• 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 Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.49-58
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• 2008

Generally, steel corrosion occurs in concrete structures due to carbonation in down-town area and underground site and it propagates to degradation of structural performance. In general diagnosis and inspection, only carbonation depth in sound concrete is evaluated but unsound concrete such as joint and cracked area may occur easily in a concrete member due to construction process. In this study, field survey of carbonation for RC columns in down-town area is performed and carbonation depth in joint and cracked concrete including sound area is measured. Probability of durable failure with time is calculated through probability variables such as concrete cover depth and carbonation depth which are obtained from field survey. In addition, service life of the structures is predicted based on the intended probability of durable failure in domestic concrete specification. It is evaluated that in a RC column, various service life is predicted due to local condition and it is rapidly decreased with insufficient cover depth and growth of crack width. It is also evaluated that obtaining cover depth and quality of concrete is very important because the probability of durable failure is closely related with C.O.V. of cover depth.

• Journal of the Korea Concrete Institute
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• v.16 no.4 s.82
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• pp.521-528
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• 2004

In general, polymer concrete has more excellent mechanical properties and durability than Portland cement concrete, but very sensitive to heat and has large deformations. In this study, the long-term creep behaviors was predicted by the short-term creep test, and then the characteristic of creep of recycled-PET polymer concrete was defined by material and experimental variables. The error in the predicted long-term creep values is less than 5 percent for all polymer concrete systems. The filler carry out an important role to restrict the creep strains of recycled PET Polymer concrete. The creep strain and specific on using the $CaCO_3$ were less than using fly-ash. The creep increases with an increase in the applied stress, but not proportional the rate of stress increase ratio. The creep behavior of polymer concrete using recycled polyester resin is not a linear viscoelastic behavior.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.453-463
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• 2018

An analysis including ASE model accessing based on the intention of behavior performance of online university students is a new approach to improve academic persistence considering the characteristics of students with extensive personal variables, a uniqueness of learning environment. This study aimed to identify the relationship between ASE model including academic stress and academic persistence, and the effect of these factors on academic persistence of online university students. Data were collected from 181 sophomores in K open university from March to June, 2018. Frequency analysis, ${\chi}^2-test$, t-test, F-test, Pearson's correlation analysis, and multiple regression analysis used for data analysis. For factors affecting academic persistence, academic stress (${\beta}=-.16$, p=.016), online learning attitude (${\beta}=.44$, p<.001), and social support among social influential factors (${\beta}=.16$, p=.045) were statistically significant and the prediction model of academic persistence showed 29% explanation power (F=15.76, p<.001). To enhance academic persistence of online university students, it is needed to develop programs to reduce academic stress, improve attitude toward online learning, and improve social support.

• Spatial Information Research
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• v.20 no.1
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• pp.9-18
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• 2012

South Korea has many landslides caused by heavy rains during summer time recently and the landslides continue to cause damages in many places. These landslides occur repeatedly each year, and the frequency of landslides is expected to increase in the coming future due to dramatic global climate change. In Korea, 81.5% of the population is living in urban areas and about 1,055 million people are living in Seoul. In 2011, the landslide that occurred in Seocho-dong killed 18 people and about 9% of Seoul's area is under the same land conditions as Seocho-dong. Even the size of landslide occurred in a city is small, but it is more likely to cause a big disaster because of a greater population density in the city. So far, the effort has been made to identify landslide vulnerability and causes, but now, the new dem and arises for the prediction study about the areal extent of disaster area in case of landslides. In this study, the diffusion model of the landslide disaster area was established based on Cellular Automata(CA) to analyze the physical diffusion forms of landslide. This study compared the accuracy with the Seocho-dong landslide case, which occurred in July 2011, applying the SCIDDICA model and the CAESAR model. The SCIDDICA model involves the following variables, such as the movement rules and the topographical obstacles, and the CAESAR model is also applied to this process to simulate the changes of deposition and erosion.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.137-145
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• 2012

According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.823-832
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• 2006

Precipitation is the most important component to the study of water and energy cycle in hydrology. In this study we investigate rainfall retrieval uncertainty from different sources of remotely sensed precipitation field and then probable error propagation in the simulation of hydrologic variables especially, runoff on different vegetation cover. Two remotely sensed rainfall retrievals (space-borne IR-only and ground radar rainfall) are explored and compared visually and statistically. Then, an offline Community Land Model (CLM) is forced with in situ meteorological data to simulate the amount of runoff and determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show there are big differences between different sources of precipitation fields in terms of the magnitude and temporal variability. The study provides some intuitions on the uncertainty of hydrologic prediction via the interaction between the land surface and near atmosphere fluxes in the modelling approach. Eventually it will contribute to the understanding of water resources redistribution to the climate change in Korean Peninsula.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.391-403
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• 2014

This study seeks to carry out a literary review of preceding studies and the health improvement model of Pender(1987) on university students majoring in nursing to explain the health improvement behaviors and identify the factors that affect their activities to provide a framework for developing a more effective nursing mediation method that promotes health improvement behaviors. The study subjects were 204 university students majoring in nursing who have had clinical practice experience. The period for data collection was from April 1to May 30, 2014 and a total of 204 copies of the questionnaire were used for analysis. For the collected data, frequency analysis, percentage, ANOVA, t-test and correlation analysis were conducted using SPSS, LISREL, and path analyss was done for hypothesis testing. The overall index of hypothesis model showed a good congruence as ${\chi}^2=.06$(p=.812), df=1, ${\chi}^2(df)=.000$, GFI=0.97, AGFI=1.0, SRMR=.002, NFI=0.947, NNFI=0.957, RMSEA=0.016, CN=266. Looking at the verification of the hypothesis presented in the model, the variables that affect health improvement behaviors were perceived disability, perceived self-efficacy, perceived social support, while stress from clinical practice, perceived health status, persistence and perceived benefits did not affect health improvement behaviors.