• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.85-91
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• 2020

Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.

• Parasites, Hosts and Diseases
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• v.58 no.6
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• pp.609-617
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• 2020

Plasmodium vivax reemerged in 1993. It has been sustained for more than 25 years and become one of the important indigenous parasitic diseases in northern and western parts of the Republic of Korea near the demilitarized zone. In particular, relapse is a significant concern for the control of malaria, as short- and long-term incubation periods vary among those infected in Korea. In this study, the prevalence of asymptomatic carriers was examined among residents of high endemic areas of vivax malaria during nonseasonal transmission of mosquitoes. Blood samples from 3 endemic regions in northwestern Korea were evaluated by microscopic examination, rapid diagnostic testing, and nested PCR to identify asymptomatic patients carrying malaria parasites in the community. However, no positive malaria case among residents of endemic areas was detected. Additionally, serological analysis was carried out to measure antibodies against 3 antigenic recombinant proteins of P. vivax, merozoite surface protein 1-19, circumsporozoite surface protein-VK210, and liver-stage antigen (PvLSA-N), by the protein array method. Interestingly, seropositivity of sera between previous exposure and samples without exposure to malaria was significantly higher using the PvLSA-N antigen than the other antigens, suggesting that PvLSA-N can be used as a serological marker to analyze the degree of exposure for malaria transmission in endemic areas. This indicates a very low asymptomatic carrier prevalence during the nonmalaria season in the endemic areas of Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.44-56
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• 2018

Researches on military weapons are actively studied to improve national defense power of each country. The military weapon system is being used not only as a weapon but also as a reconnaissance and surveillance device for places where it is difficult for people to access. If such a weapon system becomes an object of attack, military data that is important to national security can be leaked. Furthermore, if a device is taken, it can be used as a terrorist tool to threaten its own country. So, security of military devices is necessarily required. In order to enhance the security of a weapon system such as drone, it is necessary to form a chain of trust(CoT) that gives trustworthiness to the overall process of the system from the power on until application is executed. In this paper, by analyzing the trusted computing-based boot technology, we derive trusted boot technology components and classify them based on hardware dependence/independence. We expect our classification of hardware dependence/independence to be applied to the trusted boot technology of our self-development ultraprecision weapon system to improve the defense capability in our military.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.342-348
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• 2020

Uropathogenic Escherichia coli (UPEC) is a major cause of urinary tract infections (UTIs), which is one of the most common infectious diseases in humans worldwide. Since UPEC is increasingly gaining resistance to many antimicrobial agents, antibiotic therapy of UTI has recently become a great concern. This study examined the epidemiological relationship, and antimicrobial resistance patterns of 84 UPEC isolates obtained from UTI patients in Daejeon, from March to December 2017. Molecular epidemiology was investigated by multilocus sequence typing (MLST), and an antimicrobial susceptibility test was determined using an E-test. In this study, UTI was more common in females (73.8%) than in males (26.2%), and the highest incidence of UTI was observed in the age group in their 70s. Among the 84 UPEC isolates, 59 isolates (70.2%) were multidrug-resistant (MDR), and the major sequence type was ST131 (44 isolates, 52.4%). Interestingly, the rates of MDR in non-ST131 isolates (72.5%) were higher compared to ST131 isolates (68.2%). These results indicate the possibility of the development and spread of MDR in non-ST131 isolates. Effective surveillance networks and continuous research need to be conducted globally to prevent the emergence and international spread of MDR non-ST131 isolates.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Pediatric Infection and Vaccine
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• v.27 no.3
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• pp.171-179
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• 2020

Purpose: In Korea, seasonal influenza is an important respiratory illness afflicting children every year. We aimed to investigate the childhood epidemiology in Jeju during the 2017-2018 and 2018-2019 seasons. Methods: Children aged <13 years, who were tested for influenza at the Jeju National University Hospital during the 2017-2018 and 2018-2019 influenza seasons, were included. Demographics and the influenza test results were retrospectively reviewed from their medical records. Results: This study included 5,219 cases of influenza-like illness (ILI) (2017-2018: n=2,279; 2018-2019: n=2,940). The mean age of the eligible children was 2.85±2.79 years, and the most common age among ILI patients in each season was 1 year group. There were 902 (17.3%, 902/5,219) confirmed influenza cases during the 2 seasons. The rate of influenza confirmed by rapid influenza diagnostic test or polymerase chain reaction among ILI patients in the 2017-2018 and 2018-2019 seasons was 10.4% (236/2,279) and 10.3% (303/2,940) for influenza A, and 9.1% (208/2,279) and 5.3% (155/2,940) for influenza B, respectively. The mean age of influenza-confirmed cases was 4.09 years and 5.05 years in the 2017-2018 and 2018-2019 seasons, respectively (P<0.05). Weekly distribution of influenza was similar to that of ILI in the clinical sentinel surveillance system in both seasons. Conclusion: The difference in the influenza epidemic trend and age-group distribution between the 2017-2018 and 2018-2019 seasons was distinct in Jeju. Steady epidemiological studies on influenza in Jeju are needed for comparison with other regions of Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.47-52
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• 2021

