IIn the context of site response analysis, the use of shear wave velocity (  ) profiles that consider the seismological rock (  ≥ 3,000 m/s) depth is recommended. This study proposes regression analysis and machine learning-based models to predict deep   profiles for a specialized excavated rock site in South Korea. The regression model was developed by modifying mathematical expressions from a previous study and analyzing the correlation between   and model variables to predict deep   beyond 50 m. The machine learning models, designed using tree-based algorithms and a fully connected hierarchical structure, were developed to predict   from 51 m to 300 m at 1 m intervals. These models were validated by comparing them with measured deep   profiles and accurately estimating the trend of deep   variations. The proposed prediction models are expected to improve the accuracy of ground motion predictions for a specialized excavated rock site in Korea.

Environmental changes play a significant role in the introduction, dispersal, and establishment of invasive species. This study aims to predict the habitat suitability of the newly invasive pest P. absoluta in South Korea by thoroughly considering key environmental factors, including climate and land cover changes. First, the MaxEnt model was developed to simulate changes in habitat suitability using global occurrence data and future climate change scenarios. Subsequently, potential risk areas (PRAs) for P. absoluta within agricultural regions were analyzed based on land cover changes. The results indicated that under all Shared Socioeconomic Pathway (SSP) scenario combinations, the PRA for SSP1 and SSP3 in 2055 were similar, with values of 47.85% and 48.62%, respectively. However, by 2085, these areas showed a marked decrease to 39.28% and 28.52%, respectively. These findings suggest that the PRA for P. absoluta is expected to be most critical in the near future as climate and land-use changes continue to progress. This study emphasizes the urgent need for ongoing monitoring and management to prevent further invasion and spread of P. absoluta into new regions of South Korea. Additionally, it provides scientific evidence to support the development of effective control and management strategies. By thoroughly evaluating the impact of climate and land cover changes on invasive species management, this research presents a foundational framework for predicting the spread and risks of P. absoluta under future climate scenarios.

고자리꽃파리는 양파 및 마늘 등 Allium 속에 속하는 농작물의 중요한 해충으로 전 세계적으로 온대 지역에서 경제적 해충으로 취급하고 있다. 본 연구는 고자리꽃파리의 발생 기준점을 정하여 연간발생양상을 해석하고, 초기방제 시기를 설정할 수 있도록 월동 번데기 우화모형을 개발하고자 수행하였다. 고자리꽃파리 월동 번데기의 온도발육 모형으로 선형 및 비선형 모형을 추정하고, 발육기간 분포모형과 결합하여 월동번데기의 성충으로 우화시기 예측모형을 수립하였다. 비선형 모델의 경우 3-매개변수 Lactin 수식과 저온에서 온도와 발육률 간의 선형성을 높이기 위해 마지막 매개변수 (λ)를 선형모형의 절편으로 대체한 4-매개변수 수식을 사용하였다. 일일 평균기온을 이용하여 50% 성충 우화일을 예측한 결과, 선형모형 기반의 적 산온도 모형(DD, degree-day) 및 선형 또는 비선형 모형을 적용하여 발육률을 누적하는 발육률 적산 모형(RS, rate summation) 모두 실측값과 큰 차이를 보였다. 반면 시간별 온도를 입력변수를 사용한 경우, 3-매개변수 모델을 제외한 사인곡선법 기반의 DD 모형, 선형 RS 모형, 4-매개변수 비선 형 RS 모형의 평균편차는, 실제 관측치와 3일 이상 차이가 나지 않았다. 최종적으로 시간별 온도자료를 이용하고, 발육모형으로 선형과 4-매개변수 비 선형 모형을 적용하는 RS 모형을 활용 가능한 모형으로 선정하였다. 선형 RS 모형은 두 번의 포장적합(1984, 1987)에서 실제 관측값과 편차가 3일 이내로 차이가 없었다. 비선형 RS 모형은 1984년 적합에서 0.8일의 편차로 정확했지만 1987년 적합에서는 6.5일의 평균편차를 보였다.

