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Understanding costumer requirements is a precondition for clothing design and manufacturing. A good shapewear designer should know women’s preferences for their buttock shapes before making patterns. The purpose of this paper is to figure out factors affecting the beauty of women’s buttock shapes and the effect of ethnic background to buttock shapes preference.

The approach utilized both 3D virtual models and 3D printed models to detect women’s preference for their buttock shapes. As a first stage, a two-step K-means cluster method was used to classify female buttock shapes into ten groups and these ten kinds of buttock shapes were output as 3D virtual models and printed out as 3D plastic models. In a subsequent stage, 51 Caucasians, 35 African-Americans and 49 Asians were selected to rank the models separately based on their preference and choose the factors which they thought could influence the beauty of buttock shapes. Kendall’s W coefficient was tested to help assessing the ranking results. Finally, a sample girdle was designed based on the buttock shape preferences of Asian females as an example, and was tested by a model.

Results showed some correlation between ethnicity and buttock shape preference. Both methods of presentation of the shapes were equally preferred by participants. Caucasian women preferred a full, round buttock shape, which was coordinated to other parts of the body. The attractive buttock shape as judged by Asian women was curvy, not drooping and not too big. African-American women choose buttock shapes that were very full with high buttock bumps as beautiful. A sample girdle was made based on the preferences of Asian females and it proved to perform well in creating the desired shape during the trial test.

Current research in the literature about women’s buttock beauty is based on plastic surgeons’ experiences. This study provides a novel method to analyze female’s preference for their buttock shapes; a method that can also be used for other body parts. The results can also be used as an indicator for underwear designers to improve shapewear pattern designs and for consumers to evaluate the shaping ability of shapewear.

An exact pattern prototype is a prerequisite for female girdle pattern-making. The purpose of this paper is to develop new ways to make girdle pattern prototypes based on 3D technology.

This paper presented two novel methods for creating girdle pattern prototypes. The first one was the girdle's parametric foundation pattern developing method based on 3D geometric modeling. In this method, considering the different characteristics of a female's lower body shape, several models were created to define the relationship between the female's lower body shape and the pattern, such as a side-waist curvature model, an interior-posterior waist-warping model, a buttocks' parametric model and an abdomen parametric model. Then, parameters of drawing the prototype were abstracted to facilitate transforming the 3D geometric model into the 2D pattern. Another method was implemented by 3D virtual modeling and unwrapping. The whole process included surface division, surface reconstruction and surface unwrapping.

The prototypes created by these two methods were tested using the 3D virtual trying-on examination. Trial tests showed that the patterns can be dressed in the right positions on the virtual model with little pressure. This means that the proportions and shapes of the pattern are correct. The prototypes obtained through the methods proposed in this paper have good effects and high precision. Both methods can be used for making the girdle's foundation pattern.

Two pragmatic approaches of girdle's prototype building have been put forward. The parametric prototype designing method has changed the unconstrained state of free modeling. The pattern structure can be controlled by parameter constraints. In the other method, with 3D scanning and surface modeling technology, personalized girdle's pattern is generated, and the segmentation lines of the girdle can be designed flexibly according to the requirements. These findings also can be used in other tight garments' prototype making.

The purpose of this paper is to define the evaluation criteria for Chinese female lower body shapes and simplify the evaluation process of shapewear, including girdles, shaping pants, etc.

The study utilized a machine learning algorithm based on support vector regression and optimized by a genetic algorithm to construct an evaluation model for the contour beauty of Chinese female lower body shapes. A total of 64 virtual 3D models of women were measured. These models were rated by 42 raters using the Likert 9 psychological scale and data was obtained from 310 female samples.

Eight factors were selected and used to create a regression prediction model. The model achieved an accuracy of 84.7% for the training samples and 86.6% for the test samples.

The model can be utilized to assess the aesthetic appeal of the female lower body and to evaluate the shaping impact of shapewear. The research and evaluation of shapewear for the female lower body are of great significance, particularly in enhancing production efficiency.

To reduce the difficulty of the sewing process and promote the automation process of fabric sewing, a soft finger-assisted feeding method is proposed to investigate the effect of sewing process parameters on the quality of automatic sewing.

Taking cotton woven fabrics as an example, the causes of sewing deviation are firstly investigated from three aspects: fabric properties, sewing speed and sewing edge position. By simulating the sewing action of human hands, the method of reducing sewing deviation by using soft fingers to press and feed the fabric is proposed. Then, four sewing process factors, namely, robot arm end pressure, sewing machine speed, sewing needle gauge and stitch density, were selected, and three levels were set for each factor to design orthogonal sewing experiments. The sewing deviation of 1# sample under different sewing processes was measured, and the optimal parameter matching for automatic sewing of this specimen was derived.

The findings demonstrate that, while sewing cloth automatically, the sewing deviation is significantly influenced by the robotic arm's end pressure, sewing speed, and stitch density, whereas the sewing deviation is not significantly impacted by the needle number.

The findings offer fundamental information for the development of an automated sewing procedure using soft fingers, which has theoretical and real-world application value to speed up the intelligent modernization and transformation of the apparel industry.

