• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.636-645
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• 2022

In this study, we investigated the adsorption characteristics of methyl orange (MO), an anionic dye, on ginkgo shell-based activated carbon (AC). For this purpose, ACs (GS-1, GS-2, and GS-4) with different textural properties were prepared using ginkgo shells and potassium hydroxide (KOH), a representative chemical activating agent. The correlation between the textural characteristics of AC prepared and the mixing ratio of KOH was investigated using nitrogen adsorption/desorption isotherms. The MO adsorption equilibrium experiment on the prepared ACs was conducted under different pH (pH 3~11) and temperature (298~318 K) conditions, and the results were investigated by Langmuir, Freundlich, Sips and temperature-dependent Sips equations. The feasibility of the MO adsorption treatment process of the prepared AC was also investigated using the dimensionless Langmuir separation factor. The heterogeneous adsorption properties of MO for the prepared AC examined using the adsorption energy distribution function (AED) were closely related to the system temperature and textural characteristics of AC. The kinetic results of the batch adsorption performed at different temperatures can be satisfactorily explained by the homogeneous surface diffusion model (HSDM), which takes into account the external mass transfer, intraparticle diffusion, and active site adsorption. The relationship between the activation energy value obtained by the Arrhenius plot and the adsorption energy distribution function value was also investigated. In addition, the adsorption process mechanism of MO on the prepared AC was evaluated using Biot number.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• 한국노년학
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• v.40 no.4
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• pp.747-760
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• 2020

The purpose of this study is to prepare basic data necessary for the development of services or systems that can enhance the accessibility of health and medical services or enhance the efficiency of health and medical use of senior citizens by identifying factors for predicting health and medical use behavior of senior citizens in Incheon Metropolitan City through the Korea health panel data(version 1.5). Through the structural equation model established through the SPSS and AMOS, it was confirmed that the predisposing factors, health behaviors and needs factors had significant direct and indirect effects on the use of health care services. Since the imbalance in demand and supply of health and medical services is more severe than in other regions, the results of this study can be used as basic data when checking whether the current health and medical system in Incheon Metropolitan City can operate effectively in an aged society and discussing how to provide health and medical services to the elderly in Incheon.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.265-290
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• 2022

An economic evaluation was conducted using cost-benefit analysis for an integrated service platform of open access research articles. The data needed for benefit measurement were collected by conducting a series of surveys to service beneficiaries, including 1,313 academic researchers, 49 bio-industry researchers, and 102 researchers in various industries. Cost-benefit analysis and sensitivity analysis were conducted after estimating the total costs for system construction and operations, anticipated direct and indirect benefits. With respect to the cost-benefit analysis limited to direct benefits, the estimated benefit was KRW 82 billion, which is about 14 times of the total costs for eight years of the entire business period. With respect to the cost-benefit analysis for both direct and indirect benefits, BCR was estimated to be about 98.9 and NPV to be KRW 538.8 billion, indicating that economic feasibility of the project was sufficiently secured. The results of this analysis may help securing the investment to the integrated service platform for OA research products, and the benefit estimation model developed in this study would be utilized as an assessment tool during the rest of this project.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.105-114
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• 2022

As the national income level is improving, housing urbanization and economic speed are progressing rapidly, household waste and food waste are rapidly increasing. The "Waste Management Act" (founded in 1991) introduced the volume-based household waste system in 1995, and even after implementation, the odor of food waste and the prompt disposal process continue to be a social problem.For this reason, the food waste recycling business is attracting attention. In this paper, regarding the role of resource recycling such as feed, compost, and other resources of food waste, this thesis aims to reduce the inefficiency of the recycling process. Data Envelopment Analysis (DEA) of the relationship between inputs and outputs of 33 facilities nationwide, excluding facility data (insufficient) among 394, (238 public, and 156 private ones), as of the end of 2020, which is running a domestic resource recycling project This study was conducted to investigate the important role in the relative management performance of food waste recycling facilities.It was hypothesized that the influence of business history, facility capacity, capital, and machinery of a company running a food waste recycling business on sales was tested.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.153-170
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• 2022

Waterjet has been comprehensively used in urban areas owing to a suitable technique for cutting concrete and rock, and low noise and vibration. Recently, the abrasive waterjet technique has been adopted and applied by the Korea Atomic Energy Research Institute to demolish concrete plugging without disturbing and damaging In-situ Demonstration of Engineered Barrier System in the disposal research tunnel. In this study, the use of abrasive waterjet in the tunnel was evaluated for practical applicability and the existing cutting model was compared with the experimental results. As a variable for waterjet cutting, multi-cutting, water flow rate, abrasive flow rate, and standoff distance were selected for the diversity of analysis. As regarding the practical application, the waterjet facilitated path selection for cutting the concrete plugging and prevented additional disturbances in the periphery. The pump's noise at idling was 64.9 dB which is satisfied with the noise regulatory standard, but it exceeded the standard at ejection to air and target concrete because the experiment was performed in the tunnel space. The experimental result showed that the error between the predicted and measured cutting volume was 12~13% for the first cut and 16% for second cut. The standoff distance had a significant influence on the cutting depth and width, and the error tended to decrease with decrement of standoff distance.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.