• Ocean and Polar Research
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• v.38 no.2
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• pp.149-159
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• 2016

The statistical measures of the coastal environmental data are used in a variety of statistical inferences, hypothesis tests, and data-driven modeling. If the measures are biased, then the statistical estimations and models may also be biased and this potential for bias is great when data contain some outliers defined as extraordinary large or small data values. This study aims to suggest more robust statistical measures as alternatives to more commonly used measures and to assess the performance these robust measures through a quantitative evaluation of more typical measures, such as in terms of locations, spreads, and shapes, with regard to environmental monitoring data in the Busan coastal sea. The detection of outliers within the data was carried out on the basis of Rosner's test. About 5-10% of the nutrient data were found to contain outliers based on Rosner's test. After removal (zero-weighting) of the outliers in the data sets, the relative change ratios of the mean and standard deviation between before and after outlier-removal conditions revealed the figures 13 and 33%, respectively. The variation magnitudes of skewness and kurtosis are 1.36 and 8.11 in a decreasing trend, respectively. On the other hand, the change ratios for more robust measures regarding the mean and standard deviation are 3.7-10.5%, and the variation magnitudes of robust skewness and kurtosis are about only 2-4% of the magnitude of the non-robust measures. The robust measures can be regarded as outlier-resistant statistical measures based on the relatively small changes in the scenarios before and after outlier removal conditions.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.261-274
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• 2019

This paper aims to develop an appropriate methodology for establishing an investment strategy for 'demonstration of artificial rainfall technology using UAV' and that include establishment of a technology classification, set of indicators for technology evaluation, suggestion of final key technology as a whole study area. It is designed to complement the latest research trend analysis results and expert committee opinions using quantitative analysis. The key indicators for technology evaluation consisted of three major items (activity, technology, marketability) and 10 detailed indicators. The AHP questionnaire was conducted to analyze the importance of indicators. As a result, it was analyzed that the attribute of the technology itself is most important, and the order of closeness to the implementation of the core function (centrality), feasibility (feasibility). Among the 16 technology groups, top investment priority groups were analyzed as ground seeding, artificial rainfall verification, spreading and diffusion of seeding material, artificial rainfall numerical modeling, and UAV sensor technology.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1801-1810
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• 2005

The effect of longitudinal thin rectangular riblets aligned with the flow direction on turbulent channel flow has been investigated using direct numerical simulation. The thin riblets have been modeled using the immersed boundary method (IBM) where the velocities at only one set of vertical nodes at the riblets positions are enforced to be zeros. Different spacings, ranging between 11 and 43 wall units, have been simulated aiming at getting the optimum spacing corresponding to the maximum drag reduction while keeping the height/spacing ratio at 0.5. Reynolds number based on the friction velocity ${\mu}_\tau$ and the channel half depth $\delta$ is set to 150. The flow is driven by adjusted pressure gradient so that the mass flow rate is kept constant in all the simulations. This study shows similar trend of the drag ratio to that of the experiments at the different spacings. Also, this research provides an optimum spacing of around 17 wall units leading to maximum drag reduction as experimental data. Explanation of drag increasing/decreasing mechanism is highlighted.

• East Asian Journal of Business Economics (EAJBE)
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• v.11 no.2
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• pp.59-70
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• 2023

Purpose - The hospitality industry has experienced significant transformations in recent years, primarily driven by advancements in technology and changes in consumer behavior. The worldwide COVID-19 epidemic has intensified a trend of hotel guests increasingly relying on meal delivery applications. Considering the recent COVID-19 pandemic, this study sets out to investigate how hotel guests have been using food delivery apps before and during the outbreak. Research design, data, and methodology - This study utilizes a systematic literature review as its research design. A systematic literature review is a methodical, well-structured procedure for locating, analyzing, and synthesizing pertinent research articles. Result - The findings shed light on four main aspects: convenience and accessibility, safety and hygiene assurance, personalization and customization, and local exploration and cultural immersion. These findings provide valuable insights into the evolving preferences and behaviors of hotel guests in utilizing food delivery apps, particularly in the context of the COVID-19 pandemic. Conclusion - This study has contributed to the understanding of the changing role of food delivery apps among hotel guests. By recognizing the evolving dynamics and leveraging the opportunities presented by food delivery apps, hotels can adapt to meet guest expectations, enhance customer satisfaction, and thrive in the ever-changing landscape of the hospitality industry.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.173-182
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• 2016

AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.

