• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.37-46
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• 2007

In the practice of preloading method for soft ground improvement, field engineers need information of ground properties, construction works and field monitoring on ground behaviors of the site. So, integrating all these informations into one database can provide more efficient way for managing and utilizing the data for construction management. In this study, integrated system for construction management of ground improvement sites under preloading is developed. The developed system consists of database (DB) and application program. The database contains all collected data in a construction site and processed data in the system with their geographic information. All informations in the database are standardized from the result of data characterization. Application program performs various functions on managing and utilizing information in the database; pre- and post- data processing with graphic visualization of output, spatial data interpolation, and prediction of ground behavior using field measuring data. And by providing integrating informations and predictions over entire project area with comprehensible visual displays, the applicability and effectiveness of the developed system for construction management were confirmed.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.291-291
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• 2020

제주도는 강수의 지표침투성이 좋은 화산섬의 지질특성상 지표수의 개발이용여건이 취약한 관계로 용수의 대부분을 지하수에 의존하고 있다. 따라서 제주도는 정책 및 연구적으로 오랜 기간동안 지하수의 보전관리에 많은 노력을 기울여 오고 있다. 하지만 최근 기후변화로 인한 강수의 변동성 증가로 인해 지하수위의 변동성 또한 증가할 가능성이 있으며 따라서 지하수위의 급격한 하강에 대비하여 지하수위의 예측 및 지하수 취수량 관리의 필요성이 요구되고 있다. 지하수에 절대적으로 의존하고 있는 제주도의 수자원 이용 여건을 고려할 때, 지하수의 취수량 관리를 위한 지하수위의 실시간 예측이 필요한 실정이다. 하지만 기존의 예측방법에 의한 제주도 지하수위 예측기간은 충분히 길지 않으며 예측기간이 길어지면 예측성능이 낮아지는 문제점이 있었다. 본 연구에서는 이러한 단점을 보완하기 위해 딥러닝 알고리즘인 Long Short Term Memory(LSTM)를 활용하여 제주도 남동쪽 표선유역 중산간지역의 1개 지하수위 관측정에 대해 지하수위를 예측하고 분석하였다. R 기반의 Keras 패키지에 있는 LSTM 알고리즘을 사용하였고, 입력자료는 인근의 성판악 및 교래 강우관측소의 일단위 강수량자료와 인근 취수정의 지하수 취수량자료 및 연구대상 관측정의 지하수위 자료를 사용하였으며, 사용된 자료의 기간은 2001년 2월 11일부터 2019년 10월 31일까지 이다. 2001년부터 13년의 보정 및 3년의 검증용 시계열자료를 사용하여 매개변수의 보정 및 과적합을 방지하였고, 3년의 예측용 시계열자료를 사용하여 LSTM 알고리즘의 예측성능을 평가하였다. 목표 예측일수는 1일, 10일, 20일, 30일로 설정하였으며 보정, 검증 및 예측기간에 대한 모의결과의 평가지수로는 Nash-Sutcliffe Efficiency(NSE)를 활용하였다. 모의결과, 보정, 검증 및 예측기간에 대한 1일 예측의 NSE는 각각 0.997, 0.997, 0.993 이었고, 10일 예측의 NSE는 각각 0.993, 0.912, 0.930 이었다. 20일 예측의 경우 NSE는 각각 0.809, 0.781, 0.809 이었으며 30일 예측의 경우 각각 0.677, 0.622, 0.633 이었다. 이것은 LSTM 알고리즘에 의한 10일 예측까지는 관측 지하수위 시계열자료를 매우 적절히 모의할 수 있다는 것을 의미하며, 20일 예측 또한 적절히 모의할 수 있다는 것을 의미한다. 따라서 LSTM 알고리즘을 활용하면 본 연구대상지점에 대한 2주일 또는 3주일의 안정적인 지하수위 예보가 가능하다고 판단된다. 또한 LSTM 알고리즘을 통한 실시간 지하수위 예측은 지하수 취수량 관리에 활용할 수 있을 것이다.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.5
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.102-110
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• 2023

