• Journal of Plant Biotechnology
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• v.46 no.3
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• pp.228-235
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• 2019

Apple (Malus pumila) is one of the most economically important fruits in Korea. but virus infection has decreased the sustainable production of apples and caused serious problems such as yield loss and poor fruit quality. Virus or viroid infection including apple chlorotic leaf spot virus (ACLSV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), apple mosaic virus (ApMV) and apple scar skin viroid (ASSVd) have been also reported in Korea. In many cases, as apple gets infected with virus and viroid with no specific symptoms, the damage and symptoms caused by the viruses are not detected. In our research, viruses in the rootstock were eliminated for a virus-free apple dwarfing rootstock of M.9 and M.26. The virus elimination methods were apical meristem culture, thermotherapy ($37^{\circ}C$, 6 weeks) and chemotherapy($Ribavirin^{(R)}$). The detection of apple viruses was accomplished by Enzyme-linked Immuno-Sorbent Assay (ELlSA) and reverse transcription-polymerase chain reaction (RT-PCR). RT- PCR method was 10 ~ 30% more sensitive than the ELISA method. The efficiency of virus elimination was enhanced in apical meristem culture method. The acquisition rate of virus-free apple dwarfing rootstocks was 30 ~ 40% higher in apical meristem culture. After the meristem culturing of M.9, the infection ratio of ACLSV, ASPV and ASGV was 45%, 60% and 50%, respectively. In the apple dwarfing rootstock of M.26, the infection ratio of ACLSV, ASPV and ASGV was 40%, 55% and 55%, respectively. Based on this study, the best method for the production of virus-free apple dwarfing rootstocks was the apical meristem culture.

• Research in Plant Disease
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• v.25 no.1
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• pp.8-15
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• 2019

Plum pox virus (PPV) is a significant viral disease in Prunus spp. worldwide. A nationwide survey was started in Prunus spp. orchards, since PPV was first detected from peach in Korea, 2015. During 2016-2017, samples were collected from 30,333 trees in 1,985 orchards of stone fruits in 8 provinces and 4 cities, Korea and tested by RT-PCR using specific PPV primer set. As a result, 21 trees including peach (9 trees), Japanese apricot (4 trees), plum (1 tree), apricot (7 trees) in 10 orchards were infected and controlled by eradication program. Amplicons of the expected size (547 bp) were obtained from total RNA of seven peach trees in 2016, and directly sequenced. BLAST analysis revealed the highest nucleotide (NT) identity (99%) with a PPV D isolates (LC331298, LT600782) in Genbank. The seven isolates from shared nt sequence identities of 98 to 100% with one another. Phylogenetic analysis showed the isolates in peach clustered closely with the PPV-D isolates from Korea, Japan, USA, and Canada. This is, to our knowledge, the first report of the presence of PPV in Prunus spp. orchards in Korea, 2016-2017, we hope that our results and efforts will contribute to effective measures for eradication of PPV.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.24-33
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• 2021

For this study, we surveyed concentrations of 8 mycotoxins (aflatoxin B1, B2, G1, G2, ochratoxin A, fumonisin B1, B2 and zearalenone) in agricultural products used for food and medicine by liquid chromatography-tandem mass spectrometry and conducted a risk assessment. Samples were collected at the Yangnyeong Market in Seoul, Korea, between January and November 2019. Mycotoxins were extracted from these samples by adding 0.1% formic acid in 50% acetonitrile and cleaned up by using an ISOLUTE Myco cartridge. The method was validated by assessing its matrix effects, linearity, limit of detection (LOD), limit of quantification (LOQ), recovery and precision using four representative matrices. Matrix-matched standard calibration was used for quantification and the calibration curves of all analytes showed good linearity (r2>0.9999). LODs and LOQs were in the range of 0.02-0.11 ㎍/kg and 0.06-0.26 ㎍/kg, respectively. Sample recoveries were from 81.2 to 118.7% and relative standard deviations lower than 8.90%. The method developed in this study was applied to analyze a total of 187 samples, and aflatoxin B1 was detected at the range of 1.18-7.29 ㎍/kg (below the maximum allowable limit set by the Ministry of Food and Drug Safety, MFDS), whereas aflatoxin B2, G1 and G2 were not detected. Mycotoxins that are not regulated presently in Korea were also detected: fumonisin (0.84-14.25 ㎍/kg), ochratoxin A (0.76-17.42 ㎍/kg), and zearalenone (1.73-15.96 ㎍/kg). Risk assessment was evaluated by using estimated daily intake (EDI) and specific guideline values. These results indicate that the overall exposure level of Koreans to mycotoxins due to the intake of agricultural products used for food and medicine is unlikely to be a major risk factor for their health.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.881-887
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• 2023

