• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.145-166
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• 2023

This study aims to analyze the research trends of English-language journals in Korean studies published in Korea. Data were collected from four English journals in Korean studies indexed in A&HCI and SCOPUS. A total of 1,840 were selected, including 768 articles of the Korea Journal, 466 articles of The Review of Korean Studies, 285 articles of the Seoul Journal of Korean Studies, and 321 articles of the Acta Koreana, in connection with content analysis, author analysis, author keyword frequency analysis, and topic modeling. In results, the domain research of Korean studies is Humanities, followed by Social Science, and Arts and Kinesiology. These three sectors have grown significantly in publishing numbers since 2000. The subject period of the study is in the order of the modern period, late Joseon, and Japanese colonial period. Authors from domestic affiliations made up 73.34% of the total, but the proportion of authors belonging to foreign institutions continued to increase. As for author keywords, 'Korea'(41), 'Buddhism'(20), 'Koreanwar'(18), and 'Joseon'(18) were derived as top keywords. In topic modeling, six topics were identified; 'Korean culture, cultural transmission,' 'Korean modern political history,' 'Korean social democratization process,' 'Japanese colonial period,' 'Korean religious philosophy,' and 'Korean ancient history.' Through this study, it was possible to identify the interests in and research areas of the recent international academic community of Korean studies.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.471-482
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• 2022

This study aims to investigate the characteristics of wave generation in a deep ocean engineering basin and to develop a meta-model of the transfer function of the wavemaker that reflects the geometric characteristics of the deep ocean engineering basin. To this end, the two-dimensional frequency domain boundary element method was applied to achieve an efficient analysis that reflects the geometric characteristics of the deep ocean engineering basin. The developed numerical method was validated through comparison with the analytical solution. Numerical analyses were conducted for the boundary value problem of the wavemaker according to various periods and the positions of the movable bottom. The numerical results were used to investigate the effect of the geometric characteristics of the deep ocean engineering basin on the transfer function of the wavemaker, and the effect of depth on wave generation was checked by changing the position of the movable bottom. To efficiently utilize the various results of the boundary element method, a meta-model, an approximate model of the transfer function of the wave maker, was developed using a thin plate spline interpolation model. The validity of the developed meta-model was confirmed through a comparison of the results of the model tests.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.180-193
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• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.109-119
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• 2022

The computational efficiency of reverse time migration (RTM) based on numerical modeling is not secured due to the high-frequency band of several hundred Hz or higher for data acquired through a three-dimensional (3D) ultra-high-resolution (UHR) seismic survey. Therefore, this study develops an RTM program to derive high-quality 3D geological structures using UHR seismic data. In the traditional 3D RTM program, an excitation amplitude technique that stores only the maximum amplitude of the source wavefield and a domain-limiting technique that minimizes the modeling area where the source and receivers are located were used to significantly reduce memory usage and calculation time. The program developed through this study successfully derived a 3D migration image with a horizontal grid size of 1 m for the 3D UHR seismic survey data obtained from the Korea Institute of Geoscience and Mineral Resources in 2019, and geological analysis was conducted.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.3
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• pp.287-301
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• 2009

The novel cut-and-cover tunnel construction method using rib-reinforced pre-cast arch segments has been recently developed and applied for practice to secure a structural stability of high covering and wide width section tunnels. Cut-and-cover tunnels are usually damaged by the seismic behavior of backfill grounds in case of a low covering condition. Seismic analyses are performed in this study to characterize the dynamic behavior of rib-reinforced pre-cast arch cut-and-cover tunnels. Seismic analyzes for 2 lane cast-in-place and rib-reinforced pre-cast arch cut-and-cover tunnels are carried out by using the commercial FDM program (FLAC2D) considering various field conditions such as the covering height embankment slope and excavation slope. It can be concluded that the amplification of seismic wave is reduced due to an increase in the structural stiffness induced by rib-reinforcement. The results show that the rib-reinforced pre-cast arch cut-and-cover tunnels are more effective against the seismic loading, compared to the cast-in-place cut-and-cover tunnels.

• Convergence Security Journal
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• v.22 no.5
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• pp.95-105
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• 2022

The Army, Navy, and Air Force are making various efforts to develop a weapon system that incorporates the 4th industrial revolution technology so that it can be used in multi-domain operations. In order to effectively demonstrate the integrated combat power through the weapon system to which the new technology is applied, it is necessary to establish a network environment in which each weapon system can transmit and receive information smoothly. For this, it is essential to analyze the Information Exchange Requirement(IER) of each weapon system, but many IER analysis studies did not sufficiently reflect the various considerations of the actual tactical network. Therefore, this study closely analyzes the research methods and results of the existing information exchange requirements analysis studies. In IER analysis, the size of the message itself, the size of the network protocol header, the transmission/reception structure of the tactical network, the information distribution process, and the message occurrence frequency. In order to be able to use it for future IER prediction, we present a technique for calculating the information exchange requirement as a probability distribution using the Poisson distribution and the probability generating function. In order to prove the validity of this technique, the results of the probability distribution calculation using the message list and network topology samples are compared with the simulation results using Network Simulator 2.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1031-1040
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• 2023

Propagation characteristics in line-of-sight(LOS) paths in 3, 6, 10, and 17 GHz frequency bands were measured and analyzed in two different indoor corridors: second floors of Buildings D2 and E2. The measurement was designed to measure when the receiving antenna moved at 0.5 m intervals from 3 m to 30 m, while the transmission antenna was fixed. The analysis of the two indoor corridors was compared by applying basic transmission loss, root mean square (RMS) delay spread, and K-factor. For basic transmission loss, the loss coefficient of the floating intercept path loss model was higher in the indoor corridor of Building E2 than in that of Building D2. Similarly, the RMS delay spread in the time domain was greater in the indoor corridor of Building E2. However, the indoor corridor of Building D2 exhibited higher K-factor in the 3, 6, and 17 GHz bands with lower wave propagation in the 10 GHz band. Despite the 2 indoor corridors being identical, the propagation characteristics varied due to different internal structures and materials. The results provide measurement data for ITU-R Recommendations regarding various indoor environments.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• Informatization Policy
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• v.31 no.2
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• pp.3-38
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• 2024

Hallucination is a significant barrier to the utilization of large-scale language models or multimodal models. In this study, we collected 654 computer science papers with "hallucination" in the abstract from arXiv from December 2022 to January 2024 following the advent of Chat GPT and conducted frequency analysis, knowledge network analysis, and literature review to explore the latest trends in hallucination research. The results showed that research in the fields of "Computation and Language," "Artificial Intelligence," "Computer Vision and Pattern Recognition," and "Machine Learning" were active. We then analyzed the research trends in the four major fields by focusing on the main authors and dividing them into data, hallucination detection, and hallucination mitigation. The main research trends included hallucination mitigation through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), inference enhancement via "chain of thought" (CoT), and growing interest in hallucination mitigation within the domain of multimodal AI. This study provides insights into the latest developments in hallucination research through a technology-oriented literature review. This study is expected to help subsequent research in both engineering and humanities and social sciences fields by understanding the latest trends in hallucination research.