• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.851-856
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• 2018

In this study, we analyze the characteristics of wireless power transfer(WPT) based on magnetic resonance in an underwater environment and propose an equivalent model suitable for underwater WPT. The proposed underwater WPT equivalent model is constructed by expanding the free-space WPT T-model reflecting characteristics change according to media. Considering the water salinity, we propose a method to extract the parameters of the proposed model based on the S parameters. To verify the proposed model, a 6.78-MHz underwater WPT system was constructed and compared with the predicted power transfer efficiency of the model. As a result, it was confirmed that the proposed model predicts the variation of characteristics with an average error of less than 3 %.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.292-299
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• 2018

Objectives: The aim of the present study was to assess the frequency of use, and preferences regarding information and communication technologies (ICTs) among Ecuadorian patients with chronic kidney disease (CKD) undergoing hemodialysis. Methods: We conducted an anonymous cross-sectional survey-based study from January 2016 to April 2017, involving 393 patients with end-stage renal disease from 9 hemodialysis centers, in which they rated their use and preferences of various ICTs through a modified version of the Michigan Questionnaire. The questionnaire collected information regarding demographics, patients' interest in obtaining health-related information through ICTs, and interest in using ICTs as a potential way to communicate with their healthcare providers. A chi-square test for association and adjusted regression analyses were performed. Results: Among all patients who participated, 64.3% reported owning a cellphone, with less than a third reporting active Internet connection. The most used ICT for obtaining information about CKD and/or hemodialysis was web-based Internet, followed by YouTube. SMS was rated the highest to receive and seek health-related information, followed by Facebook. Younger age and higher levels of education were associated with a higher overall usage of ICTs. Finally, more than half of the patients reported interest in using WhatsApp for communicating with their healthcare providers. Conclusions: Understanding the preferences of ICTs among patients with CKD undergoing hemodialysis could help to improve their outcomes through the potential uses and benefits of ICTs. Further research is needed to assess their role in improving the care of patients with chronic diseases.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.156-170
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• 2019

Rapid increasing of smartphones has changed people's lifestyles, and location-based services are providing a platform to provide various conveniences in accordance with these changes. In particular, it may provide convenience to many users if location-based services are provided in an indoor area such as subway station. However, it is still a difficult task to ensure accurate positioning result for guiding routes in subway stations. This study proposes a KAI-R system that allows all processes to be performed in one system for indoor navigation in subway stations. The proposed system includes a new pedestrian step detection method for continuous positioning along with an improved fusion positioning algorithm.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.17-21
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• 2021

In 2020, the coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented disruptions to global health systems. The Korea has taken full-fledged actions against this novel infectious disease, swiftly implementing a testing-tracing-treatment strategy. New obligations have therefore been given to the Health Insurance Review and Assessment Service (HIRA) to devote the utmost effort towards tackling this global health crisis. Thanks to the universal national health insurance and state-of-the-art information communications technology (ICT) of the Korea, HIRA has conducted far-reaching countermeasures to detect and treat cases early, prevent the spread of COVID-19, respond quickly to surging demand for the healthcare services, and translate evidence into policy. Three main factors have enabled HIRA to undertake pandemic control preemptively and systematically: nationwide data aggregated from all healthcare providers and patients, pre-existing ICT network systems, and real-time data exchanges. HIRA has maximized the use of data and pre-existing network systems to conduct rapid and responsive measures in a centralized way, both of which have been the most critical tactics and strategies used by the Korean healthcare system. In the face of new obligations, our promise is to strive for a more responsive and resilient health system during this prolonged crisis.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.279-286
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• 2021

In this study, we looked at the appearance of Edutech, which is being put into the educational field after Corona 19, with the advent of the 4th industrial revolution. In the era of the 4th industrial revolution, the infrastructure, data, and service of Smart Stick that actively utilized ICT became the main pillars of smart education. In particular, smart education is being implemented through e-learning, smart learning, and edutech, and on this basis, it has become possible through the expansion and use of the Internet and computers, the dissemination of smart devices, and a software foundation using big data. Based on this, it was confirmed that Edutech is being implemented through the establishment of a quarantine safety net, a learning safety net, and a care safety net for individual learners and safe life based on artificial intelligence. Lastly, in order for edutech education using big data to become a discourse for everyone, it is necessary to consider artificial intelligence and ethics in the use and application of edutech.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.83-92
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• 2023

This study was established to investigate the site environment of mixed forests in Korea and to estimate the growth and yield of stands using national forest resources inventory data. The growth of mixed forests was derived by applying the Chapman-Richards model with diameter at breast height (DBH), height, and cross-sectional area at breast height (BA), and the yield of mixed forests was derived by applying stepwise regression analysis with factors such as cross-sectional area at breast height, site index (SI), age, and standing tree density per ha. Mixed forests were found to be growing in various locations. By climate zone, more than half of them were distributed in the temperate central region. By altitude, about 62% were distributed at 101-400 m. The fitness indexes (FI) for the growth model of mixed forests, which is the independent variable of stand age, were 0.32 for the DBH estimation, 0.22 for the height estimation, and 0.18 for the basal area at breast height estimation, which were somewhat low. However, considering the graph and residual between the estimated and measured values of the estimation equation, the use of this estimation model is not expected to cause any particular problems. The yield prediction model of mixed forests was derived as follows: Stand volume =-162.6859+6.3434 ∙ BA+9.9214 ∙ SI+0.7271 ∙ Age, which is a step- by-step input of basal area at breast height (BA), site index (SI), and age among several growth factors, and the determination coefficient (R²) of the equation was about 96%. Using our optimal growth and yield prediction model, a makeshift stand yield table was created. This table of mixed forests was also used to derive the rotation of the highest production in volume.