• Journal of Bio-Environment Control
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• v.23 no.4
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• pp.303-308
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• 2014

Reclaimed lands, expected as high-tech export horticultural complex, have unusual light environments due to sea fog. For adequate greenhouse design at reclaimed land, spatial light distributions in greenhouse should be required considering diffusive and direct lights. The objectives of this study were to analyze light environments and estimate spatial light distributions in greenhouse at reclaimed land by 3D greenhouse models. Total and diffusive lights were compared between reclaimed land and inland. For verification of the 3D greenhouse models, spatial light distributions and measured light intensities in greenhouse were compared with the estimated ones. Light environments at reclaimed land showed a higher diffusive irradiation than at inland, especially near sunrise and sunset. The estimated spatial light distributions in greenhouse showed good agreements with the measured ones. By using this method, we could estimate the average light intensity with time and spatial light distributions in greenhouse at specific outside light conditions. This result will be useful for analysis of light environments but also estimation of crop light inception in greenhouse at reclaimed land.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 1993.10a
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• pp.11-12
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• 1993

여름철 온실의 수경재배를 위해서는 양액의 냉각은 필수적이다. 이와 같은 양액냉각을 적절히 실행하기 위해서는 먼저 시설내의 환경예측에 의한 양액의 냉각부하 산정 및 냉각부하에 따른 설비용량 결정이 필요하다. 그동안 국내외적으로 온실의 환경예측을 위한 모델들은 많이 개발되어 있지만 대부분이 토경재배를 대상으로 온실의 변화나 난방효율의 예측을 목적으로 하고 있으며, 수경재배 온실 더우기 양액 냉각을 취급하는 경우는 찾아보기 힘들다. (중략)

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Journal of Bio-Environment Control
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• v.14 no.2
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• pp.76-82
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• 2005

Evaporative cooling pad system is one of the main cooling methods in greenhouses and its efficiency is very high. However, it has some disadvantages such as greenhouse temperature distributions are not uniform and installation cost is expensive. In this study, a CFD simulation model f3r predicting the air temperature distribution in the fan and fad cooling greenhouse was developed. The model was calibrated and validated against experimental data and a good fit was obtained. The influence of different outside wind, fan and pad height, ventilation rate, shading, and greenhouse length, were then examined. In order to reduce the internal temperature gradients, it is desired that the prevail wind direction and the fan and pad heights are considered. The simulation indicates that high ventilation rates and shading contribute to reduce the temperature gradients in the fan and pad cooling greenhouse. In order to maintain the desired greenhouse temperature, the pad-to-fan distance should be restricted according to the design climate conditions, shading and ventilation rates. The developed CFD model can be a useful tool to evaluate and design the fan and pad systems in the greenhouses with various configurations.

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.107-108
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• 2012

본 논문은 건설산업의 신뢰성 있는 온실가스 추정을 위해 건설단계에서 사용되는 장비, 자재 및 에너지 사용량에 따른 온실가스 배출량 추정방법론을 고찰하였다. 그리고 온실가스 배출량 추정모델인 Road Construction Emission Model과 시설물탄소배출량 산정 프로그램을 비교분석하였다. 건설장비만을 고려한 배출량 추정은 사용장비의 각종 특성에 따라 차이가 발생할 수 있고, 자재 및 에너지사용에 따른 배출량 추정은 장비의 사용이 대부분을 차지하는 토목공사에서 배제한 단점이 있다. 따라서, 건설산업의 온실가스배출량 측정은 장비의 사용 또는 자재 및 에너지사용에 따른 포괄적인 온실가스 배출량 추정과 이를 위한 기반확충이 이루어져야 한다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2001.11a
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• pp.59-62
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• 2001

여름철 온실내 고온 문제를 해결하기 위해 이용되는 자연환기형 포그냉방은 환기가 충분치 못할 경우 온실 내부의 습도가 증가하여 증발 효율이 떨어지는 문제가 발생한다. 제습장치를 이용하여 온실 내부의 상대습도를 낮추면 증발 냉각 효율을 높일 수 있을 것으로 생각된다. 본 연구에서는 제습장치를 이용한 포그냉방 온실에 대한 CFD 모델을 개발하여 온실의 열환경 및 수분 환경을 분석하고자 한다. (중략)

• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.280-287
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• 2012

The sources of greenhouse gases (GHG) at Hanyang University Ansan campus, including direct sources, indirect sources, and others, were investigated in order to establish the GHG inventory. Emission of GHG was calculated with the energy use from each source from 2007 and 2009. The indirect emission (56.7%) due to the electricity significantly contributed to total GHG emission. The scenario for the GHG reduction was designed for both campus administration and members. The reduction potential of GHG was simulated from 2007 to 2020 using Long-range Energy Alternatives Planning (LEAP) model. In case of GHG reduction scenario by campus administration, the GHG can be reduced by 63.34 ton $CO_{2eq}/yr$ for stationary combustion in the direct source, by 221.1 ton $CO_{2eq}/yr$ for mobile combustion in the direct source, and by 4,637.34 ton $CO_{2eq}/yr$ for lighting in the indirect source, compared to 2020 Business As Usual (BAU). In case of GHG reduction action scenario by campus members, the reduction potential of GHG was 1293.76 ton $CO_{2eq}/yr$. Overall, the total GHG emissions in 2020 by the both scenarios can be decreased by 24% compared to 2020 BAU.

• Journal of the Korea Computer Industry Society
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• v.8 no.1
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• pp.41-48
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• 2007

In this paper, a example of a development of web-based remote monitoring and control system using Java technology on TCP/IP is proposed. We implemented Client/Server socket programs using Java Socket class, and applied those to a greenhouse made from LEGO blocks and made web-based greenhouse remote monitoring and control system. Implemented System transferred states of greenhouse to client programs, and control signals from client program were transferred to Server. Greenhouse model was controlled well by the system.

• Journal of Bio-Environment Control
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• v.7 no.4
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• pp.290-297
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• 1998

In this study an optimization of greenhouse roof shape was performed to maximize solar light transmission which is one of the most important elements in greenhouse environment. To determine roof shape that maximize the total light transmissivity, a computer model for analysing light transmissivity was composed and the Genetic Algorithms was applied for solving optimization problems. By setting composite model as objective function(fitness function), the optimum combination of design variables(roof inclination angle, width ratio) was searched using Genetic Algorithms. The optimum combination of input variables for the maximum light transmissivity at Suwon in winter was found 40 degree root angle , 0.5 width ratio, for two span greenhouses and 37 $_。 / roof angle, 0.7 width ratio, for single span greenhouses.es.