Climate change significantly impacts biodiversity, particularly for endemic species in restricted habitats. This study focuses on the Korean fir (Abies koreana), an alpine conifer species in South Korea, to evaluate potential habitat changes under SSP climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5- 8.5). Using high-resolution ensemble climate data from the KMA (Korea Meteorological Administration) and integrating 10 species distribution models (SDMs) into an ensemble model, we predicted habitat suitability for the period 2010~2090. The species data was refined and constructed with a focus on location data so that it could be used in this model, using the National Ecosystem Survey, Baekdudaegan Conservation Area Survey of the National Institute of Ecology, and the National Park Natural Resources DB of National Park Service. The results identified BIO1 (mean annual temperature) and BIO13 (Precipitation of Wettest Month) as the most influential bioclimatic variables for habitat suitability. SSP1-2.6 exhibited fluctuating habitat area with partial recovery by 2070s, while SSP5-8.5 projected a near-complete loss of suitable habitats by 2090s. The ensemble model demonstrated robust performance, providing reliable predictions across all scenarios. This study highlights the substantial impact of climate change on the habitat suitability of Abies koreana and underscores the importance of understanding these changes to preserve vulnerable alpine ecosystems.

에스컬레이터는 공공시설과 다중이용시설에서 필수적인 이동 수단으로 사용되며, 특히 고령 인구 증가와 함께 사고 발생률이 꾸준히 증가하고 있다. 이에 따라 공공시설의 안전 관리를 강화하고 중대한 시민 재해를 예방하기 위한 사고 예측 기술의 필요성이 대두되고 있으며, 본 연구는 2010년부터 2022년까지 13년간의 에스컬레이터(무빙워크 포함) 사고 데이터를 활용하여 다중선형회귀분석과 로지스틱 회귀분석을 기반으로 사고 예측 모델을 개발하였다. 다중선형회귀분석을 통해 사고 발생 건수 예측 모델을 구축하였고, 로지스틱 회귀분석을 통해 전도사고의 발생 확률을 분석하여 주요 변수와 영향을 도출하였다. 연구 결과, 이용자 과실과 같은 요인이 사고 발생과 피해 심각성에 가장 큰 영향을 미치는 변수 로 확인되었다. 본 연구에서 제시된 예측 모델은 사고 예방을 위한 체계적인 안전 관리 및 정책 수립에 유용한 자료로 활용될 수 있으며, 공공 및 민간 영역에서의 ESG 활동에도 기여할 수 있을 것이다.

본 연구에서는 박스 구조물의 부재력 예측을 위한 다양한 딥러닝 모델의 정확성을 비교하고자 하였다. 이를 위해 상용 유한 요소 프로그램인 MIDAS를 이용하여 300개의 유한요소모델을 작성하고, 수치해석을 수행하여 딥러닝 모델에 적용하기 위한 학습데이 터를 생성하였다. 또한, 딥러닝 모델의 정확성을 비교하기 위해 MLP, CNN, RNN 및 LSTM과 같은 다양한 신경망 모델과 Adam, SGD, RMSprop 및 Adamax 등 최적화 알고리즘을 교차 적용하여 16개의 딥러닝 모델을 생성하였다. 그 결과 Adam 최적화 알고리즘 이 모든 모델에서 가장 우수한 성능을 보여주었으며, 특히 MLP 모델에서 가장 높은 R2 값을 나타내었다. 이를 통해, 박스 구조물의 부재력 예측을 위한 최적의 딥러닝 모델 구성은 Adam optimizer와 MLP 구조임을 확인하였다.

Rapidly changing environmental factors due to climate change are increasing the uncertainty of crop growth, and the importance of crop yield prediction for food security is becoming increasingly evident in Republic of Korea. Traditionally, crop yield prediction models have been developed by using statistical techniques such as regression models and correlation analysis. However, as machine learning technique develops, it is able to predict the crop yield more accurate than the statistical techniques. This study aims at proposing the onion yield prediction framework to accurately predict the onion yield by using various environmental factor data. Temperature, humidity, precipitation, solar radiation, and wind speed are considered as climate factors and irrigation water and nitrogen application rate are considered as soil factors. To improve the performance of the prediction model, ensemble learning technique is applied to the proposed framework. The coefficient of determination of the proposed stacked ensemble framework is 0.96, which is a 24.68% improvement over the coefficient of determination of 0.77 of the existing single machine learning model. This framework can be applied to the particular farmland so that each farm can get their customized prediction model, which is visualized by the web system.

