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Journal articles on the topic 'Light modeling'

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Author: Grafiati

Published: 11 March 2023

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1

Ivashko, P. V. "Modeling of light scattering in biotissue." Semiconductor Physics Quantum Electronics and Optoelectronics 17, no. 2 (June 30, 2014): 149–54. http://dx.doi.org/10.15407/spqeo17.02.149.

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2

Garstang, R. H. "Light Pollution Modeling." International Astronomical Union Colloquium 112 (1991): 56–69. http://dx.doi.org/10.1017/s0252921100003705.

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The growth of urban development and its accompanying outdoor lighting has made the search for new observatory sites increasingly difficult. A method of predicting the brightness of the night sky produced by a city of known population and distance is useful in making studies of prospective new observatory sites, as well as in studying the likely future deterioration of existing sites. Other sources of light pollution can be investigated using the same model. In most cases, several cities are responsible for the light pollution at a given site, and the predicted night sky brightness is the sum of the contributions of all the cities. In this paper, we shall review the surprisingly little work which has been done on predicting night sky brightnesses from model calculations.

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3

Jinguo Quan, Jinguo Quan, Bo Bai Bo Bai, Shuang Jin Shuang Jin, and Yan Zhang Yan Zhang. "Indoor positioning modeling by visible light communication and imaging." Chinese Optics Letters 12, no. 5 (2014): 052201–52204. http://dx.doi.org/10.3788/col201412.052201.

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4

Houle, C., and E. Fiume. "Light-Source Modeling Using Pyramidal Light Maps." CVGIP: Graphical Models and Image Processing 55, no. 5 (September 1993): 346–58. http://dx.doi.org/10.1006/cgip.1993.1026.

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5

Bergé, Luc, and Stefan Skupin. "Modeling ultrashort filaments of light." Discrete & Continuous Dynamical Systems - A 23, no. 4 (2009): 1099–139. http://dx.doi.org/10.3934/dcds.2009.23.1099.

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6

Wyrembeck, Edward P. "Modeling the Behavior of Light with a Light Cone." Physics Teacher 44, no. 8 (November 2006): 549. http://dx.doi.org/10.1119/1.2362953.

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7

Ting, D. Z. Y., and T. C. Mcgill. "Modeling Light-Extraction Characteristics of Packaged Light-Emitting Diodes." VLSI Design 6, no. 1-4 (January 1, 1998): 363–66. http://dx.doi.org/10.1155/1998/12165.

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We employ a Monte Carlo ray-tracing technique to model light-extraction characteristics of light-emitting diodes. By relaxing restrictive assumptions on photon traversal history, our method improves upon available analytical models for estimating light-extraction efficiencies from bare LED chips, and enhances modeling capabilities by realistically treating the various processes which photons can encounter in a packaged LED. Our method is not only capable of calculating extraction efficiencies, but can also provide extensive statistical information on photon extraction processes, and predict LED spatial emission characteristics.

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8

KUMAR, Sunil, Kunal MITRA, Ali VEDAVARZ, and Yukio YAMADA. "Modeling of Ultrashort Light Pulse Propagation in Light Scattering Media." TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C 63, no. 607 (1997): 895–900. http://dx.doi.org/10.1299/kikaic.63.895.

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9

Barylo, G. I., R. L. Holiyka, I. I. Helzhynskyi, Z. Yu Hotra, M. S. Ivakh, and R. L. Politanskyi. "Modeling of organic light emitting structures." Physics and Chemistry of Solid State 21, no. 3 (September 30, 2020): 519–24. http://dx.doi.org/10.15330/pcss.21.3.519-524.

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The paper has been shown the results of the study of the parameters of organic light-emitting structures based on the SPICE (Simulation Program with Integrated Circuit Emphasis) model studies. A SPICE model of diode structure has been developed, which is implemented in the form of a substitution scheme based on the basic components of the simulator. This model can be extended by introducing additional components of the substitution scheme, which provides higher accuracy in representing the structure specifics. Graphical results of researches of the model of OLED structure at the change of internal parameters have been presented. The obtained data well represent the parameters of real structures and are characterized by a fairly effective adaptation to the experimental data of specific samples.

