The development of fire retardants (FRs) is an approximative process of optimization. In this context, a novel water-soluble formulation with poly(sodium silicate-aluminum dihydrogen phosphate) (PSADP) has been developed, aiming at reduced hygroscopicity and enhanced leaching resistance of poplar wood in combination with nitrogen-phosphorus (NP) FR (FRNP). After treatment of wood with FRNP and PSADP in vacuum, the following data of the samples were determined: rate of hydroscopicity, leaching resistance, heat release rate (HRR), total HR (THR), effective combustion heat, mass loss, and concentration of flue gas. FR distribution in the wood’s inner surface was investigated by scanning electron microscopy (SEM) and energy-dispersive X-ray analysis (EDXA). Results show that PSADP and FRNP have favorable synergistic effects on moisture resistance and flame retardance. Smoke density (SD) of NP and PSADP treated samples shows a significant reduction relative to that of NP. NP-PSADP treated samples form more char with carbon layers of higher density. At the same time, FRNP-PSADP is evenly distributed over the inner wood surfaces and penetrates the cell cavities of the poplar wood.
Abstract.Vehicle information extraction is the key means in Intelligent Transportation System (ITS). Color plays an important role in vehicle recognition. The main challenge of vehicle color recognition is to find the dominant color. In this paper, we propose a color recognition method using convolutional neural network. We train the classifier with the network structure 'NIN' to increase the classification accuracy. The experiments are validated on our dataset and extra data, which are collected from city surveillance equipment. The proposed method outperforms other competing color recognition methods.
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