This study proposes a weight optimization technique based on Mixture Design of Experiments (MD) to overcome the limitations of traditional ensemble learning and achieve optimal predictive performance with minimal experimentation. Traditional ensemble learning combines the predictions of multiple base models through a meta-model to generate a final prediction but has limitations in systematically optimizing the combination of base model performances. In this research, MD is applied to efficiently adjust the weights of each base model, constructing an optimized ensemble model tailored to the characteristics of the data. An evaluation of this technique across various industrial datasets confirms that the optimized ensemble model proposed in this study achieves higher predictive performance than traditional models in terms of F1-Score and accuracy. This method provides a foundation for enhancing real-time analysis and prediction reliability in data-driven decision-making systems across diverse fields such as manufacturing, fraud detection, and medical diagnostics.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

The purpose of this study was to develop a more accurate model for predicting the in-situ compressive strength of concrete pavements using Internet-of-Things (IoT)-based sensors and deep-learning techniques. This study aimed to overcome the limitations of traditional methods by accounting for various environmental conditions. Comprehensive environmental and hydration data were collected using IoT sensors to capture variables such as temperature, humidity, wind speed, and curing time. Data preprocessing included the removal of outliers and selection of relevant variables. Various modeling techniques, including regression analysis, classification and regression tree (CART), and artificial neural network (ANN), were applied to predict the heat of hydration and early compressive strength of concrete. The models were evaluated using metrics such as mean absolute error (MAE) to determine their effectiveness. The ANN model demonstrated superior performance, achieving a high prediction accuracy for early-age concrete strength, with an MAE of 0.297 and a predictive accuracy of 99.8%. For heat-of-hydration temperature prediction, the ANN model also outperformed the regression and CART models, exhibiting a lower MAE of 1.395. The analysis highlighted the significant impacts of temperature and curing time on the hydration process and strength development. This study confirmed that AI-based models, particularly ANNs, are highly effective in predicting early-age concrete strength and hydration temperature under varying environmental conditions. The ability of an ANN model to handle non-linear relationships and complex interactions among variables makes it a promising tool for real-time quality control in construction. Future research should explore the integration of additional factors and long-term strength predictions to further enhance the model accuracy.

This study aimed to develop a pavement management system suitable for the climate and traffic characteristics of Gangwon Province. This research focused on analyzing the asphalt pavement performance characteristics of national highways in Gangwon Province by region and developing prediction models for the current pavement performance and annual changes in performance. Quantitative indicators were collected to evaluate the condition of national highway pavements in Gangwon Province, including factors affecting road performance, such as weather data and traffic volume. The Gangwon region was then classified according to its topography, climate, weather, traffic volume, and pavement performance. Prediction models for the current pavement performance and annual changes in performance were developed for national highways. This study also compared the predicted values for the Gangwon region using a nationwide pavement performance-prediction model from other studies with the predicted values from the developed annual changes in the performance prediction model. This study established a foundation for implementing a pavement management system tailored to the unique climate and traffic characteristics of Gangwon Province. By developing region-specific performance prediction models, this study provided valuable insights into more effective and efficient pavement maintenance strategies in Gangwon Province.

This study aimed to improve the accuracy of road pavement design by comparing and analyzing various statistical and machine-learning techniques for predicting asphalt layer thickness, focusing on regional roads in Pakistan. The explanatory variables selected for this study included the annual average daily traffic (AADT), subbase thickness, and subgrade California bearing ratio (CBR) values from six cities in Pakistan. The statistical prediction models used were multiple linear regression (MLR), support vector regression (SVR), random forest, and XGBoost. The performance of each model was evaluated using the mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The analysis results indicated that the AADT was the most influential variable affecting the asphalt layer thickness. Among the models, the MLR demonstrated the best predictive performance. While XGBoost had a relatively strong performance among the machine-learning techniques, the traditional statistical model, MLR, still outperformed it in certain regions. This study emphasized the need for customized pavement designs that reflect the traffic and environmental conditions specific to regional roads in Pakistan. This finding suggests that future research should incorporate additional variables and data for a more in-depth analysis.

The purpose of this study was to enhance the correlation between the dependent and independent variables in a prediction model of pavement performance for local roads on Jeju Island by applying K-means clustering for data preprocessing, thereby improving the accuracy of the prediction model. Pavement management system (PMS) data from Jeju Island were utilized. K-means clustering was applied, with the optimal K value determined using the elbow method and silhouette score. The Haversine formula was used to calculate the distances between the analysis sections and weather stations, and Delaunay triangulation and inverse distance weighting (IDW) were employed to interpolate the magnitude of the influencing factors. The preprocessed data were then analyzed for correlations between the rutting depth (RD) and influencing factors, and a prediction model was developed through multiple linear regression analysis. The RD prediction model demonstrated the highest performance with an R² of 0.32 and root-mean-square error (RMSE) of 1.48. This indicates that preprocessing based on the RD is more effective for developing an RD prediction model. The study also observed that the lack of pavement-age data in the analysis was a limiting factor for the prediction accuracy. The application of K-means clustering for data preprocessing effectively improved the correlation between the dependent and independent variables, particularly in the RD prediction model. Future research is expected to further enhance the prediction accuracy by including pavement-age data.