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10

Yi Xu and Daniel G. Aliaga. "Modeling Repetitive Motions Using Structured Light." IEEE Transactions on Visualization and Computer Graphics 16, no. 4 (July 2010): 676–89. http://dx.doi.org/10.1109/tvcg.2009.207.

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11

Banninger, D., and H. Fluhler. "Modeling light scattering at soil surfaces." IEEE Transactions on Geoscience and Remote Sensing 42, no. 7 (July 2004): 1462–71. http://dx.doi.org/10.1109/tgrs.2004.828190.

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12

Fu, Ling, Ralf Leutz, and Harald Ries. "Physical modeling of filament light sources." Journal of Applied Physics 100, no. 10 (November 15, 2006): 103528. http://dx.doi.org/10.1063/1.2364669.

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13

Shams-Nateri, A. "Modeling Light Reflection from Polyacrylonitrile Nanofiber." Journal of Computational and Theoretical Nanoscience 7, no. 2 (February 1, 2010): 418–22. http://dx.doi.org/10.1166/jctn.2010.1376.

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14

Eker, Zeki. "Modeling light curves of spotted stars." Astrophysical Journal 420 (January 1994): 373. http://dx.doi.org/10.1086/173567.

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15

Johnsen, G. "Notes on Modeling Light Water Reactors." Science 316, no. 5824 (April 27, 2007): 542b. http://dx.doi.org/10.1126/science.316.5824.542b.

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16

Schmidt, Richard J., and Russell C. Moody. "Modeling Laterally Loaded Light‐Frame Buildings." Journal of Structural Engineering 115, no. 1 (January 1989): 201–17. http://dx.doi.org/10.1061/(asce)0733-9445(1989)115:1(201).

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17

Abouelsaood, Ahmed A. "Modeling light scattering from mesoporous silicon." Journal of Applied Physics 91, no. 5 (March 2002): 2753–59. http://dx.doi.org/10.1063/1.1435827.

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18

Ju, Seunghwan, Heesuk Seo, and Sunghyu Han. "Light 3D Modeling with mobile equipment." Journal of the Korea Society of Digital Industry and Information Management 12, no. 4 (December 30, 2016): 107–14. http://dx.doi.org/10.17662/ksdim.2016.12.4.107.

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19

Colalongo, L., G. Verzellesi, D. Passeri, A. Lui, P. Ciampolini, and M. V. Rudan. "Modeling of light-addressable potentiometric sensors." IEEE Transactions on Electron Devices 44, no. 11 (1997): 2083–90. http://dx.doi.org/10.1109/16.641388.

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20

Moriya, Takashi, Sergei I. Blinnikov, Nozomu Tominaga, Naoki Yoshida, Masaomi Tanaka, Keiichi Maeda, and Ken'ichi Nomoto. "Light Curve Modeling of Superluminous Supernovae." Proceedings of the International Astronomical Union 9, S296 (January 2013): 86–89. http://dx.doi.org/10.1017/s1743921313009277.

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AbstractOrigins of superluminous supernovae (SLSNe) discovered by recent SN surveys are still not known well. One idea to explain the huge luminosity is the collision of dense CSM and SN ejecta. If SN ejecta is surrounded by dense CSM, the kinetic energy of SN ejecta is efficiently converted to radiation energy, making them very bright. To see how well this idea works quantitatively, we performed numerical simulations of collisions of SN ejecta and dense CSM by using one-dimensional radiation hydrodynamics code STELLA and obtained light curves (LCs) resulting from the collision. First, we show the results of our LC modeling of SLSN 2006gy. We find that physical parameters of dense CSM estimated by using the idea of shock breakout in dense CSM (e.g., Chevalier & Irwin 2011, Moriya & Tominaga 2012) can explain the LC properties of SN 2006gy well. The dense CSM's radius is about 1016 cm and its mass about 15 M⊙. It should be ejected within a few decades before the explosion of the progenitor. We also discuss how LCs change with different CSM and SN ejecta properties and origins of the diversity of H-rich SLSNe. This can potentially be a probe to see diversities in mass-loss properties of the progenitors. Finally, we also discuss a possible signature of SN ejecta-CSM interaction which can be found in H-poor SLSN.

