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To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and accuracy of predicting the thermal and moisture comfort of skin-tight clothing (also called skin-tight underwear), principal component analysis(PCA) is used to reduce the dimensions of related variables and eliminate the multicollinearity relationship among variables. Then, the optimized variables are used as the input parameters of the coupled intelligent model of the genetic algorithm (GA) and back propagation (BP) neural network, and the thermal and moisture comfort of different tights (tight tops and tight trousers) under different sports conditions is analysed. At the same time, in order to verify the superiority of the genetic algorithm and BP neural network intelligent model, the prediction results of GA-BP, PCA-BP and BP are compared with this model. The results show that principal component analysis (PCA) improves the accuracy and adaptability of the GA-BP neural network in predicting thermal and humidity comfort. The forecasting effect of the PCA-GA-BP neural network is obviously better than that of the GA-BP, PCA-BP, BP model, which can accurately predict the thermal and moisture comfort of tight-fitting sportswear. The model has better forecasting accuracy and a simpler structure. [ABSTRACT FROM AUTHOR]

Optimal ergonomic design for consumer goods (such as garments and furniture) cannot be perfectly realised because of imprecise interactions between products and human models. In this paper, we propose a new body classification method that integrates human skeleton features, expert experience, manual measurement methods, and statistical analysis (principal component analysis and K-means clustering). Taking the upper body of young males as an example, the proposed method enables the classification of upper bodies into a number of levels at three key body segments (the arm root [seven levels], the shoulder [five levels], and the torso [below the shoulder, eight levels]). From several experiments, we found that the proposed method can lead to more accurate results than the classical classification methods based on three-dimensional (3 D) human model and can provide semantic knowledge of human body shapes. This includes interpretations of the classification results at these three body segments and key feature point positions, as determined by skeleton features and expert experience. Quantitative analysis also demonstrates that the reconstruction errors satisfy the requirements of garment design and production. Practitioner summary The acquisition and classification of anthropometric data constitute the basis of ergonomic design. This paper presents a new method for body classification that leads to more accurate results than classical classification methods (which are based on human body models). We also provide semantic knowledge about the shape of human body. The proposed method can also be extended to 3 D body modelling and to the design of other consumer products, such as furniture, seats, and cars. Abbreviations: PCA: principal component analysis; KMO: Kaiser-Meyer-Olkin; ANOVA: analysis of variance; 3D: three-dimensional; 2D: two-dimensional; ISO: International Standardisation Organisation; BFB: body-feature-based [ABSTRACT FROM AUTHOR]

This paper investigates the influences of woven fabric type, impact locations and number of layers on ballistic impact performances of target panels through trauma dimension and panel surface damage mechanisms for lightweight women ballistic vest design. Three panels with 30, 35 and 40 layers of two-dimensional plain weave and another two panels with 30 and 40 layers of three-dimensional warp interlock fabrics were prepared. The three-dimensional woven fabric was manufactured using automatic Dornier weaving machine, whereas the two-dimensional fabric (with similar p-aramid fibre type (Twaron®)) was received from the Teijin Company. The ballistic tests were carried out according to NIJ Standard-0101.06 Level IIIA. Based on the result, woven fabric construction type, number of layers and target locations were directed an upshot on the trauma measurement values of the tested target panels. For example, 40 layers of two-dimensional plain weave fabric panels show lower trauma measurement values as compared to its counterpart three-dimensional warp interlock fabric panels with similar layer number. Moreover, 40 layers of two-dimensional fabric panels revealed 47% and 39% trauma depth reduction as compared to panels with 30 layers of two-dimensional fabric panel in moulded (target point 1) and non-moulded (target point 6), respectively. Due to higher amount of primary yarn involvement, two-dimensional plain weave fabric panel face higher level of local surface damages but less severe and fibrillated yarns than three-dimensional warp interlock fabrics panels. Moreover, three-dimensional warp interlock fabric panels required higher number of layers compared to two-dimensional plain weave aramid fabrics to halt the projectiles. Similarly, based on the post-mortem analysis of projectile, higher projectile debris deformation was recorded for panels having higher number of layers for both types of fabrics at similar target locations. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to investigate the increase in the electromagnetic shielding effectiveness (EMSE) of a set of five variants of three-dimensional (3D) warp interlock woven fabrics containing silver multifilament yarns arranged in a 3D orthogonal grid. The EMSE enlargement as a factor of increasing the quantity of the conductive material per unit area was investigated. The quantity of the conductive material per unit area in a 3D woven fabric can be enlarged by increasing either the yarn undulation or the number of conductive yarn systems, while the yarn density and yarn fineness are fixed. Thus, the binding depth of the conductive warp was gradually increased for the first four variants in order to increase the yarn undulation. Alternatively, the conductive weft system was doubled for the last variant with the aim of increasing the quantity of the conductive component. It should be noted that changing the weave structure requires less effort and energy while keeping the same threading of warps in the reed compared to altering the warp density. The EMSE was measured in an anechoic chamber and the shielding was satisfactory for all the variants in the frequency range of 1–6 GHz (19–44 dB). The results revealed that increasing only 7% of the waviness degree of the conductive warps led to 17% EMSE improvement due to increasing of the conductive yarns through the thickness of the variants. Moreover, no upward EMSE was detected for the last variant, despite the fact that the conductive weft system was doubled. [ABSTRACT FROM AUTHOR]