In this paper, we deal with the design of a model predictive control (MPC) for precise speed servo control of DC motor systems. The proposed controller is designed in the form of optimal control that calculates and outputs the optimized control input under constraints for each sampling. In particular, MPC designs the control inputs in advance for each sampling and predicts the outputs using them. Thus, it shows excellent control performance even in the case of disturbance or model uncertainty. The effectiveness of the proposed controller was demonstrated through computer simulations using MATLAB/Simulink and DC motor experimental system using real time controller. Moreover, the effectiveness of the proposed controller was confirmed by comparing its control performance with PID controller, which was tested under the same experimental condition as the MPC.

North Korea has repeatedly provoked using unmanned aerial vehicles (UAVs), and the threat posed by UAVs continues to escalate, as evidenced by recent directives involving the use of waste-laden balloons and the development of suicide drones. North Korea’s small UAVs are difficult to detect due to their low radar cross-section (RCS) values, necessitating the efficient deployment and operation of assets for effective response. Against this backdrop, this study aims to predict the infiltration routes of enemy UAVs by considering their perspective, avoiding key facilities and obstacles, and propose deployment strategies to enable rapid detection and response during provocations. Utilizing the Markov Decision Process (MDP) based on previous studies, this research presents a model that reflects both UAV flight characteristics and regional environments. Unlike previous models that designate a single starting point, this study addresses the practical challenge of uncertainty in initial infiltration points by incorporating multiple starting points into the scenarios. By aggregating and integrating the probability maps derived from these variations into a unified map, the model predicts areas with a high likelihood of UAV infiltration over time. Furthermore, based on case studies in the capital region, this research proposes deployment strategies tailored to the specifications of currently known anti-drone integrated systems. These strategies are expected to support military decision-making by enabling the efficient operation of assets in areas with a high probability of UAV infiltration.

This study integrates TabTransformer and CTGAN for predicting job satisfaction among South Korean college graduates. TabTransformer handles complex tabular data relationships with self-attention, while CTGAN generates high-quality synthetic samples. The combined approach achieves an accuracy of 0.85, precision of 0.83, recall of 0.82, F1-score of 0.82, and an AUC of 0.88. Cross-validation confirms the model's robustness and generalizability with a mean accuracy of 0.85 and a standard deviation of 0.008. The integration of TabTransformer and CTGAN enhances predictive accuracy and model generalizability, providing valuable insights for employment policy and research.

기후 변화에 의해 해수면 온도 상승, 태풍의 최고 강도 북상, 태풍 강도 증가가 나타나고 있으며, 미래의 태풍 강도 변화가 더 심화될 것으로 예상하고 있다. 본 논문에서는 기후 변화 시나리오에 의해서 발생할 수 있는 한반도 부근의 태풍 강도를 예측하기 위하여 딥러닝 기반 태풍 강도 예측 모델을 개발하였다. 기후 예측정보를 이용하여 미래 기후 변화 환경장 변화에 따른 태풍의 강도를 예측할 수 있도록 과거 환경장을 학습 자료로 사용하였다. 학습자료는 1980년에서 2022년까지의 태풍 발생 빈도가 높은 6~10월의 기상 및 해양 재분 석 월평균 자료와 Best Track 태풍 241개를 입력자료로 사용하였다. 환경장 변화에 따른 태풍 강도 예측을 위해 자료의 공간적인 특징과 시간적인 특징을 함께 고려하는 딥러닝 모델인 ConvLSTM 기반으로 모델을 개발하였다. 태풍 트랙 시퀀스의 각 이동 경로에 대한 월평균 환경장 자료를 모델에 학습하여 태풍의 중심 기압을 예측하였다. 태풍의 공간적 특성을 반영할 수 있도록 범위를 설정하여 입력자료로 학습하였으며, 5°⨉ 5°의 범위일 때 가장 좋은 결과를 보였다. 몬테카를로 방법을 이용한 민감도 실험을 통해 모델 예측에 가장 큰 영향을 미치는 변수는 SST로 확인되었다.