• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of Life Science
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• v.33 no.9
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• pp.746-753
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• 2023

Viral infectious diseases have been regarded as one of the greatest threats to global public health. The recent coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a stark reminder of the threat posed by emerging viral infections. Developing and producing appropriate and efficient vaccines and therapeutics are the only options to combat this pandemic. The COVID-19 pandemic has highlighted the need for novel vaccine platforms to control and prevent emerging viral diseases. Conventional vaccine platforms, including live-attenuated vaccine and inactivated vaccines, pose limitations in the speed of vaccine development, manufacturing capacity, and broad protection for emergency use. Interestingly, vaccination with the SARS-CoV-2 vaccine candidate based on the mRNA-lipid nanoparticle (LNP) platform protected against COVID-19, confirming that the nucleoside-modified candidate is a safe and effective alternative to conventional vaccines. Moreover, the prophylactic strategies against the COVID-19 pandemic have been mRNA nucleic acid-based vaccines and nanoparticle-based platforms, which are effective against SARS-CoV-2 and its variants. Overall, the novel vaccine platform has presented advantages compared with the traditional vaccine platform in the COVID-19 pandemic. This review explores the recent advancements in vaccine technologies and platforms, focusing on mRNA vaccines, digital vaccines, and nanoparticles while considering their advantages and possible drawbacks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.224-238
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• 2022

North Korea has actively participated in the international community related to environmental agreements. It has proposed various environmental policies internally since the Kim Jong-un regime. In particular, it emphasizes activities related to climate change response, the Sustainable Development Goals, and the conservation of ecosystems including forests and wetlands. In this study, a new security cooperation plan was proposed with an understanding of the climate crisis and environmental regime as a starting point. To this end, trends and recent activities for climate-environment cooperation in the international community and on the Korean Peninsula were analyzed. In addition, North Korea's conditions for cooperation on the Korean Peninsula, technology demand, and the projected future environment of the Korean Peninsula were dealt with. Ultimately, through advice of experts, we were able to discover cooperation agendas by sector and propose short-term and long-term environmental cooperation strategies for the Korean Peninsula based on them. In this study, conditions and directions for cooperation in fields of climate technology, biological resources, air/weather, water environment, biodiversity, renewable energy, bioenergy, and so on were considered comprehensively. Among 21 cooperation agendas discovered in this study, energy showed the largest number of areas. Renewable energy, forest resources, and environmental and meteorological information stood out as agendas that could be cooperated in the short term. As representative initiatives, joint promotion of 'renewable energy' that could contribute to North Korea's energy demand and carbon neutrality and 'forest cooperation' that could be recognized as a source of disaster reduction and greenhouse gas sinks were suggested.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (