1. Yurtseven M.B., Mete S. ve Onaygil S. The effects of temperature and driving current on the key parameters of commercially available, high-power, white LEDs. Lighting Research and Technology, 2016. V48, #8, pp. 943–965.
2. Jayawardena A., Liu Y., Narendran N., Analysis of three different junction temperature estimation methods for AC LEDs, Solid State Electronics, 2013. V86, pp. 11–16.
3. Yurtseven M.B., LED Işık Kaynaklı Armatür Isıl Modellenmesi ve Isıl Tasarımı Etkileyen Faktörlerin İstatistiksel Analizi, İstanbul Teknik Üniversitesi, Enerji Enstitüsü, Doktora Tezi, Haziran 2017.
4. Yurtseven M.B., Onaygil S., Ogus G. Thermal simulation and validation of LED based luminaires using two-resistor compact thermal model, Lighting Research and Technology, 2014. V46, #5, pp. 576–586.
5. Cengiz M. S., Thermal Design Calculations for LED Luminaires, International Journal of Engineering Research and Development, 2018. V10, #2, pp. 69–75.
6. ASSIST Assist Program. 2020, http://www.lrc.rpi.edu/programs/solidState/ ASSIST/index.asp
7. Çıbuk M., Cengiz M.S., Determination of Energy Consumption According to Wireless Network Topologies in Grid-Free Lighting Systems. Light & Engineering, 2020. V28, #2, pp. 67–76.
8. Çıbuk M., Reducing Energy Consumption in Single-Hop and Multi-Hop Topologies of Road Lighting Communication Network, Light & Engineering, 2020. V28, #4.
9. Efe S. B., UPFC Based Real-Time Optimization of Power Systems for Dynamic Voltage Regulation. Computer Modeling in Engineering & Sciences, 2018. V116, #3, pp. 391–406.
10. Efe S. B., Cebeci M., Power Flow Analysis by Artificial Neural Network. International Journal of Energy and Power Engineering, 2013. V2, #6, pp. 204–208.
11. Cengiz M. S., Simulation and Design Study for Interior Zone Luminance In Tunnel Lighting. Light & Engineering, 2019. V27, #2, pp. 42–51.
12. Cengiz M. S., Effects of Luminaire Angle and Illumination Topology On Illumination Parameters In Road Lighting. Light & Engineering, 2020. V28, #4.
13. Cengiz M. S., Cengiz Ç., Numerical Analysis of Tunnel LED Lighting Maintenance Factor. IIUM Engineering Journal, 2018. V19, #2, pp. 154–163.
14. Cengiz M. S., The Relationship between Maintenance Factor and Lighting Level in Tunnel Lighting. Light & Engineering, 2019. V27, #3, pp. 75–88.
15. Poppe A. Lasance C.J. On the standardization of thermal characterization of LEDs. Semiconductor Thermal Measurement and Management Symposium, 200925th Annual IEEE, 15–19 Mart 2009, San Jose, CA, ABD.
16. Petroski, J. Thermal Challenges in LED Cooling. 2020, https://www.electronics-cooling.com/2006/11/thermal-challenges-inled-cooling/
17. ANSYS: Engineering Simulation & 3D Design Software, https://www.ansys.com/, 2020.
18. Oms Lighting (2014). Cold lumens versus hot lumens. http://www.omslighting.com/ledacademy/579/4-leds-controls/41-coldlumens-versus-hot-lumens
19. Ateş S., Yurtseven M.B., Onaygil S. Design of A Chip on Board (COB) LED Based Industrial Luminaire With Thermal Simulations. Light & Engineering, 2019. V27, #2, pp. 78–87.

The paper discusses the possibility of remote detection of a continuous laser beam propagating in a scattering continental and coastal atmosphere, when it is recorded outside the axial zone. In the single scattering approximation, estimates of the radianceat the registration site are carried out, which are compared with the threshold characteristics of existing photodetectors in the visible and IR spectral regions. It is shown that the laser radiation (LR) of the beam is reliably recorded in the range of angles (0–180)° at metrological range of visibility equal (5–20) km at night conditions. At twilight, under the same conditions, detection capabilities are significantly reduced.
A significant increase of the LR beam radiance contrast with a decrease in its divergence has been shown experimentally in the field observations.
At twilight, a decrease in the beam’s radiance contrast is seen. A beam with a divergence equal to 2 ceases to be distinguishable at angles equal to (80–90)°, and a beam with a divergence of 4 – at angles (60–70)°.In this case, the contrast difference reaches up to 10 times.

1. Roy N., Reid F. Off–axis laser detection model in coastal areas, Optical Engineering, 2008. V47, pp. 1–11.
2. Cariou J.P. Off-axis detection of pulsed laser beams: simulation and measurements in the lower atmosphere, Proceedings of SPIE, 2003. V5086, pp. 129–138.
3. Michulec J. K., Schleijpen R. Influence of aerosols on off-axis laser detection capabilities, Proceedings of SPIE, 2009. V7463, pp. 1–12.
4. DeGrassie John S. Modeling off-axis laser scattering: effects from aerosol distributions, Proceedings of SPIE, 2012. V8517 (85170V).
5. Mendoza–Yero, O. Effects of off–axis laser beam propagation on beam parameters, Proceedings of SPIE, 2014. V5622.
6. Kaloshin G.A., Piazzola J. Influence of the large aerosol particles on the infrared propagation in coastal areas, Proceedings of 23rd International Laser Radar Conference, 2006. pp. 429–432.
7. Kaloshin G.A., Piazzola J., Shishkin S. Numerical modeling of influence of meteorological parameters on aerosol extinction in the marine atmospheric surface layer, Proceedings of 16th International Conference on Nucleation and Atmospheric Aerosols (ICNAA), 2004. p. 352–354.
8. Kaloshin G.A. Modeling the Aerosol Extinction in Marine and Coastal Areas, IEEE Geoscience and Remote Sensing Letters, 2020, april. URL: https://ieeexplore.ieee.org/document/9052468 (date of reference: 20.06.2020); doi: 10.1109/LGRS.2020.2980866.
9. Kaloshin G.A. Method of Building of a Visual Landing-and-Takeoff System by Means of Vortex Laser Beams, Patent of Russia No. 2695044, 2018. V32.
10. Gathman S.G. Optical properties of the marine aerosol as predicted by the Navy aerosol model, Optical Engineering, 1983. V22, #1, pp. 57–62.
11. Gathman S.G. J. van Eijk A.M. and Cohen L.H. Characterizing large aerosols in the lowest levels of the marine atmosphere, Proceedings of SPIE, 1998. V3433, pp. 41–52.
12. Shettle E.P. Models of aerosols clouds and precipitation for atmospheric propagation studies, Proceedings of AGARD Conference: Atmospheric Propagation in the UV Visible IR and MM–Wave region and Related Systems Aspects, 1989. V454, pp. 15–1 – 15–13.
13. Weichel H. (ed.) Laser Beam Propagation in the Atmosphere. – SPIE Bellingham WA. – 28.09.1990.
14. Zuev V.E. Propagation of Visible and Infra-Red Waves in Atmosphere [Rasprostranenie vidimykh i infrakrasnykh voln v atmosfere]. – Moscow: Sovetskoye radio, 1970. 496 p.
15. Zuev V.E. Propagation of Laser Radiation in Atmosphere [Rasprostranenie lazernogo izlucheniya v atmosfere]. Moscow: Radio i svyaz, 1981. 288 p.
16. Zuev V.E., Krekov G.M. Optical Models of Atmosphere [Opticheskiye modeli atmosfery]. – Leningrad: Gidrometeoizdat, 1986. 256 p.
17. Zuev V.E., Kabanov M.V., Saveliev B.A. Propagation of Laser Beams in a Scattering Environment [Rasprostranenie lazernykh puchkov v rasseivayushchey srede], Applied Optics, 1969. V8, #1, pp. 137–141.
18. Deymerdjan D. Scattering of Electromagnetic Radiation by Spherical Polydisperse Particles [Rasseyanie elektromagnitnogo izlucheniya sfericheskimi polidispersnymi chastitsami]. – Moscow: Mir, 1971. 290 p.
19. Jensen D.R., Gathman S.G., Zeisse C.R., Littfin K.M. EOPACE overview and initial accomplishment, Journal of Aerosol Science, 1999. V30, #1, pp. 53–54.
20. Jensen D.R., Gathman S.G., Zeisse C.R., Leeuw G., de Smith M.H., Frederickson P.A., Davidson K.L. Electrooptical Propagation Assessment in Coastal Environments (EOPACE): summary and accomplishments, Optical Engineering, 2001. V40, #8, pp. 1486–1498.
21. Kaloshin G.A., Gordienko A.I. Laser aids to navigation (methods), IALA Bullletin. 2003. V3, pp. 46–51.
22. Kaloshin G.A. Gordienko A.I. Laser aids to navigation (technologies), IALA Bullletin, 2004. #1, pp. 42–49.
23. Gordienko A.I. Kaloshin G.A. Laser leading beacons: summaries and perspectives, Proceedings of XV Conference IALA “Navigation and the Environment”, 2002. pp. 150–158.
24. Jensen D.R. Gathman S.G. Zeisse C.R. and Littfin K.M. EOPACE (Electrooptical Propagation Assessment in Coastal Environments) Overview and Initial Accomplishments /Proceedings of Millennium Conference on Antennas and Propagation (AP2000). – Davos Switzerland, 2000.
25. Nilsson B.A. Meteorological influence on aerosol extinction in the 0.2–40  wavelength range, Applied Optics, 1979. V18, pp. 3457–3472.
26. Nilsson B.A. Model of the relation of IR aerosol extinction to weather parameters, Proceedings of SPIE: Infrared Technology XVIII, 1992. V1762, pp. 238–250.
27. Lewis E.R., Schwartz S.E. Sea salt aerosol production: Mechanisms methods measurements and models – a critical review. Geophys. Monograph. Washington DC: AGU, 2004. 413 p.
28. Kaloshin G.A., Grishin I.A. An aerosol model of the marine and coastal atmospheric surface layer, Atmosphere, Ocean, 2011. V49, #2, pp. 112–120.
29. Kaloshin G.A. Development of the Aerosol Model of the Near-Earth Layer of Marine and Coastal Atmosphere [Razvitiye aerozolnoy modeli prizemnogo sloya morskoy i pribrezhnoy atmosfery]. Optika atmosfery i okeana, 2018. V31, #11, pp. 881–887.
30. Kaloshin G.A., Shishkin S.A. The Range Software Package for Calculations of Optical Radiation Propagation with Consideration of Aerosol Attenuation in the Near-Surface Layer of the Continental Marine and Coastal Atmosphere [Programmno-tekhnologicheskiy paket Range dlya provedeniya raschyotov rasprostraneniya opticheskogo izlucheniya s uchyotom aerozolnogo oslableniya v prizemnom sloye kontinentalnoy morskoy i probrezhnoy atmosfery], Certificate of State Registration of Computer Software No. 2012616944 dated on 08/03/2012.
31. Kaloshin G.A., Shishkin S.A. MaexPro Software Package for Calculation of Spectral Aerosol Attenuation Coefficients in the Near-Surface Layer of Marine and Coastal Atmosphere [Programma dlya raschyota spektralnykh koeffitsientov aerozolnogo oslableniya v prizemnom sloye morskoy i pribrezhnoy atmosfery MaexPro] / Certificate of State Registration of Computer Software No. 2012616945 dated 08/03/2012.
32. Kaloshin G.A., Shishkin S.A. MieCalc Software Package for Calculation of Complex Refractive Indices of a Material of Marine and Coastal Aerosol Particles [Programma dlya raschyota kompleksnykh pokazateley prelomleniya veshchestva chatits morskogo i pribrezhnogo aerozolya MieCalc]. Certificate of State Registration of Computer Software No. 2012616943 dated 08/03/2012.
33. Kaloshin G.A., Shishkin S.A. Zhukov V.V. Microphysical and Optical Characteristics of Marine and Coastal Aerosol [Mikrofizicheckiye i opticheskiye kharakteristiki morskogo i pribrezhnogo aerozolya]. Certificate of State Registration of a Database No. 2015621775 dated 12/14/2015.
34. Kaloshin G.A., Shishkin S.A. Zhukov V.V. Characteristics of scattered radiation in off–axis recording of laser radiation under field conditions, Proceedings of SPIE25th Intern. Symp. on Atmospheric and Ocean Optics: Atmospheric Physics, 2019. V11208, P. 112081C.
35. Instrument Systems. URL: http://www.instrument systems.com/ (date of reference: 20.06.2020).
36. Kaloshin G.A., Shishkin S.A., Zhukov V.V. Software Package for Control and Processing of the Data of Spectroradiometry Measurements of Scattered Radiation of Laser Beams in Atmosphere [Programma dlya upravleniya i obrabotki dannykh spektroradiometricheskikh izmereniy resseyannogo izlucheniya lazernykh puchkov v atmosfere] / Certificate of State Registration of Computer Software No. 2015618954 dated 08/20/2015.
37. Kaloshin G.A., Shishkin S.A., Zhukov V.V. Software for Control of Measurements of Laser Beam Luminance Contrast in Scattering Environments [Programma upravleniya izmereniyami kontrasta yarkosti lazernykh puchkov v rasseivayushchikh sredakh] Certificate of State Registration of Computer Software No. 2015663204 dated 12/14/2015.
38. Meshkov V.V., Matveev A.B. Basics of Light Engineering: Study Guide for Higher Education Institutions: in 2 parts. P. 2. Physiological Optics and Colourimetry [Osnovy svetotekhniki: uchebnoye posobiye dlya vuzov: v 2 ch. Ch. 2. Fiziologicheskaya optika i kolorimetriya].2nd edition, revised and supplemented. Moscow: Energoatomizdat, 1989, 432 p.
39. Luizov A.V. Eye and Light [Glaz i svet]. – Leningrad: Energoatomizdat, Leningrad Branch, 1983. 144 p.

