• Tunnel and Underground Space
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• v.19 no.6
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• pp.498-506
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• 2009

The stability assessment of a concrete liner of a compressed air storage tunnel should be performed by an approach which is different from that commonly used for the liners of road tunnels, since the liner is exposed to high air pressure. In this study, the stability analysis method for the liner of compressed air storage tunnel is proposed based on the elastic and elasto-plastic solutions of the thick-walled cylinder problem. In case of elastic analysis, the yield initiation condition at the inner boundary is considered as the failure condition of the liner, while the condition which results in the extension of yielding zone to a certain depth is taken as a failure indicator of the liner in the elasto-plastic analysis taking Mohr-Coulomb criterion. The application of the proposed method revealed that the influence of the relative magnitude of boundary loads on the stability of liner is considerable. In particular, noting that the estimation of the outer boundary load may be relatively difficult, it is thought that the precise prediction of outer boundary load is very important in the analysis. Accordingly, the emphasis is put on the selection of the liner installation time, which may govern the magnitude of outer boundary load.

• Korean Journal of Soil Science and Fertilizer
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• v.18 no.2
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• pp.169-176
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• 1985

Six analytial methods for determing the available nitrogen in soils were tested to predict the optimum fertilizer rate for the flue-cured tobacco and to test the fertility level of soils for tobacco. All methods, nitrifiable $NO_3-N$ value for 2 and 4 weeks incubation, UV absorption value at 260nm and N-value in acid digestion of 0.01 M-$NaHCO_3$ extracts, N-value extracted in boiling with $CaCl_2$ solution, and autoclave-extractable $NH_4-N$ value in 0.01 M-$CaCl_2$, were closely correlated with total nitrogen uptake as well as yield. Therefore available nitrogen indices determined from above 6 analysis method could be used for the predicting of tobacco yield without fertilizer, criteria for fertility class, and recommendable range of optimum fertilization.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1243-1252
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• 2019

The purpose of this study is to investigate the optimum conditions of biomass pretreatment with potassium hydroxide (KOH) for efficient utilization of cellulose, hemicellulose and lignin from Miscanthus. The optimization of variables was performed by response surface methodology (RSM). The variation ranges of the parameters for the RSM were potassium hydroxide 0.2~0.8 M, reaction temperature 110~190℃ and reaction time 10~90 min. The optimum conditions of alkali pretreatment from Miscanthus were determined as follows: concentration of KOH 0.47 M, reaction temperature 134℃ and reaction time 65 min. At the optimum conditions, the yield of cellulose from the solid fraction after pretreatment was predicted to be 95% by model prediction. Finally, 66.1 ± 1.1% of cellulose were obtained by verification experiment under the optimum conditions. The order contents of solid extraction were hemicellulose 26.4 ± 0.4%, lignin 3.7 ± 0.1% and ash 0.5 ± 0.04%. The yield of ethanol concentration of 96% was obtained using separated saccharification and fermentation.

• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.174-183
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• 2018

This study aims to make the stand density management diagram which is useful for establishing stand density management system in Chamaecyparis obtusa forest. By using 216 sample plots to estimate Yield-Density relationship ($R^2=0.743$), the stand density management diagram was modeled by the estimated parameters. As a result of this diagram, after planting 3,000 trees per hectare the mortality rate of this unthinned C. obtusa stands over 80 years was estimated to be equal to $12.0{\sim}18.1trees{\cdot}ha^{-1}{\cdot}year^{-1}$, and stand volume was $463.1{\sim}695.4m^3{\cdot}ha^{-1}$, and stand density was $1,555{\sim}2,038trees{\cdot}ha^{-1}$. Developed stand density management diagram for C. obtusa is effective to establish the management criteria and production objective. Therefore, this study allowed us to make the optimal forest working plan.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.195-206
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• 2019

This experiment was carried out to study the effects of different environmental conditions and cultivation techniques on productivity of forage corn in central and southern area of Korea on 2017 and 2018. Average dry matter yield of forage corn at 34 cultivation regions was 13,510kg/ha. Forage productivity of forage corn cultivated at actual production sites have positive correlation with cultivation techniques(p<0.01) but not correlated with cultivation environments. Forage productivity of forage corn have positive correlation with seeding techniques(p<0.01) but not correlated with fertilization techniques. These results suggest that practices of cultivation techniques are more important than cultivation environments for increasing the forage productivity of forage corn. Therefore, yield prediction techniques of forage corn in Korea have to be considered the practices of cultivation techniques along with soil and climate conditions.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.39-62
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• 2020

In this study, Barcelona Basic Model (BBM) was implemented into TOUGH2-MP/FLAC3D for the numerical analysis of coupled thermo-hydro-mechanical (THM) behavior of unsaturated soils and the prediction of long-term behaviors. Similar to the methodology described in a previous study for the implementation of BBM into TOUGH-FLAC, the User Defined Model (UDM) of FLAC based on the Modified Cam Clay Model (MCCM) and the FISH function of FLAC3D were used to extend the existing MCCM module in FLAC3D for the implementation of BBM into TOUGH2-MP/FLAC3D. In the developed BBM module in TOUGH2-MP/FLAC3D, the plastic strains due to change in suction increase (SI) in addition to mean effective stress are calculated. In addition to loading-collapse (LC) yield surface, suction increase (SI) yield surface is changed by hardening rules in the developed BBM module. Several numerical simulations were conducted to verify and validate the implementation of BBM: using an example presented in the FLAC3D manual for the standard MCCM, simulation results using COMSOL, and experimental data presented in SKB Reports. In addition, the developed BBM analysis module was validated by simultaneously performing a series of modeling tests that were performed for the validation of the Quick tools developed for the purpose of effectively deriving BBM parameters, and by comparing the Quick tools and Code_Bright results reported in a previous study.

• Food Science and Preservation
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• v.12 no.4
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• pp.344-349
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• 2005

Microwave-assisted process (MAP) was applied to extract antioxidant-related components from Thymus quinquecostatus var. japonica Hara. Microwave power(2,450 MHz, $0{\sim}160$ W) and extraction time($1{\sim}5\;min$) were used as independent variables($X_i$) for central composite design to yield 10 different extraction conditions. Response surface methodology was applied to predict optimum extraction conditions for dependent variables of extracts, such as total yield, total phenolics, flavonoid, and electron donation ability depending on different powers and extraction times of MAP. Determination coefficients($R^2$) of regression equations for dependent variables were higher than 0.93 excluding that of total phenolics, and microwave power was predicted more influential than extraction time in MAP (p<0.05). The optimal extraction time for each dependent variable was ranged from 3.36 to 4.97 min, but microwave power showed wide ranges depending on variables. The superimposed contour maps for maximized dependent variables illustrated extraction conditions of 64 to 100 W in microwave power and 2.9 to 4.0 min in extraction time.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.4
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• pp.231-242
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• 2018

This experiment was carried out to study the effects of different environmental conditions and production techniques on forage productivity of Italian ryegrass (IRG) in central and southern regions of Korea from 2016 to 2017. Average dry matter yield of 27 IRG cultivation regions was 6,940kg/ha. Forage productivity of IRG have positive correlation with cultivation techniques(p<0.01) but not correlated with cultivation environments. Forage productivity of IRG have positive correlation with seeding and field management techniques(p<0.01) but not correlated with fertilization techniques. This results suggests that practices of cultivation techniques are more important than cultivation environments for increasing the forage productivity of IRG. Therefore, yield prediction techniques of IRG in Korea have to be considered the practices of cultivation techniques along with soil and climate conditions.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.