The two physiological responses studied here to characterize discomfort glare under various lighting conditions had significant correlation with the subjective evaluation. The correlation between discomfort glare and physiological responses suggests an objective way to characterize and evaluate discomfort glare that may overcome the problems of conventional subjective evaluation. It also offers an explanation as to why long-term exposure to discomfort glare leads to visual fatigue and eyestrain.

Abstract:To model discomfort glare from LED road lighting, the effect of four key variables on perceived glare was explored. These variables were: the average glare source luminance (L g ), the background luminance (L b ), the solid angle of the glare source from the perspective of the viewer (ω) and the angle between the glare source and the line of sight (θ). Based on these four variables 72 different light conditions were simulated in a scaled experimental set-up. Participants were requested to judge the perceived discomfort glare of these light conditions using the deBoer rating scale. All four variables and some of their interactions had indeed a significant effect on the deBoer rating. Based on these findings, we developed a model, and tested its general applicability in various verification experiments, including laboratory conditions as well as real road conditions. This verification proved the validity of the model with a correlation between measured and predicted values as high as 0.87 and a residual deviation of about 1 unit on the deBoer rating scale. These results filled the gap in estimating discomfort glare of LED road lighting and clarified similarities of and differences in discomfort glare between LED and traditional light sources.

In a PV system, DC arc is regarded as a serious fault, which might cause circuit damage and trigger fires. The arc fault, however, is hard to detect due to the special fields of photovoltaic systems: constant direct current without zerocrossing point, sophisticated components leading to noise interruption, and usually occupying large area. Therefore, detectable characteristics are of great importance to diagnosis and alarm of fault arcs in PV systems. In this paper, we presented a classification method of separating arcing and non-arcing in the feature space. First, data sets of current signal were sampled by designing field experiments with "pull apart" method for arc ignition. Then seven features in both time and frequency domains were defined and two of them in each domain were selected to train BSVM. In order to simplify the computation, the trained BSVM network was replaced by a separating line, which was proved to have a better performance of classification. Testing results showed that this method could diagnose fault arcs with high accuracy. But whether this method is suitable for other PV systems needs to be verified in further work.

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