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2022 Vol.31, Issue 3 Preview Page

Original Articles

31 July 2022. pp. 152-162
Ahn W.Y., and H.C. Lee 2016, Automatic control system for cultivation environment of crops. J Korea Inst Inf Commun Eng 20:2167-2171. (in Korean) doi:10.6109/jkiice.2016.20.11.2167 10.6109/jkiice.2016.20.11.2167
Ariga M., S. Nakayama, and D. Nishibayasi 2018, Machine learning at work. O'Reilly Media, CA, USA, pp 137-140.
Chang Z., Y. Zhang, and W. Chen, 2019, Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187:115804. doi:10.1016/ 10.1016/
Charu C.A. 2018, Neural networks and deep learning: a Textbook. Springer, Heidelberg, Germany, pp 123-127.
Cho K., B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. doi:10.48550/arXiv.1406.1078 10.3115/v1/D14-1179
Cho K.J., K.Y. Kim, and W.M. Yang 2015, Survey of ICT apply to plastic greenhouse, rack·pinion adaption to single span and CFD analysis. Protected Hort Plant Fac 24:308-316. (in Korean) doi:10.12791/KSBEC.2015.24.4.308 10.12791/KSBEC.2015.24.4.308
Choi H.Y., T.W. Moon, D.H. Jung, and J.E. Son 2019, Prediction of air temperature and relative humidity in greenhouse via a multilayer perceptron using environmental factors. Protected Hort Plant Fac 28:95-103. (in Korean) doi:10.12791/KSBEC.2019.28.2.95 10.12791/KSBEC.2019.28.2.95
Choi Y.S., H.J. Lee, and S.T. Joung 2012, A design and implementation of web-based green house automation system. J Korea Inst Electron Commun Sci 1519-1527. (in Korean) doi:10.13067/JKIECS.2012.7.6.1519 10.13067/JKIECS.2012.7.6.1519
Fatnassi H., C. Poncet, M.M. Bazzano, R. Brun, and N. Bertin 2015, A numerical simulation of the photovoltaic greenhouse microclimate. Solar Energy 120:575-584. doi:10.1016/j.solener.2015.07.019 10.1016/j.solener.2015.07.019
François C. 2017, Deep learning with Python. Manning Publications Company, NY, USA, pp 79-92.
Gabriel K.R. 1971, The biplot graphic display of matrices with application to principal component analysis. Biometrika 58:453-467. doi:10.1093/biomet/58.3.453 10.1093/biomet/58.3.453
Hong S.W., and I.B. Lee 2014, Predictive model of microenvironment in a naturally ventilated greenhouse for a modelbased control approach. Protected Hort Plant Fac 23:181-191. (in Korean) doi:10.12791/KSBEC.2014.23.3.181 10.12791/KSBEC.2014.23.3.181
Hope T., Y.S. Resheff, and I. Lieder 2017, Learning tensorflow: A guide to building deep learning systems. O'Reilly Media, CA, USA, pp 84-91.
