• 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 the Korean Geophysical Society
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• v.2 no.3
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• pp.209-216
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• 1999

In order to get geologic information necessary for underground installation of water pipe, seismic refraction profiling was applied to the southwest side of the Seomjin River which flows between Namwon-gun, Cholabuk-do and Gokseong-gun, Cholanam-do. Before obtaining the in-line refraction data, walkaway data were recorded with 1 m geophone interval and -36∼+36 m offset range. From the walkaway data, it is interpreted that a dry soil layer with the average velocity of 585 m/s covers wet sediments with the average velocity of 1,326 m/s. The second layer overlies basements nearly horizontally with the average velocity of 4,218 m/s. Refraction profiling of 220 m long with the geophone interval of 2 m is interpreted with the Generalized Reciprocal Method (GRM). Three layers are identified with average velocities of 688 m/s, 1,473 m/s, and 3,776 m/s, respectively. The depth to the bedrock impossible for ripping ranges between two extremes, 1.51∼2.43 m and 2.25∼3.54 m, depending upon thickness of the hidden layer. A typical shortcoming of refraction method, the hidden layer problem, prevents accurate estimation in depth of the second layer.