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21

Vasundhara, R. "Modeling Uranian mutual event light curves." Planetary and Space Science 56, no. 14 (November 2008): 1791–96. http://dx.doi.org/10.1016/j.pss.2008.02.018.

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22

Kocifaj, Miroslav, and Martin Aubé. "Light pollution: Theory, modeling, and measurements." Journal of Quantitative Spectroscopy and Radiative Transfer 139 (May 2014): 1–2. http://dx.doi.org/10.1016/j.jqsrt.2014.01.009.

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23

Kuna, Lukasz, John Mangeri, Edward P. Gorzkowski, James A. Wollmershauser, and Serge Nakhmanson. "Mesoscale modeling of polycrystalline light transmission." Acta Materialia 175 (August 2019): 82–89. http://dx.doi.org/10.1016/j.actamat.2019.06.001.

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24

Gutierrez, Diego, Veronica Sundstedt, Fermin Gomez, and Alan Chalmers. "Modeling light scattering for virtual heritage." Journal on Computing and Cultural Heritage 1, no. 2 (October 2008): 1–15. http://dx.doi.org/10.1145/1434763.1434765.

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25

Цапар, Віталій Степанович, Олексій Анатолійович Жученко, and Антон Петрович Коротинський. "Simulation modeling of light flow concentrators." Proceedings of the NTUU “Igor Sikorsky KPI”. Series: Chemical engineering, ecology and resource saving, no. 3 (November 27, 2020): 22–28. http://dx.doi.org/10.20535/2617-9741.3.2020.217901.

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26

Zubko, Evgenij. "Modeling light scattering by forsterite particles." Optics Letters 40, no. 7 (March 17, 2015): 1204. http://dx.doi.org/10.1364/ol.40.001204.

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27

Lee, Raymond L., and Javier Hernández-Andrés. "Measuring and modeling twilight’s purple light." Applied Optics 42, no. 3 (January 20, 2003): 445. http://dx.doi.org/10.1364/ao.42.000445.

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28

Mobley, C. D., G. F. Cota, T. C. Grenfell, R. A. Maffione, W. S. Pegau, and D. K. Perovich. "Modeling light propagation in sea ice." IEEE Transactions on Geoscience and Remote Sensing 36, no. 5 (1998): 1743–49. http://dx.doi.org/10.1109/36.718642.

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29

Yehorchenkov, Volodymyr, Oleh Sergeychuk, and Lidiia Koval. "Principles of the exposure natural lighting modeling of premises." Theory and Building Practice 2020, no. 2 (November 20, 2020): 113–18. http://dx.doi.org/10.23939/jtbp2020.02.113.

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It’s well known that a criterion of estimating the varying natural lighting is exposure equal to the product of light intensity by its duration. Here we have made studies into the exposure in the room depending on the orientation of a light aperture and its location in space. The exposure has been considered by the example of three identical office rooms with the same light apertures oriented north, west and south and having three positions – vertical, inclined and horizontal. To calculate the annual exposure we made use of the well-known software package VELUX Daylight Visualizer 2. For convenience of analyzing the exposure there was introduced the concept of the natural exposure coefficient (NEC) which is a ratio between the exposure in the room and a simultaneous value of the outer exposure. Our studies have shown that exposure is an effective criterion to assess the indoor natural lighting in time. The existing system of estimating energy consumption in lighting buildings with the help of a simultaneous lighting is rough and does not take into account such factors as orientation of light apertures by the sides of the horizon and their location in space. The use of exposure let us improve the method of calculating energy consumption in lighting premises taking into account the light aperture location in space and their as orientation by the sides of the horizon. The numerical experiment performed has given a predicted result, namely, the most power-consuming room is the north-oriented one with the vertical light aperture and the least power-consuming room is the one with the horizontal light aperture. The room with the inclined light aperture has average energy consumption.

