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Construction Of A Psychophysical Scale Of Visual Comfort of Lighting Based On A Neural Network: preparation Of The Experiment L&E, Vol. 29, No. 3, 2021

Light & Engineering 29 (3)

Volume 29
Date of publication 06/24/2021
Pages 114–122

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Construction Of A Psychophysical Scale Of Visual Comfort of Lighting Based On A Neural Network: preparation Of The Experiment L&E, Vol. 29, No. 3, 2021
Articles authors:
Vladimir P. Budak, Ekaterina I. Ilyina

Vladimir P. Budak, Professor, Doctor of Technical Sciences. In 1981, he graduated from the Moscow Power Engineering Institute (MPEI). At present, he is the Editor-in-Chief of the Svetotekhnika / Light & Engineering journals, Professor of the Subdepartment of Light and Engineering in NRU “MPEI”. Full member of the Academy of Electrotechnical Sciences of Russia

Ekaterina I. Ilyina, M. Sc. She graduated from NRU MPEI in 2004 and works as a development engineer at AMT-Engineering LLC. She is also postgraduate student of the Light and Engineering sub-department at NRU MPEI. Her research interests are lighting quality, psychophysical visual scale perceptual, unified glare rating (UGR), neural networks, near field lighting fixtures

One of the important questions in lighting engineering is to determine the sensation of discomfort from lighting installations. There is no unified psychophysical scale for assessing the visual comfort of lighting (VCL) at any arbitrary distribution of luminance in space. This article considers a mathematical model of the scale based on a neural network (NN) as an ‘expert’ that trained to determine the comfort of perception of lighting depending on the light source’s luminance and background. The experimental data obtained at the lighting engineering department of the National Research University “MPEI” were used to train the NN. The experiment results presented in this article are consistent with the numerical scale for estimating the VCL proposed by Lakiesch and Holladay. A new model allows predicting the sensation of VCL with an accuracy of up to 70 %. This work allows formulating criteria for NN’s input and output parameters to choose a metric for evaluating NN’s performance, such as the confusion matrix, ROC curves, and a metric, such as the probability distribution for each sensation depending on the input parameters. It clearly follows, that amount of initial data is not allowed to make a final conclusion. One more experiment required considering the algorithm used to calibrate the experimental installation, instructions for observers, and the obtained results processing.
1. Gusev A.N., Izmailov Ch. A., Mikhalevskaya M.B. Measurement in psychology: general psychological practice. 2nd ed., Moscow // Smysl. 1998, p. 286.
2. Corte-Valiente A. D., Castillo-Sequera J. L., Castillo-Martinez A., Gómez-Pulido J. M., Gutierrez-Martinez J. An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems // Energies. 2017, Vol. 10, # 2, p. 175.
3. Gao Y., Lin Y., Sun Y. A wireless sensor network based on the novel concept of an I-matrix to achieve highprecision lighting control // Building and Environment. 2013, Vol. 70, pp. 223–231.
4. Luckiesh M., Holladay L.L. Glare and Visibility // Transactions of the IES. 1925, Vol. 20, pp. 221–252.
5. Luckiesh M., Guth S.K. Luminance in the visual field at borderline between comfort and discomfort // Illuminating Engineering. 1949, Vol. 44, #11, pp. 650–670.
6. Veitch J. A., Newsham G.R. Deterinants of lighting quality II: research and recommendations // American Psychological Association 104th Annual Convention, Toronto, Ontario. 1996, Vol. 08–09, pp. 1–55.
7. Budak V., Zheltov V., Meshkova T., Notfullin R. Evaluation of illumination quality based on spatialangular luminance distribution // Light & Engineering. 2017, Vol. 25, pp. 24–31.
8. Hopkinson R.G. Evaluation of Glare // Illuminating Engineering. 1957, Vol. 52, #6, pp. 305–321.
9. Kotik G. G., Matveev A.B., Pereima V.V., Tokhadze I., L. Categorical quality assessments and their interrelation on the psychophysical scale. 1975, #3, pp. 3–5
10. Budak V. P., Zheltov V.S., Meshkova T.V., Chembaev V.D. A new criterion for the quality of lighting and its approbation in laboratory conditions // Vestnik MEI. 2020, #1, pp. 73–81.
11. Haykin S. Neural networks. A comprehensive foundation, 2 ed. // N.Y., Boston, San Francisco: Prentice-Hall. 1999, pp. 32–33.
12. Beale M. H., Hagan M.T. Demuth H.B., Deep Learning ToolboxTM Getting Started Guide R2019b // Natick, MA: The MathWorks, Inc.. 2019, p. 162.
13. Lourakis M. I. A. A brief description of the Levenberg-Marquardt algorithm implemented by levmar // Technical Report, Institute of Computer Science Foundation for Research and Technology, Hellas, 2005.
14. Cortes C., Jackel L.D., Chiang W.-P. Limits on learning machine accuracy imposed by data quality // Advances in Neural Information Processing Systems 7, 1994, MIT Press, 1995, pp. 239–246.


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