1. Special issue of the Svetotekhnika journal to the 40th anniversary of the victory: 1985, issue 5:
а) Lysov, F.M. Lighting of the Memorial Site to the Heroes of the Battle of Stalingrad [Osveshcheniye memorialnogo kompleksa – pamyatnika Geroyam Stalingradskoy Bitvy]. Svetotekhnika, 1985, Vol. 5, pp. 8–10.
b) Obolensky, N.V., Shchepetkov, N.I., Yaremchuk, Yu.R. Architectural Lighting of Ensembles of the Monuments to Heroes of the Great Patriotic War [Arkhitekturnoye osveshcheniye ansambley – monumentov geroyam Velikoy Otechestvennoy Voiny]. Svetotekhnika, 1985, Vol. 5, pp. 2–5.
c) Epstein, S.N. Lighting of the Soviet Army Glory Hill [Osveshcheniye Kurgana slavy Sovetskoy Armii]. Svetotekhnika, 1985, Vol. 5, pp. 7–8.
d) Topuz, V.G. On Architectural and Artistic Lightingof the Alley of Glory Memorial and the Monument to Unknown Sailor in Odessa [Ob arkhitekturno-khudozhestvennom osveshchenii memoriala Alleya Slavy – pamyatnika Neizvestnomu matrosu v Odesse]. Svetotekhnika, 1985, # 5, pp. 11–12.
e) Lesman, E.A. Lighting of the Monuments to Partizan Glory and Pilots Defending the Sky of Leningrad [Osveshcheniye pamyatnikov Partizanskoy slavy i lyotchikam-zashchitnikam Leningradskogo neba]. Svetotekhnika, 1985, # 5, pp. 12–14.
2. Alexandrova, E.M., Nechaev, V.V., Stepanov, V.N., Tsarkov, V.M. Modernisation of Lighting of the Mamayev Kurgan Memorial Site [Rekonstruktsiya osveshcheniya memorialnogo kompleksa Mamayev Kurgan]. Svetotekhnika, 2003, # 4, pp. 23–26.
3. Basalyga, N.N., Epstein, S.N. Lighting of the Victory Monument in Minsk and the Khatyn Memorial Site [Osveshcheniye monumenta Pobedy v Minske i memorialnogo kompleksa Khatyn]. Svetotekhnika, 1985, # 4, pp. 4–5.
4. Shchepetkov, N.I. Lighting Design of a City [Svetovoy dizain goroda]. Moscow: Arkhitektura-S, 2006, pp. 66, 73 and 277.
5. Lesman, E.A. Lighting of the Monument to Heroic Defenders of Leningrad [Osveshcheniye pamyatnika geroicheskim zashchitnikam Leningrada]. Svetotekhnika, 1985, #1, pp. 2–4.
6. Bartsev, A.A., Burtseva, N.B., Chernyak, A. Sh. Lighting of the Prokhorovka Field Victory Monument [Osveshcheniye Monumenta Pobedy na Prokhorovskom pole]. Svetotekhnika, 1996, #7, pp. 26–27.
7. Gorbachyova, Z.K., Dudkina, G.D. Lighting of the Fountains of the Ensamble of the Poklonnaya Hill Victory Monument [Osveshcheniye fontanov ansamblya pamyatnika Pobedy na Poklonnoy gore]. Svetotekhnika, 1996, # 9, pp. 2–3.
8. SP 52.13330.2016. Daylighting and artificial lighting. Moscow: 2016.

1. Nakamura, S., Iwasa, M.S. Method of manufacturing p-type compound semiconductors // Patent N5306662. Apr.1994. Japan.
2. Amano, H., Akasaki, I. et.al. Method for producing a luminous element of III-group nitride. Patent #5496766. Mar. 1996. Japan.
3. Mamakin, S.S., Yunovich, A.E., Vattana, A.B., Manyakhin, F.I. Electric Properties and Fluorescence Spectra of LED’s Based on InGaN/GaN Heterojunctions with Modulated and Doped Quantum Wells [Elektricheskiye svoystva i spektry luminestsentsii svetodiodov na osnove geteroperekhodov InGaN/GaN s modulirovanno-legirovannymi kvantovymi yamami] // FTP, 2003, Vol. 37, # 9, pp. 1131–1137.
4. Voytsekhovsky, A.V., Nesmelov, S.N., Kulchitsky, N.A., Melnikov, A.A. The Influence of Dislocations on Internal Quantum Efficiency of Light-Emitting Structures Based on InGaN/GaN Quantum Wells [Vliyaniye dislokatsiy na vnutrennyuyu kvantovuyu effektivnost svetoizluchayushchikh struktur na osnove kvantovykh yam In-GaN/GaN] // Nano i mikrosistemnaya tekhnika, 2011, Vol. 8I, pp. 27–35.
5. Shim, J.-I., Shin, D.-S. Measuring the internal quantum efficiency of light-emitting diodes towards accurate reliable room-temperature characterization // Nanophotonics, 2018, September, pp. 1–15.
6. Zang, M., Bhattacharya, P., Singh, J., Hinckley, J. Direct measurement of auger recombination in In0.1Ga0.9N/ GaN quantum well and its impact on the efficiency in In0.1Ga0.9N/GaN multiply uantum well light emitting diodes // Appl. Phys. Letter, 2009, Vol. 95, # 20, pp. 1108.
7. Dai, Q., Shan, Q., Wang, J., Chhajed, S., Cho, J.M., Shubert, E.F., Crawford, M.H., Koleske, D. D., Kim, M.-H., Park, Y. Carrier recombination mechanisms and efficiency droop in GaInN/GaN light-emitting diodes // Appl. Phys. Letter, 2010, Vol. 97, # 13, pp. 3507.
8. David, A., Grundmann, M.J. Droop in InGaN lightemitting diodes: A differential carrier lifetime analysis // Appl. Phys. Letter, 2010, Vol. 96, # 10, pp. 3504.
9. David, A., Hurni, C.A., Young, N.G., Craven, M.D. Electrical properties of III-nitride LEDs recombination-based injection model and theoretical limits to electrical efficiency and electroluminescent cooling // Appl. Phys. Letter, 2016, Vol. 109, # 8, pp. 3501.
10. Hopkins, M.A., Allsopp, D.W.E., Kappers, M.J., Oliver, R.A., Humpreys, C.J. The ABC model of recombination reinterpreted: Impact on understanding carrier transport and efficiency droop in InGaN/GaN light emitting diodes // J. Appl. Phys. 2017, Vol. 122, # 23, pp. 4505.
11. Bochkaryova, N.I., Rebane, Yu.T. Shreter, Yu.G. Growth of Shockley-Reed-Hall Recombination Rate in InGaN/GaN Quantum Wells as the Major Mechanism of Loss of LED Efficiency at High Injection Levels [Rost skorosti rekombinatsii Shokli-Rida-Holla v kvantovykh yamakh InGaN/GaN kak osnovnoy mekhanizm padeniya effektivnosti svetodiodov pri vysokikh urovnyakh inzhektsii] // FTP, 2015, Vol. 49, # 12, pp. 1714–1719.
12. Prudaev, I.A., Skakunov, M.S., Lelekov, M.A., Ryaboshtan, Yu.L., Gorlachuk, P.V., Marmalyuk, A.A. Recombinant Currents in LEDs Based on Multiple Quantum Wells (AlxGa1-x)0.5In0.5P/(AlyGa1-y)0.5In0.5P [Rekombinatsyonnyye toki v svetodiodakh na osnove mnozhestvennykh kvantovykh yam (AlxGa1-x)0.5In0.5P/(AlyGa1-y)0.5In0.5P] // Izvestiya vuzov. Fizika, 2013, Vol. 56, # 8, pp. 44–47.
13. Sah C.T., Noyce R.N., Shockley W. Carrier Generation and Recombination in P-N Junctions and P-N Junction Characteristics // Proc. IRE, 1957, Vol. 45, pp. 1228–1243.
14. Choo, S.C. Carrier generation-recombination in the space-charge region of an assymmetrical p-n junction // Solid State Electronics,1968, Vol. 11, pp. 1069–1077.
15. Fedor I. Manyakhin, Arthur B. Vattana, and Lyudmila O. Mokretsova Application of the Sah-Noyce-Shockley Recombination Mechanism to the Model of the Voltage-Current Relationship of LED Structures with Quantum Wells// Light & Engineering Journal, 2020, Vol. 28, #5, pp. 31–38.
16. Goryunov, N.N., Manyakhin, F.I., Klebanov, M.P., Lukashev, N.V. Impulse Three-Frequency Method of Measurement of Charged Centre Parameters in the Space Charge Region of Semiconductor Structures [Impulsnyy tryokhchastotnyy metod izmereniya parametrov zaryazhennykh tsentrov v oblasti prostranstvennogo zaryada poluprovodnikovykh struktur] // Pribory i sistemy upravleniya, 1999, Vol. 10, pp. 46–49.
17. Shockley, W. The Theory of p – n Junctions in Semiconductors and p-n Junction Transistors // Bell Syst. Tec. J. 1949, Vol. 28, pp. 435–489.
18. Abdullaev, Zh.S., Gusev, M. Yu., Zyuganov, A.N., Torchinskaya, T.V. Parameters of Deep Centres in AlGaAs LEDs Evaluated by Capacity and Injection Spectroscopy Methods [Parametry glubokikh tsentrov v svetodiodakh AlGaAs otsenyonnyye metodami emkostnoy i inzhektsionnoy spektroskopii] // Ukr. Phys. Journal. 1989, Vol. 34, # 8, pp. 1220–1224.
19. Voytsekhovky, A.V., Gorn, D.I. Recombination Mechanisms in InGaN/GaN Structures with Quantum Wells at High Levels of Excitation [Mekhanizmy rekombinatsii v strukturakh InGaN/GaN s kvantovymi yamami pri vysokikh urovnyakh vozbuzhdeniya] // Izvestiya vuzov.Fizika, 2015, Vol. 58, # 8/2, pp. 171–173.
20. NSM Archive. Physical Properties of Semiconductors. URL: http://www.ioffe.rssi.ru/SVA/NSM/Semicond/(reference date: 28.02.2020).
21. Ladygin, E.A. The Effect of Ionising Radiation on Electronic Equipment [Deystviye pronikayushchey radiatsii na izdeliya elektronnoy tekhniki]. Moscow: Sovetskoye radio, 1980, 224 p.