This study aims to analyze the development and current trends of AI-based medical imaging devices commercialized in South Korea. As of September 30, 2023, there were a total of 186 AI-based medical devices licensed, certified, and reported to the Korean Ministry of Food and Drug Safety, of which 138 were related to imaging. The study comprehensively examined the yearly approval trends, equipment types, application areas, and key functions from 2018 to 2023. The study found that the number of AI medical devices started from four products in 2018 and grew steadily until 2023, with a sharp increase after 2020. This can be attributed to the interaction between the advancement of AI technology and the increasing demand in the medical field. By equipment, AI medical devices were developed in the order of CT, X-ray, and MR, which reflects the characteristics and clinical importance of the images of each equipment. This study found that the development of AI medical devices for specific areas such as the thorax, cranial nerves, and musculoskeletal system is active, and the main functions are medical image analysis, detection and diagnosis assistance, and image transmission. These results suggest that AI's pattern recognition and data analysis capabilities are playing an important role in the medical imaging field. In addition, this study examined the number of Korean products that have received international certifications, particularly the US FDA and European CE. The results show that many products have been certified by both organizations, indicating that Korean AI medical devices are in line with international standards and are competitive in the global market. By analyzing the impact of AI technology on medical imaging and its potential for development, this study provides important implications for future research and development directions. However, challenges such as regulatory aspects, data quality and accessibility, and clinical validity are also pointed out, requiring continued research and improvement on these issues.

• Maritime Security
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• v.7 no.1
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• pp.31-60
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• 2023

This study proposes a new convergence weapon system that combines the covert placement and detection abilities of a self-propelled mobile mine with the rapid tracking and attack abilities of supercavitating rocket torpedoes. This innovative system has been designed to counter North Korea's new underwater weapon, 'Haeil'. The concept behind this convergence weapon system is to maximize the strengths and minimize the weaknesses of each weapon type. Self-propelled mobile mines, typically placed discreetly on the seabed or in the water, are designed to explode when a vessel or submarine passes near them. They are generally used to defend or control specific areas, like traditional sea mines, and can effectively limit enemy movement and guide them in a desired direction. The advantage that self-propelled mines have over traditional sea mines is their ability to move independently, ensuring the survivability of the platform responsible for placing the sea mines. This allows the mines to be discreetly placed even deeper into enemy lines, significantly reducing the time and cost of mine placement while ensuring the safety of the deployed platforms. However, to cause substantial damage to a target, the mine needs to detonate when the target is very close - typically within a few yards. This makes the timing of the explosion crucial. On the other hand, supercavitating rocket torpedoes are capable of traveling at groundbreaking speeds, many times faster than conventional torpedoes. This rapid movement leaves little room for the target to evade, a significant advantage. However, this comes with notable drawbacks - short range, high noise levels, and guidance issues. The high noise levels and short range is a serious disadvantage that can expose the platform that launched the torpedo. This research proposes the use of a convergence weapon system that leverages the strengths of both weapons while compensating for their weaknesses. This strategy can overcome the limitations of traditional underwater kill-chains, offering swift and precise responses. By adapting the weapon acquisition criteria from the Defense force development Service Order, the effectiveness of the proposed system was independently analyzed and proven in terms of underwater defense sustainability, survivability, and cost-efficiency. Furthermore, the utility of this system was demonstrated through simulated scenarios, revealing its potential to play a critical role in future underwater kill-chain scenarios. However, realizing this system presents significant technical challenges and requires further research.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Tuberculosis and Respiratory Diseases
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• v.41 no.5
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• pp.452-461
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• 1994

