2014 IES Street and Area Lighting Conference
September 14-17, 2014 | Nashville, TN
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The assessment of the variation of average internal illuminance from tubular light guides under different outdoor climatic conditions. Computer program Radiance was used for simulations of daylighting from tubular light guides of various dimensions. The internal illuminance calculations were compared for two extreme situations in boundary conditions – sunny summer sky and winter overcast sky, determined for the climatic region of Central Europe. Daylight simulations give information about average indoor illuminance on vertical and horizontal reference plane in the dependence of various dimensions of the light guides. Results of computer daylight simulations serve for the design optimization.
The legibility levels of text and graphics can be assessed using the Legibility Index, conventionally defined as the distance at which material can be read with perfect accuracy (the legibility distance) divided by the character height. The ratio equals the inverse tangent of the visual angle subtended by the character. The definition assumes that the viewing material is perpendicular to the observer, which is not always true. Although text and graphics are, in reality, often viewed not perpendicular to the display, they are rarely studied. To examine the legibility of characters viewed not perpendicular to thedisplay, this paper has redefined the Legibility Index as the inverse square root of the solid angle subtended by the target, based on a hypothesis that the three-dimensional solid angle, rather than the two-dimensional visual angle, both subtended by the character, captures how people recognize text and graphics that usually have two significant dimensions (width and height). This hypothesis proves consistent with how retinal images of text and graphics activate cones in the fovea of a viewer’s eyes, and has also been verified in a laboratory using legibility data collected from 20 human subjects. Using these data, the calculated steady redefined Legibility Index closely matches the fact that the 20 subjects have similar recognition performance (critical legibility level with 100 percent accuracy) of text (same target) under the same lighting conditions (Lb=120 cd/m2, C%=97.9, zero ambient light) at different viewing angles (perpendicular or not perpendicular to the displays), while the calculated ratio of legibility distance to character height rapidly decreases with increased viewing angles.
This paper describes a data processing and evaluation framework for application to a lighting control sensor network. Many buildings include systems to detect occupancy and control building services. Current systems use single measurement points to detect occupancy, and there can be significant uncertainty associated with the measurement of occupancy. More effective occupancy measurement and control are provided by sensor networks. A sensor network reduces uncertainty because data from more than one detector provides converging information concerning space occupancy. While a network of sensors reduces the uncertainty associated with individual sensor measurements, the utility of a sensor network for control depends on the analysis techniques applied to the data stream, and the ability of these techniques to produce results that better correspond to occupancy than current systems. Eight data processing algorithms are described: logical functions (OR, AND, & MAJORITY); moving average; rulebased reasoning; Bayesian belief network; least squares estimation, and; artificial neural networks. Three metrics that can be applied to evaluate the effectiveness of the data fusion methods are also described: the total occupied time measured by single vs. multiple sensors; the φ coefficient, and; the number of times that a controller using the associated method would have taken an inappropriate action (that is, switching the lights off in an occupied space [a false-off] or switching the lights on in a vacant space [a false-on].
This article evaluates the application of sensor networks to lighting control, and had two goals. The first goal was to evaluate the utility of using more than one sensor to detect occupancy. Sensor networks were installed in a sample of private offices and cubicle workstations. Large individual differences in sensor response to occupancy were observed in all monitored areas: the percent difference in occupied time measured by sensors in ten private offices ranged from 16.1 percent to 74.0 percent (mean 44.7 percent): the differences between detectors measuring occupancy in 23 cubicle workstations ranged from 14 percent to 91 percent (mean 53.7 percent). These data establish the considerable uncertainty in occupancy measurement that exists with current systems. Current control systems compensate for this uncertainty by applying a long time delay before switching lights off. Since a sensor network more accurately measures occupancy, shorter time delays can be applied to reduce system operating time and save energy. Applying a 5-minute time delay to sensor network data, instead of the 20 minutes that is more typical in current single sensor applications, reduced operating time by an additional 22.4 percent to 33.3 percent (relative to the reductions that would have resulted from use of a single sensor). A longer time delay of 10 minutes applied to the sensor network data stream produced reductions of 8.4 percent to 24.7 percent, relative to the reduction that would have resulted from use of a single sensor with a 20-minute time delay.