The article proposes the methods of object shooting by means of a spectrometer based on a multi-channel radiation detector and further processing of its results allowing spectral resolution of such spectrometers significantly to increase with the same original spatial resolution. The mathematical model of the shooting process is provided. It is determined that restoration of spectral radiance of objects based on the shooting data using the proposed method is a mathematically incorrect inverse task. The Greville method, the method of wavelet transformation, the Tikhonov regularisation method, and the Godunov method were considered as methods for its solution. The results of computational modelling of the considered methods are shown and it is found that restoration of spectral radiance of objects based on the shooting data using the considered methods is possible and relative error of restoration is at a fraction of per cent scale. It is determined that the wavelet transformation method is an optimal method of solution of the incorrect spectral radiance restoration task. It is also shown that the proposed method of imaging spectrometry is applicable both when using matrix radiation detectors with increased number of narrow-band filters and when using widely spread standard three-channel matrix RGB detectors of radiation.

1. Golovin, A.D., Dyomin, A.V. The Imitation Model of the Multi-channel Offner Hyperspectrometer [Imitatsionnaya model mnogokanalnogo giperspectrometra Offnera] // Kompyuternaya optika, 2015, Vol. 39, # 4, pp. 521–528. Doi 10.18287/0134–2452–2015–39–4–521–528.
2. Larar Allen M. et al. Multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques, and applications III // Proc. SPIE International Society for Optical Engineering. 13–14 October 2010. Incheon, Korea Republic, 2010, Vol. 7857.
3. Rodionov, I.D., Rodionov, A.I., Vedeshin, L.A., Vinogradov, A.N., Egorov, V.V., Kalinin, A.P. Aviation Hyperspectral Installations for Remote Sensing Applications [Aviatsionnyie giperspektralnyie kompleksy dlya resheniya zadach distantsionnogo zondirovaniya] // Issledovaniye zemli iz kosmosa, 2013, Vol. 6, pp. 81–93.
4. Gorbunov, G.G., Chikov, K.N., Shlishevskiy, V.B. Interferential Hyper and Ultraspectral Videospectrometers for Remote Sensing Applications [Interferentsionnyie giper- i ultraspektralnyie videospektrometry dlya zadach distantsionnogo zondirovaniya] // Bulleting of SGUGiT, 2016, Vol. 1, # 33, pp. 70–94.
5. Vilaseca M., Schael B., Delpueyo X., Chorro E., Perales E., Hirvonen T., Pujol J. Repeatability, reproducibility, and accuracy of a novel pushbroom hyperspectral system // Color Research & Application, 2013, Vol. 39, No. 6, pp. 549–558. Doi 10.1002/col.21851.
6. Pozhar, V.E., Machikhin, A.S., Gaponov, M.I., Shirokov, S.V., Mazur, M.M., Sheryshev, A.E. Hyper-spectrometer Based on an Acousto-optic Tuneable Filters for UAVS // Light & Engineering, 2019, Vol. 27, # 3, pp. 99–104.
7. Palto, S.P., Alpatova, A.V., Geyvandov, A.R., Blinov, L.M., Lazarev, V.V., Yudin, S.G. Fourier Spectroscopy as a Method of Studying of Photoelectric Properties of Organic Systems [Furye-spektroskopiya kak metod izucheniya fotoelektricheskikh svoistv organicheskikh sistem] // Optika i spektroskopiya, 2018, Vol. 124, # 2, pp. 210–220. Doi 10.21883/OS.2018.02.45526.209–17.
8. Zavarzin, V.I., Mitrofanova, Yu.S. Schematic Solutions for Advanced Hyperspectral Systems [Skhemnyie resheniya dlya perspektivnoy giperspektralnoy apparatury] // Opticheskiy zhurnal, 2017, Vol. 84, # 4, pp. 12–16.
9. Golovin, A.D., Dyomin, A.V. The Imitation Model of the Multi-channel Offner Hyperspectrometer [Imitatsionnaya model mnogokanalnogo giperspectrometra Offnera] // Kompyuternaya optika, 2015, Vol. 39, # 4, pp. 521–527. Doi 10.18287/0134–2452–2015–39–4–521–528.
10. Balashov, A.A., Vagin, V.A., Golyak, I.S., Morozov, A.N., Nesteruk, I.N., Khorokhorin, A.I. Visible and Near-IR Range Fourier Spectrometer [Furye-spektrometr vidimogo i blizhnego IK diapazonov] // Radiostroeniye, 2017, Vol. 6, pp. 27–38. Doi 10.24108/rdeng.0617.0000124.
11. Arkhipov, S.A., Zavarzin, V.I., Lee, A.V. // Reflective Optical Systems for Low-dimension Hyperspectral Systems of Remote Sensing of Earth [Zerkalnyie opticheskiye sistemy dlya malogabaritnoy giperspektralnoy apparatury distantsionnogo zondirovaniya zemli iz kosmosa] // From the collection Acoustic-optic and Radar Methods of Measurement and Information Processing. Proceedings of the 10th International Science and Technical Conference. A.S. Popov Russian Society of Radio Engineering, Electronics and Communication (NTORES), 2017, pp. 262–264.
12. Veys C., Davies P., Hibbert J., Grieve B. (2017). An Ultra-Low-Cost Active Multispectral Crop Diagnostics Device. 1005–1007. Paper presented at IEEE Sensors 2017 Conference, Glasgow, United Kingdom. Doi.org/10.1109/ICSENS.2017.8234211.
13. Burns P.D., Berns R.S. Analysis Multispectral Image Capture // Color Imaging Conferenct 1996, # 1, pp. 19–22.
14. Khorokhorov, A.M., Vvedenskaya, A.V., Shirankov, A.F., Kobozev, V.S., Algorithm of Enhancement of Matrix Radiation Detectors with Bayer filters [Algoritm rasshireniya vozmozhnostey matrichnykh priyomnikov izlucheniya s filtrami Bayera / Int. Conf. Applied Optics 2018, Saint Petersburg: D.S. Rozhdestvensky Society of Optics, 2018, Vol. 2.
15. Varentsova, S.A., Trofimova, V.A., Troshchiyev, Yu.V. Reconstruction of a Signal and Dynamics of its Spectral Characteristics with a Non-regular Set of Measurements [Vosstanovleniye signala i dinamiki ego spektralnykh kharakteristik pri neregulyarnom nabore izmereniy] // J. tech. phys. 2008, Vol. 78, # 7, pp. 57–68.
16. Zhuchko, O.V., Pytyev, Yu.P. Reconstruction of Functional Dependence Using Theoretical and Possibility Methods // J. comp. math. and math. phys. 2003, Vol. 43, # 5, pp. 767–783.
17. Hardeberg J. Acquisition and reproduction of color images: colorimetric and multispectral approaches. USA: Dissertation.com, 2001.
18. Vapnik, V.N. Reconstruction of Dependences Using Empirical Data [Vosstanovleniye zavisimostey po empiricheskim dannym], Moscow: Nauka, 1979, 448 p.
19. Ivanov, V.K., Vasin, V.V., Tanana, V.P. The Theory of Linear Incorrect Problems and its Application [Teoriya lineynykh nekorrektnykh zadach i eyo prilozhenie], Moscow: Nauka, 1978, 206 p.
20. Sizikov, V.S., Lavrov, A.V. Contemporary Sustainable Mathematical and Software Methods of Distorted Spectra Reconstruction [Sovremennyie ustoichivyie matematicheskiye i programmnyie metody vosstanovleniya iskazhonnykh spektrov] // Science and Technical Bulletin of Information Technologies, Mechanics and Optics, 2018, Vol. 18, # 6.
21. Sydikhov, A. Sh., Arapov, S. Yu., Arapova, S.P. Pseudoinverse Processing of the Data of Multispectral Shooting in Stationary Zones of an Image [Psevdoinversnaya obrabotka dannykh multispektralnoy fotosyomki v statsionarnykh zonakh izobrazheniya] / Int. Conf. of Students, Postgraduates and Young Scientists Information Technologies, Telecommunications and Control Systems: book of reports, Ekaterinburg: UrFU, 2015, pp. 179–185.
22. El-Rifai I. et al. Enhanced Spectral Reflectance Reconstruction Using Pseudo-Inverse Estimation Method // Int. J. Image Process. IJIP, 2013, Vol. 7, # 3, pp. 278–285.
23. Barliani, A.G. Development of Algorithms of Equalisation and Evaluation of Accuracy of Unconstrained and Constrained Geodetic Networks Based on a Pseudonormal Solution [Razrabotka algoritmov uravnivaniya i otsenki tochnosti svobodnykh i nesvobodnykh geodezicheskikh setey na osnove psevdonormalnogo resheniya, Novosibirsk: SGGA, 2010, 135 p.
24. Morozov, V.V., Grebennikov, A.I. Methods of Solution of Incorrectly Set Problems. The Algorithm Aspect [Metody resheniya nekorrektno postavlennykh zadach. Algoritmicheskiy aspekt]. – Moscow: Moscow University Press, 1992, 319 p.
25. Vogel C.R. Computational Methods for Inverse Problems. SIAM. Philadelphia, 2002, 179 p. Doi 10.1137/1.9780898717570.
26. Gurylyova, A.V., Khorokhorov, A.M., Latyshev V.I. Comparative Analysis of Methods of Solving Incorrect Reverse Problems for Multi-channel Hyperspectroscopy [Sravnitelnyi analiz metodov resheniya nekorrektnykh obratnykh zadach dlya mnogokanalnoy giperspektrometrii] // Optika i spektroskopiya, 2019, Vol. 127, # 4, pp. 551–557. Doi 10.21883/OS.2019.10.48356.171–19.
27. Arapov, S. Yu., Arapova, S.P., Dubinin, I.S., Sergeev, A.P. Reconstruction of Reflection Spectra of Test Fields Based on Data of Multispectral Shooting [Vosstanovleniye spektrov otrazheniya testovykh poley po dannym multispektralnoy fotosyomki] / Transmission, Processing, Perception of Text and Graphic Information: proceedings of the International Science and Practical Conference. – Ekaterinburg: UrFU, 2015. – P. 21–33.
28. Arapov, S. Yu., Tarasov, D.A., Sergeev, A.P., Kolmogorov, Yu.N. Modelling of Reflection Spectra Using the Basis of Erf Functions [Modelirovaniye spektrov otrazhaniya na osnove bazisa iz funktsiy tipa integrala oshibok] // Izvestiya vysshykh uchebnykh zavedeniy. Problemy poligrafii i izdatelskogo dela, 2012, Vol. 6, pp. 17–29.
29. Novikov, L.V. Spectral Analysis of Signals in Wavelet Basis [Spektralnyi analiz signalov v bazise veivletov] // Nauchnoye priborostroeniye. 2000, Vol. 10, # 3, pp. 70–77.
30. Tarasov, D.A. Modelling of Reflection Spectra by Polynome Superposition [Modelirovaniye spektrov otrazheniya superpozitsiey polinomov] // Izvestiya vysshykh uchebnykh zavedeniy. Problemy poligrafii i izdatelskogo dela, 2012, Vol. 5, pp. 59–66.
31. Tikhonov, A.N., Arsenin, V. Ya. Methods of Solving of Incorrect Problems [Metody resheniya nekorrektnykh zadach]. Moscow: Nauka, 1986, 288 p.
32. Calvetti, D., Morigi, S., Reichel, L., Sgallari, F. Tikhonov regularization and the L-curve for large discrete ill-posed problems // J. Comput. Appl. Math. 2000, Vol. 123, pp. 423–446.
33. Hansen, C.A. Matlab Package for Analysis and Solution of Discrete Ill-Posed Problems // Numerical Algorithms, 1994, Vol. 6, pp. 1–35.
34. Morozov, V.A. Linear and Non-linear Incorrect Problems [Lineynyie i nelineynyie nekorrektnyie zadachi] // Itogi nauki i tekhniki. Ser. Math. anal. 1973, Vol. 11, pp. 129–178.
35. Goncharskiy, A.V., Leonov, A.V., Yagola, A.G. Generalised Residual Principle [Printsip obobshchyonnoy nevyazki] // J. Comp. Math. & Math. Phys. 1973, Vol. 13, # 2, pp. 294–302.
36. Godunov, S.K., Antonov, A.G., Kirilyuk, O.P., Kostin, V.I. Guaranteed Accuracy of Solution of a System of Linear Equations in Euclidean Spaces [Garantirovannaya tochnost resheniya sistem lineynykh uravneniy v evklidovykh prostranstvakh]. Novosibirsk: Nauka: Sib. branch, 1988, 456 p.
37. Vapnik, V.N., Mikhalskiy, A.I. On Searching of Dependences Using the Method of Ordered Risk Minimisation [O poiske zavisimostey metodom uporyadochennoy minimizatsii riska] // Automat. & telemech. 1974, Vol. 10, pp. 10–21.
38. Connah, D., Alsam, A., Hardeberg, J.Y. Multispectral imaging: How many sensors do we need? // J. Imaging Sci. Technol. 2006, Vol. 50, #. 1, pp. 45–52. Doi 10.2352/j.imagingsci.technol. (2006)50:1(45).
39. Helling, S., Seidel, E., Biehlig, W. Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera / Proc. of CGIV (2nd European Conference on Color in Graphics, Imaging and Vision). 2004, Vol. 2, pp. 254–258.
40. Imai, F.H., Berns, R.S. Spectral estimation using trichromatic digital cameras // Proc. Int. Symp. Multispectral Imaging and Color Reproduction for Digital Archives. Chiba: Society of Multispectral Imaging of Japan, 1999, pp. 42–48.
41. Masahiro, Y., Hideaki, H., Nagaaki, O. Beyond Red–Green–Blue (RGB): Spectrum-Based Colour Imaging Technology // J. Imaging Sci. Technol. 2008, Vol. 52, #. 1, pp. 1–15.

