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Shuoshuo Wang,Wubin Chen,Lina Wang,Juming Yao,Guocheng Zhu,Baochun Guo,Jiri Militky,Mohanapriya Venkataraman,MING ZHANG 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.108 No.-
Ultraviolet radiation is extremely harmful to humans and often occurs in high-temperature weather. Thedevelopment of intelligent textiles based on UV protection and thermal regulation is paramount. In thisresearch, we use coaxial electrospinning technology to prepare anti-ultraviolet smart thermo-regulatingnanofiber membranes. The zinc oxide (ZnO) and octadecane were incorporated into nanofibers with thepolyacrylonitrile (PAN)/ZnO as sheath and the octadecane as core successfully. The composite nanofibershave excellent comprehensive properties, the highest melting enthalpy is 111.38 J/g, and the UPF value is86.21. This multifunctional nanofiber membrane has broad prospects in outdoor products, electroniccomponent protection, and military products.
Features Fusion with Adaptive Weights for Pedestrian Classification
Junbo Zhao,Shuoshuo Chen,Weizi Liu,Xiaoxiao Chen 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
In this paper, we study the problem of pedestrian classification, which could lead to an improvement of performance of the Pedestrian Detection Systems. Since the traditional approaches merely focus on the recognition of pedestrian, the device would keep alerting the drivers even if the pedestrians are walking on a safe track. We attempt to classify pedestrians in order to make those devices, equipped in the cars, more intelligent and pragmatic. We propose a method to extract features including HOGs (Histogram of Oriented Gradient), LTPs (Local Ternary Pattern), Color Names and to fuse them efficiently. The three features are weighted fused depending on the size of patches as well as each patch’s gradient value which is computed via a 3<SUP>*</SUP>3 Sobel operator. Afterwards we will train a random forest with 50 discriminative decision trees, using the fused features. Our method is tested on the images of humans from INRIA dataset. The experimental results show that our method of features fusion, with adaptive weights assigned to the different features, yields a significant gain of 12.9% in mean AP (Average Precision) over the simple features concatenation framework. Accordingly, our method is practicable for classifying pedestrians.