• 전기학회논문지
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• 제65권1호
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• 한국물환경학회지
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• 제37권5호
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Geomechanics and Engineering
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• 제38권5호
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

• Safety and Health at Work
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• 제10권4호
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Ecology and Resilient Infrastructure
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• 제10권4호
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• pp.107-115
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• 2023

고 탁도의 원수는 정수장 운영 및 수 생태 환경에 부정적인 영향을 줄 수 있어 관리가 필요한 수질 인자이며, 하천의 탁도 예측을 통해 고 탁도의 원수의 효율적 관리를 수행하기 위해 관련분야에 대한 연구가 지속되고 있다. 본 연구에서는 대표적인 앙상블 머신러닝 알고리즘 중 하나인 LightGBM (light gradient boosting machine)을 이용하여 탁도를 예측하는 다중 분류 모형을 구축하였다. 모형의 구축을 위해 입력자료를 탁도값에 따라 탁도가 낮은 경우부터 높은 경우까지 4개의 class로 구분하였으며, class 1 - 4에 속하는 자료수는 각각 945개, 763개, 95개, 25개로 분류되었다. 구축한 모형의 class 1 - 4에 대한 정밀도 (Precision) 각각 0.85, 0.71, 0.26, 0.30 재현율 (Recall)은 각각 0.82, 0.76, 0.19, 0.60로 데이터 수가 적은 소수 class에서 상대적으로 모형이 성능이 낮은 경향을 보였다. 데이터 불균형을 해소하기 위해 over-sampling알고리즘 중 SMOTE를 적용한 결과 개선된 모형의 class 1 - 4에 대한 정밀도 및 재현율은 각각 0.88, 0.71, 0.26, 0.25 및 0.79, 0.76, 0.38, 0.60으로 데이터 불균형 해소를 통해 모형의 재현율이 크게 개선되는 것을 확인할 수 있었다. 또한 데이터 구성비율이 모형성능에 미치는 영향에 대한 확인을 위하여 입력자료의 구성비를 다양하게 하고 각각의 자료로 구축된 모형의 결과를 비교하여 입력자료 구성비에 따른 모형성능의 차이를 분석하였으며, 모형 입력자료의 구성비의 적정한 산정을 통해 모형의 성능을 향상시킬 수 있음을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• 한국터널지하공간학회 논문집
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• 제24권2호
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• pp.217-230
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• 2022

쉴드TBM (Tunnel Boring Machine) 터널 시공에 있어 막장압 관리는 막장면 붕괴, 지반침하 등을 방지하여 막장 안정성을 유지하는 데 중요한 역할을 담당한다. 특히, 챔버 내부의 굴착토로 막장압을 조절하는 토압식 쉴드TBM의 경우, 이수식 쉴드TBM에 비해 막장압의 관리가 어렵다. 본 연구에서는 국내 토압식 쉴드TBM 터널 시공 현장의 지반조건 및 굴진특성 데이터를 분석하여, 토압식 쉴드TBM 터널의 세그먼트 링별 막장압 예측모델을 제시하였다. 예측모델의 입력특성으로 7가지를 선정하였으며, 912개의 학습 데이터 세트(Training data set)와 228개의 시험 데이터 세트(Test data set)를 확보하였다. 최적의 토압식 쉴드TBM 막장압 예측모델 선정을 위하여 KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), XGB (eXtreme Gradient Boosting) 모델의 하이퍼파라미터(Hyperparameter)를 최적화하여 예측성능을 비교한 결과, RF 모델이 7.35 kPa의 평균 제곱근 오차(Root Mean Square Error, RMSE)로 가장 우수한 성능을 나타냈다. 추가적으로, RF 모델의 특성 중요도(Feature importance) 분석을 수행한 결과, 입력특성 중 수압의 영향도가 0.38로 가장 높았으며, 전반적으로 지반조건이 굴진특성보다 높은 중요도를 보여주었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권4호
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• pp.173-178
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• 2023

한국 문학이 세계적으로 관심을 받게 됨에 따라 해외 출판시장에서의 수요가 지속적으로 증가하고 있다. 따라서 해외 출판시 도서 판매량의 예측과 과거 해외 독자들의 선호도가 높았던 도서들의 특징을 분석하는 것이 중요하다. 본 논문에서는 최근 5년간 해외 출간된 도서 중에서 굿셀러로 분류되는 누적 5천 부 이상 판매 여부 예측 모델을 제안하고 굿셀러의 요인이 되는 변수들을 분석하였다. 이를 위해, XGBoost, Gradient Boosting, Adaboost, LightGBM, Random Forest의 다섯 개 앙상블 학습 모델과 Support Vector Machine, Logistic Regression, Deep Learning을 적용한 결과, 불균형 데이터 문제 해결에 앙상블 알고리즘이 큰 효과를 보였음을 확인했으며, 그 중에서도 LightGMB 모델이 99.86%의 AUC 값을 얻어 가장 좋은 예측 성능을 보임을 검증하였다. 예측을 위해 사용된 변수 중 가장 중요한 변수는 작가의 해외 출간 횟수로 나타났으며, 평점 평균, 상위 출판 시장 규모를 가진 국가에서 출판 여부와 평점 참여자 수 등이 중요한 변수로 나타났다. 또한, 굿셀러 도서에 대한 독자들의 반응을 분석하기 위해서, 굿셀러 도서 중에서도 가장 많이 판매된 4권의 작품 리뷰에 대해 텍스트 마이닝을 실시하였다. 분석 결과 스토리, 등장인물, 작가 순으로 관심을 둔 리뷰가 많았음을 알 수 있었으며, 평점이 낮은 리뷰로부터 번역 키워드가 도출된 것으로 보아, 번역에 대한 지원을 확대하는 것이 필요할 것으로 보인다.

• 한국산업정보학회논문지
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• 제28권2호
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• pp.1-9
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• 2023

유방암 관련 기존 AI 연구는 보조적인 진단 예측이나 임상적 요인에 따른 진료 결과를 예측하는 주제가 많았다. 또한 연구기관의 코호트 자료나 일부 환자 자료를 이용하는 경우가 대부분이었다. 본 논문에서는 건강보험심사평가원이 보유하고 있는 전 국민 유방암 환자의 전수 데이터를 활용하여 유방암 환자의 40~50대와 다른 연령대 간의 생존 여부 예측과 생존 여부에 미치는 요인의 차이점을 분석했다. 그 결과, 환자들의 생존 여부 예측 정밀도는 40~50대가 평균 0.93으로 60~80대 0.86 보다 높았으며, 요인에 있어서도 40~50대는 치료횟수(46%)가, 60~80대는 나이(32%)의 변수 중요도가 제일 높았다. 기존 연구와 성능 비교 결과, 평균 정밀도가 0.90으로 기존 논문의 정밀도 0.81보다 높았다. 적용 알고리즘별 성능 비교 결과, 의사결정나무(Decision Tree), 랜덤포레스트(Random Forest) 및 그래디언트부스팅(Gradient Boosting)의 전체 평균 정밀도는 0.90, 재현율은 1.0으로 연령대 그룹 내에서 동일하였으며, 다층퍼셉트론(Multi-Layer Perceptron)의 정밀도는 0.89, 재현율은 1.0 이었다. 심평원의 전 국민 심사청구 빅데이터 가치 활용을 제고하기 위해 비전문가용 머신러닝 자동화(Auto ML) 도구를 사용한 더 많은 연구가 진행되기를 바란다.