• 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.

• Korean Journal of Forensic Psychology
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• v.13 no.1
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• pp.75-97
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• 2022

Profiling is recognized as a representative application area of domestic criminal psychology, and the National Police Agency profiler is firmly established as a profession. However, compared to the social awareness, the recognition and utilization within the police is not high. In this study, we tried to identify factors affecting the intention to use profiling by identifying the perception of investigators who request and use profiling from a profiler when a violent incident occurs. To this end, the relationship between the perception of the importance of factors considered by investigators in the criminal investigation process and the attitude toward profiling on the intention to use profiling was verified through the path model. As a result of a survey of 340 police investigators, the investigator's perception of the importance of investigation elements was divided into two factors: the importance of normative investigative elements (evidence collection and legal judgment, etc.) and factual investigative elements (criminal analysis, criminal information system analysis, etc.). Among them, the importance of factual investigative elements were found to have a positive effect on the intention to use it by mediating the attitude toward profiling. On the other hand, in the case of the importance of normative investigative elements, it was found to have a negative effect on the attitude toward profiling. These results suggest that the perception that investigators have about investigation, which is their main work area, plays a role in determining whether to request profiling as well as attitude towards profiling. Based on the research results, strategies necessary to activate the use of profiling were discussed.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.3
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• pp.97-103
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• 2021

Perilla frutescens (P. frutescens) is an important herb used for many purposes such as medicinal, aromatic, and functional food in Asian countries and has beneficial effects such as antioxidant activity, anti-inflammation activity, anti-depression activity, and anxiolytic activity. However, there have been no studies on the protective effect of P. frutescens extract (PFE) on amnesia in vivo. The present study aimed to investigate whether PFE protects memory deficit using a scopolamine-induced mice model and elucidate the underlying mechanisms involved. The protective effect of PFE against scopolamine-induced memory deficits was investigated using Y-maze, passive avoidance, and Morris water maze tests. Furthermore, the potential mechanisms of PFE in improving memory capabilities related to the cholinergic system and antioxidant activity were examined. PFE significantly increased spontaneous alternation in the Y-maze test, step-through latency in the passive avoidance test, and swimming time in the target quadrant in the probe test when compared to the scopolamine-treated group. Likewise, PFE significantly decreased escapes latency in the Morris water maze test. PFE could not regulate cholinergic function in acetylcholine level and acetylcholine esterase activity. However, PFE increased DPPH radical scavenging activity dose-dependently and total polyphenol content was 127.7±1.2 ㎍ GAE/mg. The results showed that the PFE could be a preventive and/or therapeutic candidate for memory and cognitive dysfunction in Alzheimer's disease.