KIEE - The Transactions of the Korean Institute of Electrical Engineers

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S. W. Cain, E. M. McGlashan, P. Vidafar, J. Mustafovska, S. P. N. Curran, X. Wang, A. Mohamed, V. Kalavally, and A. J. K. Phillips, “Evening home lighting adversely impacts the circadian system and sleep,” Scientific Reports, vol. 10, no. 1, pp. 1-10, 2000.

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P. R. Boyce, N. H. Eklund, and S. N. Simpson, “Individual lighting control: Task performance, mood, and illuminance,” Journal of the Illuminating Engineering Society, vol. 29, no. 1, pp. 131-142, 2000.

3

A. Pandharipande, and G. R. Newsham, “Lighting controls: Evolution and revolution,” Lighting Research & Technology, vol. 50, no. 1, pp. 115-128, 2018.

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K. W. Houser, and T. Esposito, “Human-centric lighting: Foundational considerations and a five-step design process,” Frontiers in Neurology, vol. 12, 2021.

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H. I. Kwon, B. H. Baek, and Y. S. Jeon, “A study of the business model development of human-centric lighting: Based on eco-science methodology,” Energies, vol. 14, no. 16, 2021.

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N. Trivellin, M. Meneghini, M. Ferretti, D. Barbisan, M. D. Lago, G. Meneghesso, and E. Zanoni, “Effects and exploitation of tunable white light for circadian rhythm and human-centric lighting,” Proceedings of IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2015.

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K. Choi, and H. J. Suk, “Dynamic lighting system for the learning environment: Performance of elementary students,” Optics Express, vol. 24, no. 10, pp. A907-A916, 2016.

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E. Guerry, L. Caumon, G. Zissis, C. Caumon, and E. Becheras, “Human-centric lighting for the benefit of the elderly,” Proceedings of International Conference on Energy Efficiency in Domestic Appliances and Lighting & International Symposium on the Science and Technology of Lighting (EEDAL/LS: 17), pp. 1-4, 2022.

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E. N. Madias, E. Patrikios, C. Grigoropoulos, and L. Doulos, “Design and development of a sustainable environmentally friendly and human-centric luminaire,” Proceedings of IOP Conference Series: Earth and Environmental Science, vol. 1123, no. 1, 2022.

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F. Roberts, M. White, S. Memon, B. J. He, and S. Yang, “The application of human-centric lighting in response to working from home post-COVID-19,” Buildings, vol. 13, no. 10, 2023.

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S. R. Lim, D. Kang, O. A. Ogunseitan, and J. M. Schoenung, “Potential environmental impacts of light-emitting diodes (LEDs): Metallic resources, toxicity, and hazardous waste classification,” Environmental Science & Technology, vol. 45, no. 1, pp. 320-327, 2011.

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S. Suresh, H. N. S. Anusha, T. Rajath, P. Soundarya, and S. V. P. Vudatha, “Automatic lighting and control system for classroom,” International Conference on ICT in Business Industry & Government (ICTBIG), pp. 1-6, 2016.

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A. Pandharipande, and G. R. Newsham, “Lighting controls: Evolution and revolution,” Lighting Research & Technology, vol. 50, no. 1, pp. 115-128, 2018.

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X. Yin, and S. L. Keoh, “Personalized ambience: An integration of learning model and intelligent lighting control,” IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 666-671, 2016.

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G. Ma, and X. Pan, “Research on a visual comfort model based on individual preference in China through machine learning algorithm,” Sustainability, vol. 13, no. 14, 2021.

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J. Zhang, Z. Chen, A. Wang, Z. Li, and W. Wan, “Intelligent personalized lighting control system for residents,” Sustainability, vol. 15, no. 21, 2023.

17

A. G. Putrada, M. Abdurohman, D. Perdana, and H. H. Nuha, “Machine learning methods in smart lighting toward achieving user comfort: a survey,” IEEE Access, vol. 10, pp. 45137-45178, 2022.