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30

Xiaofei Fan and Gang Yao. "Modeling Transient Pupillary Light Reflex Induced by a Short Light Flash." IEEE Transactions on Biomedical Engineering 58, no. 1 (January 2011): 36–42. http://dx.doi.org/10.1109/tbme.2010.2080678.

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31

Bhutiani, R., D. R. Khanna, and Kumar S. Chandra. "Light-limited population dynamics of phytoplankton: modeling light and depth effects." Environmentalist 29, no. 1 (August 25, 2008): 93–105. http://dx.doi.org/10.1007/s10669-008-9184-2.

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32

Johnson, R. Scott, and Alan N. Lakso. "Approaches to Modeling Light Interception in Orchards." HortScience 26, no. 8 (August 1991): 1002–4. http://dx.doi.org/10.21273/hortsci.26.8.1002.

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33

Bolibok, Leszek, Michał Brach, Stanisław Drozdowski, and Michał Orzechowski. "Modeling light conditions on the forest floor." Forest Research Papers 74, no. 4 (December 1, 2013): 335–44. http://dx.doi.org/10.2478/frp-2013-0032.

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Abstract Contemporary models of light conditions on the forest floor can be divided into two categories: undercanopy models that allow the light conditions in a stand under the canopy to be simulated, and models that take into account shielding from the side. Under-canopy models precisely estimate the availability of wavelengths of light spatially distributed under the canopy of stands: however, these models require a large amount of data on the spatial structure of forest stands. The other class of models describe the light conditions on a particular open surface. These incorporate shielding from the side and are easier to use as they require less data than under-canopy models. In practice, in forest conditions, such models require data on the size, shape and geographical location of surveyed surfaces (e.g. gaps and cut areas) and on the height of the surrounding stand. Often, these data are available in databases, such as the State Forest Information System (SKP), orcan otherwise be obtained relatively easily (and inexpensively). Compared to under-canopy models, these models provide a cheap way to obtain useful information on variation in the light environment that affects the microclimate for regenerating plants on clearcuts and canopy gaps.

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34

Correa, E. C., V. Diaz-Barcos, J. Fuentes-Pila, P. Barreiro, and M. C. González. "MODELING OVOPRODUCT SPOILAGE WITH RED LED LIGHT." Acta Horticulturae, no. 802 (December 2008): 265–72. http://dx.doi.org/10.17660/actahortic.2008.802.34.

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35

Eaton, Michael, Kale Harbick, Timothy Shelford, and Neil Mattson. "Modeling Natural Light Availability in Skyscraper Farms." Agronomy 11, no. 9 (August 24, 2021): 1684. http://dx.doi.org/10.3390/agronomy11091684.

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Lighting is a major component of energy consumption in controlled environment agriculture (CEA) operations. Skyscraper farms (multilevel production in buildings with transparent glazing) have been proposed as alternatives to greenhouse or plant factories (opaque warehouses) to increase space-use efficiency while accessing some natural light. However, there are no previous models on natural light availability and distribution in skyscraper farms. This study employed climate-based daylight modeling software and the Typical Meteorological Year (TMY) dataset to investigate the effects of building geometry and context shading on the availability and spatial distribution of natural light in skyscraper farms in Los Angeles (LA) and New York City (NYC). Electric energy consumption for supplemental lighting in 20-storey skyscraper farms to reach a daily light integral target was calculated using simulation results. Natural lighting in our baseline skyscraper farms without surrounding buildings provides 13% and 15% of the light required to meet a target of 17 mol·m−2·day−1. More elongated buildings may meet up to 27% of the lighting requirements with natural light. However, shading from surrounding buildings can reduce available natural light considerably; in the worst case, natural light only supplies 5% of the lighting requirements. Overall, skyscraper farms require between 4 to 11 times more input for lighting than greenhouses per crop canopy area in the same location. We conclude that the accessibility of natural light in skyscraper farms in dense urban settings provides little advantage over plant factories.