The article reviews the importance of insolation as a factor of prevention and containment of infectious diseases and epidemics. The authors consider insolation not as a mean of curing the Сoronavirus Disease (WHO fairly calls such possibility “a myth”) but as a means to lower the risks of dissemination of the infection, to reduce viability of the virus in the environment, to support human protective immune mechanisms affecting susceptibility of the population as a whole, severity and recovery time, i.e. both sanitary and hygienic and prevention factors of the COVID‑19 epidemic containment. Apart from the germicidal and virucidal sanitising effects of solar rays, the article reviews anti-epidemic capabilities of insolation as a microclimate factor and a psychological and physiological regulator of human protective capabilities as well as the insolation standards as a mechanism of development density regulation. It is impossible to efficiently combat massive dissemination of highly contagious infections without concerted utilisation of all available means and measures: both medical and preventive and organisational. The unprecedented mobilisation of healthcare systems and large-scale restrictive quarantine measures are under special attention of the society. This article reviews the importance of insolation as a universal natural anti-epidemic factor which is undeservedly placed in the end of the list of effective infection combating measures.

1. Obolensky, N.V. Architecture and the Sun [Arkhitektura i solntse]. Moscow: Stroyizdat, 1988.
2. Moscow Ecological and Climatic Characteristics Handbook (Based on Observations of the Meteorological Laboratory of Moscow State University) [Spravochnik ekologo-klimaticheskikh kharakteristik g. Moskvy (po nablyudeniyam meteorologicheskoy laboratorii MGU)]. Vol. 1. Moscow: Moscow State University Press. 2003, pp. 35–37.
3. URL: https://iz.ru/986191/anna-urmantcevamariia-nediuk/minusy-pliusa-koronavirus-luchshe-vsego-rasprostraniaetsia-pri‑8–9degc/ (date of reference: 22.04.2020).
4. Boyce P. Light and Health [Svet i zdorovye]. Svetotekhnika, 2006, # 2, pp. 43–48.
5. Anisimov Vladimir N. Light Desynchronosis and Health// Light & Engineering, 2019, Vol. 27, # 3, pp. 14–25.
6. Gašper Čož Senior Living – Lighting, Circadian Rhythm and Dementia II// Light & Engineering, 2019, Vol. 27, #5, pp. 9–14.
7. Light Engineering Handbook [Spravochnaya kniga po svetotekhnike] / Edited by Ju.B. Aizenberg. 4th Issue, Section Fifteen. Light and Health. Non-Visual Functions of Light [Svet i zdorovye. Nezritelnyie funktsii sveta]. Moscow: 2019, pp. 809–813.
8. Saatov Kh.I. On Wound Healing in Conditions of Body Exposure to Ionising Radiation and Insolation [K osobennostyam zazhivleniya ran v usloviyakh vozdeystviya na organizm ioniziruyushchey radiatsii i insolyatsii]: Thes. of Cand. of Med.: Vol. 1–2 / I.P. Pavlov Samara Medical Institute,1967.
9. Popovskiy Yu.B. The History of Sanitary and Epidemiological Standardisation of Insolation of Residential Premises in USSR and Russian Federation [Istoriya sanitarno-epidemiologicheskogo normirovaniya insolyatsii zhilykh pomeshcheniy v SSSR i Rossiyskoy Federatsii]. National Association of Scientists. 2015, Vol. 6–3 (11), pp. 27–30.
10. Shmarov I.A., Zemtsov V.A., Korkina E.V. Insolation: Standardisation and Calculation Practice [Insolyatsiya: praktika normirovaniya i raschyota] // Zhilishchnoye stroitelstvo, 2016, # 7, pp. 48–53.
11. Shchepetkov, N.I. Open Letter to Chief Sanitary Inspector of the Russian Federation A. Yu. Popova [Otrkytoye pismo Glavnomu sanitarnomu vrachu RF A. Yu. Popovoy]// Svetotekhnika, 2017, Vol. 6, pp. 100.

Architectural design of a facade, both at the aesthetic point of view and from the point of view of internal daylighting performance of the building, can be considered as a complex task. In this study, we implement a multi-objective evolutionary algorithm to formally exploration the process of reconstruction of the education building’s facade. The purpose of this research is to create a facade configuration by considering the size and location of elements and their materials when creating a suitable internal daylight distribution. The total construction cost of the building’s exterior and the daylight performance of the building’s interior are considered as objectives. The problem formulation includes two conflicting objectives, which are to increase daylighting aspect on each floor and reduce the total construction cost of the facade. To detect the approximation of Pareto fronts, including non-dominated solutions, we used a fast and elitist multi-objective genetic algorithm (NSGA-II). Computational and architectural results show that NSGA-II is efficient enough to demonstrate eligible facade design alternatives.

1. Sariyildiz I. Performative computational design. in Keynote speech in: Proceedings of ICONARCH-I: International congress of architecture-I, Konya, Turkey, 15–17 November 2012. 2012. Selcuk University.
2. Cruz-Correia R., et al., Integration of Hospital data using Agent Technologies–a case study. Ai Communications, 2005. V18, #3, pp. 191–200.
3. Joseph A. and M. Rashid, The architecture of safety: hospital design. Current opinion in critical care, 2007. V13, #6, pp. 714–719.
4. Walch J.M., et al. The effect of sunlight on postoperative analgesic medication use: a prospective study of patients undergoing spinal surgery. Psychosomatic Medicine, 2005. V67, #1, pp. 156–163.
5. Ulrich R.S., et al. Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, 1991. V11, #3, pp. 201–230.
6. Pechacek C.S., M. Andersen, and S.W. Lockley Preliminary method for prospective analysis of the circadian efficacy of (day) light with applications to healthcare architecture. Leukos, 2008. V5, #1, pp. 1–26.
7. Nabil A. and J. Mardaljevic Useful daylight illuminances: A replacement for daylight factors. Energy and buildings, 2006. V38, #7, pp. 905–913.
8. Aripin S. ‘Healing architecture’: A study of daylight in public hospital designs in Malaysia. 2010.
9. Shikder S.H., M. Mourshed, and A.D. Price, Optimisation of a daylight-window: hospital patient room as a test case. 2010.
10. Moon P. and D.E. Spencer, Illumination from a non-uniform sky. Illuminating Engineering, 1942. V37, #10, pp. 707–726.
11. Mardaljevic J., L. Heschong, and E. Lee, Daylight metrics and energy savings. Lighting Research and Technology, 2009. V41, #3, pp. 261–283.
12. Deb K., et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 2002. V6, #2, pp. 182–197.
13. Zitzler E., M. Laumanns, and L. Thiele. SPEA2: Improving the strength Pareto evolutionary algorithm. in Eurogen. 2001.
14. Ekici B., et al. Addressing the high-rise form finding problem by evolutionary computation. in 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. IEEE.
15. Ugurlu C., et al. Evolutionary computation for architectural design of restaurant layouts. in 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. IEEE.
16. Ugurlu C., et al. Identification of sustainable designs for floating settlements using computational design techniques. in 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. IEEE.
17. Cubukcuoglu C., et al. Multi-objective harmony search algorithm for layout design in theatre hall acoustics. in Evolutionary Computation (CEC), 2016 IEEE Congress on. 2016. IEEE.
18. Cubukcuoglu C., et al., A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements. Algorithms, 2016. V9, #3, p. 51.
19. Cubukcuoglu C., et al. Multi-objective optimization through differential evolution for restaurant design. in Evolutionary Computation (CEC), 2016 IEEE Congress on. 2016. IEEE.
20. Dursun O., B. Ekici, and S. Sarıyıldız. Time-cost optimization at the conceptual design stage using differential evolution: Case of single family housing projects in Germany. in 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. IEEE.
21. Kirimtat A., et al. Multi-objective optimization for shading devices in buildings by using evolutionary algorithms. in Evolutionary Computation (CEC), 2016 IEEE Congress on. 2016. IEEE.

The research examined the changing of colour difference by the control colours depending on the choice of colour space when working with matrix photo detector. The spectral characteristics of photo detectors from different manufacturers noticeably differ from each other and from the addition the difference in colour quality between different digital devices. A software method for studying the colour rendition of the image obtained by digital devices based on the selection of an individual colour space for each matrix photo detector is proposed. To analyze and evaluate the capabilities of the spectral characteristics of matrix photo detectors, the control colour method based on the Mansell Atlas was used. The analysis of the obtained parameters of 14 colours was carried out according to various criteria for seven colour spaces: sRGB, AdobeRGB, DCI-P3 RGB, M1N1P1, PAL / SECAM, Wide Gamut RGB, ProPhoto RGB. Also studied the influence of the choice of colour space on the change in the coordinates of the source 6,500 K. Based on the colour differences of the control colours, it is possible to choose the optimal colour space for working with a specific matrix photo detector. The latter will reduce colour distortion at the initial stage of image registration. The ways for improving the colorimetric method of control colours are proposed as applied to digital devices at the software level.

