• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• Journal of Korean Medicine Rehabilitation
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• v.31 no.4
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• pp.87-103
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• 2021

Objectives The purpose of this study was to investigate the development process and describe the diagnosis methods, theories and treatments of Mongolian traditional medicine and Korean traditional medicine through literature records and prior studies. Methods Literature records and previous studies on traditional medicine of both countries were collected through various sites in Mongolia (Esan, Mongoliajol, Kok, Yumpu, Scribd, Science and Technology Foundation [STF]) and Korea (Koreanstudies Information Service System [KISS], Korea Institute of Science and Technology Information [KISTI], National Digital Science Library [NDSL], Research Information Sharing Service [RISS], Oriental Medicine Advanced Searching Integrated System [OASIS]). Also the English database was searched through PubMed. In the case of Mongolian traditional medicine, medical books published in Mongolia were mainly referenced and used for research. Results Studying the development process, basic concepts and the system of diagnosis and treatment of the two traditional medicine, several commonalities and differences were revealed. Conclusions This study showed that the scope of diagnosis methods between Mongolian and Korean traditional medicine were slightly different, and that the medical terminology for the diagnosis method had slightly different contents from each other. Although there were many similarities in treatments of Mongolian and Korean traditional medicine, the Chuna therapy is found in Korean traditional medicine only. The basic theories constituting traditional medicine were the same, but the five-element theory used by the two countries differs in the following two factors. Mongolia uses elements of air and space as the theory of five elements, while Korea uses elements of wood and iron.

• Clinical and Experimental Pediatrics
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• v.64 no.4
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• pp.180-187
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• 2021

Background: Severe perinatal asphyxia results in multiple organ involvement, neonate hospitalization, and eventual death. Purpose: This study aimed to investigate the predictive factors of death in newborns with hypoxic-ischemic encephalopathy (HIE) receiving selective head cooling. Methods: This cross-sectional descriptive-retrospective study was conducted from 2013 to 2018 in Fatemieh Hospital of Hamadan and included 51 newborns who were admitted to the neonatal intensive care unit with a diagnosis of HIE. Selective head cooling for patients with moderate to severe HIE began within 6 hours of birth and continued for 72 hours. The required data for the predictive factors of death were extracted from the patients' medical files, recorded on a premade form, and analyzed using SPSS ver. 16. Results: Of the 51 neonates with moderate to severe HIE who were treated with selective head cooling, 16 (31%) died. There were significant relationships between death and the need for advanced neonatal resuscitation (P=0.002), need for mechanical ventilation (P=0.016), 1-minute Apgar score (P=0.040), and severely abnormal amplitude-integrated electroencephalography (a-EEG) (P=0.047). Multiple regression of variables or data showed that the need for advanced neonatal resuscitation was an independent predictive factor of death (P=0.0075) and severely abnormal a-EEG was an independent predictive factor of asphyxia severity (P=0.0001). Conclusion: All cases of neonatal death in our study were severe HIE (stage 3). Advanced neonatal resuscitation was an independent predictor of death, while a severely abnormal a-EEG was an independent predictor of asphyxia severity in infants with HIE.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.183-188
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• 2019

As social perceptions of pets change, cultural attitudes toward pets are becoming more friendly. In particular, dogs have been living familiarly and closely with humans for a long time. In the changing times, various services are being used to improve the understanding of dogs and to prevent companion dogs and increase awareness of respect for life. Therefore, in this paper, we implemented a smart lead line in which IoT service and application technology are linked to the walking dog's automatic lead line. To do this, we developed a smart dog lead line by designing and implementing an integrated module in connection with heterogeneous sensors and linking it with a dog lead line. Finally, a smart dog lead line was used to collect the dog's biological signals in real time, identify the location of the dog, and provide a notification system. Through this, we believe that the culture of dog culture can be further grown.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Journal of International Academy of Physical Therapy Research
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• v.12 no.1
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• pp.2261-2266
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• 2021

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of 'Download All Images' and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

• Journal of the Korean Society of Food Culture
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• v.37 no.2
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• pp.143-152
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• 2022

Galactosemia is a rare genetic metabolic disease caused by galactose and its metabolites generated during carbohydrate metabolism, which is relatively rare in Asian countries, including Korea. Patients with galactosemia should be treated with a galactose-restricted diet. However, information is lacking about the exact content of galactose in food, and dietary guidelines for patients with galactosemia in Korea. This study aims to recognize the difficulties faced by parents and clinical experts of patients with galactosemia, and understand their demands. Totally, 5 parents of children diagnosed with galactosemia and 5 clinical professionals participated in the focus group interviews. The parents' interview focused on the daily life of the patient, which included diet and social difficulties. The clinical experts mainly answered about medical care, including the number and status of patients, and their suggestions for effective treatment. Most parents were worried about social isolation due to conflicts in the family as well as in society due to a lack of understanding of the disease. The clinical experts stated the absence of a disease management system as the greatest problem. An integrated support system for patients with galactosemia, which includes appropriate dietary guidelines by considering the domestic environment, is required.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.757-768
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• 2022

In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.

• Journal of East-West Nursing Research
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• v.28 no.2
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• pp.179-189
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• 2022

Purpose: Based on the results of Grandey's Emotion Regulation Process Model and previous studies, this study was conducted to identify the relationship between emotional labor, communication competency, emotional intelligence, social support, and burnout, and to identify factors affecting burnout of nurses in outpatient department. Method: The participants were 190 nurses with more than six months of experience working at the outpatient department of a general hospital in Seoul. Data were collected from April 5 to May 28, 2021, and analyzed using SPSS/WIN 25.0. Results: Significant variables affecting burnout were emotional labor, communication competency, emotional intelligence, education, and total clinical experience. Social support showed a statistically significant negative correlation with burnout, but did not affect burnout. Burnout showed a statistically significant a positive correlation with emotional labor, and showed a negative correlation with communication competency, emotional intelligence and social support. We found a negative correlation between burnout and subjective health status. Emotional labor had a positive effect on burnout. Emotional intelligence, clinical experience for more than 10 years, communication competency, and education for masters or higher negatively affected burnout, respectively. They accounted for 49.2% of the total variance of burnout. Conclusion: Based on the results of this study, it is necessary to reduce emotional labor, one of the major predictors of burnout for outpatient care. In order to prevent emotional labor that results in burnout, an integrated program that improves emotional intelligence and communication competency should be developed.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.565-572
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• 2023

This study was conducted to analyze the status radiological imaging examinations assessment in Korea medical institutions conducted in the public sector and suggest a direction for improvement. Among the assessment of medical institutions, the main assessment related to radiographic imaging examinations are the certification evaluation of medical institutions and the adequacy assessment of radiographic imaging examinations. The certification evaluation of medical institutions evaluates the image inspection operation process, provision of accurate results, and compliance with safety management procedures. In the assessment of adequacy of radiographic imaging examinations, structural indicators related to manpower and equipment, patient evaluation implementation rate, and exposure reduction programs were included. However, for safer and higher-quality radiological imaging examinations, it is necessary to increase the participation rate of medical institutions in certification evaluations. In addition, it is necessary to improve the manpower indicator, and incentive payments can be considered to induce quality improvement of medical institutions in the future. Integrated management of radiation exposure at the national level should also be carried out simultaneously.