• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.15-20
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• 2022

This study is about the development of a device that reduces bacterial diffusion and odor by improving the water quality of the toilet using ozone water. In public toilets used by an unspecified number of people, various pollutants and pathogens are present in the toilet. These substances that are harmful to the human are dispersed in the form of aerosols into the air through toilet flushing. Aerosols containing various contaminants may flow into the user's respiratory tract or spread to the skin and cover, serving as a medium for various diseases. In order to prevent this spread, it is essential to continuously maintain cleanliness inside the toilet. Therefore, in this study, ozone water that can improve the water quality of the toilet was used as a way to keep the toilet environment clean. A device that is mounted in a toilet tank and continuously generates ozone water to improve pollutants inside the toilet was designed and developed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.134-143
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• 2020

With the worldwide spread of African swine fever, interest in livestock epidemics is growing. Livestock transport vehicles are the main cause of the spread of livestock epidemics, but no empirical quarantine procedures and standards related to the mobility of livestock transport vehicles in South Korea. This study extracted livestock-related vehicles' trajectory by utilizing the facility visit history data from the Korea Animal Health Integrated System and the DTG (Digital Tachograph) data from the Korea Transportation Safety Authority and presented them as exposure indexes aggregating the link-time occupancy of each vehicle. As a result, a total of 274,519 livestock-related vehicle trajectories were extracted, and exposure values by link and zone were quantitatively derived. Through this study, it is expected that prior monitoring of livestock transport vehicles and the establishment of post-disaster prevention policies would be provided.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Particle and aerosol research
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• v.18 no.4
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• pp.87-95
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• 2022

Currently, droplet protective screens (DPSs) are used to prevent the spread of respiratory diseases. As virus particles can maintain their infective in indoor environments, recent studies have investigated the risk of airborne transmission. However, the ability of DPSs to block airborne transmission has not been verified yet. In this study, the preventive ability of DPSs against droplet and airborne transmission was evaluated. Moreover, the effectiveness of a Portable air purifier (PAP) was investigated. According to results, in a simulated room where an infectious person spoke, the DPS blocked more than 90% of the micron-sized droplets (with a diameter larger than 1 ㎛) transmitted to the front of the infectious person. However, sub-micron droplets (with a diameter smaller than 1 ㎛) passed through the DPS and spread in a room. However, the PAP reduced the amount of both micron and sub-micron droplets transmitted to the front of the infectious person. When the PAP airflow direction was set from the DPS surface to the free space near the infectious person, improved prevention against droplet and airborne transmission was recorded. However, airborne transmission was accelerated when the PAP airflow direction was set from the free space to the DPS surface.

• Textile Coloration and Finishing
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• v.33 no.4
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• pp.302-308
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• 2021

Air filters are being used in countless places from industrial sites to everyday life. The spread of the COVID-19 virus, which started in 2019, is disrupting people's daily lives, and the importance of air filters as a basic means to prevent the spread of these diseases is further highlighted. In this study, the purpose was to develop another type of air filter media with excellent barrier properties that can replace PP meltblown nonwoven fabrics widely used commercially due to its excellent electrostatic properties, differential pressure and filtration efficiency. Therefore, wet-laid nonwoven for air filters were manufactured using plexi-filaments formed through flash spinning and having various fiber diameter from hundreds of nanometers to tens of micrometers, and its applicability as an air-filter media was investigated compared to the meltblown nonwoven. As a result of the performance evaluation, it was found that the filtration efficiency and barrier performance at 0.3㎛ was superior to that of the meltblown nonwoven of the same weight, although the differential pressure was high due to morphological properties of the plexi-filament.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.309-322
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• 2023

The Hawkes process is a point process with self-exciting characteristics. It has been mainly used to describe seismic phenomena in which aftershocks occur due to the main earthquake. Recently, it has been used to explain various phenomena with self-exciting properties, such as the spread of infectious diseases and the spread of news on SNS. The Hawkes process can be flexibly modified according to the characteristics of events by using various types of excitation functions. Since it is difficult to implement a maximum likelihood estimator numerically, estimation methods have been improved until recently. In this paper, the conditional intensity function and excitation function are explained to describe the Hawkes process. Then, existing examples of Hawkes processes used in seismic, epidemiological, criminal, and financial fields are described and estimation methods are introduced. I analyze earthquakes that occurred in gyeongsang-do, Korea from November 2017 to December 2022, using R package ETAS.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.329-338
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• 2022

As the non-face-to-face economic situation developed due to the COVID-19 pandemic, untact stock groups appeared in the stock market. This study proposed the Korea COVID-19 fear index following the spread of infectious diseases in the COVID-19 pandemic situation and analyzed the influence on the untact stock and contact stock returns. The results of the empirical analysis are as follows. First, as a result of the Granger causality analysis using the Korea COVID-19 fear index, significant causality was found in the return of contact stocks such as Korean Air, Hana Tour, CJ CGV, and Paradise. Second, as a result of stock price prediction based on the LSTM model, Kakao, Korean Air, and Naver's prediction performance was high. Third, the investment performances of the Alexander filter entry rule using the predicted stock price were high in Naver futures and Kakao futures. This study can find a difference from previous studies in that it analyzed the influence of the spread of the COVID-19 pandemic on untact and contact stocks in the COVID-19 situation where the non-face-to-face economy is in full swing.

• Journal of Appropriate Technology
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• v.6 no.1
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• pp.21-27
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• 2020

The ASF, which originated in Africa and threatens the world, landed in Asia in 2018 in China, and became a stern threat to the security of the Korean Peninsula when North Korea officially reported the ASF to the OIE in May 2019. In 1921, Montgomery, a British veterinary pathologist, made headlines by naming the African swine fever "African Swine Fever," or ASF, a disease caused by a high fatality virus that existed in East Africa. The ASF, which was a pandemic of endemic diseases in Africa, landed in Portgal around 1957 and swept through farms in Lisbon, Portugal. The ASF continued to settle in Spain and Portugal, causing 40 years of damage until the end of the 1990s, and is now in progress after landing on the Italian island of Sardinia in 1978. The virus, which landed in Portiport of Georgia on the Black Sea coast of the Black Sea in 2007, spread to Russia and caused massive damage to China in 2018, then rapidly spread to Vietnam, Laos and Myanmar in May 2019 and spread across the country, causing massive damage to the pork industry and is now in progress. Just three months after confirming the outbreak in North Korea, the outbreak at farms in Paju and Yeoncheon was confirmed on Sept. 16, 2019, leaving South Korea with the stigma of ASF-causing countries, and although the ASF's nationwide expansion has been blocked, it is currently underway in wild boars. If the ongoing ASF in the two Koreas becomes indigenous, it would be a major disaster not only for the pork industry but also for the Korean Peninsula economy. Under the current circumstances, it is impossible to focus only on veterinary areas limited to South Korea, ruling out risk factors from the ASF outbreak. Currently, it is difficult to prevent damage to the pork industry due to the ASF outbreak due to the poor water defense reality in North Korea, and as it is adjacent to China, which has a high risk of developing various epidemic diseases, there is a need for the two Koreas to jointly conduct quarantine and quarantine on the border areas. First of all, I think rapid exchange of information and education on ASF and other diseases is necessary before establishing a joint defense system on the Korean Peninsula. It is important to conduct thorough quarantine and disinfection of ASF-generated areas in North Korea, and areas bordering China and Russia, and jointly conduct thorough quarantine and control of livestock and livestock products in circulation. Cooperation by the South and North Korean water defense industries to prevent the protracted ASF on the Korean Peninsula by all means and methods is essential.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.219-225
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• 2020

Importance of touch equipment and smart learning increases and public institutions and educational facilities are applying smart devices to their daily environments. However, users of public smart devices are at risk of being exposed to the direct and indirect spread of infectious diseases. This study develops an exo-finger that wraps the fingertips of smart device users and is intended to have a disease prevention effect when used on public equipment. An exoskeletal body was fabricated by inserting a secondary material which is a mixture of the activating material, carbon black (CB) and a macromolecular polymer (elastomer) into a mold. This device was confirmed to have a touch function when the CB content was 0.030 wt% or higher, and the content of the elastomer was varied so that it could have a friction force similar to that when a person touches a smart device (a friction coefficient of 2.5). Through experiments, it was concluded that the CB content had little effect on the friction coefficient. As a result of testing the completed prototype on a smart device, it was proven that the developed exoskeletal device can be useful in situations where it is impossible to touch due to wearing protective gears, or when equipment such as gloves is used to prevent the spread of infectious diseases.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.