Mobile QR Code QR CODE : Journal of the Korean Institute of Illuminating and Electrical Installation Engineers

Journal of the Korean Institute of Illuminating and Electrical Installation Engineers

ISO Journal TitleJ Korean Inst. IIIum. Electr. Install. Eng.
Online Submission

Journal Search

  1. Home
  2. Archive
  3. 2024-02 (Vol.38 No.1)
  4. 10.5207/JIEIE.2024.38.1.001
XML PDF INFO REF

References

1 
I. Chew, D. Karunatilaka, C.P. Tan, and V. Kalavally, “Smart lighting: The way forward? Reviewing the past to shape the future,” Energy and Buildings, vol. 149, pp. 180-191, 2017.DOI
2 
T. Labeodan, C. D. Bakker, A. Rosemann, and W. Zeiler, “On the application of wireless sensors and actuators network in existing buildings for occupancy detection and occupancy-driven lighting control,” Energy and Buildings, vol. 127, pp. 75-83, 2016.DOI
3 
H. Zou, et al., “WinLight: A WiFi-based occupancy-driven lighting control system for smart building,” Energy and Buildings, vol. 158, pp. 924-938, 2018.DOI
4 
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.DOI
5 
S. W. Cain, et al., “Evening home lighting adversely impacts the circadian system and sleep,” Scientific Reports, vol. 10, no. 1, pp. 1-10, 2000.DOI
6 
A. Pandharipande and G. R. Newsham, “Lighting controls: Evolution and revolution,” Lighting Research & Technology, vol. 50, no. 1, pp. 115-128, 2018.DOI
7 
A. Zarindast and J. Wood, “A data-driven personalized smart lighting recommender system,” Front. in Big Data, vol. 14, no. 4, p. 706117, 2021.DOI
8 
K. Choi and H. J. Suk, “Dynamic lighting system for the learning environment: Performance of elementary students,” Optics Express, vol. 24, no. 10, pp. A907-16, 2016.DOI
9 
D. Cupkova, et al., “Intelligent human-centric lighting for mental wellbeing improvement,” International Journal of Distributed Sensor Networks, vol. 15, no. 9, 2019.DOI
10 
S. H. Yoon, et al., “Indoor surveillance camera based human centric lighting control for smart building lighting management,” International Journal of Advanced Culture Technology, vol. 8, no. 1, pp. 207-212, 2020.URL
11 
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.DOI
12 
S. Suresh, et al., “Automatic lighting and control system for classroom,” 2016 International Conference on ICT in Business Industry & Government (ICTBIG), pp. 1-6, 2016.URL
13 
A. Pandharipande and G. R. Newsham, “Lighting controls: Evolution and revolution,” Lighting Research & Technology, vol. 50, no. 1, pp. 115-128, 2018.DOI
14 
Korea Meteorological Administration (KMA), “Open MET data portal,” [online] Available:http://www.data.kma.go.kr (Accessed 9 JAN 2024)URL
15 
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, p. 9840, 2022.DOI
16 
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.DOI
17 
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.URL
18 
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.DOI
19 
M. Tan and P. Shao, “Prediction of student dropout in e-Learning program through the use of machine learning method,” International Journal of Emerging Technologies in Learning, vol. 10, no. 1, pp. 11-17, 2015.URL
20 
C. Sitaula and M. B. Hossain, “Attention-based VGG-16 model for COVID-19 chest X-ray image classification,” Applied Intelligence, vol. 51, pp. 2850-2863, 2021.DOI
21 
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,” Annu Int Conf IEEE Eng Med Biol Soc., pp. 5570-5573, 2012.DOI
22 
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.DOI
23 
S. B. Choi, J. Y. Choi, J. S. Park, and D. W. Kim, “ATLS hypovolemic shock classification by prediction of blood loss in rats using regression models,” Shock, vol. 46, no. 1, pp. 92-98, 2016.DOI