1. Zhbanova, VL. Colour separation systems for matrix pho-todetectors: monograph (Sistemy cvetodeleniya matrichnyh fotopriemnikov) [In Russian]. Smolensk: Universum. 2018. 186 p. ISBN978–5–91412–392–2
2. Domasev, M.V., Gnatyuk, S.P. Colour, colour management, colour calculations and meas-urements (Cvet, upravlenie cvetom, cvetovye raschety i izmereniya) [In Russian]. Saint Pe-tersburg, ‘’Piter ‘’. 2009. P. 224.
3. Richard F. Lyon, Paul M. Hubel. Eyeing the Camera: into the Next Century. IS&T Re-porter “The window on imaging”. 2002. Vol. 17. № 6.
4. Cepeda-Negrete, J. Dark image enhancement using per-ceptual colour transfer / J. Cepeda-Negrete, R. Sanchez-Yanez, F. Correa-Tome, R. Lizarraga-Morales / IEEE Access. 2018. Vol. 6. P. 14935–14945. DOI: 10.1109/ACCESS.2017.2763898.
5. Lozhkin, L.D., Osipov, O.V., Voronoj, A.A. Colour Correction in Tri-Colour Colour Devic-es (Cvetokorrekciya v trekhcvetnyh ustrojstvah cvetovosproizvedeniya) [In Russian]. Computer optics. 2017. Т. 41, № 1. P. 88–94. DOI: 10.18287/2412–6179–2017–41–1–88–94.
6. Gao, H. An improved gray-scale transformation method for pseudo-colour image en-hancement / H. Gao, W. Zeng, J. Chen. Computer Optics. 2019. Vol. 43, Issue. 1. P. 78–82. – DOI: 10.18287/2412–6179–2019–43–1–78–82.14.
7. Kanaeva, I.A. Colour and brightness correction methods for creating panoramic images [In Russian]. I.A. Kanaeva, Yu.A. Bolotova. Computer optics. 2018. Т. 42, № 5. P. 885–897. DOI: 10.18287/2412–6179–2018–42–5–885–897.
8. Zhbanova, V.L. Research into methods for determining colour differences in the CIELAB uniform colour space // Light & Engineering, 2020,. Vol. 28, #3, pp. 53–59.
9. Zhbanova, V.L, Parvuyusov Yu.B. Еxperimental investigation of the colour-separation system of photodetector array // Journal of Optical Technology, 2019, Vol. 86, #6, pp. 177–182 (DOI (CrossRef): 10.1364/JOT.86.000177).
10. Zhbanova, V.L., Nubin V.V. A method of improving colour rendition of digital photo- and videocameras // Light & Engineering, 2014, Vol. 22, # 2, pp. 84–89.
11. Krivosheev, MI, Kustarev, AK. Colour measurements [In Russian]. Moscow “Ener-goatomizdat”, 1990. 240 p.
12. CIE (Commission Internationale de l’Eclairage). Publication № 116. Industrial colour difference evaluation, 1995.
13. CIE (Commission Internationale de l’Eclairage). Publication № 135/2:1999. Colour rendering (TC133 closing remarks).
14. GOST 7721–89 Illuminants for colour measurements. Types. Technical requirements. Marking.

he current state of LED lighting systems with parallel power supply by photovoltaic modules and central power supply network is analysed. The approach to implementation of parallel operation of LED luminaire powered by two sources of power is presented. It is simple, cheap and highly reliable as compared to the existing solutions. Based on this approach, four diagrams are developed which are applicable correspondingly to lighting applications and characteristics of photovoltaic modules and power consumers. The first and the second diagrams contain minimal quantity of transformers, but a number of operational constraints shall be taken into account when using them. The third diagram contains standard transformers and implies minimal number of various constraints, which makes it an optimal solution for the low-power lighting system being designed. The fourth diagram is expensive due to utilisation of equipment with automatic maximum power point tracking (the MPPT technology); it provides maximum possible energy efficiency of the lighting systems but the advantages of the MPPT technology apply only to high-power systems.
It is preferable to use such objects where lighting is mostly required during daytime as consumers of such systems (shopping malls, underground passages, storage facilities, poultry farms, etc.). A positive aspect is increase in reliability of consumer power supply since power supply of LED luminaires will be also provided by an additional source.
The proposed approach leads to reduction of power consumption for LED lighting, saving of fossil energy sources and therefore to ecologisation of the environment.

1. Global Market Outlook For Solar Power 2019–2023 / Aurélie Beauvais, Naomi Chevillard, MarianoGuillén Paredes, Máté Heisz, Raffaele Rossi, Michael Schmela. Belgium, Brussels: SolarPower Europe, 2019, 91 р. URL: https://www.solarpowereurope.org (date of reference: 09.09.19).
2. Stromgestehungskosten Erneuerbare Energien / Christoph Kost, Shivenes Shammugam, Verena Jülch, Huyen-Tran Nguyen, Thomas Schlegl. Deutschland, Freiburg: Fraunhofer-Institut für Solare Energiesysteme ISE, 2018, 41 р. URL: https://www.ise.fraunhofer.de (date of reference: 09.09.19).
3. Renewables 2018 global status report / Hannah E. Murdock, Rana Adib [et al.] France, Paris: REN21, 2018. – 324 p. – ISBN978–3–9818911–3–3 –URL: http://www.ren21.net/wp-content/uploads/2018/06/ GSR_2018_Highlights_final.pdf (date of reference: 09.09.19).
4. Advancing the Global Renewable Energy Transition / Hannah E. Murdock, Rana Adib [et al.] – France, Paris: REN21, 2018, 51 p. ISBN978–3–9818911–2–6 – URL: http://www.ren21.net/wp-content/uploads/2018/06/GSR_2018_Highlights_final.pdf (date of reference: 09.09.19).
5. Wang B., Sechilariu M., Locment F. Intelligent DC microgrid with smart grid communications: control strategy consideration and design // IEEE Тransactions on Smart Grid, 2012, 3(4), pp. 2148–2156. DOI: 10.1109/TSG.2012.2217764.
6. Dragičević T., Lu X., Vasquez J.C., Guerrero J.M. DC Microgrids-Part I: A Review of Control Strategies and Stabilization Techniques // IEEE Transactions on Power Electronics, 2016, 31(7, pp. 4876–4891. DOI: 10.1109/TPEL.2015.2478859.
7. Dragičević T., Lu X., Vasquez J.C., Guerrero J.M. DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues // IEEE Transactions on Power Electronics, 2016, 31(5), pp. 3528–3549. DOI: 10.1109/TPEL.2015.2464277.
8. Justo J.J., Mwasilu F., Lee J., Jung J.W. AC-microgrids versus DC-microgrids with distributed energy resources: A review // Renewable and Sustainable Energy Reviews, 2013, Vol. 24, pp. 387–405. DOI: 10.1016/j.rser.2013.03.067.
9. De Zoysa H.B.H., Guruge P.A., Kalingamudali S.R.D., Kularatna N., Kanishka G. Designing and Constructing a DC Microgrid with Uninterrupted Power Supply Capability and Optimizing its Energy Usage by Smart Controlling System / IEEE Industrial-Electronics-Society (IES) International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), New Zeland, Hamilton: IEEE, 2018, pp. 351–356.
10. Ali А., Lange J., Elrayyah A., Sozer Y., De Abreu-Garcia J.A., Mpanda A.A. Hybrid Flyback LED Driver with Utility Grid and Renewable Energy Interface / 33nd Annual IEEE Applied Power Electronics Conference and Exposition. USA, TX, San Antonio: IEEE, 2018, pp. 3377–3384.
11. Vieira J.A.B., Mota A.M. Implementation of a Stand-Alone Photovoltaic Lighting System with MPPT Battery Charging and LED Current Control / IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control, Japan, Yokohama: IEEE, 2010, pp. 185–190.
12. Shen C.L., Ko Y.X. Hybrid-input power supply with PFC (power factor corrector) and MPPT (maximum power point tracking) features for battery charging and HB-LED driving // Energy – Elsevier Ltd, 2014, Vol. 72, pp. 501–509. DOI: 10.1016/j.energy.2014.05.072.
13. Pandey A.K., Tyagi V.V., Selvaraj J.A.L., Rahim N.A., Tyagi S.K. Recent advances in solar photovoltaic systems for emerging trends and advanced applications // Renewable & Sustainable Energy Reviews, Elsevier Ltd, 2016, Vol. 53, pp. 859–884. DOI: 10.1016/j.rser.2015.09.043.
14. Solar Power Plants in Russia: Reality and Prospects [Solnechnyie elektrostantsii na territorii Rossii: realii i perspektivy] // Alternative Energy [web-site], 2019. https: //altenergiya.ru/sun/solnechnye-elektrostancii-vrossii.html (date of reference: 09.09.19).

1. Kandinsky W. On the Spiritual in Art [O dukhovnom v iskusstve]. Moscow: Arkhimed, 1992, 112 p.
2. Medvedev L.G. Formation of a Graphic Imagery in Drawing Classes: A Study Guide for Students of Art and Graphic Faculties of Pedagogical Institutes [Formirovaniye graficheskogo khudozhestvennogo obraza na zanyatiyakh po risunku]. Moscow: Prosvechsheniye, 1986, 159 p.
3. Zinchenko V.P. Osip Mandelshtam’s Walking Stick and Mamardashvili’s Pipe. On Basics of Organic Psychology [Posokh Osipa Mandelshtama i trubka Mamardashvili. K nachalam organicheskoy psikhologii]. – Moscow: Novaya shkola, 1997, 336 p.
4. Yakimanskaya I.S. Main Lines of Visual Thinking Research [Osnovnyie napravleniya issledovaniy obraznogo myshleniya]. Moscow: Voprosy psikhologii, 1985.
5. Kandinsky W. Point and Line to Plane [Tochka i liniya na ploskosti] / trans. from German by E. Kozina. Saint Petersburg: Azbuka, 2001, 236 p.
6. Khan-Magomedov S.O. Architecture of Soviet Avant-Garde: in 2 books. Book One: Problems of Form Making. Masters and Movements [Arkhitektura sovetskogo avangarda: v 2 kn. Kniga pervaya: Problemy formoobrazovaniya. Mastera i techeniya]. Moscow: Stroyizdat, 1996.
7.Galperin P. Ya. Mind Action as a Basis for Formation of a Thought and an Image [Umstvennoye deistviye kak osnova formirovaniya mysli i obraza]. Voprosy psikhologii, 1957, Vol. 6.
8. Kamenskaya G.V. The Methodological Recommendations on Design of Outdoor Architectural Lighting of Buildings and Structures [Metodicheskiye rekomendatsii po proektirovaniyu naruzhnogo arkhitekturnogo osveshcheniya zdaniy i sooruzheniy] / G.V. Kamenskaya, L.I. Petrova et al. Moscow, TsNIIEP of Eng. Equipment, 1977, 63 p.
9. The MSC programme. URL: https://www.light.aau.dk/msc-education/ (date of reference: 15.09.2018).
10. Professional studies master programme lighting design. URL: https://studieren.de/fileadmin/europe/germany/_study/docs/Master_Lighting_Design.pdf (date of reference: 15.09.2018).
11. Lighting M.S. URL: http://catalog.rpi.edu/preview_program.php?catoid=11&poid= 2431&returnto=256 (date of reference: 15.09.2018).
12. Architectural lighting design courses. URL: https://www.kth.se/en/ studies/master/architectural-lighting-design/course-overview‑1.268138 (date of reference: 15.09.2018).
13.Lekus E. Yu., Bystryantseva N.V. Lighting Design: Light as Material, Technology, Form: Joint Monograph [Svetovoy dizain: svet kak material, tekhnologiya, forma] // Proceedings of the international scientific conference “Material-Technology-Form as the Universal Triad in Design, Architecture, Fine and Decorative Arts”. Moscow: Stroganov Moscow State Academy of Arts and Industry, 2018, 537 p.
14. Bystryantseva N.V. Comprehensive Approach to Establishment of a Light Environment in a Night-Time City: extended abstract of Cand. Arch. Dissertation [Kompleksniy podkhod v sozdanii svetovoy sredy vechernego goroda]. Moscow: Moscow Architectural Institute (State Academy), 2015, 27 p.
15. Sokolova M.A. View from Inside. Design of Architectural Space: Interior. Study Guide [Vzglyad iznutry. Proektirovaniye arkhitektirnogo prostranstva: interier]. – Moscow: BooksMArt, 2016.
16. Matveev A.B. Aesthetics of Lighting [Estetika osvashcheniya] // Svetotekhnika, 1995, Vol. 4–5, pp. 2–4.
17. Shchepetkov N.I. Lighting Design of a City [Svetovoy dizain goroda]. Moscow: Arkhitektura-S, 2006. – 317 p.
18. Feshchenko V., Koval O. Creation of a Sign. Articles on Linguistic Aesthetics and Semiotics of Art [Sotvoreniye znaka. Ocherki o lingvoestetike i semiotike iskusstva]. – Moscow: Yazyki slavyanskoy kultury, 2014.
19. Batova, A.G. Principles of Design of Outdoor Lighting of Architectural Objects: extended abstract of Cand. Arch. Dissertation [Printsipy proektirovaniya naruzhnogo osveshcheniya arkhitekturnykh obiektov]. Moscow: Moscow Architectural Institute (State Academy), 2012, 27 p.

