关系数		 显著性
(双侧)
腰围 0.149 0.851 -0.350 0.650
臀围 0.942 0.058 -0.974* 0.026
裆围 0.978* 0.022 -0.967* 0.033
裤口围 0.893 0.107 -0.976* 0.024

表3

臀围截面厚度、宽度与松量之间的回归分析"

模型 非标准化系数 标准系数 t值 Sig. B的95.0%置信区间
B 标准误差 试用版 下限 上限
常量 24.233 0.274 — 88.286 0.000 23.052 25.414
自变量:松量 0.042 0.030 0.711 1.432 0.289 -0.085 0.170
因变量:厚度
常量 30.172 1.064 — 28.355 0.002 25.593 34.750
自变量:松量 0.507 0.115 0.952 4.412 0.048 0.013 1.000
因变量:宽度

图4

不同松量下截面间隙量"

表4

间隙量回归预测方程"

极角/(°) 回归预测方程 R2
-90 y=-0.239x2+4.183x+0.785 0.976
-70 y=0.008x3-0.049x2+6.800 0.912
-30 y=0.255x2-3.48x+15.234 0.915
0 y=0.032x3-4.989x+36.468 0.957
20 y=0.008x3-0.016x2+4.578 0.991
50 y=0.409x2-6.538x+30.977 0.995
80 y=0.104x2-2.769x+18.438 1.000

表5

短裤间隙量预测值与实际值两配对样本T检验结果"

配对
样本		 极角/
(°)		 成分差分 Sig.
(双侧)
均值 标准差 标准误
预测值-
实际值		 -90 0.038 0.180 0.09 0.703
-70 -0.218 1.002 0.501 0.693
-30 -0.033 0.9498 0.4749 0.949
0 0.3283 1.84 0.9201 0.745
20 -0.241 0.4938 0.2469 0.399
50 -0.043 0.2059 0.103 0.701
80 -0.005 0.0035 0.017 0.062
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