Recently, the share of wind power in energy markets has sharply increased with the active development of renewable energy internationally. In particular, large-scale wind farms are being developed far from the coast to make use of abundant wind resources and to reduce noise pollution. In addition to the electromagnetic interference (EMI) caused by offshore wind farms to coastal or air surveillance radars, it is necessary to investigate the EMI on global maritime distress and safety system (GMDSS) communications between ship and coastal stations. For this purpose, this study investigates whether the transmitted field of MF/HF band from a ship would be subject to interference or attenuation below the threshold at a coastal receiver. First, using geographic information system digital maps and 3D CAD models of wind turbines, the area of interest is electromagnetically modeled with patch models. Although high frequency analysis methods like Physical Optics are appropriate to analyze wide areas compared to its wavelength, the high frequency analysis method is first verified with an accurate low frequency analysis method by simplifying the surrounding area and turbines. As a result, the received wave power is almost the same regardless of whether the wind farms are located between ships and coastal stations. From this result, although wind turbines are large structures, the size is only a few wavelengths, so it does not interfere with the electric field of MF/HF distress communications.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.35-42
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• 2021

Purpose: To evaluate and analyze the adequacy of breast magnetic resonance imaging (MRI)s taken before publication of the 2018 recommendation in South Korea. Materials and Methods: We enrolled 87 cases of breast MRIs, from January 2010 to November 2013, taken at external hospitals in the study. Breast MRI protocol elements are divided into three categories based on the recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance: (1) Essential elements for breast MRI protocol; (2) Element to consider when evaluating imaging quality; and (3) Optional element for breast MRI protocol. Also, we divided enrolled cases into three groups based on their conducting locations -- (1) Primary hospitals, (2) Secondary hospitals, and (3) Tertiary hospitals-and analyzed them for the adequacy of imaging protocols based on the 2018 recommendation. We used a Chi-square test and Fisher's exact test to identify differences between categorical variables. Results: Over 98% of the criteria for 'essential elements for breast MRI protocol' were satisfied when compared with the 2018 Recommendation. Over 96% of the criteria for 'elements to consider when evaluating imaging quality' were also satisfied, except for the slice thickness (83.9%). Optional elements for breast MRI protocol were satisfied with various percentages. There were no statistically significant differences between groups of tertiary, secondary, and primary hospitals; however, 3 tesla of MRI (P = 0.04), subtraction image protocol (P = 0.032), and DWI protocol (P = 0.03) were used more frequently in the tertiary hospitals than in the others. Conclusion: We found that the categories of 'essential elements' and 'elements to consider when evaluating imaging quality' were satisfied at 98% and 96%, respectively, when compared with the 2018 Recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.2
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• pp.133-141
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• 2022

Candida is one of the most common causes of bloodstream infections and a leading cause of morbidity and mortality among hospitalized patients. The purpose of this study was to provide important information for formulating empirical treatment plans for candidemia by investigating the antifungal resistance rate of Candida. Among the Candida strains (973 cases) isolated from blood culture tests at the S hospital in 2009~2018, 4.7% (N=44) comprising the Candida spp. (932 strains) showed resistance to fluconazole. The resistant strains included C. albicans, C. parapsilosis, C. tropicalis, and C. glabrata. In addition Candida spp. (947 strains) showed resistance to amphotericin B (N=6, 0.6%), flucytosine (N=23, 2.4%) and voriconazole (N=24, 3.1%). C. albicans was resistant to fluconazole (N=23, 6.9%) and voriconazole (N=21, 6.0%), The statistical analysis showed that C. albicans and non-albicans Candida species were resistant to fluconazole (P=0.039) and voriconazole (P<0.001). A monitoring system to understand the rate of candidiasis infections in a hospital setting is required. It is also important to make the right choice of the antifungal agent based on drug susceptibility patterns. Therefore, an infection surveillance policy that tracks Candida resistance through regular antifungal susceptibility tests is necessary.