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36

Górecki, Krzysztof, Jacek Dąbrowski, and Ewa Krac. "Modeling Solar Cells Operating at Waste Light." Energies 14, no. 10 (May 16, 2021): 2871. http://dx.doi.org/10.3390/en14102871.

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The article concerns the investigations of solar cells irradiated by waste light. The measurement method and instruments used are presented. Using this method, the spectra of the light emitted by different light sources are presented and the results of measurements of sensitivity characteristics of the selected solar cell are shown. On the basis of the obtained results of the measurements, a new model of a solar cell dedicated for SPICE is formulated. In this model, an influence of spectrum characteristics of the modeled solar cell on its photocurrent is taken into account. The correctness of this model is verified experimentally for all the considered lighting sources. It is proved that photocurrent is the highest for irradiation using a classical bulb, whereas it is the lowest for a fluorescent lamp.

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37

Boccard, Mathieu, Corsin Battaglia, Franz-Josef Haug, Matthieu Despeisse, and Christophe Ballif. "Light trapping in solar cells: Analytical modeling." Applied Physics Letters 101, no. 15 (October 8, 2012): 151105. http://dx.doi.org/10.1063/1.4758295.

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38

He, Ming, Frank Lam, and Ricardo O. Foschi. "Modeling Three-Dimensional Timber Light-Frame Buildings." Journal of Structural Engineering 127, no. 8 (August 2001): 901–13. http://dx.doi.org/10.1061/(asce)0733-9445(2001)127:8(901).

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39

An, Feng, Matthew Barth, George Scora, and Marc Ross. "Modeling Enleanment Emissions for Light-Duty Vehicles." Transportation Research Record: Journal of the Transportation Research Board 1641, no. 1 (January 1998): 48–57. http://dx.doi.org/10.3141/1641-06.

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A comprehensive modal emissions model for light-duty cars and trucks is being developed under the sponsorship of NCHRP Project 25-11. Model development has been described previously for vehicles operating under stoichiometric and enrichment conditions. A modal emissions model is presented for vehicles operated under enleanment conditions. Enleanment typically occurs with sharp deceleration or load reduction events, and sometimes during long deceleration. Under enleanment conditions, the air/fuel ratio is lean and incomplete combustion or misfire occurs. Preliminary research indicates that enleanment emissions (particularly for hydrocarbons) contribute significantly to a vehicle’s overall emissions. An enleanment emissions module has been developed on the basis of second-by-second emission measurements generated at the College of Engineering—Center for Environmental Research and Technology’s vehicle testing facility using the Federal Test Procedure, US06, and a specially designed modal emission cycle (MEC01). On the basis of more than 200 vehicles tested and modeled, lean-burn hydrocarbon emissions (HClean) account for 10 to 20 percent of the overall HC emissions under the various test cycles. HClean emission contributions vary greatly from vehicle to vehicle, ranging from near 0 to more than 30 percent of total HC emissions of individual vehicles. After detailed analysis of the second-by-second emission data over the modal emission cycle MECO1, it was found that enleanment hydrocarbons emissions are mostly associated with rapid load reduction events and long deceleration events. The former is most likely to cause extremely high levels of HC as short spikes, and the latter is mostly associated with longer-lasting HC puffs. A methodology has been developed to characterize and model enleanment hydrocarbons emissions associated with these two events. The model estimates are compared with measurements, with encouraging results.

