Pavement temperature is a critical factor in winter road maintenance as it directly affects operational decisions related to de-icing, antiicing, and other safety measures. Accurate forecasting of pavement temperature enables road agencies to optimize maintenance strategies, reduce operational costs, and improve roadway safety outcomes. This study proposes a novel machine-learning algorithm, termed LSTMCNN, which integrates convolutional neural networks (CNNs) with long short-term memory (LSTM) networks for pavement temperature prediction. The proposed model enables the LSTM component to capture sequential dependencies, whereas the CNN component extracts local and spatial features embedded in time-series temperature records. Therefore, the proposed model can effectively identify long-range temporal relationships while uncovering localized or spatial features within the dataset. The input data—comprising pavement, atmospheric, and soil temperatures—were obtained at the entrance of a tunnel where a multivehicle pile-up due to black ice had occurred previously. The proposed LSTM-CNN model achieved an average prediction error of 0.61 ℃ and was benchmarked against other well-established machine-learning models, including Transformer and standalone LSTM architectures. The results show that the proposed algorithm delivers statistically superior predictive performance. The LSTM-CNN approach offers significant potential for enhancing the efficiency and effectiveness of winter road maintenance operations.

최근 고층 건축물의 설계 시 풍향 변화에 따른 내풍 안전성 평가는 필수적이나, 유한요소해석은 막대한 시간과 비용이 소요 된다. 본 연구에서는 풍공학 표준 모형인 CAARC 건축물을 대상으로 해석 속도를 혁신적으로 단축하면서 물리적 타당성을 확보한 물 리정보 신경망을 포함한 다양한 대체모델 체계를 제안한다. 이 연구에서는 해석 자동화 파이프라인을 구축하여 0°-360° 풍향별 구조 응답 데이터를 생성하였으며, 3차 스플라인 보간, 다항회귀, 가우시안 프로세스 회귀, 다층 퍼셉트론과 물리정보 신경망의 성능을 정량 적으로 비교하였다. 분석 결과, 물리정보 신경망 모델은 결정계수 0.9991, 평균 절대 오차 5.848×10-10으로 가장 우수한 성능을 기록하 였다. 특히 물리정보 신경망은 학습 데이터가 없는 외삽 구간에서도 물리적 제약 조건을 통해 실제 유한요소해석 결과와 일치하는 응 답을 성공적으로 재구성하였다. 또한 단일 케이스 유한요소해석 대비 약 31배 이상의 연산 속도 향상을 보여 실시간 구조 응답 평가의 가능성을 확인하였다. 본 연구의 결과는 정적 등가 풍하중에 대한 풍향별 최대 변위 예측에 한정되며, 가스트·와류 진동 등 동적 거동 의 직접 예측은 향후 연구 과제이다.

도로의 기하구조(종단경사, 평면 곡선반경)는 차량의 속도 변화, 제동 거리, 원심력 등에 직접적인 영향을 미쳐 주행 안전성과 사고 위험을 결정짓는 핵심적인 설계 요소이다(Park et al., 2008). 따라서 도로 유지관리 측면 에서 이러한 기하선형 정보를 정밀하게 측정하고 관리하는 것은 필수적이나, 준공 후 장기간이 경과하거나 관 리 체계가 다원화된 경우 데이터가 누락되어 통합적인 활용에 한계가 있다. 이에 본 연구는 설계도면이 부재한 대규모 도로망의 안전 진단 및 위험 구간 판단 근거를 마련하기 위해, GIS(Geographic Information System) 기반 노드·링크 시스템의 평면선형 데이터와 공개 DEM(Digital Elevation Model)을 활용하여 전국 고속국도의 기하구조를 추정하는 경제적이고 보편적인 방법론을 제안하고 자 한다.

This study develops a scientific fishing-ground exploration framework for the Korean large purse-seine fishery, where traditional experience-based searching has become increasingly unreliable under rapid climate variability. AIS-derived fishing locations from 2021 to 2023 were integrated with HYCOM-based temperature and salinity fields and MODIS-Aqua chlorophyll-a data to construct a unified environmental – fishing dataset. After multicollinearity screening and principal component analysis, temperature and salinity at 30 m depth and chlorophyll-a were selected as representative predictors. Using these variables, a generalized additive model (GAM) with background-sampled pseudo-absence data and monthly maximum entropy (MaxEnt) models were developed to quantify nonlinear habitat – environment relationships and predict monthly and seasonal mackerel fishing occurrences. Model performance was evaluated using independent data from 2024. GAM exhibited relatively stable predictive performance across months with generally high AUC and TSS values whereas MaxEnt showed pronounced seasonal variability and was effective in identifying potential habitat structures based on presence-only environmental conditions. Spatial predictions from both models showed good agreement with observed fishing-ground distributions during specific seasons, reproducing high-suitability zones associated with seasonal thermal – salinity fronts and productivity gradients. These results provide insights into the environmental mechanisms governing purse-seine fishing grounds and demonstrate the complementary roles of GAM for operational prediction and MaxEnt for potential habitat exploration.