1. Gagliardi, R.M., Karp, Sh. Optical Communications: trans. from Eng. / Eds. A.G. Sheremetyev. Moscow: Svyaz, 1978, 424 p.
2.Meshkov, V.V. Basics of Light Engineering: Study Guide for Higher Education Institutions. P. 1 [Osnovy svetotekhniki: ucheb. posobie dlya vuzov. Ch. 1]. 2nd edition, revised. Moscow: Energiya, 1979, 368 p.
3. Leonidov A.V. Changes in irradiance and illuminance on Earth surface during 11 – year solar activity cicle // Light & Engineering, 2020, Vol. 28, # 2, pp. 61–66.
4. Allen, C.W. Astrophysical Quantities (Handbook) [Astrofizicheckiye velichiny (Spravochnik)]. Trans. from Eng. Moscow: Mir, 1977, 279 p. (Allen C.W. Astrophysical quantities. – London: The Athlone Press, 1973, 279 p.)
5. Martynov, D. Ya. Practical Astrophysics Course [Kurs prakticheskoy astrofiziki]. – Moscow: Nauka, 1977.
6. Schwabe H. Sonnenbeobachtungen im Jahre 1843 // Astronomische Nachrichten, 1844, Vol. 21, p. 233.
7. Schwarzer, K. Lighting with Adjustable Colour – Study and Optimisation of Colour Lighting Control Systems / Summary Report, 2006. URL: http://www.bocom.eu/rus/сatalog/downloads/Farblichtstadie.rus.pdf (date of reference: 07/30/2019).
8. Kononovich, E.V., Moroz, V.I. General Astronomy: Study Guide [Obshchiy kurs astronomii: uchebnoну posobie] / Eds. V.V. Ivanov. 2nd edition, revised. Moscow: Editorial URSS, 2004, 544 p.
9. Leonidov A.V. Calculation of the Natural Illumination of the Earth’s Surface at Different States of Cloud Cover // Izvestiya – Atmospheric and Oceanic Physics. 2019, Vol. 55, # 11, pp. 1592–1601.
10. Leonidov A.V. On optical receivers in pathway implicated in control of human circadian rhythm Biophysics, 2016, Vol. 61, # 6, pp. 1002–1010.
11. Brainard G.C., Glickman G.L. The biological potency of light in humans: significance to health and behavior / CIE152: 2003.
12. Brainard, G.K., Glickman, G.L. Biological Effect of Light on Human Health and Behaviour [Biologicheskoye vliyaniye sveta na zdorovye i povedeniye cheloveka] // Svetotekhnika, 2004, Vol. 1, pp. 4–8.
13. Thapan K., Arendt J., Skene D.J. An action spectrum for melatonin suppression: evidence for a novel non – rod, non – cone photoreceptor system in humans // J. Physiol. 2001, Vol. 535, pp. 261–267.
14. Brainard G.C., Provencio I. Photoreception for the neurobehavioral effects of light in humans / CIE031:2006 “Proc. 2nd CIE Expert Symposium on “Lighting and Health”, pp. 6–21.
15. Wentzel, E.S. Probability Theory [Teoriya veroyatnostey]. 2nd edition, revised and supplemented. Moscow: Fizmatgiz, 1962, 564 p.
16. Levin, B.R. Theoretical Basics of Statistical Radio Engineering [Teoreticheskiye osnovy statisticheskoy radiotekhniki]. Book one, Moscow: Sovetskoye radio, 1966, 728 p.
17. Leonidov, A.V. On conditions of occurrence ofseasonal disorders in human circannual and circadian rhythm // Biophysics, 2014, Vol. 59, #1, pp. 157–161.

Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scaled problems with incomplete information. Hence, intelligent optimization algorithms, which are inspired by natural phenomena such as physics, biology, chemistry, mathematics, and so on have been proposed as working solutions over time. Many of the intelligent optimization algorithms are based on physics and biology, and they work by modelling or simulating different nature-based processes. Due to philosophy of constantly researching the best and absence of the most effective algorithm for all kinds of problems, new methods or new versions of existing methods are proposed to see if they can cope with very complex optimization problems. Two recently proposed algorithms, namely ray optimization and optics inspired optimization, seem to be inspired by light, and they are entitled as light-based intelligent optimization algorithms in this paper. These newer intelligent search and optimization algorithms are inspired by the law of refraction and reflection of light. Studies of these algorithms are compiled and the performance analysis of light-based i ntelligent optimization algorithms on unconstrained benchmark functions and constrained real engineering design problems is performed under equal conditions for the first time in this article. The results obtained show that ray optimization is superior, and effectively solves many complex problems.

1. Hussain K., Salleh M.N. M., Cheng S., Shi Y., “Metaheuristic research: a comprehensive survey”, 2018, Artificial Intelligence Review, pp. 1–43.
2. Nabaei A., Hamian M., Parsaei M.R., Safdari R., Samad-Soltani T., Zarrabi H., A. Ghassemi, “Topologies and performance of intelligent algorithms: a comprehensive review”, Artificial Intelligence Review, 2018. V49, #1, pp. 79–103.
3. Alatas B., “Chaotic bee colony algorithms for global numerical optimization”, Expert Systems with Applications, 2010. V37, #8, pp. 5682–5687.
4. Kashan A. H., “A new metaheuristic for optimization: Optics inspired optimization”, Computers & Operations Research, 2015. V55, pp. 99–125.
5. Kashan A. H., “An effective algorithm for constrained optimization based on optics inspired optimization (OIO)”, Computer-Aided Design, 2015. V63, pp. 52–71.
6. Kaveh A., Khayatazad M., “A new meta-heuristic method: ray optimization”, Computers & structures, 2012. V112, pp. 283–294.
7. Kaveh A., Khayatazad M., “Ray optimization for size and shape optimization of truss structures”, Computers & Structures, 2013. V117, pp. 82–94.
8. Tsoulos I. G., “Modifications of real code genetic algorithm for global optimization”, Applied Mathematics and Computation, 2008. V203, pp. 598–607.
9. Camp C. V., Bichon J., “Design of space trusses using ant colony optimization”, Journal of Structural Engineering, 2004. V130, pp. 741–51.
10. Camp C. V., “Design of space trusses using big bang–big crunch optimization”, Journal of Structural Engineering, 2007. V133, pp. 999–1008.
11. Perez R. E., Behdinan K., “Particle swarm approach for structural design optimization”, Computers & Structures, 2007. V85, pp. 1579–88.
12. Kaveh A., Talatahari S., “Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures”, Computers & Structures, 2009. V87, pp. 267–83.
13. Kaveh A., Ghazaan M.I., Bakhshpoori T., “An improved ray optimization algorithm for design of truss structures”, Periodica Polytechnica, Civil Engineering, 2013. V57, #2, p. 97.
14. Kaveh A., Javadi S.M., “Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm”, Acta Mechanica, 2014. V225, #6, pp. 1595–1605.
15. He Q., Wang L., “An effective co-evolutionary particle swarm optimization for constrained engineering design problems”, Engineering Applications of Artificial Intelligence, 2007. V20, #1, pp. 89–99.
16. Yildiz A. R., “A novel particle swarm optimization approach for product design and manufacturing”, The International Journal of Advanced Manufacturing Technology, 2009. V40, pp. 5–6.
17. Wang J., Yin Z., “A ranking selection-based particle swarm optimizer for engineering design optimization problems”, Structural and Multidisciplinary Optimization, 2008. V37, #2, pp. 131–147.
18. Mahdavi M., Fesanghary M., Damangir E., “An improved harmony search algorithm for solving optimization problems”, Applied mathematics and computation, 2007. V188, #2, pp. 1567–1579.
19. Hedar A. R., Fukushima M., “Derivative-free filter simulated annealing method for constrained continuous global optimization”, Journal of Global Optimization, 2006. V35, #4, pp. 521–549.
20. Mezura-Montes E., Coello C.C., Landa-Becerra R., “Engineering optimization using simple evolutionary algorithm”, In 15th IEEE International Conference on Tools with Artificial Intelligence, 2003. Pp. 149–156.
21. Badrloo S., “A new method for solving combinatorial optimization problems with permutation based solution structure using optics inspired optimization”, Azad University, Science and Research Branch, Iran, 2015.
22. Moghadasi M., “Design of image processing methods using league championship algorithm and optics inspired optimization”, Azad University, Science and Research Branch, Iran, 2015.
23. Badrloo S., Kashan A.H., “A new method for the travelling salesman problem based on Optics Inspired Optimization”, International Conference on Modern Research in Management and Industrial Engineering, Iran, 2015.
24. Badrloo S., Kashan A.H., “A new method for the quadratic assignment problem based on Optics Inspired Optimization”, International Conference on Modern Research in Management and Industrial Engineering, Iran, 2015.
25. Lalwani P., Banka H., Kumar C., “CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization”, Peer to Peer Networking and Applications, 2017. V10, #3, pp. 453–471.
26. Abdulla A. E., Nishiyama H., Kato N., “Extendingthe lifetime of wireless sensor networks: A hybrid routing algorithm”, Computer Communications, 2012. V35, #9, pp. 1056–1063.
27. Yu J., Qi Y., Wang G., Gu X., “A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution”, AEU-International Journal of Electronics and Communications, 2012. V66, #1, pp. 54–61.
28. Sabet M., Naji H.R., “A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks”, AEU-International Journal of Electronics and Communications, 2015. V69, #5, pp. 790–799.
29. Ozdemir M. T., Ozturk D., “İki bölgeli güç sistemininin optikten esinlenen optimizasyon algoritması ile optimal yük frekans kontrolü”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 2016. pp. 28–2.
30. Muller S. D., Marchetto J., Airaghi S., Koumoutsakos P., “Optimization based on bacterial chemotaxis”, IEEE Transactions on Evolutionary Computation, 2002. V6, pp. 16–29.
31. Altay E. V., Alatas B., “Bird swarm algorithms with chaotic mapping”, Artificial Intelligence Review, 2020. Pp. 1–42.
32. Alatas, B., “Sports inspired computational intelligence algorithms for global optimization”. Artificial Intelligence Review, 2019. V52, #3, pp. 1579–1627.
33. Bingol H., Alatas B., “Chaotic league championship algorithms”, Arabian Journal for Science and Engineering, 2016. V41, #12, pp, 5123–5147.
34. Belegundu A. D., Arora J.S., “A study of mathematical programming methods for structural optimization. Part I: Theory”, International Journal for Numerical Methods in Engineering, 1985. V21, #9, pp. 1583–1599.
35. Ragsdell K. M., Phillips D.T., “Optimal design of a class of welded structures using geometric programming”, Journal of Engineering for Industry, 1976, V98, #3, pp. 1021–1025.

1. Zheleznikova O.E., D. Mohammad S.D., Mikhalkova A.N., Mikaeva S.A. Lighting with led light sources / automation. Modern technology// 2019. T. 73. # 12, pp. 540–543.
2. Zheleznikova, O. E. on the effectiveness of LED lighting for visual work / / O. E. Zheleznikova, S.A. Amelkina, L.V. Sinitsina// Light & Engineering, 2018, #2, pp. 6–10.
3. Zhuravleva Yu.a., Kovalenko O. Yu., Mikaeva S.A., Atishev A.V., Nemov V.V. Investigation of the influence of the form factor of led lamps for household lighting on their lighting characteristics // Energy Security and Energy Saving, 2019, No. 6, pp. 24–27.
4. GOST R54815–2011/IEC/PAS62612: 2009. LED lamps with a built-in control device for General lighting at a voltage of more than 50 V. Operational requirements, Entered 2012–07–01, Moscow: Standardinform, 2012, 16 p.
5. GOST R55702–2013. Light sources are electric. Methods for measuring electrical and light parameters. “Yes,” I said. 2013–11–08, Moscow, Standardinform, 2013. 44 p.
6. GOST R54350–2015. Lighting devices. Lighting requirements and test methods. – Entered 2016–01–01. Moscow: Standardinform, 2015, 45 p.
7. Nesterkina N.P. about the characteristics of led filament lamps with a power of 4, 6, 8 W / N.P. Nesterkina, A.S. Kondrashin / / in the collection: Problems and prospects of development of domestic lighting, electrical engineering and energy materials of the XIII All-Russian scientific and technical conference with international participation in the IV all-Russian lighting forum with international participation, 2017, pp. 358–366.
8. Mikaeva S.A., Zheleznikova O. E., Sinitsyna L.V. Complex of modern research equipment for light measurements / / automation and modern technologies, 2012, #12, pp. 33–36.
9. Appendix to certificate no. 64752 photocolorimetric measuring Installation. – [Electronic resource]. – Access mode: http://fundmetrology.ru/ 10_tipy_si/11/view. aspx?num=qJbKqJpWgBeM / – Title from the screen.
10. Nestyorkina N.P., Kovalenko O. Yu., Zhuravlyova Yu.A. Analysis of characteristics of led lamps with T8 bulb by various manufacturers// Light & Engineering, 2019, Vol. 27, № 6, pp. 82–87.
11. J. Liu, C. Xu, H. Zheng, and S. Liu, “Numerical analysis and optimization of thermal performance of LED filament light bulb,” in 2017 IEEE67th Electronic Components and Technology Conference (ECTC), pp. 2243–2248, Orlando, FL, USA. – 2017.
12. C. Xu, Z. Zhang, J. Chu et al., “Thermal dissipation enhancement of LED filament bulb by ionic wind” in 2016 17th International Conference on Electronic Packaging Technology (ICEPT), pp. 1212–1215, Wuhan, China, 2016.