Huh M.H. 2017, Representing variables in the latent space. Korean J Appl Stat 30:555-566. (in Korean) doi:10.5351/KJAS.2017.30.4.555 10.5351/KJAS.2017.30.4.555
Hwang I.C., H. Noh, D. Yang, and M. Kim 2021, Prediction of paprika yield using multiple linear regression. J Korean Inst Commun 46:21-11. (in Korean) doi:10.7840/kics.2021.46.11.2048 10.7840/kics.2021.46.11.2048
Kim D.H., B.M. Jenkins, T.R. Rumsey, M.W. Yore, and N.J. Kim 2007, Simulation and model validation of a horizontal shallow basin solar concentrator. Solar Energy 81(4):463-475. doi:10.1016/j.solener.2006.08.007 10.1016/j.solener.2006.08.007
Kim H.S. 2001, Prediction of cooling effect for fog cooling system in greenhouse by CFD simulation. Master Diss., Seoul National University, Seoul, Korea, pp 1-45. (in Korean)
Kim S.Y., and Y.J. Jung 2017, First learning machine learning. Hanbit Media, Seoul, Korea, pp 137-140. (in Korean)
Kwon H.W., K.C. Oh, Y. Choi, Y.G. Chung, and J. Kim 2021, Development and application of machine learning‐based prediction model for distillation column. Int J Intell 36:1970-1997. doi:10.1002/int.22368 10.1002/int.22368
Lee I.B., and T.H. Short 1999, Analysis of the efficiency of natural ventilation in multi-span greenhouse using CFD simulation. J Bio-Env Con 8:9-18. (in Korean)
Lee I.B., N.K. Yun, T. Boulard, J.C. Roy, S.H. Lee, G.W. Kim, S.K. Lee, and S.H. Kwon 2006a, Development of an aerodynamic simulation for studying microclimate of plantcanopy in greenhouse: (1) Study on aerodynamic resistance of tomato canopy through wind tunnel experiment. J Bio-Env Con 15:289-295. (in Korean)
Lee I.B., N.K. Yun, T. Boulard, J.C. Roy, S.H. Lee, G.W. Kim, S.W. Hong, and S.H. Sung 2006b, Development of an aerodynamic simulation for studying microclimate of plantcanopy in greenhouse: (2) Development of CFD model to study the effect of tomato plants on internal climate of greenhouse. J Bio-Env Con 15:296-305. (in Korean)
Lee J.K., J.W. Oh, Y.J. Cho, and D.H. Lee 2020, A research about time domain estimation method for greenhouse environmental factors based on artificial intelligence. Protected Hort Plant Fac 3:277-284. (in Korean) doi:10.12791/KSBEC.2020.29.3.277 10.12791/KSBEC.2020.29.3.277
Muller A.C., and S. Guido 2017, Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, CA, USA, pp 27-31.
Na M.H, Y. Pack, and W. Cho 2017, A study on optimal environmental factors of tomato using smart farm data. J Korean Data Inf Sci Soc 28:1427-1435 doi:10.7465/jkdi.2017.28.6.1427 10.7465/jkdi.2017.28.6.1427
Oh K.C., H.W. Kwon, J.W. Roh, Y.Y. Cho, H.D. Park, H.T. Cho, and J.H. Kim 2020, Development of machine learningbased platform for distillation column. Korean Chem Eng Res 58:565-572. (in Korean) doi:10.9713/kcer.2020.58.4.565 10.9713/kcer.2020.58.4.565
Park Y.M., S.M. Gang, J.H. Chae, and J.J. Lee 2018, Classification method of plant leaf using DenseNet. J Korea Multimedia Soc 21:571-582. doi:10.9717/kmms.2018.21.5.571 10.9717/kmms.2018.21.5.571
Song Y.E., A.Y. Moon, S.Y. An, and H.Y. Jung 2019, Prediction of smart greenhouse temperature-humidity based on multidimensional LSTMs. J Korean Soc Precis Eng 36(3):239-246. (in Korean) doi:10.7736/KSPE.2019.36.3.239 10.7736/KSPE.2019.36.3.239
Tadj N., T. Bartzanas, D. Fidaros, B. Draoui, and C. Kittas 2010, Influence of heating system on greenhouse microclimate distribution. Trans ASABE 53:225-238. doi:10.13031/2013.29498 10.13031/2013.29498
Yu I.H., N.K. Yun, M.W. Cho, H.R. Ryu, and D.G. Moon 2014, Development of CFD model for analyzing the air flow and temperature distribution in greenhouse with air-circulation fans. Korean J Agric Sci 41:461-472. (in Korean) doi:10.7744/cnujas.2014.41.4.461 10.7744/cnujas.2014.41.4.461
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 31
  • No :3
  • Pages :152-162
  • Received Date :2022. 03. 21
  • Revised Date :2022. 06. 10
  • Accepted Date : 2022. 06. 20