Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (05): 113-118.doi: 10.13475/j.fzxb.20180608606

• Apparel Engineering • Previous Articles     Next Articles

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 E-mail:zfy166@zstu.edu.cn

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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

CLC Number: 

  • TS941.17

Fig.1

Style of sample shorts. (a) Front; (b) Back"

Tab.1

Size specification of sample shorts cm"

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

Fig.2

Structure of CMAC neural network"

Fig.3

Silhouette of different ease allowance. (a) Minimum enclosing rectangle; (b) Ratio of thickness to width"

Tab.2

Correlation analysis of silhouette and ease allowance"

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

Tab.3

Regression analysis of thickness, width and ease allowance of hip cross section"

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

Fig.4

Intervals of different ease allowance. (a) Waist; (b) Hip; (c) Thigh; (d) Leg opening"

Tab.4

Regression equation of interval"

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

Tab.5

Results of two paired samples T test between predicted values and actual values of shorts interval"

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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