Background: Tumor necrosis factor(TNF)-$\alpha$ and Interleukin(lL)-$1{\beta}$ are thought to play a major role in the pathogenesis of the septic syndrome, which is frequently associated with adult respiratory distress syndrome(ARDS). In spite of many reports for the role of TNF-$\alpha$ in the pathogenesis of ARDS, including human studies, it has been reported that TNF-$\alpha$ is not sensitive and specific marker for impending ARDS. But there is a possibility that the results were affected by the diversity of pathogenetic mechanisms leading to the ARDS because of various underlying disorders of the study group in the previous reports. The purpose of the present study was to evaluate the roles of TNF-$\alpha$ and IL-$1{\beta}$ as a predictable marker for development of ARDS in the patients with septic syndrome, in which the pathogenesis is believed to be mainly cytokine-mediated. Methods: Thirty-six patients of the septic syndrome hospitalized in the intensive care units of the Asan Medical Center were studied. Sixteens suffered from ARDS, whereas the remaining 20 were at the risk of developing ARDS(acute hypoxemic respiratory failure, AHRF). In all patients venous blood samples were collected in heparin-coated tubes at the time of enrollment, at 24 and 72 h thereafter. TNF-$\alpha$ and IL-$1{\beta}$ was measured by an enzyme-linked immunosorbent assay (ELISA). All data are expressed as median with interquartile range. Results: 1) Plama TNF-$\alpha$ levels: Plasma TNF-$\beta$ levels were less than 10pg/mL, which is lowest detection value of the kit used in this study within the range of the $mean{\pm}2SD$, in all of the normal controls, 8 of 16 subjects of ARDS and in 8 in 20 subjects of AHRF. Plasma TNF-$\alpha$ levels from patients with ARDS were 10.26pg/mL(median; <10-16.99pg/mL, interquartile range) and not different from those of patients at AHRF(10.82, <10-20.38pg/mL). There was also no significant difference between pre-ARDS(<10, <10-15.32pg/mL) and ARDS(<10, <10-10.22pg/mL). TNF-$\alpha$ levels were significantly greater in the patients with shock than the patients without shock(12.53pg/mL vs. <10pg/mL) (p<0.01). There was no statistical significance between survivors(<10, <10-12.92pg/mL) and nonsurvivors(11.80, <10-20.8pg/mL) (P=0.28) in the plasma TNF-$\alpha$ levels. 2) Plasma IL-$1{\beta}$ levels: Plasma IL-$1{\beta}$ levels were less than 0.3ng/mL, which is the lowest detection value of the kit used in this study, in one of each patients group. There was no significant difference in IL-$1{\beta}$ levels of the ARDS(2.22, 1.37-8.01ng/mL) and of the AHRF(2.13, 0.83-5.29ng/mL). There was also no significant difference between pre-ARDS(2.53, <0.3-8.34ngfmL) and ARDS(5.35, 0.66-11.51ng/mL), and between patients with septic shock and patients without shock (2.51, 1.28-8.34 vs 1.46, 0.15-2.13ng/mL). Plasma IL-$1{\beta}$ levels were significantly different between survivors(1.37, 0.4-2.36ng/mL) and nonsurvivors(2.84, 1.46-8.34ng/mL). Conclusion: Plasma TNF-$\alpha$ and IL-$1{\beta}$ level are not a predictable marker for development of ARDS. But TNF-$\alpha$ is a marker for shock in septic syndrome. These result could not exclude a possibility of pathophysiologic roles of TNF-$\alpha$ and IL-$1{\beta}$ in acute lung injury because these cytokine could be locally produced and exert its effects within the lungs.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.