1. Marsh A.J. The Application of Shading Masks in Building Simulation / 9th Int. IBPSA Conf. “Building Simulation 2005”, Montreal, Canada, 2005.
2. Harkness, E.L., Mehta, M.L. Solar Radiation Control in Buildings [Regulirovaniye solnechnoy radiatsii v zdaniyakh]; Trans. from Eng. by G.M. Ayrapetova, Moscow: Stroyizdat, 1984,177 p.
3. Kupriyanov, V.N., Spiridonov, A.V. Calculation of Shading Device Parameters [Raschyot parametrov solntsezachshitnykh ustroystv] // Stroitelstvo i rekonstruktsiya, 2019, Vol. 3 (83), pp. 54–62.
4. Spiridonov, A.V., Dvoretsky, A.T. Insolation and Shading [Insolyatsiya i solntsezachshita] / Light Engineering Handbook [Spravochnaya kniga po svetotekhnike]. 4th issue / Edited by Yu.B. Eisenberg and G.V. Boos. Moscow: Editorial Board of the Svetotekhnika journal, 2019, pp. 553–566.
5. SP 370.1325800.2017 Solar Shading Devices in Buildings. Design rules.
6. Dvoretsky A.T., Spiridonov A.V., Shubin I.L., Klevets K.N. Accounting of Climatic Features in Designing Solar Shading Devices // Light & Engineering, 2018, Vol. 26, # 2, pp. 162–166.
7. Scientific and Applied Handbook on Climate in the USSR [Nauchno-prikladnoy spravochnik po klimatu SSSR]. Series 3. Long-Term Data. Parts 1–6. Issue 1–34, Leningrad: Gidrometeoizdat, 1989–1998.
8. Dvoretsky, A.T., Spiridonov, A.V., Mirofanova, S.A., Denisova, T.V. On Necessity of Determination of Degree-Days of the Building Cooling Period in Russia [O neobkhodimosti opredeleniya graduso-sutok perioda okhlazhdeniya zdaniy na territorii Rossii] // Zhilishchnoye stroitelstvo, 2019, Vol. 8, pp. 46–49.
9. Sergeychuk, O.V. Geometry Computer Model of Atmospheric Radiation for Energy-Efficient Contruction [Geometrichna komp’yuterna model Atmospheric Radiation dlya energoeffektivnogo budivnitstva] // Energozberezhennya v budivnitstve ta arkhitekturi, 2011, Vol. 1, pp. 22–28.
10. ISO 52016–1:2017 “Energy performance of buildings – Energy needs for heating and cooling, internal temperatures and sensible and latent heat loads – Part 1: Calculation procedures”.
11. Sergeychuk, O.V., Buravchenko, V.S., Andropova, O.V. et al. Features of the Method of Calculation of Solar Access in the National Annex to DSTU B EN ISO 13790 [Osobennosti metodiki raschyota postupleniy v natsionalnom prilozhenii k DSTU B EN ISO 13790] // Energoefektivnist’ v budivnitstve ta arkhitekturi, 2014, Vol. 6, pp. 267–272.
12. Dvoretsky, A.T., Morgunova, M.A., Sergeychuk, O.V., Spiridonov, A.V. Methods of Design of Stationery Shading Devices [Metody proektirovaniya statsionarnykh solntsezachshitnykh ustroystv] // Stroitelnyie materialy, oborudovaniye, tekhnologii XXI veka, 2018, Vol. 11–12, pp. 6–10.

The main purpose of new studies investigating pantograph catenary interaction in electric rail systems is to detect malfunctions. In the pantograph catenary interaction studies, cameras with non-contact error detection methods are used extensively in the literature. However, none of these studies analyse lighting conditions that improve visual function for cameras. The main subject of this study is to increase the visibility of cameras used in railway systems. In this context, adequate illuminance of the test environment is one of the most important parameters that affect the failure detection success. With optimal lighting, the rate of fault detection increases. For this purpose, a camera, and a LED luminaire 18 W was placed on a wagon, one of the electric rail system elements. This study considered CIE140–2019 (2nd edition) standards. Thanks to the lighting made, it is easier for cameras to detect faults in the electric trains on the move. As a result, in scientific studies, especially in rail systems, the lighting of mobile test environments, such as pantograph-catenary, should be optimal. In environments where visibility conditions improve, the rate of fault detection increases.

1. Yaman O., Pantograf-Katener Sistemlerinde Görüntü İşleme Tabanli Temassız İzleme Yöntemlerinin Geliştirilmesi, Yüksek Lisans Tezi, Firat Üniversitesi, Fen Bilimleri Enstitüsü, 2014, p. 122.
2. Karaköse E., Rayli Sistemlerde Pantograf-Katener Sisteminin Modellenmesi, Simülasyonu ve Ariza Teşhis Yöntemlerinin Geliştirilmesi, Yüksek Lisans Tezi, Firat Üniversitesi, Fen Bilimleri Enstitüsü, 2014, p. 190.
3. Aydın I., Karaköse E., Karaköse M., Gençoğlu M.T., Akın E. A new computer vision approach for active pantograph control, 2013 International Conference on Innovations in Intelligent Systems and Applications, 2013, pp. 1–5.
4. Karakose E., Gençoğlu M.T., Karakose M., Yaman O., Aydin, I., Akin E. (2018) A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems, Journal of Intelligent Manufacturing, 2018. V29, #4, pp. 839–856.
5. Parlakyıldız S., Gençoğlu M.T., Cengiz M.S. 2018. Development of Rail Systems from Past to Present. International Conference on Multidisciplinary, Science, Engineering and Technology (2018 Dubai, BAE)
6. Cengiz Ç., Yapıcı, İ., Cengiz M.S. 2018. Fourier Analysis in Rail Systems. International Conference on Multidisciplinary, Science, Engineering and Technology (2018 Dubai, BAE).
7. Cengiz M.S., The Relationship Between Maintenance Factor and Lighting Level in Tunnel Lighting, Light & Engineering, 2019. V27, #3, pp. 75–84.
8. Cengiz M.S., Cengiz, Ç., Numerical Analysis of Tunnel LED Lighting Maintenance Factor, IIUM Engineering Journal. 2018. V19, #2, pp. 154–163.
9. Efe S.B., Varhan D. Interior Lighting of A Historical Building By Using LED Illumınators: A Case Study Of Fatih Paşa Mosque, Light & Engineering, 2020. V28, #4, pp. 77–83.
10. Cengiz M.S., A Simulation and Design Study for Interior Zone Luminance in Tunnel Lighting, Light & Engineering, 2019. V27, #2, pp. 42–51.
11. Sabato, A. and Niezrecki, C. Feasibility of Digital Image Correlation for railroad tie inspection and ballast support assessment. Measurement, 2017. V103, pp. 93–105.
12. Manikandan, R., Balasubramanian, M. and Palanivel, S. Machine Vision Based Missing Fastener Detection In Rail Track Images Using Svm Classifier. International Journal on Smart Sensing and Intelligent Systems, 2017. V10, #4, pp. 574–589. 13. Biswas R., Khan R.A., Islam S. and Uddin J. A Novel Approach to Detect and Classify the Defective of Missing Rail Anchors in Real-time. International Journal of Emerging Technology and Advanced Engineering, 2016. V6, #12, pp. 270–276.
14. Liu J., Li B., Xiong Y., He B., and Li L., 2015. Integrating the symmetry image and improved sparse representation for railway fastener classification and defect recognition. Mathematical Problems in Engineering, 2015.
15. Gibert X., Patel V.M. and Chellappa R. Robust fastener detection for autonomous visual railway track inspection. IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. pp. 694–701.
16. Gibert X., Patel V.M., and Chellappa R. Deep multitask learning for railway track inspection. IEEE Transactions on Intelligent Transportation Systems, 2017. V18, #1, pp. 153–164.
17. Fan H., Cosman P.C., Hou Y. and Li B. High-Speed Railway Fastener Detection Based on a Line Local Binary Pattern. IEEE Signal Processing Letters, 2018. V25, #6, pp. 788–792.
18. Yuan X., Liu B. and Chen H. Algorithm and program design for fastener locating and detection using wavelet transformation and template matching. IEEE17th International Conference on Communication Technology (ICCT), 2017. pp. 1116–1121.
19. Liu J. and Li B. Railway fastener defects recognition algorithm based on computer vision. International Conference on Electronic Science and Automation Control (ESAC2015), 2015. pp. 285–288.
20. Trinh H., Haas N., Ying Li., Otto C., Pankanti S. Enhanced rail component detection and consolidation for rail track inspection, IEEE Workshop on Applications of Computer Vision (WACV), 2012. pp. 289–295.
21. Feng H., Jiang Z., Xie F., Yang P., Shi J., Chen L. Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE Transactions on Instrumentation and Measurement, 2014. V63, #4, pp. 877–888.
22. Babenko P. Visual inspection of railroad tracks, Doctoral dissertation, University of Central Florida Orlando, Florida, 2009.
23. Ying L., Trinh T., Haas N., Otto C., Pankanti S. Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection, IEEE Transactions on Intelligent Transportation Systems, 2014. V15, pp. 760–770.
24. Sawadısavı S.V. Development of Machine-Vision Technology for Inspectıon of Railroad Track, Graduate College of the University of Illinois at Urbana-Champaign, 2010.
25. Mao Q., Cui H., Hu Q. and Ren X. A rigorous fastener inspection approach for high-speed railway from structured light sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 2017.
26. Aytekin Ç., Rezaeitabar Y., Dogru S. and Ulusoy I. Railway fastener inspection by real-time machine vision. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015. V45, #7, pp. 1101–1107.
27. Santur Y., Karaköse M. and Akin, E. A new rail inspection method based on deep learning using laser cameras. International Artificial Intelligence and Data Processing Symposium (IDAP), 2017. pp. 1–6.
28. Santur Y., Karaköse M. and Akin E. Big data framework for rail inspection. International Artificial Intelligence and Data Processing Symposium (IDAP), 2017. pp. 1–4.
29. Wang C., Li Y., Ma Z., Zeng J., Jin T. and Liu H. Distortion Rectifying for Dynamically Measuring Rail Profile Based on Self-Calibration of Multiline Structured Light. IEEE Transactions on Instrumentation and Measurement, 2018.
30. Xiong Z., Li Q., Mao Q. and Zou Q.. A 3D laser profiling system for rail surface defect detection. Sensors, 2017. V17, #8, p. 1791.
31. Yunjie Z., Xiaorong G., Lin L., Yongdong P. and Chunrong Q. Simulation of Laser Ultrasonics for Detection of Surface-Connected Rail Defects. Journal of Nondestructive Evaluation, 2017. V36, #4, p. 70.
32. Wohlfeil J. Vision based rail track and switch recognition for self-localization of trains in a rail network. In Intelligent Vehicles Symposium (IV), 2011. pp. 1025–1030.
33. Johansson A., Pålsson B., Ekh M., Nielsen J.C., Ander M.K., Brouzoulis J. and Kassa E. Simulation of wheel–rail contact and damage in switches & crossings. Wear, 2011. V271, #1, pp. 472–481.
34. Palsson B. Optimisation of railway switches and crossings, Chalmers University of Technology, 2014.
35. Singh A. K., Swarup A., Agarwal A. and Singh D. Vision based rail track extraction and monitoring through drone imagery. ICT Express, 2017.
36. Li Q., Shi Z., Zhang H., Tan Y., Ren S., Dai P. and Li W. A cyberenabled visual inspection system for rail corrugation. Future Generation Computer Systems, 2018. V79, pp. 374–382.
37. Zhuang L., Wang L., Zhang Z. and Tsui K.L. Automated vision inspection of rail surface cracks: A double-layer data-driven framework. Transportation Research Part C: Emerging Technologies, 2018. V92, pp. 258–277.
38. Min Y., Xiao B., Dang J., Yue B. and Cheng T. Real time detection system for rail surface defects based on machine vision. EURASIP Journal on Image and Video Processing, 2018. V1, p. 3.
39. Biao Y. U. E. and MIN Y.Z. An Adaptive Background Adjusting Algorithm for Rail Surface Defect Image Segmentation. DEStech Transactions on Computer Science and Engineering, 2017.
40. Gan J., Li Q., Wang J. and Yu H. A Hierarchical Extractor-Based Visual Rail Surface Inspection System. IEEE Sensors Journal, 2017. V17, #23, pp. 7935–7944.
41. Shang L., Yang Q., Wang J., Li S. and Lei W. Detection of rail surface defects based on CNN image recognition and classification. 20th International Conference on Advanced Communication Technology (ICACT), 2018. pp. 45–51.
42. Faghih-Roohi S., Hajizadeh S., Núñez A., Babuska R. and De Schutter B. Deep convolutional neural networks for detection of rail surface defects. International Joint Conference Neural Networks (IJCNN), 2016. pp. 2584–2589.
43. Liu Z., Wang W., Zhang X. and Jia W. Inspection of rail surface defects based on image processing. International Asia Conference on Automation and Robotics Informatics in Control, 2nd, 2010. V1, pp. 472–475.
44. Dubey A., and Jaffery Z. Maximally Stable Extremal Region Marking (MSERM) based Railway Track Surface Defect Sensing, IEEE Sensors Journal, 2016.
45. Steenbergen M. and Dollevoet R. On the mechanism of squat formation on train rails–Part I: Origination, International Journal of Fatigue, 2013. V47, pp. 361–372.
46. Tastimur C., Yetis H., Karaköse M. and Akın E., Rail Defect Detection and Classification with Real Time Image Processing Technique. International Journal of Computer Science and Software Engineering (IJCSSE), V5,#12, pp. 283–290.
47. Zheng S. Chai X. An X., Li L. Railway Track Gauge Inspection Method Based on Computer Vision, International Conference on Mechatronics and Automation (ICMA), 2012. pp. 1292–1296.
48. CIE140:2000 – Road Lighting Calculations. CIE140, International Commission on Illumination, Road Lighting Calculations, Vienna-Austria, 33 (2000).
49. CIE140:2019 Road Lighting Calculations. CIE140, International Commission on Illumination, 2nd Edition ISBN:978–3–902842–56–5.
50. Cengiz Ç., Kaynaklı M., Gencer G., Eren M., Yapici İ., Yildirim S., Cengiz M.S., Selection Criteria and Economic Analysis of LEDs, Book of Abstracts, Imeset Int. Conf. Mult. Sci. Eng. Tech., October 27–29, 2017, Bitlis, Turkey.
51. Gencer G., Eren M., Yildirim S., Kaynaklı M., Palta O., Cengiz M.S., Cengiz Ç., Numerical Approach to City Road Lighting Standards, Book of Abstracts, Imeset Int. Conf. Mult. Sci. Eng. Tech., October 27–29, 2017, Bitlis, Turkey.
52. Yıldırım S., Yapıcı İ., Atiç S., Eren M., Palta O., Cengiz Ç., Cengiz M.S., Yurci Y., Numerical Analysis of Productivity and Redemption Periods in LED Illimunation. Imeset Book of Abstracts, Int. Conf. Mult. Sci. Eng. Tech., 12–14 July 2017, Baku.
53. Onaygil S., TEDAŞ Genel Müdürlüğü Meslek İçi Eğitim Semineri, TEDAŞ Basımevi, Ankara, 2005, pp. 1–70.
54. Onaygil S., 2007. TEDAŞ Genel Müdürlüğü Meslek İçi Eğitim Semineri-Gölbaşı Eğitim Tesisleri, Yol aydnlatma Semineri 23–24 Ocak 2007.
55. Güler Ö. & Onaygil S., The effect of luminance uniformity on visibility level in road lighting, Lighting Research Technology, 2002. V35, pp. 199–215.
56. Çıbuk M., Cengiz M.S. Determination of Energy Consumption According to Wireless Network Topologies in Grid-Free Lighting Systems. Light & Engineering, 2020. V28, #2, pp. 67–76.
57. Çıbuk M., Reducing Energy Consumption in Single-Hop and Multi-Hop Topologies of Road Lighting Communication Network, 2020. V28, #4 pp. 91–102.
58. Efe S. B., UPFC Based Real-Time Optimization of Power Systems for Dynamic Voltage Regulation. Computer Modeling in Engineering & Sciences, 2018. V116, #3, pp. 391–406.
59. Efe S. B., Cebeci M. Power Flow Analysis by Artificial Neural Network. International Journal of Energy and Power Engineering, 2013. V2, #6, pp. 204–208.
60. Cengiz, M.S. Simulation and Design Study for Interior Zone Luminance In unnel Lighting. Light & Engineering, 2019. V27, #2, pp. 42–51.
61. Cengiz M.S. Effects of Luminaire Angle and Illumination Topology on Illumination Parameters in Road Lighting. Light & Engineering, 2020. V28, #4 pp. 47–56.

