• Journal of Gastric Cancer
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• v.23 no.2
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• pp.264-274
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• 2023

Purpose: In this study, polymerase chain reaction (PCR)-based microsatellite instability (MSI) testing was comprehensively analyzed and compared with immunohistochemistry (IHC) for mismatch repair (MMR) protein expression in patients with gastric cancer (GC). Materials and Methods: In 5,676 GC cases, PCR-based MSI testing using five microsatellites (BAT-26, BAT-25, D5S346, D2S123, and D17S250) and IHC for MLH1 were performed. Reevaluation of MSI testing/MLH1 IHC and additional IHC for MSH2, MSH6, and PMS2 were performed in discordant/indeterminate cases. Results: Of the 5,676 cases, microsatellite stable (MSS)/MSI-low and intact MLH1 were observed in 5,082 cases (89.5%), whereas MSI-high (MSI-H) and loss of MLH1 expression were observed in 502 cases (8.8%). We re-evaluated the remaining 92 cases (1.6%) with a discordant/indeterminate status. Re-evaluation showed 1) 37 concordant cases (0.7%) (18 and 19 cases of MSI-H/MMR-deficient (dMMR) and MSS/MMR-proficient (pMMR), respectively), 2) 6 discordant cases (0.1%) (3 cases each of MSI-H/pMMR and MSS/dMMR), 3) 14 MSI indeterminate cases (0.2%) (1 case of dMMR and 13 cases of pMMR), and 4) 35 IHC indeterminate cases (0.6%) (22 and 13 cases of MSI-H and MSS, respectively). Finally, MSI-H or dMMR was observed in 549 cases (9.7%), of which 47 (0.8%) were additionally confirmed as MSI-H or dMMR by reevaluation. Sensitivity was 99.3% for MSI testing and 95.4% for MMR IHC. Conclusions: Considering the low incidence of MSI-H or dMMR, discordant/indeterminate results were occasionally identified in GCs, in which case complementary testing is required. These findings could help improve the accuracy of MSI/MMR testing in daily practice.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.1-9
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• 2023

In this paper, we propose a method to recognize the braille blocks for embedded devices in real time through deep learning. First, a deep learning model for braille block recognition is trained on a high-performance computer, and the learning model is applied to a lightweight tool to apply to an embedded device. To recognize the walking information of the braille block, an algorithm is used to determine the path using the distance from the braille block in the image. After detecting braille blocks, bollards, and crosswalks through the YOLOv8 model in the video captured by the embedded device, the walking information is recognized through the braille block path discrimination algorithm. We apply the model lightweight tool to YOLOv8 to detect braille blocks in real time. The precision of YOLOv8 model weights is lowered from the existing 32 bits to 8 bits, and the model is optimized by applying the TensorRT optimization engine. As the result of comparing the lightweight model through the proposed method with the existing model, the path recognition accuracy is 99.05%, which is almost the same as the existing model, but the recognition speed is reduced by 59% compared to the existing model, processing about 15 frames per second.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.65-85
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• 2022

In this study, in order to derive the priority of indicators and sustainability areas of smart ports, which means ports in the digital era, previous studies and ESG, which have recently been indispensably introduced in all industries worldwide, were studied together. A hierarchical structure was established with upper evaluation items and 20 lower evaluation items in four areas (operational, environmental, social, and governance), and a relative evaluation method of weighting items among the AHP techniques was applied. The pairwise comparison questionnaire consisted of a 9-point scale proposed by Satty (1980). A survey was conducted targeting working-level workers who perform sustainability or ESG(Environmental, Social, Governance)-related work at four representative port authorities in Korea (Busan, Incheon, Ulsan, Yeosu Gwangyang). In order to increase the accuracy of the analysis results, AHP analysis was conducted on 17 questionnaires with a consistency ratio of 0.1 or less. As a result of the analysis, it was confirmed that among the four areas representing the sustainability of domestic smart ports, the operation area had the highest priority, followed by the environment area. In addition, looking at the overall priorities for the 20 detailed indicators, indicators such as operational efficiency, operational planning, energy management, and pollution measurement and management system were found to have high priority. On the other hand, it was confirmed that the social and the governance areas had relatively low importance compared to other areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.37-51
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• 2023

Spatial information is essential for interpreting urban phenomena. Methodologies for spatializing urban information, especially when it lacks location details, have been consistently developed. Typical methods include Geocoding using structured address information or place names, spatial integration with existing geospatial data, and manual tasks utilizing reference data. However, a vast number of documents produced by administrative agencies have not been deeply dealt with due to their unstructured nature, even when there's demand for spatialization. This research utilizes the natural language processing model BERT to spatialize public documents related to urban planning. It focuses on extracting sentence elements containing addresses from documents and converting them into structured data. The study used 18 years of urban planning public announcement documents as training data to train the BERT model and enhanced its performance by manually adjusting its hyperparameters. After training, the test results showed accuracy rates of 96.6% for classifying urban planning facilities, 98.5% for address recognition, and 93.1% for address cleaning. When mapping the result data on GIS, it was possible to effectively display the change history related to specific urban planning facilities. This research provides a deep understanding of the spatial context of urban planning documents, and it is hoped that through this, stakeholders can make more effective decisions.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.12-36
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• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.572-581
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• 2023

Purpose: The purpose of this study is to improve the irregularity of the stress-strain curve and to ensure accuracy when calculating the damping effect by preventing members from moving in the off-plane direction due to eccentricity when loads are applied. Method: The specifications of the steel strips used in this study are the same, but the curvature of the strips to constitute each damper is different. Each steel strip with different curvature was arranged in an triangle, three dampers with different curvature were made, and repeated load tests were conducted, and the amount of energy dissipation was calculated to measure the performance of the damper. Result: The amount of energy dissipation significantly decreases compared to the case where there is no initial curvature, and the change in the test energy dissipation amount according to the size of the curvature is not large, and the presence or absence of the hyperbolic rate is considered an important variable. Conclusion: The period is about 78.7% longer from T=0.3 to T=0.536sec, and the response spectrum acceleration is reduced from Sa=0.54g to Sa=0.229g, so the damping effect of the damper is sufficient.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.529-541
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• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Imaging Science in Dentistry
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• v.53 no.3
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• pp.193-198
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• 2023

Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion: AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.

• Korean Journal of Culture and Social Issue
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• v.25 no.2
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• pp.157-177
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• 2019

In Study 1, the author translated the material which is modified by Han(2017) and allows researchers to examine misinformation effect about background (temporal structure of event) and object information. Eighty-five Korean participants were participated in Study 1 and tested their memory after misled by temporal and object post-event information about a story. The translated material could produce misinformation effect in both types of information. In Study 2, a 3-way ANOVA was conducted with combined data collected from Korea and the U.S to test the effects of three IVs (whether misled or not, the type of information and the nationality of the participants) on memory after misled by temporal and object information. As results, the main effects of all three IVs, the 2-way interaction effect of whether misled or not and the type of information, and the 3-way interaction effect of all the three IVs were statistically significant. In sum, the higher accuracy rate was obtained when the participants were not misled, and they were more accurate about the information about object. Americans tended to be more accurate. The misinformation effect was larger when the participants were misled by object information. The 2-way interaction effect was found only in the Korean sample. In the discussion, the implication of the current study was discussed.