• Journal of the Korean Solar Energy Society
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• v.36 no.5
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• pp.31-50
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• 2016

To develope a greenhouse fog cooling system to control the temperature and relative humidity simultaneously to the target value, a theoretical analysis and experiments were done. The control process includes the measuring of environmental variables, setting and coding of the water and heat balance equations to maintain the target temperature and relative humidity in greenhouse, calculating of the open level of the greenhouse roof window that governs the natural ventilation and spray water quantity, and operating of the motor to open/close the roof window and pump to spray for water. The study results were shown to be very good because the average air temperature in the greenhouse was kept to be about $28.2^{\circ}C$ with the standard deviation of about $0.37^{\circ}C$ compared to the target temperature of $28^{\circ}C$ and the average relative humidity was about 75.2% compared to the target relative humidity was 75% during the experiments. The average outside relative humidity was about 41.0% and the average outside temperature was $27.2^{\circ}C$ with the standard deviation of about $0.54^{\circ}C$. The average solar intensity in the greenhouse was 712.9 W. The wind velocity of outside greenhouse was 0.558 m/s with the standard deviation of 0.46 m/s.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.110-113
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• 2005

Relative humidity of air In the greenhouse has to be maintained at 70 to 80 percents to provide a better growth condition of crops. To control relative humidity of air in the greenhouse, a dehumidifier functioning by refrigeration cycle was designed and manufactured in this study. And, results of its performance test in the greenhouse site were reported. The developed dehumidifier has separated condenser and evaporator in the heat exchanger part in order to increase dehumidifying capacity at a low temperature condition. When the conditions of incoming air into the dehumidifier were temperature of $15\~25^{\circ}C$ and relative humidity of $0\~95\%$, quantity of condensed water per hour, ie, dehumidification rate was $4.7\~7.0\;kg/hr$. Relative humidity difference was not greater than 5 percents at various locations in the greenhouse due to proper distributing of dehumidified air through vinyl duct. Thermal energy output from the developed dehumidifier was about 8,5000 kcal/hr that was 7 percents of maximum greenhouse heating load of 10 a.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.247-255
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• 2007

During the winter season in Korea, the relative humidity of greenhouse at night often exceeds 90% because air temperature inside the greenhouse is usually controlled using a heater with all of windows closed to minimize heat loss, thereby requiring the use of a dehumidifier that can maintain optimum humidity levels of $70{\sim}80%$ to provide a good growth condition of crops. Also, such a high humid condition can cause the development of a pest, such as insects, fungi or diseases. However, the use of most conventional dehumidifiers for low temperature dehumidification is limited because their performance is degraded due to frost accumulation on the evaporator coil. This study was carried out to develop a refrigeratory-based dehumidifier suitable for low temperature dehumidification in greenhouse cultivation. The developed dehumidifier consists of a condenser and an evaporator installed separately so that relative and absolute humidity levels can be reduced when air passed through the condenser and evaporator, respectively. The prototype dehumidifier showed a dehumidification capacity of $5{\sim}7kg/h$ when air with a temperature of $15{\sim}25^{\circ}C$ and a relative humidity of $70{\sim}95%$ came into the dehumidifier. Under the condition that either temperature or relative humidity was fixed, the amount of condensed water was proportional to the levels of both temperature and relative humidity.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.262-268
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• 1998

This study were performed to find the cooling effect and to provide design data during summer season. for the Pad ＆ Fan system of Venlo type greenhouse. The temperature and humidity variation along the greenhouse width and wind velocity in the greenhouse were surveyed. Also, the influence of shading on the cooling effect were analyzed. While Pad ＆ Fan system were operated, the temperature, humidity and wind velocity in greenhouse were different by the distance from the Pad and the height from the ground. The temperature difference between Pad and Fan was about 8.1$^{\circ}C$~10.4$^{\circ}C$ without shading and about 4.4$^{\circ}C$~5.5$^{\circ}C$ with shading.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.935-942
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• 2015

According to growing of the plant cultivation in a greenhouse environment, the demand of a system to control the greenhouse easier has increased. Currently, the methods to control by the mobile App represent the information in a greenhouse environment with a simple numerical data or compose only the contents with a limited degree of freedom. In order to solve these problems, this paper presents a system that can be viewed or controlled greenhouse conditions in near / remote distance using augmented reality and MQTT communication protocol, RTSP media streaming protocol. The proposed method is implemented in Android smartphone environment and acts monitoring the information (temperature, humidity, illuminance) obtained by greenhouse's sensors and transmits the real time greenhouse's video using RTSP in the remote distance, and controls the values of temperature, humidity, illuminance for the greenhouse using the augmented reality in the near distance.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.93-99
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• 2017

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

• Journal of Bio-Environment Control
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• v.10 no.1
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• pp.10-14
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• 2001

The cooling effect of a fog cooling system has a close relationship to air flow and relative humidity in the greenhouse. From the VETH chart for cooling design, a cooling efficiency can be improved by means of increasing the air exchange rate and the amount of sprayed water. In the no shading experimental greenhouse by time control, when average air exchange rate was 0.77 times.min$^{-1}$ and spray water amount was 2,009g, inside temperature of the greenhouse was 31$^{\circ}C$ that was almost close to outside temperature and cooling efficiency was 82%. When average air exchange rate was close to temperature of the greenhouse that was no cooling and 70% shading greenhouse environment. When average air exchange rate was 2.59times.min$^{-1}$ , spray water amount was 2,009g and shading rate was 70%, inside relative humidity of the greenhouse was increased was 2,009 g and shading rate was 70%, inside relative humidity of the greenhouse was increased, but temperature was not decreased. When average air exchange rate was 2.33 times.min$^{-1}$ and spray water amount was 2,009g, inside temperature was 31.4 and at that time maximum wind speed at the air inlet of greenhouse was 1.9m.s$^{-1}$ . Since time controller sprayed amount of constant water at a given interval, some of sprayed water remained not to be evaporated, which increased relative humidity and decreased cooling efficiency. Because the shading screen prevented air flow in the greenhouse, it also caused the evaporation efficiency to be decreased. In order to increase cooling efficiency, it was necessary to study on controling by relative humidity and air circulation in the greenhouse.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.2
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• pp.39-45
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• 2012

In recent years the USN(Ubiquitous Sensor Networks) technology has been applied in the greenhouse in order to control temperature and humidity automatically. In this paper, we proposed a control algorithm using feedback linearization techniques based on a mathematical model for temperature and humidity environment. Especially, Control algorithm is presented to the operation of the ventilator affecting on the temperature and humidity system at the same time. The System has been designed taking into account the disturbance(External temperature, soil temperature, external humidity, solar radiation and wind). In conclusion, I will present a way to control multiple actuator through simulations. The proposed control algorithm is validated using the Matlab/Simulink tools.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.250-254
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• 1998

This paper shows how the environmental parameters(temperature, humidity) in the greenhouse are influenced by air movement produced by air circulation fans. When the fans were used, they could make indoor temperature and humidity homogenious, but there was no significant difference in the location and number of fans. When the fans were not used during the night time, there was no significant difference in the temperature and humidity, but the standard deviation was reduced by using the fans.