Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

The purpose of this study is to examine learners’ perceptions of AI-based machine translation (MT) in high school ‘Reading British and American Literature’ classes. This research explored how students perceived the impact of MT on their class participation, learning motivation, confidence in English use, and improvement in English ability. The study also examined how the effectiveness of MT use differed according to students’ English proficiency levels. A total of 153 third-year students participated in a nine-week English literature course. Data were collected through an online survey and statistically analyzed. The findings reveal that students showed positive perceptions regarding class participation, learning motivation, confidence in English use, and improvement in English ability. Notably, participation in the English literature classes using AI-based MT was significantly higher than that in other English classes. Analysis by English proficiency levels showed no significant differences in class participation and affective factors (learning motivation and confidence). However, lower-proficiency learners perceived greater improvement in English proficiency compared to higher-proficiency learners. These results suggest that incorporating AI-based MT in English literature classes can create an inclusive learning environment that supports learners across different proficiency levels, particularly benefiting lower-proficiency students in terms of improvement in English ability.

본 연구는 성장 단계별 돼지의 평균 사료 섭취량을 추정하고, 각 매개변수 간의 상관분석을 통해 변수를 선별한 후, 기계학습 기반 회귀분석을 통해 돼지의 사료 섭취량(FI)을 예측하는 모델을 만들고자 한다. 본 실험은 2023년 9월 14일부터 2023년 12월 15일까지 93일 동안 진행하였다. 사료는 09:00와 17:00 하루에 2회 제공하였으며, 제공된 사료의 양은 돼지의 평균 체중의 5%를 지급하였다. 돼지의 몸무게(PBW)는 매일 09:00에 이동식 돈형기를 사용하여 측정하였다. 축산환경관리시스템(LEMS) 센서를 이용하여, 돈사 내 온도(RT), 상대습도(RH), NH3를 5분 간격으로 수집하였다. 성장 단계를 3단계로 나누었으며, 각 GS1, GS2 및 GS3으로 명명하였다. 각 성장 단계별 평균 사료 섭취량과 표준편차를 구하여, 유의미성과 성장 단계별 사료 섭취의 경향을 분석하였다. 각 모델의 성능평가( , RMSE, MAPE) 시 8:2의 비율로 데이터를 분할하여, 정확도 검증을 수행하였다. 연구 결과 성장 단계별 돼지의 사료 섭취량에 유의미한 차이(p < 0.05)가 있음과 돼지가 성장할수록 일정한 양의 사료를 섭취하는 것을 확인하였다. 또한 각 변수의 상관분석 시 FI와 PBW에서 강한 상관관계가 나타났으며(R > 0.94), 각 모델의 성능평가 결과 RFR 모델이 가장 높은 정확성(  = 0.959, RMSE = 195.9, MAPE = 5.739)을 보였다.

As global greenhouse gas reduction regulations are strengthened and the demand for eco-friendly energy increases, renewable energies, including offshore wind power, are growing rapidly. Unlike onshore wind power generation, offshore wind power is located in the ocean. As a result, the offshore wind power substructure is exposed to low temperatures, corrosion, and continuous fatigue loads. Therefore, selecting appropriate materials and welding techniques is crucial for durability. In this study, FCAW welding was performed on S355ML steel (EN10025) for offshore wind power applications. After the welding process, the mechanical properties of the welded joint were evaluated through tensile, low-temperature impact, and hardness tests to assess the welding condition. The study revealed that the tensile and yield strength of the welded joint were superior to those of the base material. Additionally, the impact strength at low temperatures was confirmed to exceed the standard.

A cold roll-bonding (CRB) process is applied to fabricate an AA1050/AA5052 layered sheet. In the process, commercial AA1050 and AA5052 sheets of 1 mm thickness, 40 mm width and 300 mm length are stacked onto each other, and then reduced to a thickness of 0.5 mm through a 2-pass cold rolling process without lubricant. The roll-bonded AA1050/AA5052 layered sheet is then annealed for 1 h at various temperatures from 200 to 400 °C. The specimens annealed at temperatures below 250 °C showed a typical deformation structure in which the grains were elongated along the rolling direction. However, the specimens annealed at temperatures higher than 300 °C exhibited recrystallization structures in both the AA1050 and AA5052 regions. All the roll-bonded and subsequently annealed specimens showed an inhomogeneous distribution of hardness in the thickness direction, in which the hardness in the AA5052 regions was higher than that in the AA1050 regions. As the annealing temperature increased, the tensile and yield strengths decreased and the elongation increased gradually. The mechanical properties were compared to those of commercial AA1050 and AA5052 materials and CRBed AA5052-2L materials from a previous study.

