Chemical protective clothing comfort study: thermal insulation and evaporative resistance from fabric to garment

The relationship between the Rct and Ret of textile material used in CPC and that obtained from CPC garment was investigated. It was found that the Rct and Ret of CPC fabric are reliable predictor for the Rct and Ret of CPC garments respectively. Air gap contributes significantly to the increase of the Rct of CPC garments and fabrics. Heat dissipation by water vapor transfer through CPC is a complex process and different from other kinds of clothing due to its low permeability or impermeability. Further studies on the influential factors of Ret of CPC garments are needed

Analysis of heat stress associated with wearing chemical protective clothing using a numerical model

A numerical model was applied to evaluate heat stress under different thermal environmental conditions, activity intensities, and the effect of movement status on clothing properties when wearing a typical CPC. It was concluded that the ambient temperature and metabolic rate is strongly associated with heat stress and reduced the tolerance time. Although the manikin movement greatly affected the thermal insulation and evaporative resistance of CPC, the effects of movement on heat stress can be neglected

A Stealthy and Robust Fingerprinting Scheme for Generative Models

This paper presents a novel fingerprinting methodology for the Intellectual Property protection of generative models. Prior solutions for discriminative models usually adopt adversarial examples as the fingerprints, which give anomalous inference behaviors and prediction results. Hence, these methods are not stealthy and can be easily recognized by the adversary. Our approach leverages the invisible backdoor technique to overcome the above limitation. Specifically, we design verification samples, whose model outputs look normal but can trigger a backdoor classifier to make abnormal predictions. We propose a new backdoor embedding approach with Unique-Triplet Loss and fine-grained categorization to enhance the effectiveness of our fingerprints. Extensive evaluations show that this solution can outperform other strategies with higher robustness, uniqueness and stealthiness for various GAN models