• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of Preventive Medicine and Public Health
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• v.56 no.2
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• pp.111-127
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• 2023

Objectives: Pre-pregnancy diet has an important role in preparing for healthy generation. However, evidence on this issue has been scarce. A scoping review synthesising current evidence will support the demand to map 'what has been researched' on pre-pregnancy diet and maternal and child health. Methods: Systematic search was performed using PICOS (Population, Intervention, Comparison, Outcomes, and Study design) framework in electronic databases. Articles were screened for eligibility, summarized, and the quality was assessed using the National Institute of Health assessment tool. The review structure complies with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guide. Results: Forty-two articles were included after full-text screening. Twenty-five studies were in high-income countries (HICs), six in each upper-middle income, five in lower-middle income countries (LMICs), and one in low-income countries (LIC). Based on the regions: North America (n=16), Europe (n=5), South America (n=4), Australia (n=4), Asia (n=5), Middle East (n=2), and sub-Saharan Africa (n=1). The two-most observed diet-related exposures were dietary pattern (n=17) and dietary quality (n=12). The most assessed outcome was gestational diabetes mellitus (n=28) and fetal and newborn anthropometry (n=7). The average quality score±standard deviation was 70±18%. Conclusions: Research related to pre-pregnancy diet is still concentrated in HICs. The context of diet may vary; therefore, future research is encouraged in LMICs and LICs context, and Mediterranean, South-East Asia, Pacific, and African regions. Some maternal and child nutrition-related morbidity, such as anemia and micronutrient deficiencies, have not been discussed. Research on these aspects will benefit to fill in the gaps related to pre-pregnancy diet and maternal and child health.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.248-248
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• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.511-517
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• 2006

Flood damage is one of the most important and influential natural disaster which has an effect on human beings. Local concentrated heavy rainfall in urban area yields flood damage increase due to insufficient capacity of drainage system. When the excessive flood occurs in urban area, it yields huge property losses of public facilities involving roadway inundation to paralyze industrial and transportation system of the city. To prevent such flood damages in urban area, it is necessary to develop adequate inundation analysis model which can consider complicated geometry of urban area and artificial drainage system simultaneously. In this study, an urban flood analysis model adopting the unstructured computational grid was developed to simulate the urban flood characteristics such as inundation area, depth and integrated with subsurface drainage network systems. By the result, we can make use of these presented method to find a flood hazard area and to make a flodd evacuation map. The model can also establish flood-mitigation measures as a part of the decision support system for flood control authority.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Journal of Korean Dental Hygiene Science
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• v.1 no.2
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• pp.21-39
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• 2018

The purpose of this study was to examine the learning competencies of students at a college from September 1 to November 30, 2017, in an effort to provide some information on how to foster learning competencies in college years, which lay the foundation for work and social lives. 1. The learning competencies of the subjects consisted of academic vision, student identity, cognitive regulation, emotional regulation, learning management and creating learning environments. Out of five points, they scored the highest in academic vision and student identity with 3.34, followed by learning management with 3.20, creating learning environments with 3.18, emotional regulation with 3.16 and cognitive regulation with 3.14. 2. There were statistically significant differences in academic vision according to age, the area of major, the academic credential of their fathers, commuting time, military service experience and career plans. 3. There were statistically significant differences in student identity and cognitive regulation according to gender, age, the area of major, the academic credential of their fathers, commuting time, military service experience and career plans. 4. There were statistically significant differences in emotional regulation according to age, the area of major, the academic credential of their fathers, commuting time, career plans and daily mean study hours. 5. There were statistically significant differences in learning management according to gender, age, the area of major, grade point average, the academic credential of their fathers, career plans and daily mean study hours. 6. There were statistically significant differences in creating learning environments according to gender, age, the area of major, the academic credential of fathers, commuting time, career plans and daily mean study hours. As they were poorest at the cognitive regulation area among the areas of learning competencies, self-directed learning programs that deal with how to study, learning process, how to take notes and arrange them, how to link different pieces of acquired knowledge and how to map out study plans should be developed to give support to students.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.33 no.2
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• pp.67-77
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• 2015

The purpose of this paper is to draw historic valuable resources to conserve through the analysis about the preservation status of historic urban environment in Suwon Hwaseong Fortress. As for the conservation of urban environment, it is important to protect the resources showing historical continuity and to manage the resources remaining characteristics of place, the analysis of the preservation status is focused on the perspective of preservation of physical form and land use. This paper makes progress through three phases. First, in order to understand urban environment in Hwaseong Fortress overall, it compares land registration original map in 1911 to current map in 2014 by the four items of topography, water environment, streets and sites. Next, changes of four items in urban environment have been reviewed further by the research of maps, relative literatures, field survey and interview, and are classified according to the criteria of preservation-partially preservation-disappearance. After analysing preservation status, valuable urban historic cultural resources are drawn separately by being preserved continually and by being preserved partially but remaining characteristics of place. As a result, natural factors of topography and waterway and urban factor of streets are remained considerably preserved. And even if these factors are changed, the ground environment features support to understand historic urban context. Second, as preservation of topography, water environment, streets and sites are closely related to each other, integrated conservation frameworks are needed to enhance urban historic landscape. Third, modern historic resources in Hwaseong are remained unchanged and thus it is necessary to understand urban historic environment by the layers of various times besides Joseon Dynasty period. Fourth, historic sites and streets which had been preserved through urban development process are destroyed by recent historic cultural restoration policies, therefore urban historic resources worthy of conservation should be treated prudently.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.