The purpose of this paper is to gain an in-depth understanding into the research progress, hot spots and future trends in smart gripping technology in the field of apparel smart manufacturing.

This work scrutinised the current research status of the five automatic grasping methods for garment fabrics including the pneumatic suction grasping, the electrostatic grasping, the intrusive grasping and the dexterous grasping. Specifically, the principles, characteristics, main devices and the impact on garment production were discussed.

In particular, soft finger of the dexterous grasping method has good flexibility and adaptability in the process of fabric grasping, which provides a new solution for garment production automation. Up to now, the reviewed method in general exhibit good grasping speed, high grasping stability and flat grasping process. However, in the face of complex fabric materials which are thin and flexible and do not return their original shapes when deformed in practical applications, the gripper for automatic fabric grasping need new technological breakthroughs in the positioning accuracy, grab efficiency and flexible grasping.

The outcomes offered an overview of the research status and future trends of the automatic grasping methods for garment fabrics in the field of apparel intelligent manufacturing. It could not only provide scholars with convenience in identifying research hot spots and building potential cooperation in the follow-up research but also assist beginners in searching core scholars and literature of great significance.

Whole garment knitwear is one of the knitwear development trends in the foreseeable future for its advantages of environmental friendliness and wearing comfort. However, the development of new styles of the whole garment knitwear requires both fashion design and computer programming, which makes it time-consuming and difficult. In this paper, a whole garment knitted skirt template Library is introduced to solve this problem.

The template library composes of silhouette module, design element module and parts shaping technology module. It was built based on a comprehensive investigation of design and technology. By adhering to the principle of similarity and reusability, the template library of whole garment knitted skirts was established through the innovative design and hierarchical classification of compressed patterns and package patterns.

With the template library, more than 7.7 × 1025 package pattern templates can be generated through the permutation and combination of the package pattern templates of design elements and parts shaping technology.

The results indicated that it can accelerate the design process and improve the design efficiency of new styles with the template library. This approach can also provide inspiration for the designers and realize rapid response and personalized customization of knitted garment production. In addition, the whole garment templates can be applied into constructing other types of clothing template libraries, such as jackets, pants, etc.

This paper aims to develop sliding mode control (SMC) methods for second-order multi-agent systems (MAS) in the presence of mismatched uncertainties.

Based on the disturbance observer (DOB), discontinuous and continuous sliding mode protocols are designed to achieve finite-time consensus in spite of the disturbances.

Compared with integral SMC, numerical simulation results show that the proposed control methods exhibit better performance with respect to reduction of chattering.

The main contributions are the following: MAS described with mismatched uncertainties are considered; both discontinuous and continuous sliding mode controllers are considered; with the proposed sliding mode controller, the desired sliding surface can be reached in finite time and the DOB is introduced in the controller to alleviate the chattering phenomenon.

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.

Customization, a marketing strategy through providing personalized products, might be a new solution to motivate consumer feedbacks in electronic commerce (e-commerce) websites. Taking the dual-value of customization (emotional involvement and uniqueness expression) as the theoretical basis, this study aims to investigate the impact of customization on consumer's word-of-mouth (WOM) behaviors and contents by motivating: (1) more, (2) faster, (3) positive at first and then negative, (4) longer and (5) more helpful WOMs.

A field study was conducted with multi-sourced data: customer order data from a Chinese retailer and WOM data from Amazon.com. The two datasets were matched to filter out 463 online reviews among 6,892 customers who placed customized orders. Heckman's two-stage model, logistic regression, Ordinary least squares regression, Tobit regression, analysis of covariance and Lind–Mehlum U Test were used in the data analysis.

This study has found that (1) customization level motivates WOM behaviors including WOM posting and WOM speed, (2) an inverted U-shaped relationship exists between customization level and consumer rating and (3) customization level has a significantly positive impact on WOM helpfulness but not on WOM length.

This study advances theoretical development in the area of WOM motivators by proposing a new product-centric approach, customization, to stimulate voluntary WOMs. Empirical field research that analyzes consumer's real responses to customization is in scarcity. The dual-value of customized products is proposed as the underlying mechanism to explain the impact of customization level on consumer's WOM behaviors/contents. An interesting inverted U-shaped relationship is found between customization level and customer rating. This research provides nuanced practical guidance for websites, companies and consumers.

This study aims to examine the mechanism of how hotel executive brand identity influences physical facility quality, customer-based brand equity (CBBE) and employee-based brand equity (EBBE).

The study introduces a multilevel model and collects 925 executive and 1,978 employee responses from 62 upscale hotels in China.

Executive brand identity positively affects employee brand internalization, which leads to positive EBBE. Meanwhile, executive brand identity positively influences the physical facility quality, which leads to positive CBBE.

This study considers the tangible (physical facilities) and intangible (employees) elements of hotel services to comprehensively investigate the brand equity formation. By applying multilevel structural equation modeling, the study examines the bidirectional relationship between organizations and employees in the brand value transformation process.