• Korea Trade Review
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• v.48 no.3
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• pp.131-150
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• 2023

The purpose of this study was to find a countermeasure to the U.S. import refusals for the Korean healthcare and life sciences industry. To this end, an analysis of trends during the pandemic was conducted using the KITA Border Rejection Database, which includes information on items and types of import refusals. The reason for rejection was also analyzed according to the FDA violation codes. The degree of countermeasure for import refusals was identified by measuring the unit rejection rate (URR). The results of the analysis showed that the major U.S. import refusals for the Korean healthcare and life sciences industry had expanded from contact lenses to COVID-19 diagnostic kits and drugs after the pandemic broke out. The major reasons for import refusals were non-compliance with the Predicate Device and Drugs Act and non-approval by the FDA for products and facilities. On the other hand, the unit rejection rate (URR) of major items in the Korean healthcare and life sciences industry was measured higher than the industry average. The results therefore showed a low level of response to U.S. import refusals. The results of the analysis of reasons for import refusals by item according to FDA violation codes were as follows. First of all, the main violation for contact lenses and COVID-19 diagnostic kits corresponded to misbranding. This was often due to the fact that Korean companies did not provide the relevant notices and information required by the FDA. Many cases also failed to demonstrate a substantial equivalency compared to predicate devices already on the market. On the other hand, applications for new unapproved drugs were not accepted as they had yet to pass relevant regulations that would prove their safety and efficacy. In conclusion, import refusals for the Korean healthcare and life sciences industry were found to be closely related to technical barriers to trade (TBT).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.545-548
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• 2016

Big data is all data generated in the digital environment which has a variety of large and a short life cycle. The amount and type of data are becoming more and more produced on a larger scale, as a smart phone and the internet are popular, and consequently it has been converted into time for users to take advantage and extract only the valuable and useful data from the generated big data. Big data can also be applied to the medical industry and health sectors. It has created the synergy to be fused with ICT such as IoT, smart healthcare, and so on. However, there will be challenges like data security in order securely to use a meaningful and useful vast amounts of data. In this study, we analyze the future prospects of the healthcare, applications and issues of medical big data, and the expected challenges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.503-504
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• 2017

Recently, economic growth in the world has increased interest in healthy life, and the smart health care industry is growing. In the field of smart healthcare, wearable-type biometric information measurement technology has been highlighted due to the importance of IoT technology. The purpose of this study is to develop a wearable heart - rate monitoring system that can be applied to wearable health care and glove - type monitoring that enables convenient monitoring of heart rate during activity. For this purpose, a glove - type wearable health care system was developed and its performance was evaluated. Experimental results showed that the heartbeat monitoring was possible even in the presence of actual daily activities.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.47 no.6
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• pp.147-153
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• 2015

Wet-tissue has been used for baby wipe, cleansing pads, industrial wipes, pain relief, personal hygiene, pet care, and healthcare at home, care facilities, restaurant, and hospital. Raw materials of wet-tissue are mainly natural fibers and synthetic fibers such as cotton, rayon, PET (polyethylene terephthalate) and so on. In this study, electrolytic water of NaCl solution was used as fluid in wet-tissue, and the effect of raw materials on antibacterial rate of wet-tissue was investigated. Rayon (100%) showed an excellent antibacterial rate compared with cotton (100%) and rayon:PET (50:50). Antibacterial rate increased as Cl concentration of electrolytic water increased. Absorption of rayon:PET (50:50) was uneven and antibacterial rate of wet-tissue slightly increased by increase of Cl concentration. Antibacterial rate of wet-tissue was 100% under the conditions of more than 1.5 mL of electrolytic water dosage, and dropped under 50% after storage period of 48 hours.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.299-302
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• 2014

Due to the convergence between digital devices and the development of wireless communication technology, bit-signal sensor miniaturization, building an Electronic Medical Record (EMR) which is a digital version of a paper chart that contains all of a patient's medical history and the information of Electronic Health Record (EHR), Ubiquitous healthcare (u-Healthcare) that can monitor their health status and provide personal healthcare service anytime and anywhere. Also, the appearance of cloud computing technology is one of the factors that accelerate the development of u-healthcare service. However, if the individual information to be used maliciously during the u-healthcare service utilization, leads to serious problems directly related to the individual's life because if it goes beyond the level of simple health screening and treatment, it may not provide accurate and reliable healthcare services. For this reason, we analyzed a variety of security issues related to u-healthcare service in cloud computing environment and described about directions of secure health information sharing system construction. In addition, we suggest the future developmental direction for th activation of u-healthcare industry.

• Journal of Radiation Protection and Research
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• v.46 no.1
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• pp.14-23
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• 2021

Background: For radiological protection and control, the International Commission on Radiological Protection (ICRP) provides the nominal risk coefficients related to radiation exposure, which can be extrapolated using the excess relative risk and excess absolute risk obtained from the Life Span Study of atomic bomb survivors in Hiroshima and Nagasaki with the dose and dose-rate effectiveness factor (DDREF). Materials and Methods: Since it is impossible to directly estimate the radiation risk at doses less than approximately 100 mSv only from epidemiological knowledge and data, support from radiation biology is absolutely imperative, and thus, several national and international bodies have advocated the importance of bridging knowledge between biology and epidemiology. Because of the accident at the Tokyo Electric Power Company (TEPCO)'s Fukushima Daiichi Nuclear Power Station in 2011, the exposure of the public to radiation has become a major concern and it was considered that the estimation of radiation risk should be more realistic to cope with the prevailing radiation exposure situation. Results and Discussion: To discuss the issues from wide aspects related to radiological protection, and to realize bridging knowledge between biology and epidemiology, we have established a research group to develop low-dose and low-dose-rate radiation risk estimation methodology, with the permission of the Japan Health Physics Society. Conclusion: The aim of the research group was to clarify the current situation and issues related to the risk estimation of low-dose and low-dose-rate radiation exposure from the viewpoints of different research fields, such as epidemiology, biology, modeling, and dosimetry, to identify a future strategy and roadmap to elucidate a more realistic estimation of risk against low-dose and low-dose-rate radiation exposure.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.