The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) ...The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) to tackle the problem of query images with various intra-class variations,e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images.The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.展开更多
A qualitative and quantitative workplace assessment was carried out to determine naphtha exposure in a tyre manufacturing industry. A qualitative chemical health risk assessment was conducted to identify naphtha hazar...A qualitative and quantitative workplace assessment was carried out to determine naphtha exposure in a tyre manufacturing industry. A qualitative chemical health risk assessment was conducted to identify naphtha hazard at the workplace. Quantitative assessment using Portable VOC Monitor, Automatic Sampling Pump and personal air sampling pump was used to determine VOC concentrations, organic solvents, and individual air naphtha respectively. The risk rating of naphtha was estimated to be 5. The mean VOC concentration was in the range of 2.43 to 92.93 ppm. Repair area had the highest VOC concentration while the lowest was in the moulding area. Each work station had significant differences for VOC concentrations (p 〈 0.001). Laboratory analysis found various solvents including 2-methyl pentane, hexane, methyl cyclopentane, heptane, cyclohexane and toluene which were present in the liquid naphtha. Only xylene has been detected in the making and moulding areas with a range of 2 to 5 ppm. Meanwhile, the air naphtha concentrations of the exposed workers were significantly higher than those unexposed. The risk of naphtha exposure was qualitatively significant and not adequately controlled. Naphtha was detected in all work stations since it is the main solvent used. The "Repair Area" was significantly more contaminated than the other area.展开更多
基金supported in part by the National Natural Science Foundation of China(61673402,61273270,60802069)the Natural Science Foundation of Guangdong Province(2017A030311029,2016B010109002,2015B090912001,2016B010123005,2017B090909005)+1 种基金the Science and Technology Program of Guangzhou of China(201704020180,201604020024)the Fundamental Research Funds for the Central Universities of China
文摘The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) to tackle the problem of query images with various intra-class variations,e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images.The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.
文摘A qualitative and quantitative workplace assessment was carried out to determine naphtha exposure in a tyre manufacturing industry. A qualitative chemical health risk assessment was conducted to identify naphtha hazard at the workplace. Quantitative assessment using Portable VOC Monitor, Automatic Sampling Pump and personal air sampling pump was used to determine VOC concentrations, organic solvents, and individual air naphtha respectively. The risk rating of naphtha was estimated to be 5. The mean VOC concentration was in the range of 2.43 to 92.93 ppm. Repair area had the highest VOC concentration while the lowest was in the moulding area. Each work station had significant differences for VOC concentrations (p 〈 0.001). Laboratory analysis found various solvents including 2-methyl pentane, hexane, methyl cyclopentane, heptane, cyclohexane and toluene which were present in the liquid naphtha. Only xylene has been detected in the making and moulding areas with a range of 2 to 5 ppm. Meanwhile, the air naphtha concentrations of the exposed workers were significantly higher than those unexposed. The risk of naphtha exposure was qualitatively significant and not adequately controlled. Naphtha was detected in all work stations since it is the main solvent used. The "Repair Area" was significantly more contaminated than the other area.