PSC-I girder bridges constitute the largest proportion among highway bridges in Korea. According to the precision safety diagnosis data for the past 10 years, approximately 41.3% of the PSC-I bridges have been graded as C. Furthermore, with the increase in the aging of bridges, preemptive management is becoming more important. Damage and deterioration to the deck and girder with a long replacement cylce can have considerable impacts on the service and deterioration of a bridge. In addition, the high rate of device damages, including expansion joints and bearings, necessitates an investigation into the influence of the device damage in the structural members of the bridge. Therefore, this study defined representative PSC-I girder bridges with single and multiple spans to evaluate heterogeneous damages that incorporate the damage of the bridge member and device with the deterioration of the deck. The heterogeneous damages increased a crack area ratio compared to the individual single damage. For the single-span bridge, the occurrence of bearing damage leads to the spread of crack distribution in the girder, and in the case of multi-span bridges, expansion joint damage leads to the spread of crack distribution in the deck. The research underscores that bridge devices, when damaged, can cause subsequent secondary damage due to improper repair and replacement, which emphasizes the need for continuous observation and responsive action to the damages of the main devices.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.681-689
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• 2023

Objective : Subarachnoid hemorrhage (SAH) is a condition characterized by bleeding in the subarachnoid space, often resulting from the rupture of a cerebral aneurysm. Delayed cerebral ischemia caused by vasospasm is a significant cause of mortality and morbidity in SAH patients, and inflammatory markers such as systemic inflammatory response index (SIRI), systemic inflammatory index (SII), neutrophil-to-lymphocyte ratio (NLR), and derived NLR (dNLR) have shown potential in predicting clinical vasospasm and outcomes in SAH patients. This article aims to investigate the relationship between inflammatory markers and cerebral vasospasm after aneurysmatic SAH (aSAH) and evaluate the predictive value of various indices, including SIRI, SII, NLR, and dNLR, in predicting clinical vasospasm. Methods : A retrospective analysis was performed on a cohort of 96 patients who met the inclusion criteria out of a total of 139 patients admitted Trakya University Hospital with a confirmed diagnosis of aSAH between January 2013 and December 2021. Diagnostic procedures, neurological examinations, and laboratory tests were performed to assess the patients' condition. The Student's t-test compared age variables, while the chi-square test compared categorical variables between the non-vasospasm (NVS) and vasospasm (VS) groups. Receiver operating characteristic (ROC) curve analyses were used to evaluate the diagnostic accuracy of laboratory parameters, calculating the area under the ROC curve, cut-off values, sensitivity, and specificity. A significance level of p<0.05 was considered statistically significant. Results : The study included 96 patients divided into two groups : NVS and VS. Various laboratory parameters, such as NLR, SII, and dNLR, were measured daily for 15 days, and statistically significant differences were found in NLR on 7 days, with specific cut-off values identified for each day. SII showed a significant difference on day 9, while dNLR had significant differences on days 2, 4, and 9. Graphs depicting the values of these markers for each day are provided. Conclusion : Neuroinflammatory biomarkers, when used alongside radiology and scoring scales, can aid in predicting prognosis, determining severity and treatment decisions for aSAH, and further studies with larger patient groups are needed to gain more insights.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.64-73
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• 2024

As the seriousness of habitat destruction caused by development projects emerges, the importance of environmental impact assessment (EIA) is increasing to preserve biodiversity. In previous studies, research is being conducted to quantitatively evaluate the biodiversity impact of development factors and surrounding environmental factors on the landscape scale, but research on the factors affecting the reduction of biodiversity based on development projects is insufficient. This study examined whether independent variables (size of development project, type of the development, DEM, ecosystem and nature map, distance from the green land, distance from the protected area), which have been proven to effect biodiversity through the previous researches, have a significant effect on the change of richness index (RI) through multi-class logistic regression analysis, T-test, and analysis of the development type. As a result, only the size of development project and the first richness index in EIA showed p-value less than 0.05. And it was confirmed that the reduction in biodiversity was significantly changed in the following construction types: installation of sports facilities, energy development, and development of industrial location and industrial complex. Since the results of this study confirmed that the impact of the variables may be inconsistent depending on the analysis scale, additional study of necessary indicators at the development project is needed to analyze biodiversity changes in EIA accurately.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.