• The Journal of Bigdata
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• v.5 no.1
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• pp.181-188
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• 2020

As the size of the airport terminal grows in line with the rapid growth of aviation passengers, the advanced baggage handling system that combines various data technologies has become an essential element in order to handle the baggage carried by passengers swiftly and accurately. Therefore, this study introduces the method of analyzing the baggage handling capacity of domestic airports through the latest data analysis methodology from the process point of view to advance the operation of the airport BHS and the main points based on event log data. By presenting an accurate load prediction method, it can lead to advanced BHS operation strategies in the future, such as the preemptive arrangement of resources and optimization of flight-carrousel scheduling. The data used in the analysis utilized the APIs that can be obtained by searching for "Korea Airports Corporation" in the public data portal. As a result of applying the method to the domestic airport BHS simulation model, it was possible to confirm a high level of predictive performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.1-6
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• 2013

Three Surface cracks are among the more common flaws in aircraft and pressure vessel components. Accurate stress intensity analyses and crack growth rate data of surface-cracked components are needed for reliable prediction of their fatigue life and fracture strengths. Three Dimensional finite element method (FEM) was used to obtain the stress intensity factor for surface cracks existing in structures. A geometry model, i.e. a solid containing one or several 3D cracks is defined. Nodes are generated by bucket method, and quadratic tetrahedral solid elements are generated by the Delaunay triangulation techniques. To examine accuracy and efficiency of the present system, the stress intensity factor for a semi-elliptical surface crack in cylindrical structures subjected to pressure is calculated. Analysis results by present system showed good agreement with those by ASME equation and Raju-Newman's equation.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1891-1900
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• 2021

Fine particulate matter (FPM; diameter ≤ 2.5 ㎛) is frequently found in metropolitan areas due to activities associated with rapid urbanization and population growth. Many adolescents spend a substantial amount of time at school where, for various reasons, FPM generated outdoors may flow into indoor areas. The aims of this study were to estimate FPM concentrations and categorize types of FPM in schools. Meteorological and chemical variables as well as satellite-based aerosol optical depth were analyzed as input data in a random forest model, which applied 10-fold cross validation and a grid-search method, to estimate school FPM concentrations, with four statistical indicators used to evaluate accuracy. Loose and strict standards were established to categorize types of FPM in schools. Under the former classification scheme, FPM in most schools was classified as type 2 or 3, whereas under strict standards, school FPM was mostly classified as type 3 or 4.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.148-155
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• 2021

Gibberellins (GAs) are essential phytohormones for plant growth that influence developmental processes and crop yields. Recent functional genomic analyses of model plants have yielded good characterizations of the canonical GA signaling pathways and related genes. Although Panax ginseng has long been considered to have economic and medicinal importance, functional genomic studies of the GA signaling pathways in this crucial perennial herb plant have been rarely conducted. Here, we identified and performed functional analysis of the GA signaling-related genes, including PgGID1s, PgSLY1s, and PgRGAs. We confirmed that the physiological role of GA signaling components in P. ginseng was evolutionarily conserved. In addition, the important functional domains and amino acid residues for protein interactions among active GA, GID1, SCF^SLY1, and RGA were also functionally conserved. Prediction and comparison of crystallographic structural similarities between PgGID1s and AtGID1a supported their function as GA receptors. Moreover, the subcellular localization and GA-dependent promotion of DELLA degradation in P. ginseng was similar to the canonical GA signaling pathways in other plants. Finally, we found that overexpression of PgRGA2 and PgSLY1-1 was sufficient to complement the GA-related phenotypes of atgid1a/c double- and rga quintuple-mutants, respectively. This critical information for these GA signaling genes has the potential to facilitate future genetic engineering and breeding of P. ginseng for increased crop yield and production of useful substances.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.109-125
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• 2014

Suitable climate condition is essential for stable growth of crops which directly leads to an increase in crop production. Preceding domestic researches mostly used crop models to predict grain or crop yield in relation to climate change. However, the use of various models and input data based on foreign background lowered the reliability for result. Therefore in this study, we evaluated domestic applicability by comparing and analyzing various crop models developed abroad. In addition, we selected models based on the possibility of acquiring input data and suggested domestic applicability.