In this study, design and implementation of a new cost-efficient daylight-based lighting control system is proposed to provide energy saving in a public building with a conventional lighting system. Energy gain recovery and regional daylight utilization coefficients are obtained by conducting daylight measurements in all indoor spaces of the building where the proposed lighting system will be applied. Daylight value is continuously transferred to the control system through the pyranometer placed outside and the need for artificial lighting is calculated by using sectional daylight utilization coefficients. Thereby, maximum benefit from daylight is realized when unnecessary energy consumption for artificial lighting is reduced. Experimental measurement results show that the proposed daylight-based lighting control system provides an average energy efficiency of the building at the level of 60 %. Additionally, the required investment, such as operating cost and payback period for converting an existing conventional lighting system into the proposed system, are discussed in detail. Cost analysis shows that the payback period of the proposed system can be reduced by 5 years compared to the conventional system.

1. Paz J.F.D., Bajo J., Rodríguez S., Villarrubia G., Corchado J.M., Intelligent system for lighting control in smart cities, Information Sciences, 2016. V372, pp. 241–255.
2. Radulovic D., Skok S., Kirincic V., Energy efficiency public lighting management in the cities, Energy, 2011. V36, #4, pp. 1908–1915.
3. Zou H., Zhou Y., Jiang H, Chien Z.C., Xie L., Spanos C.J., WinLight: A WiFi-based occupancy-driven lighting control system for smart building, Energy and Buildings, 2018. V158, pp. 924–938.
4. Rosenberg E., Calculation method for electricity end-use for residential lighting, Energy, 2014. V66, pp. 295–304.
5. Yahiaoui A., Experimental study on modelling and control of lighting components in a test-cell building, Solar Energy, 2018. V166, pp. 390–408.
6. Darula S., Review of the current state and future development in standardizing natural lighting in ınteriors, Light & Engineering, 2018. V26, #4, pp. 5–26.
7. Santamouris M., Dascalaki E., Passive retrofitting of office buildings to improve their energy performance and indoor environment: the office project, Building and Environment, 2002. V37, pp. 575–578.
8. Chi D. A., Moreno D., Navarro J., Correlating daylight availability metric with lighting, heating and cooling energy consumptions, Building and Environment, 2018. V132, pp. 170–180.
9. Doulos L., Tsangrassoulis A., Topalis F.V., Multicriteria decision analysis to select the optimum position and proper field of view of a photosensor, Energy Conversion and Management, 2014. V86, pp. 1069–1077.
10. Barbón A., Pardellas A., Fernández-Rubiera J.A., Barbón N., New daylight fluctuation control in an optical fiber-based daylighting system, Building and Environment, 2019. V153, pp. 35–45.
11. Ovcharov A.T., Selyanin Y.N., Antsupov Y.V., A hybrid illumination complex for combined illumination systems: concepts, state of the problem, practical experience, Light & Engineering, 2018. V26, #2, pp. 20–28.
12. Dubois M.C., Blomsterberg A., Energy saving potential and strategies for electric lighting in future North European, low energy office buildings: A literature review, Energy and Buildings, 2011. V43, pp. 2572–2582.
13. Shen E., Hu J., Patel M., Energy and visual comfort analysis of lighting and daylight control strategies, Building and Environment, 2014. V78, pp. 155–170.
14. Chew I., Kalavally V., Oo N.W., Parkkinen J., Design of an energy-saving controller for an intelligent LED lighting system, Energy and Buildings, 2016. V120, pp. 1–9.
15. Yu X., Su Y., Daylight availability assessment and its potential energy saving estimation –A literature review, Renewable and Sustainable Energy Reviews, 2015. V52, pp. 494–503.
16. Sahana S., Roy B., Development and performance analysis of a cost-effective integrated light controller, Light & Engineering, 2019. V27, #6, pp. 73–81.
17. Alexei K. Soloviev, Nina A. Muraviova, Sergei V. Stetsky, Comfort light environment under natural and combined lighting method of their characteristics definition with subjective expert appraisal using, Light & Engineering, 2018. V26, #3, pp. 124–131.
18. Dun W., Optimization of intelligent illumination in university classroom based on FMRAS control algorithm, Light & Engineering, 2018. V26, #2, pp. 52–59.
19. Cheng R., Classroom lighting energy-saving control system based on machine vision technology, Light & Engineering, 2018. V26, #4, pp. 143–149.
20. Jain S., Garg V., A review of open loop control strategies for shades, blinds and integrated lighting by use of real-time daylight prediction methods, Building and Environment, 2018. V135, pp. 352–364.
21. Pandharipande A., Caicedo D., Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches, Energy and Buildings, 2015. V104, pp. 369–377.
22. Soori P.K., Vishwas M., Lighting control strategy for energy efficient office lighting system design, Energy and Buildings, 2013. V66, pp. 329–337.
23. Nikolaevich A. V., Light desynchronosis and heal, Light & Engineering, 2019. V27, #3, pp. 14–25.
24. Ihm P., Nemri A., Krarti M., Estimation of lighting energy savings from daylighting, Building and Environment, 2009. V44, #3, pp. 509–514.
25. Gentile N., Dubois M, Laike T., Daylight harvesting control systems design recommendations based on a literature review, Environment and Electrical Engineering (EEEIC), 2015 IEEE15th International Conference on Rome, Italy, 10–13 June, ISBN: 978–1–4799–7994–3, 2015. pp. 632–637.
26. Pandharipande A., Willems F.M.J., Daylight-adaptive lighting control using light sensor calibration prior-information, Energy and Buildings, 2014. V73, pp 105–114.
27. Tang S., Kalavally V., Ng K.Y., Parkkinen J., Development of a prototype smart home intelligent lighting control architecture using sensors onboard a mobile computing system, Energy and Buildings, 2017. V138, pp. 368–376.
28. Meugheuvel N.V.D.M., Pandharipande A., Caicedo D., Hof P.P.J.V.D., Distributed lighting control with daylight and occupancy adaptation, Energy and Buildings, 2014. V75, pp. 321–329.
29. Türkiye Cumhuriyeti Enerji Piyasası Düzenleme Kurumu [Republic of Turkey Energy Market Regulatory Authority]. URL: https://www.epdk.org.tr/Detay/Icerik/3–0–1/tarifeler (referance date: 29.10.2019)