A novel approach for fit analysis of thermal protective clothing using three-dimensional body scanning

The garment fit played an important role in protective performance, comfort and mobility. The purpose of this study is to quantify the air gap to quantitatively characterize a three-dimensional (3-D) garment fit using a 3-D body scanning technique. A method for processing of scanned data was developed to investigate the air gap size and distribution between the clothing and human body. The mesh model formed from nude and clothed body was aligned, superimposed and sectioned using Rapidform software. The air gap size and distribution over the body surface were analyzed. The total air volume was also calculated. The effects of fabric properties and garment size on air gap distribution were explored. The results indicated that average air gap of the fit clothing was around 25–30 mm and the overall air gap distribution was similar. The air gap was unevenly distributed over the body and it was strongly associated with the body parts, fabric properties and garment size. The research will help understand the overall clothing fit and its association with protection, thermal and movement comfort, and provide guidelines for clothing engineers to improve thermal performance and reduce physiological burden.     

Open Innovation and Team Leaders’ Innovation Traits

Open innovation has transformed internal novel resource allocation and facilitated diverse interorganizational cooperation models. Previous studies have investigated open innovation archetypes and their formation. However, most researchers have neglected the crucial role that team leaders play in integrating internal and external resources and connecting creative ideas in open innovation. This study investigated the influence of open innovation archetypes and team leaders’ innovation traits on team performance. On the basis of the literature review, the analytical hierarchy process was applied to construct a framework for assessing and selecting team leaders in open innovation. The analysis was completed based on samples from 45 valid respondents (88% return rate) in Taiwan’s high-tech industry. The study results suggested that innovation knowledge is the most crucial innovation criterion for a leader, particularly core knowledge and advanced knowledge. Among the various sub-criteria, extrinsic motivation was the second most important sub-criterion. Leaders with strong innovation motivations are suitable to lead inbound open innovation projects.     

Prevalence and personality correlates of Facebook bullying among university undergraduates

The purpose of the present study was to examine the prevalence of cyber-bullying through Facebook in a sample of 226 Greek university undergraduates, and to explore whether big five personality characteristics, narcissism, as well as attitudes toward Facebook, technological knowledge and skills were predictive of such behavior. Participants completed a self-report questionnaire measuring the above constructs. Results indicated that almost one third of the sample reported Facebook bullying engagement at least once during the past month, with male students reporting more frequent involvement than females. Bullying through Facebook was predicted by low Agreeableness and more time spent on Facebook only for males, whereas for females none of the studied variables predicted engagement in Facebook bullying. Findings are discussed in terms of prevention and intervention strategies.     

论合体女式上衣结构设计与纸样——以双排扣小立领款式为例

主要通过设计、打板、制作一件双排扣小立领款式,来分析该款式的款式特点、规格设计和主要部位在结构上的处理,以此强调领和袖子在服装设计中的重要性,最后通过款式的全面分析得出合体女式上衣的结构设计与纸样。     

数控机床轴向定位过盈套的设计

对数控机床主轴单元中轴向定位元件过盈套进行了结构尺寸设计。用概率分析方法分析各种加工误差对配合紧固性的影响，并做了实验验证。探讨了在保证使用性能前提下，如何制定适用于我国数控机床主轴单元用的系列标准。     

电加热实现过盈配合的工艺改进

介绍一种投资少、简单实用、低耗清洁、适合多部位和不同部件使用的小型加热盘——电加热工艺法。解决了形状特异、大而重的金属件加热处理后,在热胀冷缩状态下,满足过盈配合的需要。通过工艺改进与传统方法对比、分析,阐述了在机械制造领域中的应用效果。     

用三次样条函数拟合模具型腔廓线

用样条函数拟合曲线具有型值点个数不受限制,保证曲线光滑,便于模具型腔曲线分析等优点。因此,这里采用三次样条函数拟合曲线构造模具型腔廓线,其型值点由已有模具型腔测得。然后分析构造出模具型腔廓线的成形规律,使型腔表面缺损处可获得精确的修正。     

Automatic training of a min-max neural network for function approximation by using a second feed forward network

This research is concerned with a gradient descent training algorithm of a min-max network which we will refer to as the target network. Training makes use of a helper feed-forward network (FFN) to represent the performance function used in training the target network. A helper FFN is trained because the mathematical form of the performance function for the target network in terms of its trainable parameters, p, is not differentiable. Values for the parameter vector, p, of the target network are generated randomly and performance values are determined to produce the data for training the helper FFN with its own connection matrices. Thus we find an approximation to the mathematical relationship between performance values and p by training an FFN. The input to this FFN is a value for p and the output is a performance measure. The transfer function of the helper FFN provides a differentiable function for the performance function of the parameter vector, p, for the target network allowing gradient search methods for finding the optimum p for the target network. The method is successfully tried in approximating a given function and also on training data produced by a randomly selected min-max network.     

Training of a discrete recurrent neural network for sequence classification by using a helper FNN

This research is concerned with a gradient descent training algorithm for a target network that makes use of a helper feed-forward network (FFN) to represent the cost function required for training the target network. A helper FFN is trained because the cost relation for the target is not differentiable. The transfer function of the trained helper FFN provides a differentiable cost function of the parameter vector for the target network allowing gradient search methods for finding the optimum values of the parameters. The method is applied to the training of discrete recurrent networks (DRNNs) that are used as a tool for classification of temporal sequences of characters from some alphabet and identification of a finite state machine (FSM) that may have produced all the sequences. Classification of sequences that are input to the DRNN is based on the terminal state of the network after the last element in the input sequence has been processed. If the DRNN is to be used for classifying sequences the terminal states for class 0 sequences must be distinct from the terminal states for class 1 sequences. The cost value to be used in training must therefore be a function of this disjointedness and no more. The outcome of this is a cost relationship that is not continuous but discrete and therefore derivative free methods have to be used or alternatively the method suggested in this paper. In the latter case the transform function of the helper FFN that is trained using the cost function is a differentiable function that can be used in the training of the DRNN using gradient descent.Acknowledgement. This work was supported by a discovery grant from the Government of Canada. The comments made by the reviewers are also greatly appreciated and have proven to be quite useful.     

毫米波频段频率选择表面的分析计算

首先介绍了如何用辅助场量的FDTD方法分析计算毫米波波段的频率选择表面,然后给出了一种能快速计算多层FSS频率响应的方法——拟合电路法,并和FDTD计算的结果比较,结果吻合较好。