• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.68-75
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• 2022

In this study, in order to directly evaluate the prestress of the PSC structure, a new sensor module based on the measurement of the deformation of concrete was proposed using hetero-core optical fibers and performance tests were performed. In a hetero-core optical fiber, optical loss occurs when a specific part of the transmission path is bent, and the amount of optical loss changes linearly according to the magnitude of the curvature. In order to confirm the measurement performance of the sensor module and the applicability of the optical fiber, the sensor module was deformed and the light passing through the optical fiber was converted into wattage and measured. It can be seen that the light passing through the optical fiber has a linearity of 0.9333 in relation to the deformation while generating the maximum deformation of 0.5 mm at a rate of 0.12 mm/min in a cylindrical concrete specimen with a diameter of 15 cm and a height of 35 cm in which the sensor module is embedded. Based on the results of this experiment, it is judged that it is possible to directly evaluate the prestress of a PSC structure by embedding a sensor module using a hetero-core optical fiber in the structure and measuring the compression deformation in concrete. It is judged that it can be used as useful data for the development of a sheath tube integrated sensor module to be applied to be applied to the girder model experiment.

• Journal of Biomedical Engineering Research
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• v.43 no.3
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• pp.136-146
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• 2022

In this study, it is intended to provide basic data that can help develop a cardiovascular simulator for performance evaluation of pulse wave detectors by identifying the development status of domestic and overseas cardiovascular simulators. A total of 119 papers were selected by excluding duplicate literature, gray literature, and literature not related to a cardiovascular simulator. Based on the selected literature, the research trend of cardiovascular simulators was analyzed. As a result of analyzing the purpose of the study, most of the simulators were developed to evaluate the hemodynamic properties of artificial hearts and valves. In addition, it was used for simulation evaluation or hemodynamic studies such as pulse wave studies. As a result of analyzing configurations of the simulators, a heart most often consisted of only one left ventricle. For blood vessels, the Windkessel model was most often constructed using chambers and valves. In most studies, blood was reproduced by mixing glycerin and water to reproduce both density and viscosity. In addition, as a result of analysis from the perspective of medical device performance evaluation, simulators for evaluating artificial heart and artificial valves have been studied a lot, whereas simulators for blood pressure, pulse wave, and blood flow devices have been relatively insignificant. Based on the review results, we suggested considerations when developing a simulator for performance evaluations of a pulse wave detector.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.15-25
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• 2022

As the incidence and mortality of serious disasters in the construction industry are the highest, various efforts are being made in Korea to reduce them. Among them, risk assessment is used as data for disaster reduction measures and evaluation of risk factors at the construction stage. However, the existing risk assessment involves the subjectivity of the performer and is vulnerable to the domestic construction site. This study established a DB classification system for risk assessment with the aim of early identification and pre-removal of risks by quantitatively deriving risk factors using BIM in the risk assessment field and presents a methodology for risk assessment using BIM. Through this, prior removal of risks increases the safety of construction workers and reduces additional costs in the field of safety management. In addition, since it can be applied to new construction methods, it improves the understanding of project participants and becomes a tool for communication. This study proposes a framework for deriving quantitative risks based on BIM, and will be used as a base technology in the field of risk assessment using BIM in the future.

• Journal of Korea Entertainment Industry Association
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• v.14 no.2
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• pp.205-212
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• 2020

The purpose of this study was to investigate the recognition levels of students in occupational therapy on the function of the dementia care center. The study included 357 students in occupational therapy and the study was conducted from September 2019 to December 2019 based on the questionnaire about the function of the dementia care center. The major results of this study were as follows: Among the types of projects of the dementia care center, 'consultation and registration management' scored the highest. There were significant differences in recognition levels of 7 projects of dementia care center according to 'age' among general characteristics. There were significant differences in recognition levels of 6 projects except 'a visiting model' according to 'grade'. There were significant differences in recognition levels of 'rest area business for dementia' and 'support for families with dementia' according to 'region'. There was a significant differences in recognition levels of 'dementia care village' according to 'experience of using dementia care center'. There were significant differences in recognition levels of 'consultation and registration management', 'support for families with dementia', 'dementia care village' according to 'experience of clinical practice'. This study will provide basic data for the study of community based rehabilitation and help to establish the curriculum of occupational therapy.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.159-167
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• 2021

The main purpose of this study is to closely investigate the constructive relationship between leisure satisfaction, leisure ability, exercise satisfaction, and consistent motor behavior patterns of the elderly's participation in leisure life sports. In order to achieve this, we surveyed and utilized the sampling of conveniences that selected Yongin and Seongnam city in Gyeonggi-do sports clubs, and accumulated samples of the elderly in living sports. The sample size was 239 elderly people. Based on this, according to the purpose of the study, statistical techniques, spss 23.0, and amos programs were used for analysis. The satisfaction analysis used in the research for a data analysis includes frequency analysis, exploatory factor analysis, confirmatory facor analysis, reliability analysis, correnlation analysis and structure equation model analysis. The research has drawn the following conclusions, based on the above mentioned research method and its procedures: First, it affects the satisfaction of the leisure satisfaction movement. Second, leisure ability affects exercise satisfaction. Third, exercise satisfaction affects the consistent exercise persistence and immersion satisfaction of exercise. As a result, it can be seen that the leisure immersion according to the experience of leisure competence has an effect on the satisfaction of the exercise. As a result, and the continuation of the exercise.

• 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.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.