World Applied Sciences Journal 9 (12): 1359-1364, 2010 ISSN 1818-4952 © IDOSI Publications, 2010
Using Self Organization Method to Establish Nonlinear Sizing System 1
A.H. Doustaneh, 2M. Gorji and 2M. Varsei
1
Department of Textile, Kashan Branch, Islamic Azad University, Kashan, Iran Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Abstract: Sizing systems have been widely used for large scale clothing industries. There is some dissatisfaction because of loosing of fit, small cover factor and large numbers of size charts. The objective of this paper is to establish a sizing system using artificial neural network. A nonlinear sizing system is created by self organizing method (SOM) and compared with Statistical method (SM). The result showed that the number of size chart and the aggregate loss of fit reduced by using self organizing method. This study suggests that by using of SOM, the cover factor and aggregate loss of fit is improved and the number of size chart is reduce, making sizing system more suitable for clothing industries. Key words: Neural Network % Dynamic sizing system % Nonlinear size chart % Clothing industries INTRODUCTION The most important criteria for evaluating sizing system is the greatest percentage of population that is covered, fewest number of sizes chart that is proposed and betterfit that they are made [1]. Such a sizing system can determine production numbers and proportion of sizes to be produced, resulting in accurate material control and production planning [2]. Since these three criteria are in compromising conflict some research has been done to optimize these criteria. Chung and et al [1] developed sizing systems for Taiwanese elementary- and high-school students. A two-stage cluster analysis was performed for the classification of figure types. In their research twelve sizing systems were established systematically by age group, gender and garment type. Salusso et al [3] examined Principal Component Sizing System (PCSS) methodology as an alternative approach to advancing the mathematical efficiency and effectiveness of apparel sizing. This analysis demonstrated that the PCSS method was mathematically capable of classifying 95% of subjects with only 25 size categories. Honey and Olds [4] compared the three-dimensional (3D) shapes of a sample of 18-30 year old Australian women, to the 3D shapes assumed by the Standards Australia (SA) garment sizing system, using the newly developed L-statistic, to suggest methods of improving current garment sizing systems. Hsu and Wang [2] established systems for using a decision tree technique to determine the pants sizes of army soldiers.
They obtained only 42 size groups, that was much fewer than the number in the previous sizing systems. Hsu [5] proposes a bust-to-waist girth ratio approach for identifying female Body shapes. His analytical results indicated that the girth ratio approach can reflect fine distinctions among different female figures to classify body shapes for the development of new body measurement charts. Linear sizing system has the advantages of easy grading and size labeling. But, the disadvantage is that the structural constraints in the linear system may result in a loose fit [1]. Ng and coworkers [6] applied genetic algorithm (GA) technique to build the size table automatically according to the specification of the user. They use a nonlinear size table that was made up of varying increments of measurements from one size to another size to perform better than a liner size table. They showed that GA method can perform than clustering method. Neural network, has been successfully applied in many fields in textiles, including prediction of characteristics of textiles; identification, classification and analysis of defects; process optimization; and marketing and planning [7-12], prediction of various comfort-related properties such as human sensory perceptions and overall comfort index [13-17], prediction of steady-state thermal resistance and maximum instantaneous heat transfer Qmax of a fabric [18]. However, research on establishing sizing systems using neural network is lacking.
Corresponding Author: A.H. Doustaneh, Department of Textile, Kashan Branch, Islamic Azad University, Kashan, Iran. E-mail:
[email protected].
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Self-organizing in networks is one of the most fascinating topics in the neural network field. Such networks can learn to detect regularities and correlations in their input and adapt their future responses to that input accordingly [19]. One of the problems of proposed sizing system for apparel factory is that they are static. Different clothing need to different size charts. For example the size charts of under wear, suit or crew suit are different because of their different importance of fitness. In this study artificial neural network was used to establish a dynamic sizing system. The feasibility of using self organizing method (SOM) was investigated for creating a nonlinear sizing system. The results are compared with statistical method. MATERIALS AND METHOD Data Preparation: Thirty anthropometric data of 670 Iranian men were measured. Before the data are mined, they must be examined and purified. In this study, samples containing missing or abnormal data were omitted. 18 of the 670 samples had missing or abnormal data and were thus deleted, leaving a total of 652 valid samples for further processing. Statistical analyses were performed using SPSS 13 software. Neural Network and Self Organization Method: A commercial software package, Matlab 7 (Neural Network toolbox), was used for writing the SOM program. Neural networks have been trained to perform complex functions in various fields of application including pattern recognition, identification, classification, speech and vision and control systems. Self-organizing in networks is one of the most fascinating topics in the neural network field. Such networks can learn to detect regularities and correlations in their input and adapt their future responses to that input accordingly. The neurons of competitive networks learn to recognize groups of similar input vectors. Self-organizing maps learn to recognize groups of similar input vectors in such a way that neurons physically near each other in the neuron layer respond to similar input vectors [19]. The architecture for this SOM is shown in Fig. 1. Where R is the number of elements in input vector P (in this study P is height and bust girth dimensions), IW is input weight matrix. The ||ndist|| box in this figure accepts the input vector p and the input weight matrix IW and produces a vector n having Selements.
Fig. 1: Architecture for SOM[19] The elements are the negative of the distances between the input vector and vectors IW formed from the rows of the input weight matrix. The competitive transfer function C accepts a net input vector for a layer and returns neuron outputs of 0 for all neurons except for the winner, the neuron associated with the most positive element of net input n. The winner's output is 1. In This architecture no bias is used. The competitive transfer function produces a1 for output element a1i corresponding to i*, the winning neuron. All other output elements in a1 are 0. Now, however, as described above, neurons close to the winning neuron are updated along with the winning neuron [19]. Validating the Sizing System: Different ways for evaluating a sizing system can be found in literature [1,2,6, 5,20]. Honey and Olds [4] used L-statistic as a new method for quantifying the lack of fit between two sets of dimensions defining 3D shapes. Ng and coworkers [6] used cover factor for evaluating their proposed size charts. Some researchers have used aggregate loss of fit for this purpose [5, 20]. This measurement is using average of Euclidean function for calculating aggregate loss of fit between the proposed size and individual using the following formula: Aggregate loss of fit=(3s (assigned height-actual height)2+(assigned bustactual bust)2 )/Number of individuals
The ideal value for aggregate loss of fit when using two control dimensionswould be s[(2.54)2 + (2.54)2]=3.591, that can be calculated using the number of body dimensions considered -allowing for 2.54cm deviation of the body dimension from the assigned size [5, 20].
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Row Data
Data Preparation Deleting abnormal data
Classified population to 3 height categories Using first self organization network (1 layer 3 nodes)
Classified each height categories to 3 subgroup based on bust girth Using second self organization network (1 layer 3 nodes)
Established size charts based on bust girth for each of sub groups in previous step Using third self organization network (1 layer, numbers of nodes= (Round (Range of bust girth /4))
The percentage of size chart
