• Atmosphere
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• v.27 no.1
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• pp.55-65
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• 2017

This study investigates how Eurasian snow cover in spring (March and April) is associated with Korean temperature during summer (June-July-August). Two leading modes of Eurasian snow cover variability in spring for 1979~2015 are obtained by Empirical Orthogonal Function (EOF) analysis. The first EOF mode of Eurasian snow cover is characterized by a zonally elongated pattern over the whole Eurasian region and its principal component is more correlated with Korean temperature during June. On the other hand, the second EOF mode of Eurasian snow cover is characterized by an east-west dipole-like pattern, showing positive anomalies over eastern Eurasian region and negative anomalies over western Eurasian region. This dipole-like pattern is related with Korean temperature during August. The first leading mode of Eurasian snow cover is associated with anomalous high (low) pressure over Korea (Sea of Okhotsk) during June, which might be induced by much evaporation of soil moisture in Eurasia during March. On the other hand, the second mode of Eurasian snow cover is associated with a wave train resembling with Eurasian (EU)-like pattern in relation to the Atlantic sea surface temperature forcing, leading to the anomalous high pressure over Korea during August. Understanding these two leading modes of snow cover in Eurasian continent in spring may contribute to predict Korean summer temperature.

• Journal of the Korean Solar Energy Society
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• v.28 no.1
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• pp.19-24
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• 2008

The energy crisis is culminating for the life of the fossil fuel in the future which is come to end at $30{\sim}40$ years. Moreover above 90% of the energy in our country depend on importing and the crisis is more seγious than it of other countries. So architects devote low energy house research and it means underground space research have become public opinion. But there is not an accurate and utility method calculating the heating load of underground space. In this study it is proposed that the heating load is calculated by setting adiabatic thichness of soil and predicting underground temperature. The prediction of the underground temperature is calculated by the latitude, the level, the distance from sea, the condition of earth surface.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.71-81
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• 2011

Adfreeze bond strength is a primary design parameter, which determines bearing capacity of pile foundation in frozen ground. It is reported that adfreeze bond strength is influenced by various affecting factors like freezing temperature, confining pressure, characteristics of pile surface, soil type, etc. However, several limited researches have been performed to obtain adfreeze bond strength, for past studies considered only few affecting factors such as freezing temperature and type of pile structures. Therefore, there exists a limitation of estimating the design parameter of pile foundation with various factors in frozen ground. In this study, artificial neural network algorithm was involved to predict adfreeze bond strength with various affecting factors. From past five studies, 137 data for various experimental conditions were collected. It was divided by 100 training data and 37 testing data in random manner. Based on the analysis result, it was found that it is necessary to consider various affecting factors for the prediction of adfreeze bond strength and the prediction with artificial neural network algorithm provides enough reliability. In addition, the result of parametric study showed that temperature and pile type are primary affecting factors for adfreeze bond strength. And it was also shown that vertical stress influences only certain temperature zone, and various soil types and loading speeds might cause the change of evolution trend for adfreeze bond strength.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Journal of the Korean GEO-environmental Society
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• v.13 no.11
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• pp.51-60
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• 2012

In order to characterize the shear strength of the frozen sand for foundation design in cold region and prediction of adfreeze bond strength, many researchers developed test techniques and carried out many tests to analyze shear strength properties of the frozen sand for half a century. However, many studies for shear strength properties of the frozen sand have been carried out with limited circumstances, even though shear strength of the froze sand can be affected by various influence factors such as soil type, temperature conditions, and magnitude of normal stress. In this study, direct shear test equipment was used to analyze the shear strength characteristics of the frozen sand. Direct shear test equipment was designed for cold weather, and the direct shear tests were carried out inside of large-scaled low temperature chamber. Three soil types-two uniform sands and one well graded soil were used to analyze the shear strength of the frozen sand with three different temperature conditions and three different vertical confining pressures. In this research, a series of direct shear tests for shear strength of the frozen sand have been conducted to demonstrate the efficiency of effectiveness of the test equipment and low temperature chamber. This research also showed that shear strength of the froze sand increased with decreasing temperature condition, but the influence of vertical confining pressure was insignificant to the shear strength of the frozen sand.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.20 no.4
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• pp.185-191
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• 2010

To effectively utilize resources of reject ash and dredged soil, globular shape-formed artificial lightweight aggregate were manufactured in 8~10 mm size. Starting materials were changed various grading and composition, sintered at $1050{\sim}1250^{\circ}C$. The specific gravity, water absorptance of artificial lightweight aggregates were measured on the basis of the KS. In this study could make a prediction about application of bloating mechanism by ferrous materials and alkali/alkali-earth oxide at high temperature.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.27-35
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• 2007

The majority of farm managers growing the garden products in greenhouse concern massively about the diagnosis and prevention of the breeding and extermination for pests. especially, the managing problem for pests turns up as main issue. In the paper, we first build a wireless sensor network with soil and environment sensors such as illumination, temperature and humidity. And then we design and implement multimedia pest predication and management system which is able to predict and manage various pest of garden products in greenhouse. The proposed system can support the database with information about the pests by building up wireless sensor network in greenhouse compared with existing high-priced PLC device as well as collect various environment information from soil, the interior of greenhouse, and the exterior of greenhouse. To verify the good capability of our system, we implemented several GUI interface corresponding desktop. web, and PDA mobile platform based on real greenhouse model. Finally, we can confirm that our system work well prediction and management of pest of garden products in greenhouse based on several platforms.

• Coupled systems mechanics
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• v.11 no.2
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• pp.107-119
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• 2022

The aim of the work presented in this paper is development of numerical model for prediction of temperature distribution in pavement according to the measured meteorological parameters, with introduction of non-linear heat transfer coefficient which is a function of temerature difference between the air and the pavement. Developed model calculates heat radiated from the pavement back in the air, which is an important part of the heat trasfer process in the open air surfaces. Temperature of the pavement surface, heat radiation together with many meteorological parameters were measured in series during two years in order to validate the model and calibrate model parameters. Special finite element method for temperature heat transfer towards the soil together with the time integration scheme are used to solve the governing equation. It is proved that non-linear heat transfer coefficient, which is a function of time and temperature difference between the air and the pavement, is required to decribe this phenomena. Proposed model includes heat tranfer coefficient callibration for specific climate region, through the iterative inverse procedure.

• Water for future
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• v.26 no.2
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• pp.99-105
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• 1993

A practical flood forecasting model(FFM) is suggested. The output of the model is the results which the initial condition of meteorological parameters and soil moisture are projected on the future. The physically based station model for rainfall forecasting(RF) and the storage function model for runoff prediction(RP) are adopted respectively. Input variables for FFM are air temperature, pressure, and dew-point temperature at the ground level and the flow at the rising limb(FRL). The constant parameters for FFM are average of optimum values which the past storm events have. Also loss rate of rainfall can predicted by FRL.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.173-180
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• 2004

This paper presents the effect of varying boundary conditions such as ground subsidence, internal pressure and temperature variation for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function incorporating with von-Mises failure criteria is used in order to estimate the probability of failure mainly associated with three cases of ground subsidence. Using stresses on the buried pipelines, we estimate the probability of pipelines with von-Mises failure criterion. The effects of varying random variables such as pipe diameter, internal pressure, temperature, settlement width, load for unit length of pipelines, material yield stress and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the pipeline crossing ground subsidence regions which have different soil properties.