본 연구는 표현 형질 생육 데이터인 엽장, 엽 수와 기상 데이 터인 생육도일을 활용하여 여러 기계 학습을 통해 마늘의 생 체중을 예측하는 모델을 개발하고자 하였다. 검증 데이터에 서 random forest 모델의 결정계수가 0.924, 평균제곱근오차 (g)는 13.583 그리고 평균절대오차는 8.885로 가장 우수하였 다. 평가 데이터에서는 Catboost 모델이 결정계수가 0.928, 평균제곱근오차(g)는 13.486 그리고 평균절대오차는 9.181 로 가장 우수하였다. 그러나 Catboost, Random forest 그리고 LightGBM 모델을 0.5, 0.3 그리고 0.2 가중치를 두어 학습한 Weighted ensemble 모델이 마늘 생체중 예측의 검증 및 평가 에 있어서 검증 데이터의 결정계수가 0.922, 평균제곱근오차 (g)가 13.752 그리고 평균절대오차는 8.877이었으며 평가 데 이터에서는 결정계수가 0.923, 평균제곱근오차(g)가 13.992 그리고 평균절대오차가 9.437로 두 번째로 우수한 결과를 나 타내었다. 이러한 결과들을 종합적으로 미루어 보았을 때, Weighted ensemble 모델이 모델의 안정성 측면에서 최적의 모델이라고 판단하였다. 따라서 농가들이 표현 형질과 기상 데이터만으로도 기계학습 기법을 통하여 마늘의 생체중 예측 을 통해 작형 모니터링이 가능할 것으로 보이며 추가적으로 다년도 데이터 취득과 검증을 통하여 성능을 고도화가 가능할 것으로 판단된다.

Battery electrodes, essential for energy storage, possess pores that heavily influence their mechanical properties based on the level of porosity and the nature of the pores. The irregularities in pore shape, size, and distribution complicate the accurate determination of these properties. While stress-strain measurements can shed light on a material’s mechanical behavior and predict compression limits, the complex structure of the pores poses significant challenges for accurate measurements. In this research, we introduce a simulation-driven approach to derive stress-strain data that considers porosity. By calculating relative density and the rate of volume change under compression based on porosity, and applying pressure, we conducted a parametric study to identify the elastic modulus (E) in relation to the rate of volume change. This information was utilized within a material modeling equation, generating stress-strain (S-S) curves that were further analyzed to replicate the compression behavior of the electrode material. The outcomes of this study are expected to improve the prediction accuracy of mechanical properties for porous electrode materials, potentially enhancing battery performance and refining manufacturing processes.

Magnesium alloys, among various non-ferrous metals, are utilized in diverse fields from the automotive industry to aerospace due to their light weight and excellent specific strength. In the previous Part I study, fiber laser BOP experiments were conducted to derive basic welding characteristics and appropriate bu竹 welding conditions. Subsequently, in the Part II experiment, butt welding was performed, and through tensile tests, hardness tests, and cross-sectional observations, it was found that at laser power of 2.0 kW and welding speed of 50 mm/s, 93% of the base metafs tensile strength and 63.4% of its elongation could be achieved. In this Part III experiment, the microstructures of the base metal and the center of the weld were observed in butt-welded specimens. Through this, laser power and welding speed, on the mechanical behavior and microstructure of magnesium alloys were analyzed

This study examines career trajectories among women with career breaks, using data from the 2019 National Survey of Women on Career Breaks (n=1,138). The data underwent preprocessing, including outlier detection, feature scaling, and class imbalance correction with SMOTEENN. Three machine learning models were evaluated, with the Random Forest model achieving the best performance. Key predictors included flexible leave policies, social insurance, remote work options, and job security. The findings highlight the importance of supportive organizational policies in retaining female employees. Future research should explore longitudinal impacts and additional variables like organizational culture.