본 연구에서는 Romanoff(1957)의 실측 데이터를 사용하여 머신러닝 기반 상수도관의 부식 깊이를 예측하였다. 이를 실제 상수도관망에 적용하여 누적피해도를 분석하였다. 예측한 부식깊이를 사용하여 누적피해도를 분석하였으며 MCS(Monte Carlo Simulation)를 적용한 누적피해도와 비교 분석하였다. 부식깊이 예측모델에 따른 부식깊이를 분석한 결과 MLP-ReLU 모델이 가장 부식속도가 가장 빠르며 MLP-sigmoid가 가장 부식속도가 느렸다. 천안시 신방동과 성환읍 상수도관망에 MCS를 적용한 누적피해도 분석법과 머신러닝을 적용한 누적피해도를 비교 분석하였다. 신방동에서는 두 분석법 모두 2번 상수도관이 먼저 사용 한계에 도달하였으며 성환읍에서는 4번 상수도관이 가장 먼저 사용 한계에 도달하였다. 사용 한계에 가장 먼저 도달한 상수도관은 다른 상수도관보다 사용 년수가 오래되었거나 수압이 높은 것으로 확인되었다. MCS를 적용한 누적피해도 분석 결과 신방동의 경우 45년 만에 사용 한계를 초과한 반면 성환읍의 경우 47년 만에 사용 한계를 초과했다.

This study proposes a weighted ensemble deep learning framework for accurately predicting the State of Health (SOH) of lithium-ion batteries. Three distinct model architectures—CNN-LSTM, Transformer-LSTM, and CEEMDAN-BiGRU—are combined using a normalized inverse RMSE-based weighting scheme to enhance predictive performance. Unlike conventional approaches using fixed hyperparameter settings, this study employs Bayesian Optimization via Optuna to automatically tune key hyperparameters such as time steps (range: 10-35) and hidden units (range: 32-128). To ensure robustness and reproducibility, ten independent runs were conducted with different random seeds. Experimental evaluations were performed using the NASA Ames B0047 cell discharge dataset. The ensemble model achieved an average RMSE of 0.01381 with a standard deviation of ±0.00190, outperforming the best single model (CEEMDAN-BiGRU, average RMSE: 0.01487) in both accuracy and stability. Additionally, the ensemble's average inference time of 3.83 seconds demonstrates its practical feasibility for real-time Battery Management System (BMS) integration. The proposed framework effectively leverages complementary model characteristics and automated optimization strategies to provide accurate and stable SOH predictions for lithium-ion batteries.

Given the hazards posed by black ice, it is crucial to investigate the conditions that contribute to its formation. Two ensemble machinelearning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were employed to forecast the occurrence of black ice using atmospheric data. Additionally, explainable artificial intelligence techniques, including Feature Importance (FI) and partial dependence Plot (PDP), were utilized to identify atmospheric conditions that significantly increase the likelihood of black ice formation. The machinelearning algorithms achieved a forecasting accuracy of 90%, demonstrating reliable performance. FI analysis revealed distinct key predictors between the algorithms: relative humidity was the most critical for RF, whereas wind speed was paramount for XGBoost. The PDP analysis identified the specific atmospheric conditions under which black ice was likely to form. This study provides detailed insights into the atmospheric precursors of frost/fog-induced black ice formation. These findings enable road managers to implement proactive winter road maintenance strategies, such as optimizing anti-icing patrol routes and displaying warnings on various message signs, thereby enhancing road safety.

This study evaluated the short- and long-term prediction performances of a transformer-based trajectory-forecasting model for urban intersections. While a previous study focused on developing the basic structure of a transformer model for future trajectory prediction, the present study aimed to determine a practical prediction sequence length. To this end, multiple transformer models were trained with output sequence lengths ranging from 1 s to 10 s, and their performances were compared. The trajectory data used for training were generated through a microscopic traffic simulation, and the model accuracy was assessed using the metrics average displacement error (ADE) and final displacement error (FDE). The results demonstrate that the prediction accuracy decreases significantly when the output trajectory length exceeds 3 s. Specifically, straight-driving trajectories exhibit rapidly increasing errors, while turning trajectories maintained a relatively stable accuracy. In contrast, for turning-driving trajectories, prediction errors increased sharply during short-term forecasting, but the increase was more gradual in long-term forecasts. Additionally, the long-term prediction models produced higher errors even in the initial 1-second outputs, implying a tendency toward conservative inference under uncertain future scenarios. This conservative behavior is likely influenced by the model’s effort to minimize the overall loss across a broader prediction window, especially when trained with Smooth L1 loss function. This study provides practical insights into model design for edge-computing environments and contributes to the development of reliable short-term trajectory prediction systems for urban ITS applications.

