• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.971-979
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• 2023

Small ships (<499 GT) constitute 46% of the existing ships, therefore, it can be concluded that they produce relatively high CO₂ gas emissions. Operating in optimal trim conditions can reduce the resistance of the ship, which results in fewer greenhouse gases. An affordable way for trim optimization is to adjust the weight distribution to obtain an optimum longitudinal center of gravity (LCG). Therefore, in this study, the effect of LCG changes on the resistance of a small planing ship is studied using empirical and numerical analyses. The Savitsky method employing Maxsurf resistance and the STAR-CCM+ commercial computational fluid dynamics (CFD) software is used for the empirical and numerical analyses, respectively. Finally, the total resistance from the ship design process is compared to obtain the optimum LCG. To summarize, using numerical analysis, optimum LCG is achieved at the 46.2% length overall (LoA) at Froude Number 0.56, and 43.4% LoA at Froude Number 0.63, which provides a significant resistance reduction of 41.12 - 45.16% compared to the reference point at 29.2% LoA.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.91-91
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• 2020

국내 복분자는 서양에서 유래한 서양복분자(Rubus occidentalis)와 국내 자생종을 개량한 토종복분자(Rubus coreauns)가 혼용되고 있으며, 전통 한방약재로서 미숙과를 중심으로 국내에서 유통되고 있는 복분자의 영양성분적인 특성과 주요성분인 ellagic acid 함량을 비교하였다. 토종복분자는 광양에서 재배되고 있는 복분자를 수집하였으며, 외래종 서양복분자는 고창에서 재배되고 있는 복분자를 수집하여 사용하였다. 나머지는 국내에서 유통되고 있는 국내산과 중국산 복분자를 경동약령시장과 금산약령시장으로부터 구매하여 사용하였다. 영양성분으로는 조지방, 조단백질 그리고 탄수화물 함량을 측정하였으며, 유리당, 지방산, 유리 아미노산 17종의 함량을 측정하였다. 조지방의 경우 토종복분자는 1.90 %, 서양복분자는 3.03 % 이였으며, 중국산 유통품은 2.28 %, 기타 국내산 유통품의 경우 2.89 %으로 중국산복분자의 조지방 함량이 낮은 것을 확인하였다. 그러나 탄수화물 함량의 경우 70.28~71.85 %로 복분자간의 함량에 큰 차이를 발견할 수 없었다. 유리당의 경우에는 토종복분자의 경우 glucose가 19.03 mg/g, fructose 16.29 mg/g이 측정되었고 고창 서양복분자의 경우 glucose가 16.29 mg/g, fructose 12.76 mg/g이 측정되어 유리당의 총 함량은 차이가 없는 것으로 확인되었으나 토종복분자의 경우 glucose의 함량이 조금 높은 것을 확인하였다. 복분자의 지방산 조성을 비교한 결과 고창 서양복분자에서 불포화지방산의 함량이 19.49 mg/g 으로 광양 토종복분자의 7.69 mg/g에 비하여 월등히 높은 것을 확인되었으며, 불포화지방산 중 linoleic acid (12.19 mg/g), oleic acid (1.88 mg/g)와 linolenic acid (5.43 mg/g) 함량이 높았다. 복분자의 아미노산의 함량은 광양 토종복분자의 경우 4.50 mg/g, 고창 서양복분자의 경우 5.05 mg/g으로 유리아미노산의 함량은 유사한 것으로 나타났다. 특히 아미노산 17종 성분 중 asparagine(0.65~0.84 mg/g), arginine(0.51~1.00 mg/g)과 threonine(0.99~1.63 mg/g)의 함량이 높았다. 지표성분으로 ellagic acid의 함량은 광양 토종복분자의 경우 2.56 mg/g, 고창의 서양복분자의 경우 3.16 mg/g으로 측정되어 서양복분자가 조금 높은 것으로 나타났다. 국내 유통되고 있는 중국산 복분자의 ellagic acid의 경우 2.99 mg/g, 기타 국내산 유통 복분자의 경우 2.83 mg/g으로 광양 토종복분자와 유사한 것으로 나타났다. 위의 연구결과는 국내에서 유통되는 토종 및 서양 복분자를 원료로 하는 기능성식품에 대한 제품개발의 기초자료로서 활용 될 수 있을 것으로 기대한다.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.97-115
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• 2023