• Ocean and Polar Research
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• v.34 no.2
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• pp.201-218
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• 2012

The purpose of this study is to investigate climatological variations from the sea surface temperature (SST), chlorophyll-a concentration (Chl-a), and phytoplankton size class (PSC), using NOAA AVHRR, SeaWiFS, and MODIS data in the South Sea of Korea (SSK) and East China Sea (ECS). 26-year monthly SST and 13-year monthly Chl-a and PSC data, separated by whole and nine-different areas, were used to understand seasonal and inter-annual variations. SST and Chl-a clearly showed seasonal variations: higher SST and Chl-a were observed during the summer and spring, and lower values occurred during the winter and summer. The annual and monthly SST over 26 years increased by $0.2{\sim}1.0^{\circ}C$. The annual and monthly Chl-a concentration over 13 years decreased by $0.2{\sim}1.1mg/m^3$. To determine more detailed spatial and temporal variations, we used the combined data with monthly SST, Chl-a, and PSC. Between 1998 and 2010, the inter-annual trend of Chl-a decreased, with decreasing micro- and nano-size plankton, and increasing pico-size plankton. In regional analysis, the west region of the study area was spatially and temporally correlated with the area dominated by decreasing micro-size plankton; while the east region was less sensitive to coastal and land effects, and was dominated by increasing pico-size plankton. This phenomenon is better related to one or more forcing factors: the increased stratification of ocean driven by changes occurring in spatial variations of the SST caused limited contributions of nutrients and changed marine ecosystems in the study area.

• Informatization Policy
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• v.24 no.3
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• pp.91-107
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• 2017

Digital forensics is the process of uncovering and interpreting electronic data and materials found in digital device in relation to crime. The goal of the process is to preserve any evidence in its most original form which shall be having the force of law. The digital forensic market is increasing with a growth of ICT in domestic and global market. Many countries including U.S. are actively performing researched regarding a structured investigation by collecting, identifying and validating the digital information for the purpose of reconstructing past events which so does in academic society in Korea. This paper is to understand overall research trend about digital forensics and derive future strategy by integrating the result of meta-analysis into practices based on five criteria - main theme and topic, analysis phase, technical method for analysis, author's affiliation, and unit of analysis and method. 239 papers are analyzed, which were selected out of 470 papers published for 10 years (2007~2016) in academic journal on the list of KCI (Korea Citation index). The results of this analysis will be used to examine the characteristics of research in the field of digital forensics. The result of this research will contribute to understanding of the research trend and characteristics leading the technology-driven academia, through which measures for further research development and facilitation are suggested.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.3
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• pp.21-30
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• 2018

Driven by the recent energy saving trend, conventional silicon based power conversion modules are being replaced by modules using silicon carbide. Previous papers have focused mainly on the electrical advantages of silicon carbide semiconductors that can be used to design switching devices with much lower losses than conventional silicon based devices. However, no systematic study of their thermomechanical reliability in power conversion modules using finite element method (FEM) simulation has been presented. In this paper, silicon and silicon carbide based power devices with three-phase switching were designed and compared from the viewpoint of thermomechanical reliability. The switching loss of power conversion module was measured by the switching loss evaluation system and measured switching loss data was used for the thermal FEM simulation. Temperature and stress/strain distributions were analyzed. Finally, a thermal fatigue simulation was conducted to analyze the creep phenomenon of the joining materials. It was shown that at the working frequency of 20 kHz, the maximum temperature and stress of the power conversion module with SiC chips were reduced by 56% and 47%, respectively, compared with Si chips. In addition, the creep equivalent strain of joining material in SiC chip was reduced by 53% after thermal cycle, compared with the joining material in Si chip.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.