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40

Troy, Jeff R., Nick D. Holmes, and M. Clay Green. "Modeling artificial light viewed by fledgling seabirds." Ecosphere 2, no. 10 (October 2011): art109. http://dx.doi.org/10.1890/es11-00094.1.

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41

SAITO, Kazuya, Taketoshi NOJIMA, and You GOTOU. "618 Modeling of Light-Weight Core Structures." Proceedings of the Dynamics & Design Conference 2006 (2006): _618–1_—_618–5_. http://dx.doi.org/10.1299/jsmedmc.2006._618-1_.

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42

Klenin, Konstantin, Markus Hammermann, and Jörg Langowski. "Modeling Dynamic Light Scattering of Supercoiled DNA." Macromolecules 33, no. 4 (February 2000): 1459–66. http://dx.doi.org/10.1021/ma9914467.

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43

Eker, Zeki. "Modeling Light Curves of Spotted Stars: Erratum." Astrophysical Journal 430 (July 1994): 438. http://dx.doi.org/10.1086/174419.

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44

Lee, H. C., E. J. Breneman, and C. P. Schulte. "Modeling light reflection for computer color vision." IEEE Transactions on Pattern Analysis and Machine Intelligence 12, no. 4 (April 1990): 402–9. http://dx.doi.org/10.1109/34.50626.

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45

Storrs, Mark, David J. Mehrl, John F. Walkup, and Thomas F. Krile. "Volterra series modeling of spatial light modulators." Applied Optics 37, no. 32 (November 10, 1998): 7472. http://dx.doi.org/10.1364/ao.37.007472.

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46

Li, Z. Q., and Z. M. Simon Li. "Comprehensive Modeling of Superluminescent Light-Emitting Diodes." IEEE Journal of Quantum Electronics 46, no. 4 (April 2010): 454–61. http://dx.doi.org/10.1109/jqe.2009.2032426.

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47

YURTSEVER, ULVI. "MODELING LOSSY PROPAGATION OF NON-CLASSICAL LIGHT." International Journal of Quantum Information 09, no. 02 (March 2011): 739–50. http://dx.doi.org/10.1142/s0219749911006806.

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The lossy propagation law (generalization of Lambert-Beer's law for classical radiation loss) for non-classical, dual-mode entangled states is derived from first principles, using an infinite-series of beam splitters to model continuous photon loss. This model is general enough to accommodate stray-photon noise along the propagation, as well as amplitude attenuation. An explicit analytical expression for the density matrix as a function of propagation distance is obtained for completely general input states with bounded photon number in each mode. The result is analyzed numerically for various examples of input states. For N00N state input, the loss of coherence and entanglement is super exponential, as predicted by a number of previous studies. However, for generic input states, where the coefficients are generated randomly, the decay of coherence is very different; in fact no worse than the classical Beer-Lambert law. More surprisingly, there is a plateau at a mid-range interval in propagation distance where the loss is in fact sub-classical, following which it resumes the classical rate. The qualitative behavior of the decay of entanglement for two-mode propagation is also analyzed numerically for ensembles of random states using the behavior of negativity as a function of propagation distance.

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48

Kruger, A. "Modeling light‐emitting diode dynamic pressure transducers." Review of Scientific Instruments 57, no. 5 (May 1986): 914–20. http://dx.doi.org/10.1063/1.1138834.

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49

Ferry, Vivian E., Albert Polman, and Harry A. Atwater. "Modeling Light Trapping in Nanostructured Solar Cells." ACS Nano 5, no. 12 (November 18, 2011): 10055–64. http://dx.doi.org/10.1021/nn203906t.

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50

Shimada, Atsushi, Hajime Nagahara, and Rin-ichiro Taniguchi. "Background light ray modeling for change detection." Journal of Visual Communication and Image Representation 38 (July 2016): 55–64. http://dx.doi.org/10.1016/j.jvcir.2016.02.013.

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