In recent years, the structure of the population has been changing rapidly, with a declining birthrate and aging population, and the inequality of population distribution is expanding. At this point, changes in population estimation methods are required, and more accurate estimates are needed at the subregional level. This study aims to estimate the population in 2040 at the 500m grid level by applying an explainable machine learning to Busan in order to respond to this need for a change in population estimation method. Comparing the results of population estimation by applying the explainable machine learning and the cohort component method, we found that the machine learning produces lower errors and is more applicable to estimating areas with large population changes. This is because machine learning can account for a combination of variables that are likely to affect demographic change. Overestimated population values in a declining population period are likely to cause problems in urban planning, such as inefficiency of investment and overinvestment in certain sectors, resulting in a decrease in quality in other sectors. Underestimated population values can also accelerate the shrinkage of cities and reduce the quality of life, so there is a need to develop appropriate population estimation methods and alternatives.

• Composites Research
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• v.37 no.1
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• pp.46-52
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• 2024

The development of transportation methods has improved human transportation convenience and made it possible to expand the travel radius of people with disabilities who have difficulty moving. However, in the case of WAV (wheelchair Accessible Vehicle), the safety that may occur in a vehicle accident is still lower than that of regular passenger seats. In particular, in the case of a rear-end collision that may occur in a defenseless situation, it can cause fatal neck injuries to disabled passengers. Therefore, a more detailed design plan must be reflected in the headrest to be applied to WAV. In this study, a multi-cell headrest was proposed to implement local compression characteristic distribution of the headrest during rear-end collision of WAV. Afterwards, a correlation analysis was performed between the passenger's NIC (Neck Injury Criterion) and impact energy absorption using the data set construction through analysis and the clustering results using k-means clustering. As a result of clustering, it was confirmed that data clusters with similar characteristics were formed, and a correlation analysis between NIC and impact energy absorption through the characteristics of each cluster was performed. As a result of the analysis, it was confirmed that the softer the cell compression characteristics in Mid3 and Mid6, the more impact energy absorption increases, and the harder the cell compression characteristics in Front2, Mid3, and Mid6, the more effective it is in reducing NIC.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.591-603
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• 2020

Purpose The purpose of this study was to describe the clinical features and chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) pneumonia. Materials and Methods An Institutional Review Board-approved retrospective review was performed for 51 laboratory-confirmed COVID-19 pneumonia patients. Patients were divided into two groups depending on their clinical status: mild and severe. Clinical characteristics and chest CT findings were compared between the two groups. Results Among the 51 patients (22 men, 29 women; mean age, 56.5 ± 16 years; range, 22-88 years), 37 (72.5%) were in the mild group and 14 (27.5%) were in the severe group. The patients in the severe group (68.7 ± 12.5 years) were older than the patients in the mild group (51.8 ± 14.9 years, p < 0.001). Premorbid conditions and decreased lymphocyte counts were more often observed in the severe group than in the mild group (71% vs. 41%, p = 0.049 and 86% vs. 32%, p = 0.001, respectively). On chest CT, most patients exhibited a mixed ground-glass opacification (GGO) with consolidation (76%) or a GGO (22%) pattern. The majority of lesions were predominantly bilateral in the lower lung with a posterior, peripheral distribution. The patients in the severe group had higher severity scores than those in the mild group. Conclusion Patients with laboratory-confirmed COVID-19 pneumonia have typical chest CT findings that provide important information regarding expected disease severity.