기후 변화로 인해 해수면 상승과 폭풍해일 발생 빈도가 증가하면서, 해안 지역에서의 재난 위험이 심화되고 있다. 본 연구는 NOAA의 GFS(Global Forecast System) 모델과 일본 기상청의 JMA-MSM(Japan Meteorological Agency Meso-Scale Model) 데이터를 기반으로 딥 러닝 기술을 활용하여 폭풍해일 예측 알고리즘을 개발하고, 두 모델에서 제공하는 대기 데이터를 입력 변수로 사용하여 예측 성능을 비 교하는 것을 목표로 한다. CNN(Convolutional Neural Network), LSTM(Long Short-Term Memory), Attention 메커니즘을 결합한 모델을 설계하고, 조위관측소의 관측 자료를 학습 데이터로 사용하였다. 과거 한반도에 직접적인 영향을 미쳤던 네 개의 태풍 사례를 통해 모델 성능을 검 증한 결과, JMA-MSM 기반 모델이 GFS 기반 모델에 비해 서해, 남해, 동해에서 각각 평균 RMSE를 0.34cm, 0.73cm, 1.86cm, MAPE를 0.15%, 0.36%, 0.68% 개선하였다. 이는 JMA-MSM의 고해상도 자료가 지역적 기상 변화를 정밀하게 반영했기 때문으로 분석된다. 본 연구는 해안 재난 대비를 위한 폭풍해일 예측의 효율성을 높이고, 추가 기상 데이터를 활용한 향후 연구의 기반 제공이 기대된다.

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.

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module ‘Matminer’, 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the ‘Fe’ compositional ratio. The feature importance method provided by ‘scikit-learn’ was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

본 논문에서는 대규모 실시간 매칭의 생존 게임에서 플레이를 위한 유저들의 소셜 관계에 대해 연구한다. 특 히 “사전 팀 구성”을 통한 자의적인 팀 구성이 어떤 방식으로 유저들을 연결하는 지 연구하고자 한다. 다수 의 사람 간 집단 역학에서 나타나는 특성이나 패턴에 대한 조사를 중심으로 하였으며, 개인의 특성은 보조적 인 수단으로만 사용된다. 이번 연구에서는 게임을 플레이하는 유저들의 익명화 된 대규모 데이터를 활용하며 이에 대한 간소화된 집계 방법을 제안한다. 데이터 세트에는 사전 팀 구성에 관한 11,259만 줄의 속성이 포 함되어 있으며, 데이터에서 우리는 250만개의 노드와 1,182만개의 무방향 에지가 있는 협업 네트워크를 구성 하여 대규모 게임 내 협동 네트워크를 만듭니다. 연결 정도, 경로 길이, 클러스터링 및 소속 하위 컴포넌트의 크기 등 네트워크에 관한 수치를 통해 게임내 소셜 활동에 대한 이해를 높이고자 한다. 본 논문에서는 다음 의 두가지 특성을 중심으로 결론을 제시한다. 첫째, 네트워크 내에는 대규모로 연결된 2개(전체의 44% 및 2%)와 나머지의 파편화된 하위 컴포넌트로 구성 되어있다. 이 대규모 컴포넌트 중 작은 쪽은 한국 유저로만 구성되어 있다. 둘째, 컴포넌트 크기 별 평균 연결 거리와 군집화 계수, k-core를 확인함으로써 기타 다른 네 트워크 대비 이웃 간 연결이 강하면서 전체적으로는 비교적 멀리 떨어져 있음을 확인한다.

본 연구는 네트워크 이상 감지 및 예측을 위해 벡터 자기회귀(VAR) 모델의 사용을 비교 분석한다. VAR 모 델에 대한 간략한 개요를 제공하고 네트워크 이상 체크로 사용 가능한 두 가지 버전을 검토하며 두 종류의 VAR 모델을 통한 경험적인 평가를 제시한다. VAR-Filtered moving-common-AR 모델이 단일 노드 이상 감지 성능에서 우수하며, VAR-Adaptive Learning 버전은 몇 개의 노드 간 이상을 효과적으로 식별하는 데 특히 효 과적이며 두 가지 주요VAR 모델의 전반적인 성능 차이에 대한 근본적인 이유도 분석한다. 각 기술의 장단점 을 개요로 제공하고 성능 향상을 위한 제안도 제시하고자 한다.

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the ‘Cheongmyeong Gaual’ variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37′ N 128°32′ E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.