• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1219-1228
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• 2022

Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical method for statistical model, machine learning, and statistical learning. Finally, we discussed several considerations for choosing the prediction modeling method.

• Journal of Distribution Science
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• v.22 no.4
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• pp.79-89
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• 2024

Purpose: This study aims to enhance airport service quality by examining their impact on brand image, advocacy, and mediating brand trust in the aviation service distribution sector. Research Design, Data, and Methodology: Using existing literature, we propose a structural model exploring the relationships between key components which are service quality, brand trust, brand Image and brand advocacy. An online survey, based on prior literature, was administered to 287 Koreans who have experienced using facilities or services at Incheon International Airport (IIA). Statistical analysis employed confirmatory factor analysis (CFA) and structural equation modelling (SEM). Results: Research findings show significant impacts of airport service quality on brand trust. Increased brand trust positively influences airport brand image and advocacy. Conclusion: The study emphasizes the aviation industry's potential to boost brand trust through improved airport service quality via users' interactions. Service quality is critical factors in building brand trust. The findings emphasize the critical role of service quality in fostering brand trust. It underscores the importance of user's satisfaction with service quality in fostering brand trust which can lead to brand image and brand advocacy. The aviation industry should formulate policies and strategies to enhance brand trust improved service quality, thereby improving brand image and brand advocacy.

• Wind and Structures
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• v.38 no.5
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• pp.381-398
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• 2024

This study presents a mode analysis of 3D turbulent velocity data around a square-section building model to identify the dynamic system for Kármán-type vortex shedding. Proper orthogonal decomposition (POD) was first performed to extract the significant 3D modes. Magnitude-squared coherence was then applied to detect the phase consistency between the modes, which were roughly divided into three groups. Group 1 (modes 1-4) depicted the main vortex shedding on the wake of the building, with mode 2 being controlled by the inflow fluctuation. Group 2 exhibited complex wake vortexes and single-sided vortex phenomena, while Group 3 exhibited more complicated phenomena, including flow separation. Subsequently, a third-order polynomial regression model was used to fit the dynamics system of modes 1, 3, and 4, which revealed average trend of the state trajectory. The two limit cycles of the regression model depicted the two rotation directions of Kármán-type vortex. Furthermore, two characteristic periods were identified from the trajectory generated by the regression model, which indicates fast and slow motions of the wake vortex. This study provides valuable insights into 3D mode morphology and dynamics of Kármán-type vortex shedding that helps to improve design and efficiency of structures in turbulent flow.

• Particle and aerosol research
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• v.20 no.3
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• pp.103-114
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• 2024

Liquid-spray cleaning has recently been considered an eco-friendly cleaning method in the semiconductor industry because it efficiently cleans contaminated wafers without using any chemicals, relying instead on direct momentum transfer through dropwise impaction. Previous researches are mainly divided into two groups, such as modelling studies predicting the cleaning effect of single-droplet impact and experimental works for measuring particle removal efficiency (PRE) that essentially accompanies multiple droplet impacts. Here, we developed a Monte Carlo model to connect the single-droplet based model to the ensemble effect of multiple droplet impacts in real cleaning experiments, and thereby predict the PREs from the impaction conditions of droplets and the diameters of target particles. Additionally, we developed a two-fluid supersonic nozzle system, capable of spraying 10-60 ㎛ droplets under control of impact velocity, with aims to validate the model predictions of PREs for 15-130 nm contaminant particles on a Si wafer. We confirmed that the model predictions are in agreement with the experimental data within 7% and the cleaning time needs to be controlled for ensuring the efficient removal of particles.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.3
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• pp.108-119
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• 2024

The Reynolds-averaged Navier-Stokes (RANS) simulations are commonly used in industrial applications due to their computational efficiency. However, the linear eddy viscosity model (LEVM) used in RANS often fails to accurately capture the anisotropy of Reynolds stress in complex flow conditions. To enhance RANS predictive accuracy, data-driven closure models, such as Tensor Basis Neural Network (TBNN) and Tensor Basis Random Forest (TBRF), have been proposed. However existing models, including TBNN and TBRF, have limitations in capturing the nonlocal patterns of turbulence models, resulting in irregular and unsmooth predictions. Convolutional neural networks (CNNs) are considered as an alternative approach, but their reliance on discretization poses challenges when dealing with arbitrarily designed meshes in RANS simulations. In this study, we propose a nonlinear convolutional neural operator as the RANS closure model. Our model satisfies Galilean invariance, can learn nonlocal physics, and recovers high-resolution physics even when trained on undersampled grids. The model outperforms existing TBNN and TBRF models, successfully predicting smooth fields of Reynolds stress in flows with adverse pressure gradients, separations, and streamline curvature, where existing models struggle or fail to provide accurate predictions.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.376-387
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• 2017

This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.33-40
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• 2009

