• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.21-30
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• 2023

Accurate segmentation of the kidney tumor is necessary to identify shape, location and safety margin of tumor in abdominal CT images for surgical planning before renal partial nephrectomy. However, kidney tumor segmentation is challenging task due to the various sizes and locations of the tumor for each patient and signal intensity similarity to surrounding organs such as intestine and spleen. In this paper, we propose a semi-supervised learning-based mean teacher network that utilizes both labeled and unlabeled data using a kidney local guided map including kidney local information to segment small-sized kidney tumors occurring at various locations in the kidney, and analyze the performance according to the kidney tumor size. As a result of the study, the proposed method showed an F1-score of 75.24% by considering local information of the kidney using a kidney local guide map to locate the tumor existing around the kidney. In particular, under-segmentation of small-sized tumors which are difficult to segment was improved, and showed a 13.9%p higher F1-score even though it used a smaller amount of labeled data than nnU-Net.

• Korea Trade Review
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• v.45 no.4
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• pp.137-152
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• 2020

The increase in national income, the expansion of transportation network, the increase in leisure time, and the influx of foreign tourists in the era of internationalization, the influx of the outside population of Jeju region increased rapidly until 2020. However, the corona 19 (Covid-19) incident that began in January 2020 has hit the entire industry, and the tourism industry in Jeju has also been greatly damaged. However, in the second half of 2020, with some calming of the Corona 19 situation and difficult to leave overseas, the number of visitors to Jeju Island is increasing again as Koreans choose Jeju Island as their domestic tourism. This study analyzed the capacity of Jeju's external population based on the Contingent Valuation Method, and based on this, attempted to suggest policy recommendations for Jeju. The size of accommodations such as the density of visitors, toilets, and rest areas were excluded from consideration, and the level of securing the parking lot already exceeded the capacity, and the rate of securing the parking lot was 93.4%. In the case of accommodation, the total number of available rooms is 88,691, even if one guest per room is assumed, which is 32,372,215 per year, which is sufficient in terms of visitor capacity. To analyze the aspects of psychological capacity, this study analyzed whether the residents are feeling psychological discomfort through three methods of road congestion, garbage disposal, and sewage treatment through Contingent Valuation Method. However, the inconvenience caused by the increase of visitors and the effect of continuous population influx is working in combination, and it has the limitation that the effects of these independent factors cannot be specifically separated. As a result of the study, discomfort has already been recognized in terms of psychological capacity among the factors of capacity, and it was estimated that a cost of about 45 billion won per year was incurred as a result of deriving psychological costs through Contingent Valuation Method. In the future, a policy review is needed to resolve or maintain the perception of this discomfort through continuous management. Accordingly, it is necessary to recognize that the increase of visitors leads to the psychological discomfort of the residents, and to seek a policy alternative that can simultaneously increase the number of visitors and the comfort of the residence.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.63-75
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• 2024

Drinking water is an essential element to ensure the basic human right to live, and the quality of clean water must always be ensured. However, domestic water facilities, which were installed intensively in the early 2000s, are deteriorating. The accidents such as discoloration of water such as chromaticity and turbidity as well as leakage of substances frequently occur. However, since it is virtually impossible to replace all water pipes, the detailed standards for maintenance of water pipe network facilities established in 2021 require water pipe cleaning. The swab pig method, one of the water pipe cleaning methods, is a method of physically removing substances in pipes and is evaluated as having the highest cleaning efficiency. However, Swab is highly likely to be damaged or deformed during the cleaning process, and may even be lost. Therefore, in this study, the material of the pig was changed to a material with high compressibility, and it was made as close as possible to the inner wall of the water pipe. And, to maximize cleaning efficiency, a rotation swab pig with a rotation blade was developed. In addition, high-strength wire and winding equipment were additionally developed to eliminate the possibility of loss and to determine the location of the pig. The inlet and outlet are connected with wires, and after verifying the performance of each detailed technology, the technology was applied on a test bed with a 30m section. As a result of the application, the performance of the technology was verified by measuring the process time and evaluating applicability.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.47-58
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• 2024

This study were conduced experimentally to analyze the heat-generating performance, flexural strength, and microstructure of conductive mortar mixed with micro steel fiber and multi-wall carbon nanotube (MWCNT). In the conductive mortar heat-generating performance and flexural strength tests, the mixing concentration of MWCNT was selected as 0.0wt%, 0.5wt%, and 1.0wt% relative to the weight of cement, and micro steel fibers were mixed at 2.0vol% relative to the volume. The performance experiments were conducted with various applied voltages (DC 10V, 30V, 60V) and different electrode spacings (40 mm, 120 mm) as parameters, and the flexural strength was measured at the curing age of 28 days and compared and analyzed with the normal mortar. Furthermore, the surface shape and microstructure of conductive mortar were analyzed using a field emission scanning electron microscope (FE-SEM). The results showed that the heat-generating performance improved as the mixing concentration of MWCNT and the applied voltage increased, and it further improved as the electrode spacing became narrower. However, even if the mixing concentration of MWCNT was added up to 1.0 wt%, the heat-generating performance was not significantly improved. As a result of the flexural strength test, the average flexural strength of all specimens except the PM specimen and the MWCNT mixed specimens was 4.5 MPa or more, showing high flexural strength due to the incorporation of micro steel fibers. Through FE-SEM image analysis, Through FE-SEM image analysis, it was confirmed that a conductive network was formed between micro steel fibers and MWCNT particles in the cement matrix.

• IMMUNE NETWORK
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• v.23 no.6
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• pp.45.1-45.22
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• 2023

Interstitial lung disease (ILD) involves persistent inflammation and fibrosis, leading to respiratory failure and even death. Adult tissue-derived mesenchymal stem cells (MSCs) show potential in ILD therapeutics but obtaining an adequate quantity of cells for drug application is difficult. Daewoong Pharmaceutical's MSCs (DW-MSCs) derived from embryonic stem cells sustain a high proliferative capacity following long-term culture and expansion. The aim of this study was to investigate the therapeutic potential of DW-MSCs in experimental mouse models of ILD. DW-MSCs were expanded up to 12 passages for in vivo application in bleomycin-induced pulmonary fibrosis and collagen-induced connective tissue disease-ILD mouse models. We assessed lung inflammation and fibrosis, lung tissue immune cells, fibrosis-related gene/protein expression, apoptosis and mitochondrial function of alveolar epithelial cells, and mitochondrial transfer ability. Intravenous administration of DWMSCs consistently improved lung fibrosis and reduced inflammatory and fibrotic markers expression in both models across various disease stages. The therapeutic effect of DW-MSCs was comparable to that following daily oral administration of nintedanib or pirfenidone. Mechanistically, DW-MSCs exhibited immunomodulatory effects by reducing the number of B cells during the early phase and increasing the ratio of Tregs to Th17 cells during the late phase of bleomycin-induced pulmonary fibrosis. Furthermore, DW-MSCs exhibited anti-apoptotic effects, increased cell viability, and improved mitochondrial respiration in alveolar epithelial cells by transferring their mitochondria to alveolar epithelial cells. Our findings indicate the strong potential of DW-MSCs in the treatment of ILD owing to their high efficacy and immunomodulatory and anti-apoptotic effects.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.216-233
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• 2020

This study set out to review tomb culture in the Gyeongju region during the Bronze Age, and also examine the patterns of dolmens during their end phase. For these purposes, the study analyzed 18 tomb relics from the Bronze Age and nine from the early Iron Age. Gyeongju belongs to the Geomdan-ri cultural zone. Approximately 120 tombs from the Bronze Age have been excavated in the Gyeongju region. There are fewer tombs than dwellings in the region, which is a general characteristic of the Geomdan-ri cultural zone. Although the number of tombs is small, the detailed structure of the dead body is varied. During the Bronze Age, tombs in the Gyeongju region were characterized by more prolific construction of pit tombs, dolmens with boundaries, and stacked stone altars than were the cases in other areas. There is a great possibility that the pit tombs in the Gyeongju region were influenced by their counterparts in the northeastern parts of North Korea, given the spindle whorl artifacts buried at the Dongsan-ri sites. Dolmens with boundaries and stacked stone altars are usually distributed in the Songguk-ri cultural zone, and it is peculiar that instances of these are found in large numbers in the Gyeongju region as part of the Geomdanri cultural zone. Even in the early Iron Age, the building of dolmens with boundaries and stacked stone altars continued in the Gyeongju region under the influence of the Bronze Age. A new group of people moved into the area, and they crafted ring-rimmed pottery and built wooden coffin tombs. In the early Iron Age, new rituals performed in high places also appeared, and were likely to provide venues for memorial services for heavenly gods in town-center areas. The Hwacheon-ri Mt. 251-1 relic and the Jukdong-ri relic are ruins that exhibit the aspect of rituals performed in high places well. In these rituals performed in high places, a stacked stone altar was built with the same form as the dolmens with boundaries, and a similar rock to the cover stone of a dolmen was used. People continued to build and use dolmens with boundaries and stacked stone altars while sustaining the Bronze Age traditions, even into the early Iron Age, because the authority of dolmens was maintained. Some dolmens with boundaries and stacked stone altars, known as being Bronze Age in origin, would have continued to be used in ritual practices until the early Iron Age. Entering the latter half of the second century B.C., wooden coffin tombs began to propagate. This was the time when the southern provinces, including the Gyeongju region, were included in the East Asian network, with the spread of ironware culture and the arrival of artifacts from central China. Around this time, dolmen culture faded into history with a new era beginning in its place.