• The Journal of Bigdata
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• v.5 no.2
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• pp.231-240
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• 2020

With the development in the IT industry and the growth in the game industry, user's game data is recorded in seconds according to various plays and options, and a vast amount of game data can be analyzed based on Bigdata. Combined with business, Bigdata is used to discover new values for profit creation in various fields, but it is utilized in the game industry in insufficient ways. In this study, considering the characteristics of the subdivided lines, we constructed a win-loss prediction model for each line using the game data of League of Legends, and derived the importance of variables. This study can contribute to planning of strategies for general game users to get information about team members in advance and increase the win rate by using the record search sites.

• Herbal Formula Science
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• v.30 no.4
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• pp.293-304
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• 2022

Objectives : Sagunja-Tang is a famous prescription used in Korean medicine for the purpose of promoting vital energy, and there are few studies using Sagunja-Tang on cerebrovascular diseases yet. As previous studies confirmed that Sagunja-tang is highly likely to be used effectively for stroke, this study was intended to predict the mechanism through which Sagunja-tang would act effectively on stroke. Methods : In this study, a network pharmacology analysis method was used, and oral bioavailability (OB), drug likeness (DL), Caco-2 and BBB permeability were utilized to select compounds with potential activity. For the values of each variable used in this study, OB ≥ 30%, DL ≥ 0.18, Caco-2 ≥ 0, and BBB ≥ 0.3 were applied. Using the above variables, the relations between target genes and diseases that are presumed to be involved in the selected bioavailable compounds were constructed in a network format, and proteins thought to play a major role were identified. Results : Among the compounds included in Sagunja-Tang, 26 bioavailable compounds were selected and it was confirmed that these compounds can be effectively used in cerebrovascular diseases such as Alzheimer's disease and stroke. These compounds are considered to act on proteins related in cell death and growth. The most important mechanism of action was predicted to be apoptosis, and the protein that is thought to play the most key action in this mechanism was caspase-3. Conclusions : In our future study, Sagunja-Tang will be used in an ischemic stroke mouse model, and the mechanism of action will be explored focusing on apoptosis and cell proliferation.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• Journal of Environmental Science International
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• v.17 no.6
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• pp.611-621
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• 2008

This study intends to provide the necessary basic data needed for predicting the water quality and examining changes in water quality on the basis of the hydrological changes: an outflow or the character of a flow by investigating the interaction of the parameters through the estimation of optimal parameters need for predicting the water quality of the dam basin and the sensitivity among those estimated parameters. Im-Ha Dam in the upstream area of the Nakdong River was selected for analysis, and the water quality survey data necessary for parameter estimation was based on the monthly water quality data (water temperature, BOD, T-N and T-P) between December 1, $2005{\sim}$November 31, 2006. K1C(the saturated growth rate of plant plankton), K1RC (endogenous respiratory quotient of plankton), KDC(deoxidized ratio), K71C(minealized ratio of dissolved organic phosphorus), K83C(mineralized ratio of dissolved organic nitrogen) have been considered as the factors of the water quality performed in this water quality simulation, that is, the most effective parameters on BOD, T-N and T-P. In the result of the analysis of the sensitivity, KDC(deoxidized ratio) was the most sensitively reacted parameter on BOD and it was K71C(mineralized ratio of dissolved organic phosphorus) and K83C(mineralized ratio of dissolved organic nitrogen) on T-N and T-P. It is considered that it will be possible to apply the most optimal parameter to an analysis of the water quality simulation at Im-Ha Ho basin in the goal year by examining the interaction of the parameters through the parameters sampling which are able to applicable to prediction of the water quality and the analysis of the its sensitivity, in the future, also the analysis on the basis of the hydrological conditions: an outflow or the character of a flow will be needed.