In field surveys using the dipole-dipole electrical resistivity method, we often encounter negative apparent resistivity. The term 'negative apparent resistivity' refers to apparent resistivity values with the opposite sign to surrounding data in a pseudosection. Because these negative apparent resistivity values have been regarded as measurement errors, we have discarded the negative apparent resistivity data. Some people have even used negative apparent resistivity data in an inversion process, by taking absolute values of the data. Our field experiments lead us to believe that the main cause for negative apparent resistivity is neither measurement errors nor the influence of self potentials. Furthermore, we also believe that it is not caused by the effects of induced polarization. One possible cause for negative apparent resistivity is the subsurface geological structure. In this study, we provide some numerical examples showing that negative apparent resistivity can arise from geological structures. In numerical examples, we simulate field data using a 3D numerical modelling algorithm, and then extract 2D sections. Our numerical experiments demonstrate that the negative apparent resistivity can be caused by geological structures modelled by U-shaped and crescent-shaped conductive models. Negative apparent resistivity usually occurs when potentials increase with distance from the current electrodes. By plotting the voltage-electrode position curves, we could confirm that when the voltage curves intersect each other, negative apparent resistivity appears. These numerical examples suggest that when we observe negative apparent resistivity in field surveys, we should consider the possibility that the negative apparent resistivity has been caused by geological structure.

• Korean Journal of Environment and Ecology
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• v.26 no.5
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• pp.812-826
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• 2012

The selection rate of tree burials (TB) is still low in spite of increasing concerns about TB and government's efforts to increase TB participation. It is necessary to understand the factors affecting TB selection. This study investigated the relationship between major variables (attitude: ATT; subjective norm: SN; perceived behavioral control: PBC) of Ajzen's theory of planned behavior (TPB), additional variable (custom: CUST), and intention to select TB by using structural equation modelling (SEM). Samples were selected from Gwang-ju citizens using proportionate stratified sampling (PST) by region during September of 2011. Four hundred and twelve responses were used for data analysis. The model showed fair goodness of fit. All four variables (ATT, SN, PBC, CUST) influenced intention to select TB. The four variables explained 53.0% of intention to select TB. SN(${\beta}$=0.459) was the most predictive variable on the intention, followed by ATT(${\beta}$=0.247), PBC(${\beta}$=0.152), and CUST(${\beta}$=0.102) in decreasing order. The results were discussed and some suggestions to increase the intention of tree burial selection were made.

• Journal of Distribution Science
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• v.15 no.8
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• pp.75-86
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• 2017

Purpose - The online-to-offline (O2O) business model has brought considerable changes to the traditional Chinese business model. The main difference between O2O and pure online consumption is that O2O offers a richer experience and word-of-mouth. it is easier to trigger online word-of-mouth. However, few scholars have been concerned about the impact of experiential value on customer satisfaction and online word-of-mouth (e-WOM) in the study of O2O. This study takes the O2O business model in China's catering industry as its research object and uses structural equation modelling to analyze the impact of online and offline experiential values on customer satisfaction and e-WOM. Research design, data, and methodology - According to previous researches, consumer experiential value is mainly divided into return on investment (economy and efficiency), service excellence, playfulness and aesthetics. According to the characteristics of O2O in China's catering industry, this study divides the online experience value into efficiency and economy (return on investment). The offline part is divided into return on investment (economy and efficiency), service excellence, playfulness and aesthetics. Using a web-based survey, we collected 303 valid samples. Structural equation modelling was used to create the research model. Results - The results show that efficiency (online) and service excellence (offline) have a significant effect on customer satisfaction. Economics (online) and playfulness (offline) have a positive impact on customers' e-WOM. In addition, the higher the customer satisfaction, the greater the positive impact on the spread by word of mouth. However, aesthetic(offline) and return on investment(offline) have no significant impact to customer satisfaction and e-WOM. Conclusions - The study findings show that the key to boost customer satisfaction in the catering industry is to improve product quality and service. Although traditional competitive strategies such as online discount have been questioned by many scholars about their decreasing effectiveness, they are indispensable means to attract online traffic and trigger e-WOM. The traditional enterprises can reconstruct traditional business processes through the O2O model to effectively improve customer satisfaction and word of mouth by improving the experiential value of economy and efficiency. Additionally, it can be used as the natural advantages of online communication to induce customers to engage in word of mouth and attract more potential customers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.29-36
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• 2004

This study describes the development of coordinate transformation tool for transforming the digital map using newly derived transformation parameters which are determined from the data referred to the local geodetic datum and the geocentric datum (ITRF2000) and the distortion modelling derived from collocation method. We prepared 190 common points and used 107 points to calculate 7 transformation parameters. In order to evaluate an accuracy of coordinate transformation, 83 common points were tested. In this study, we used Molodensky-Badekas model to derive the 7 transformation Parameters. An accuracy of 0.22m was obtained applying 7 Parameters transformation and the distortion modelling together. It shows that the accuracy of coordinate transformation is improved 72% against the result of 7 parameters transformation only. We developed the transformation tool, GDKtrans, which can be transformed the digital map of scales 1/50,000, 1/25,000 and 1/5,000. We also analyzed the digital map of l/5,000 at six urban areas by GPS observations. The result shows less RMSE of about 1.9 m and large disagreement at position and features. Consequently, we suggests that l/5,000 digital map is necessary of whole revision.