• Informatization Policy
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• v.18 no.3
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• pp.3-24
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• 2011

The purpose of this paper is to indicate possible future research directions for social network services(SNS) by reviewing past and recent trends in SNS studies. The framework used for the analysis is the New Media Evolutionary Model(NMEM) proposed by Wimmer and Dominick, a four-phase system for research on new media development. Although early forms of SNS emerged in the late 1990s, most research in this field has been published in the past five years. We searched for SNS-related articles published from 2006 to August 2011 from academic journal archives in information systems, communication, marketing, and other fields, and classified them according to the NMEM to analyze the current state of SNS research. Researchers in this field have so far focused on the first two phases of the model(the media itself and use of the media), but little research has been conducted on the third(effects of the media) and fourth phases(improvements in the media). Although SNS research is still in its early stages, we suggest the need for more studies on the effects of SNS and how it can be improved. Very few studies test existing theories or build new theories related to SNS. Thus, a more rigorous approach towards SNS research is warranted, and future research should focus on theory building and testing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.9
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• pp.796-805
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• 2016

As a part of the first stage in the helicopter flight simulator development, this study numerically evaluates handling qualities of the dynamics model. The flight dynamics model was generated using public information for AS365 N2, the target aircraft of the simulator. The flight simulator is under development as a pilot training and research tool for firefighting missions. The assessment of the model intends to validate general characteristics and suitability before the model is enhanced with flight test data. The evaluation is based on the ADS-33E-PRF(Aeroautical Design Standard Performance Specification Handling Qualities Requirement) criteria, with consideration of category of the aircraft, missions, and environment. The numerical operations follow required or recommended procedures of flight test for compliance demonstration. Evaluation results are evaluated according to the rating specified in maneuverability ADS-33E-PRF. Results have identified to provide a satisfactory platform for flight dynamic model in the general helicopter simulator generated based on the RotorLibFDM, and can be used as a base for basic training and research.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.513-524
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• 2022

Recently, detecting damages of civil infrastructures from digital images using deep learning technology became a very popular research topic. In order to adapt those methodologies to the field, it is essential to explain robustness of deep learning models. Our research points out that the existing pixel-based deep learning model evaluation metrics are not sufficient for detecting cracks since cracks have linear appearance, and proposes a new evaluation methodology to explain crack segmentation deep learning model more rationally. Specifically, we design, implement and validate a methodology to generate tolerance buffer alongside skeletonized ground truth data and prediction results to consider overall similarity of topology of the ground truth and the prediction rather than pixel-wise accuracy. We could overcome over-estimation or under-estimation problem of crack segmentation model evaluation through using our methodology, and we expect that our methodology can explain crack segmentation deep learning models better.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.5
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• pp.60-67
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• 2019

In general, a reinforced concrete slabs are known to have a high fire resistance performance due to thermal properties of concrete materials. However, according to previous research, the thermal behavior of voided slabs is reported to be different from that of conventional RC solid slabs, and the differences seem to be caused by the air layer formed inside the voided slab. Therefore, it is difficult to estimate the temperature distribution of the voided slab under fire by using the existing methods that do not take into account the air layer inside the voided slab. In this study, a numerical analysis model was proposed to estimate the temperature distribution of voided slabs under fire, and evaluated. Heat transfer of slabs under fire is generally caused by conduction, convection and radiation, and time-dependent temperature changes of slab can be determined considering these phenomena. This study proposed a numerical method to estimate the temperature distribution of voided slabs under fire based on a finite difference method in which a cross-section of the slab is divided into a number of layers. This method is also developed to allow consideration of heat transfer through convection and radiation in air layer inside of slabs. In addition, the proposed model was also validated by comparison with the experimental results, and the results showed that the proposed model appropriately predicts the temperature distribution of voided slabs under fire.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.568-585
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• 2022

In the present study, we proposed a numerical method for simulating thermally induced fracture slip using a grain-based distinct element model (GBDEM). As a part of DECOVALEX-2023, the thermo-mechanical loading test on a saw-cut rock fracture conducted at the Korea Institute of Civil Engineering and Building Technology was simulated. In the numerical model, the rock sample including a saw-cut fracture was represented as a group of random Voronoi polyhedra. Then, the coupled thermo-mechanical behavior of grains and their interfaces was calculated using 3DEC. The key concerns focused on the temperature evolution, thermally induced principal stress increment, and fracture normal and shear displacements under thermo-mechanical loading. The comparisons between laboratory experimental results and the numerical results revealed that the numerical model reasonably captured the heat transfer and heat loss characteristics of the rock specimen, the horizontal stress increment due to constrained displacement, and the progressive shear failure of the fracture. However, the onset of the fracture slip and the magnitudes of stress increment and fracture displacement showed discrepancies between the numerical and experimental results. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Journal of The Korean Association For Science Education
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• v.33 no.7
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• pp.1367-1384
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• 2013

The purpose of this study was to develop a Science Academic Emotion Scale for Elementary Students. To make a scale, authors extract a core of 14 emotions related to science learning situations from Kim & Kim (2013) and literature review. Items on the scale consisted of 14 emotions and science learning situations. The first preliminary scale had 174 items on it. The number of 174 items was reduced and elaborated on by three science educators. Authors verified the scale using exploratory factor analysis, confirmatory factor analysis, inter-item consistency and concurrent validity. The second preliminary scale consisted of 141 items. The preliminary scale was reduced to seven factors and 56 items by applying exploratory factor analysis twice. The seven factors include: enjoyment contentment interest, boredom, shame, discontent, anger, anxiety, and laziness. The 56 items were elaborated on by five science educators. The scale with 56 items was fixed with seven factors and 35 items to get the final scale by applying confirmatory factor analysis twice. Except for Chi-square and GFI (Goodness of Fit Index), other various goodness of fit characteristics of the seven factors and 35 items model showed good estimated figures. The Cronbach of the scale was 0.85. The Cronbach of seven factors are 0.95 in enjoyment contentment interest, 0.81 in boredom, 0.87 in shame, 0.82 in discontent, 0.87 in anger, 0.77 in anxiety, 0.81 in laziness. The correlation coefficient was 0.59 in enjoyment contentment interest, 0.54 in anxiety, 0.42 in shame, and 0.28 in boredom, which were estimated using the Science Academic Emotion Scale and National Assessment System of Science-Related Affective Domain (Kim et al., 1998). Based on the results, authors judged that the Science Academic Emotion Scale for Elementary Students achieved an acceptable validity and reliability.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.1007-1013
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• 2013

This study aims to analyze the aerodynamics when the geometry of the turbine rotor is modified. The turbine used in this study is a small engine used in the APU of a helicopter. It is difficult to improve the performance of small engines owing to the structural weakness of the blade tip. Therefore, the improvement of the hub geometry is investigated in many ways. The working fluid of a turbine is a high-temperature and high-pressure gas. The heat transfer rate of the turbine surface should be considered to avoid the destruction of blade owing to the heat load. The SST turbulence model gives an excellent prediction of the aerodynamic behavior and heat transfer characteristics when the numerical simulations are compared with the experimental results. In conclusion, the aerodynamic efficiency is improved when a bulbous design is applied to the leading edge near the hub. The endwall loss is reduced by 15%.