• Smart Media Journal
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• v.12 no.8
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• pp.18-25
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• 2023

The COVID-19 outbreak in 2020 caused continuous damage worldwode, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are colsed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administraion and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.181-200
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• 2020

This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.968-978
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• 1999

The effective elastic properties of materials containing spherical inclusions were calculated by the elastic wave scattering theory. In the formulation additional scattering fields by the presence of random multiple scatterers that affects the effective properties were found by the single scattering approximation. In calculating the scattering fields the ensemble average on the displacements and strains inside the scatterer was found from the static approximation at long wavelength limit. The displacements were assumed to be equal to the incident field, while the strains were calculated by Eshelby's equivalent inclusion principle on the single inclusion problem. Four different models were considered and they reflected different degrees of multiple scattering effects based on the approximation introduced in the process of embedding the inclusion in the matrix. The expressions for the effective elastic constants were given in each model, and their relations to the results obtained from other scattering theory and elasticity theory were discussed. The theoretical predictions were compared with experimental results on the epoxy matrix composites containing tungsten particles of different sizes and volume fractions

• Journal of Soil and Groundwater Environment
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• v.18 no.3
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• pp.119-133
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• 2013

In the present study, several groundwater vulnerability assessment methods were applied to an agricultural area of Gumma in Korea. For the groundwater intrinsic vulnerability assessment, the performance of DRASTIC, SINTACS and GOD models was compared and an ensemble approach was suggested. M-DRASTIC and multi-linear regression (MLR) models were applied for the groundwater specific vulnerability assessment to nitrate of the study site. The correlation coefficient between the nitrate concentration and M-DRASTIC index was as low as 0.24. The result of the MLR model showed that the correlation coefficient is 0.62 and the areal extents of livestock farming and upland field are most influential factors for the nitrate contamination of groundwater in the study site.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.41-60
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• 2004

A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases. becomes increasingly large over time. To mine information from this massive data. it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise. if we use only recent or part of a whole data to mine information or pattern. there can be loss of information. which may be useful. To avoid this problem. we suggest a method that efficiently accumulates information. in the form of rule sets. over time. It takes much smaller storage compared to traditional mining methods. These accumulated rule sets are used as prediction models in the future. Based on theories of ensemble approaches. combination of many prediction models. in the form of systematically merged rule sets in this study. is better than one prediction model in performance. This study uses a customer data set that predicts buying power of customers based on their information. This study tests the performance of the suggested method with the data set alone with general prediction methods and compares performances of them.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.247-251
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• 2010

It is very difficult to understand and estimate the heat transfer and flow characteristics in the combustor, which is one of main components in the Auxiliary Power Unit (APU), because its flow filed has very complex structure. In this paper, specified is characteristics of injection and flow through different air goles in the liner, which consist of large circular holes film cooling holes, and tangential air swirl holes. The durability of the liner depends on whether the surface of the liner is exposed to the hot gas over 1000 $^{\circ}C$ of a temperature or net. It is proved that the locations of hot spots estimated from the calculation using CFD are matched well with that from the test. In this study, CFD simulations were performed to examine the heat transfer and temperature distributions in and about a liner wall with film cooling on the wall. This computational study is based on the ensemble average continuity, compressible Navier-Stokes, energy, and PDF combustion equations closed by the standard $k-{\varepsilon}$ turbulence model with standard wall functions for the gas phase and the Fourier equations for conduction in the solid phase.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.5
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• pp.612-618
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• 2004

Stereoscopic PIV measurements were made in the wind tunnel with the actual size waterjet model. The main wind tunnel provides the vehicle velocity while the secondary wind tunnel adjusts the jet issuing velocity. Experiments were performed at the range of jet to vehicle velocity ratio (JVR), 3.75 to 8.0 and the Reynolds number of 220,000 based on the jet velocity and the hydraulic diameter of the waterjet intake duct. Wall pressure distributions were measured for various JVRs. Three dimensional velocity fields were obtained at the inlet and outlet of the intake duct. It is found that severe acceleration is occurred at the lip region while deceleration is noticeable at the ramp side. The detailed three dimensional velocity fields can be used as the accurate velocity input for the CFD simulation. It is interesting to note that there are many different types of vortices in the instantaneous velocity field. It can be considered that those vortices are generated by the corner of rectangular section of the intake and Gortler vortices due to the curved wall. However, typical secondary flow with a pair of counter rotating vortex pair is clearly seen in the ensemble averaged velocity field.

• International Journal of Contents
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• v.15 no.4
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• pp.8-15
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• 2019

This paper presents a solution of the 'Quick, Draw! Doodle Recognition Challenge' hosted by Google. Doodles are drawings comprised of concrete representational meaning or abstract lines creatively expressed by individuals. In this challenge, a doodle is presented as a sequence of sketches. From the view of at the sketch level, to learn the pattern of strokes representing a doodle, we propose a sequential model stacked with multiple convolution layers and Long Short-Term Memory (LSTM) cells following the attention mechanism [15]. From the view at the image level, we use multiple models pre-trained on ImageNet to recognize the doodle. Finally, an ensemble and a post-processing method using the minimum cost flow algorithm are introduced to combine multiple models in achieving better results. In this challenge, our solutions garnered 11th place among 1,316 teams. Our performance was 0.95037 MAP@3, only 0.4% lower than the winner. It demonstrates that our method is very competitive. The source code for this competition is published at: https://github.com/ngxbac/Kaggle-QuickDraw.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.1
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• pp.90-97
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• 1992

An estimation method for the amplitude and phase response of transmission line is discussed. and applied to narrow band ISDN subscriber line. The ABCD parameters of line are evaluated from four impedance values: the standard termination impedence at activated and deactivated stares, and the input impedances of line which can be estimated at each state. Estimating input impedence, the “chirp” signal is used as incident signal and noise effect can be reduced by ensemble averaging. These ABCD parameter estimations might be applicable to ether uniform or nonuniform line. Cleary the magnitude and phase response can be obtained from estimated ABCD parameters. The numerical simulation results for N ISDN subscriber line model are included, and the estimation error introduced by deviation in load impedence is also anlyzed.