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X. Fu, D. Feng, X. Jiang, and T. Wu, “The effect of correlated color temperature and illumination level of LED lighting on visual comfort during sustained attention activities,” Sustainability, vol. 15, no. 4, 2023.

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G. M. Huebner, D. T. Shipworth, S. Gauthier, C. Witzel, P. Raynham, and W. J. E. R. Chan, “Saving energy with light? Experimental studies assessing the impact of color temperature on thermal comfort,” Energy Research & Social Science, vol. 15, pp. 45-57, 2016.

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R. Chen, M. C. Tsai, and Y. S. Tsay, “Effect of color temperature and illuminance on psychology, physiology, and productivity: An experimental study,” Energies, vol. 15, no. 12, 2022.

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X. Hao, X. Zhang, J. Du, M. Wang, and Y. Zhang, “Pedestrians’ psychological preferences for urban street lighting with different color temperatures,” Frontiers in Psychology, vol. 13, 2022.

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J. K. Kim, K. B. Lee, and S. G. Hong, “ECG-based biometric authentication using random forest,” Journal of the Institute of Electronics and Information Engineers, vol. 54, no. 6, pp. 100-105, 2017.

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J. D. Suh, “Foreign exchange rate forecasting using the GARCH extended random forest model,” Journal of Industrial Economics and Business, vol. 29, no. 5, pp. 1607-1628, 2016.

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O. M. Mustafa, O. M. Ahmed, and V. A. Saeed, “Comparative analysis of decision tree algorithms using Gini and Entropy criteria on the Forest Covertypes Dataset,” The International Conference on Innovations in Computing Research, pp. 185-193, 2024.

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D. W. Lee, and S. W. Lee, “Hourly prediction of particulate matter (PM2.5) concentration using time series data and random forest,” KIPS Transactions on Software and Data Engineering, vol. 9, no. 4, pp. 129-136, 2020.

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J. W. Chung, “Occupancy detection model in living spaces using random forest and indoor environment measurement data,” Journal of The Institute of Electronics and Information Engineers, vol. 58, no. 9, 2021.

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H. J. Lee, J. I. Choi, S. S. Kim, and J. K. Dong, “A study of illumination of dental laboratories,” Korean Academy of Stomatognathic Function and Occlusion, vol. 30, no. 4, pp. 289-298, 2014.

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Korea Meteorological Administration, Available: https://data.kma.go.kr/cmmn/main.do (Accessed 5 Sep 2024).

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M. Rodionova, A. Skhvediani, and T. Kudryavtseva, “Prediction of crash severity as a way of road safety improvement: The case of Saint Petersburg, Russia,” Sustainability, vol. 14, no. 16, pp. 9840, 2022.

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I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection,” IEEE Access, vol. 6, pp. 33789-33795, 2018.

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A. M. Prasad, L. R. Iverson, and A. Liaw, “Newer classification and regression tree techniques: Bagging and random forests for ecological prediction,” Ecosystems, vol. 9, pp. 181-199, 2006.

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J. Y. Choi, S. K. Kim, J. M. Koo, and D. W. Kim, “A survival prediction model of rats in uncontrolled acute hemorrhagic shock using the random forest classifier,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5570-5573, 2012.

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H. S. Shin, and C. S. Park, “Development of a machine learning model for a chiller using random forest algorithm and data pre-processing,” Journal of the Architectural Institute of Korea Structure & Construction, vol. 33, no. 9, pp. 67-74, 2017.

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Y. Z. Ni, and S. N. Lee, “Comparison of prediction performance of machine learning classification model using 2022 FIBA Men's Basketball Asian Cup match results,” Korean Society for Measurement and Evaluation in Physical Education and Sports Science, vol. 24, 2022.

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J. H. Roh, S. H. Min, and M. S. Kong, “Analysis of fire prediction performance of image classification models based on convolutional neural network,” Fire Science and Engineering, vol. 36, no. 6, pp. 70-77, 2022.

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F. Abramovich, and M. Pensky, “Classification with many classes: Challenges and pluses,” Journal of Multivariate Analysis, vol. 174, 2019.

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers