• Journal of Gastric Cancer
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• v.24 no.4
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• pp.436-450
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• 2024

Purpose: This study aims to explore the relationship between soy food consumption and gastric cancer (GC) risk, accounting for Helicobacter pylori infection status. Materials and Methods: We analyzed data from patients with GC and healthy individuals prospectively enrolled by 6 hospitals between 2016 and 2018. Dietary intake was evaluated using questionnaires that categorized seven dietary habits and 19 food groups. Multivariate logistic regression models were applied to examine associations. Model I adjusted for various epidemiological factors, while Model II included further adjustments for H. pylori infection. Primary exposures examined were consumption frequencies of nonfermented, unsalted soy foods (soybean/tofu) and fermented, salty soy foods (soybean paste stew). Results: A total of 5,535 participants were included, with 1,629 diagnosed with GC. In Model I, the frequency of soybean/tofu consumption was inversely related to GC risk; adjusted odd ratios (aORs) were 0.62 (95% confidence interval [CI], 0.48-0.8), 0.38 (95% CI, 0.3-0.49), 0.42 (95% CI, 0.33-0.53), and 0.33 (95% CI, 0.27-0.42) for 1 time/week, 2 times/week, 3 times/week, and ≥4 times/week. Consumption of 2 servings/week of soybean paste stew showed the lowest GC association, forming a V-shaped curve. Both low (aOR, 4.03; 95% CI, 3.09-5.26) and high serving frequencies of soybean paste stew (aOR, 2.23; 95% CI, 1.76-2.82) were associated with GC. The association between soy foods and GC in Model II was similar to that in Model I. The soy food-GC associations were consistent across sexes in Model I. Nonetheless, the positive correlation between frequent consumption of soybean paste stew (≥5 times/week) and GC was more pronounced in women (aOR, 7.58; 95% CI, 3.20-17.99) compared to men (aOR, 3.03; 95% CI, 1.61-5.88) in Model II. Subgroup analyses by H. pylori status and salty diet revealed a consistent inverse relationship between soybean/tofu and GC risk. In contrast, soybean paste stew showed a V-shaped relationship in H. pylori-positive or salty diet groups and no significant association in the H. pylori-negative group. Conclusions: Soybean/tofu intake is consistently associated with a decreased risk of GC. However, the relationship between soybean paste stew consumption and GC risk varies, depending on H. pylori infection status and dietary salt intake.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.317-325
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• 2024

This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.400-407
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• 2024

The role of unmanned aerial vehicles (UAVs) in modern warfare is increasingly significant, making their capacity for autonomous missions essential. Accordingly, autonomous target detection/identification based on captured images is crucial, yet the effectiveness of AI models depends on image sharpness. Therefore, this study describes how to determine the field of view (FOV) of the camera and the flight position of the UAV considering the required spatial resolution. Firstly, the calculation of the size of the acquisition area is discussed in relation to the relative position of the UAV and the FOV of the camera. Through this, this paper first calculates the area that can satisfy the spatial resolution and then calculates the relative position of the UAV and the FOV of the camera that can satisfy it. Furthermore, this paper propose a method for calculating the effective range of the UAV's position that can satisfy the required spatial resolution, centred on the coordinate to be photographed. This is then processed into a tabular format, which can be used for mission planning.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.107-125
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• 2023

The pace of sustainability transition within the maritime industry has been accelerating. This shift primarily necessitates changes in the industry's heavy reliance on fossil fuel-driven ecosystems. Additionally, numerous sustainability laws and regulations, such as the EU's CBAM and IMO's EEXI, have been implemented. This transition is poised to amplify the competitive edge of firms equipped with greater resources, as it introduces substantial operational burdens due to expensive eco-friendly fuel adoption and regulatory compliance. To diverge from the traditional competitive landscape, this paper aims to explore innovative maritime finance models enabling domestic firms to gain competitive advantages on a global scale. Employing analogical reasoning and modeling as a research method, this paper demonstrates that maritime firms can leverage the sustainability transition by aligning sustainable maritime operations with ETS (Emission Trading Schemes). Expanding on this novel approach, the paper delves into potential connections between CCM (Compliance Carbon Market), VCM (Voluntary Carbon Market), and digital asset exchanges. This newly proposed digital/net-zero maritime ecosystem holds the potential to significantly impact the shipping, shipbuilding, and ship finance industries, positioning Busan as a sustainable maritime finance hub. This study holds significance as pioneering research that may stimulate subsequent case-based studies and offer strategic guidance to market participants and policymakers as the maritime industry moves towards a net-zero transition

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.157-164
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• 2024

In this paper, we propose a discussion that the feasibility of deploying a deep learning-based detector on the resource-limited board. Although many studies evaluate the detector on machines with high-performed GPUs, evaluation on the board with limited computation resources is still insufficient. Therefore, in this work, we implement the deep-learning detectors and deploy them on the compact board by parsing and optimizing a detector. To figure out the performance of deep learning based detectors on limited resources, we monitor the performance of several detectors with different H/W resource. On COCO detection datasets, we compare and analyze the evaluation results of detection model in On-Board and the detection model in On-GPU in terms of several metrics with mAP, power consumption, and execution speed (FPS). To demonstrate the effect of applying our detector for the military area, we evaluate them on our dataset consisting of thermal images considering the flight battle scenarios. As a results, we investigate the strength of deep learning-based on-board detector, and show that deep learning-based vision models can contribute in the flight battle scenarios.

• Journal of The Korean Association For Science Education
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• v.44 no.4
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• pp.313-323
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• 2024

This study aims to explore educational methods to help students and citizens, who are exposed to numerous manufactured risks, better understand the nature of science and technology. It also seeks to develop their ability to identify, analyze, and evaluate the risks associated with science and technology, ultimately enabling them to live safer lives in society. To achieve this, through an extensive literature review, we explored the definition of risk, the necessity of risk education, and the relationship between SSI (Socioscientific Issues) education and risk education. Based on the results, we proposed the SSI-CURE (Socioscientific Issues Centered on the Understanding of Risk and its Evaluation) model, which can systematically educate about risks in the context of SSI. The SSI-CURE model proceeds through the following four steps: 1) Confrontation of SSI, 2) Understanding the Nature of Science and Technology with SSI, 3) Risk Assessment in SSI, and 4) Enactment of Countermeasures for SSI. These steps represent the key elements for education on risks in the context of SSI: Conceptual understanding of risks (risk knowledge), competencies necessary for discussing or addressing risk situations (risk competency), scientific content knowledge needed to understand risks (knowledge in science), and knowledge required to understand the causes of risks and their impacts (knowledge about science). We expect that the SSI-CURE model can be used not only as a guide for instruction but also as a representative framework for developing programs to educate about risks in the SSI context.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.197-203
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• 2024

The algal blooms in rivers can negatively affect water source management and water treatment processes, necessitating continuous management. In this study, a multi-classification model was developed to predict the concentration of chlorophyll-a (chl-a), one of the key indicators of algal blooms, using Tabular Prior Fitted Networks (TabPFN), a novel deep learning algorithm known for its relatively superior performance on small tabular datasets. The model was developed using daily observation data collected at Buyeo water quality monitoring station from January 1, 2014, to December 31, 2022. The collected data were averaged to construct input data sets with measurement frequencies of 1 day, 3 days, 6 days, 12 days. The performance comparison of the four models, constructed with input data on observation frequencies of 1 day, 3 days, 6 days, and 12 days, showed that the model exhibits stable performance even when the measurement frequency is longer and the number of observations is smaller. The macro average for each model were analyzed as follows: Precision was 0.77, 0.76, 0.83, 0.84; Recall was 0.63, 0.65, 0.66, 0.74; F1-score was 0.67, 0.69, 0.71, 0.78. For the weighted average, Precision was 0.76, 0.77, 0.81, 0.84; Recall was 0.76, 0.78, 0.81, 0.85; F1-score was 0.74, 0.77, 0.80, 0.84. This study demonstrates that the chl-a prediction model constructed using TabPFN exhibits stable performance even with small-scale input data, verifying the feasibility of its application in fields where the input data required for model construction is limited.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.169-174
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• 2024

This study purports to identify the factors that contribute to the classification of age groups or generations of Koreans. Independent variables such as respondents' attitudes toward welfare, attitudes toward equity, education level, perception of inequality in Korean society, tax awareness, and health status are included in the model that were put into the analysis with the main interest. Since this study does not construct any hypothesis prior to analysis, the nature of this study can be said exploratory. The data utilized for the analysis are from the 17th year of the Korean Welfare Panel collected in 2022, and a linear discrimination analysis technique will be used. First and foremost, a theoretical review of the generational classification will be conducted through domestic and international literature in the past. To date, there is no quantitative studies in Korea that have a significant influence on the generational classification. Therefore, in this study, a theoretical review of political tendencies and values, which are estimated to have a significant influence on the generational classification, that is, the difference between generations, will be significant. The perception and attitude toward welfare will be discussed in the review of values. Next, analysis models, analysis techniques, and variables to be used in the analysis will be introduced. After

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.