• Journal of the Korean Institute of Landscape Architecture
• /
• v.43 no.5
• /
• pp.65-73
• /
• 2015

The purpose of this study was to compare the Mean Radiant Temperature(MRT) under two landscaping shade membranes, white and black, with those of natural outdoor spaces at summer midday. An additional perforated black shading net was applied and compared for the consideration of the practical application. The average MRT at the height of 2.4m, 10cm below the membranes of black, white, and perforated black were $49.1^{\circ}C$, $41.6^{\circ}C$ and $36.8^{\circ}C$ respectively, while that of open sky was $41.8^{\circ}C$. This indicates that a closer position to the darker membrane caused a higher MRT. At the height of 1.1m and 1.7m, the difference of MRT between the black and the white membranes was slight, while the value of white was unexpectedly higher than the black. The MRT of black perforated net showed the lowest value at every height. The black membrane absorbed more solar radiation than the white, which caused the greater release of long wave radiation and higher temperature near the membrane itself. In spite of the higher albedo of the white membrane, the higher solar radiation transmittance rate of which seemed to cause the slightly higher MRT than the black at the hight of 1.1m and 1.7m. In summary, the performance of the black membrane was slightly better than the white in terms of the air conditioning of the human related space around the height of 1.1m and 1.7m, when the shading membranes were at 2.5m height.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.4
• /
• pp.131-143
• /
• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.