• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.25-40
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• 2007

Excessive earth pressure is one of the major mechanical factors in the deformation and damage of Cut-and-Cover Tunnel lining in shallow tunnels and portals of mountain tunnels (Kim, 2000). Excessive earth pressure may be attributed to insufficient compaction and consolidation of backfill material due to self-weight, precipitation and vibration caused by traffic (Komiya et al., 2000; Taylor et al., 1984; Yoo, 1997). Even though there were a lot of tests performed to determine the earth pressure acting on the tunnel lining, unfortunately there were almost no case histories of studies performed to determine remedial measures that reduce differential settlement and excessive earth pressure. In this study the installation of geotextile mat was selected to reduce the differential settlement and excessive earth pressure acting on the cut-and-cover tunnel lining. In order to determine settlement and earth pressure reduction effect (reinforcement effect) of geotextile mat reinforcement, laboratory tunnel model tests were performed. This study was limited to the modeling of rigid circular cut-and-cover tunnel constructed at a depth of $1.0D\sim1.5D$ in loose sandy ground and subjected to a vibration frequency of 100 Hz. Model tests with varying soil cover, mat reinforcement scheme and slope roughness were performed to determine the most effective mat reinforcement scheme. Slope roughness was adjusted by attaching sandpaper #100, #400 and acetate on the cut slope surface. Mat reinforcement effect of each mat reinforcement scheme were presented by the comparison of earth pressure obtained from the unreinforced and mat reinforced model tests. Soil settlement reduction was analyzed and presented using the Picture Analysis Method (Park, 2003).

• Journal of the Korean Geotechnical Society
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• v.23 no.8
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• pp.17-33
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• 2007

Downhole seismic method is widely used for obtaining shear wave velocity profile of a site because it is simple and economical. Determining accurate travel time of shear wave is very important to obtain reliable result in downhole seismic method. In this paper, comparison study of various travel time determination methods was performed. Numerical study and model chamber test were performed for effective comparison study. Signal traces were acquired by performing downhole test at each numerical simulation and soil box test. Travel time data for each signal traces were determined by using six different methods and Vs profiles were evaluated. Comparing travel time data and Vs profiles with the reference value, the first arrival picking method proved to be ambiguous and unreliable. Other methods also did not always provide accurate results and the magnitude of error was dependent on the signal to noise ratio. Cross-correlation method proved to be the most adequate method for the field application and it was verified additionally with field data.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.117-123
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• 2023

The size of an optical surface can significantly affect the performance of an optical system, and high spatial frequency errors have a greater impact. Therefore, it is crucial to measure the surface figure error with high frequency. To address this, a new method called rotational prism stitching interferometer (RPSI) is proposed in this study. The RPSI is a type of stitching interferometer that enhances spatial resolution, but it differs from conventional stitching interferometers in that it does not require the movement of either the mirror tested or the interferometer itself to obtain sub-aperture interferograms. Instead, the RPSI uses a beam expander and a rotating Dove prism to select particular sub-apertures from the entire aperture. These sub-apertures are then stitched together to obtain a full-aperture result proportional to the square of the beam expander's magnification. The RPSI's effectiveness was demonstrated by measuring a 40 mm diameter spherical mirror using a three-magnification beam expander and comparing the results with those obtained from a commercial interferometer. The RPSI achieved surface testing results with nine times higher sampling density than the interferometer alone, with a small difference of approximately 1 nm RMS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.105-135
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• 2022

As COVID-19 has led to a surge in e-commerce Buy Now Pay Later(BNPL) has become preferred choice among millennials. In Korea Coupang followed by Naver Pay offers a deferred payment, aiming to create customer lock-in effect, save credit card processing fee and lay the groundwork for entering into new financial services. However the literature related to the influential factors of customers' usage intention toward a deferred payment is scarce. For the study, a multi-group analysis was carried out to find differences between Naver shopping and Coupang. The results revealed that the important factors that affect a deferred payment adoption were compatibility, impulsive buying tendency in Naver shopping, whereas compatibility, relative advantage, additional value in Coupang(listed in order of most important). In addition, impulsive buying tendency had a positive effect on adoption intention in Naver shopping and on perceived risk in Coupang. The results imply that Naver shopping need to focus on managing delinquency while Coupang should provide sufficient information on how late fees and credit rating downgrade work and try not to make a deferred payment option stand out. In order to increase adoption rate it is recommendable to narrow down target segment of a deferred payment and expand it to a specialized vertical such as travel.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.203-213
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• 2022

To understand an underlying geological structure via seismic imaging, the velocity information of the subsurface medium is crucial. Although the full-waveform inversion (FWI) method is considered useful for estimating subsurface velocity models, 3D FWI needs a lot-of computing power and time. Herein, we compare the calculation efficiency and accuracy of frequency-domain 2D and 3D acoustic FWIs. Thereafter, we demonstrate that the artifacts from 2D approximation can be partially suppressed via frequency-domain 2D FWI by employing diffraction angle filtering (DAF). By applying DAF, which employs only big reflection angle components, the impact of noise and out-of-plane reflections can be reduced. Additionally, it is anticipated that the DAF can create long-wavelength velocity structures for 3D FWI and migration.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.119-125
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• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.489-498
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• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.