본 연구는 수동식 감자 파종기구를 활용한 인력파종 및 트랙터 부착형 파종기를 활용한 기계파종을 비교하여 강원도 고랭지 지역의 여름감자 파종작업에 대한 작업효율 및 경제성을 분석하였다. 작업효율은 일정 면적을 파종하는 데 소요되는 시간으로 도출하였다. 동일한 작업효율을 가지는 조건에서 인력파종 및 기계파종의 연간 고정비와 연간 유동비를 산출하여 각 파종작업에 대한 경제성 분석을 수행하였다. 이때 고정비로는 감가상각비, 이자 및 수리비를, 유동비로는 인건비와 유류비를 고려하였다. 또한, 총비용 소요 분석을 통해 트랙터 부착형 파종기가 수동식 감자 파종기구 대비 경제성이 높아지는 시점을 도출하였다. 분석 결과, 기계파종이 인력파종 대비 약 4.6배 높은 작업효율을 보였으며 파종률은 두 방식에서 모두 100%로 나타났다. 경제성의 경우 구매 비용은 트랙터 부착형 파종기가 높았으나, 인건비 측면에서 비용 절감 효과를 보였다. 이에 따라 4년 9개월 이후부터 트랙터 부착형 파종기의 총 소요비용이 수동식 감자 파종기구의 총 소요비용 대비 낮아지는 것으로 나타났다. 따라서 장기적인 관점에서 기계파종이 인력파종 대비 생력화 효과가 크며 경제성도 높은 것으로 판단된다.

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.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

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.

고밀도폴리에틸렌(HDPE)은 대표적인 열가소성 플라스틱으로서 재활용성, 내충격성 등이 우수하여 차세대 친환경 소형선박용 재료로 각광받고 있다. 그러나, HDPE의 열변형온도는 하절기의 선체 온도와 비슷한 수준이며, 재료의 열팽창 특성으로 인해 선박 구조용 재료로서의 적용 가능성에 대한 기술적 검토가 필요하다. 본 연구에서는 선박이 일상적으로 겪을 수 있는 상온 이상 온도범위에서 HDPE 의 기계적·열적 물성치를 도출하였고, 모의설계한 HDPE 어선을 대상으로 연중 태양복사열과 기관실 내부 온도가 가장 높은 하절기 운용 상황을 모사한 열전달 해석을 통해 구조부재들의 온도변화 및 분포, 응력 및 변위 구배 등을 확인하고자 하였다. 혹서기의 높은 기온과 강한 태양복사에 노출되는 HDPE 선체의 온도는 재료의 열변형온도 수준까지 상승하며 열팽창에 의한 변형 및 국부응력을 보여 설계 및 검토단계에서 선체 변형을 고려할 필요가 있고 향후 선체변형 대응방안 및 구조안전성 평가기준 마련 시 온도상승에 따른 물성치 변화를 고려할 필요가 있다.

In this study, a composite material suitable for flexible transparent electrodes was fabricated using Norland Optical Adhesive 68 (NOA 68), an ultraviolet (UV) curable polymer, and silver nanowires (Ag nanowire, AgNW). The mechanical behavior of this composite was then analyzed. A AgNW network structure was embedded in the NOA 68 polymer and cured using UV energy. The composite was prepared with an AgNW network structure formed approximately 4 μm from the top of the NOA 68 matrix. Tensile test specimens were prepared according to ASTM standards, and tensile tests were conducted at room temperature in air. Scanning electron microscopy (SEM) and tensile tests were used to analyze the changes in mechanical behavior according to UV exposure time and the presence of AgNW. The results showed that as UV curing time increased, the yield strength of the composite increased while the elongation decreased. Regardless of the presence of the AgNW filler, the stress-strain curves of the ductile polymer exhibited the typical mechanical behavior of semi-crystalline polymers as UV curing time increased, characterized by strain softening. It was also confirmed that the composite impregnated with AgNW exhibited higher strength in response to changes in mechanical properties due to UV curing.

This study investigated the optimal process conditions and mechanical properties of Cu-10Sn alloys produced by the powder bed fusion (PBF) method. The optimal PBF conditions were explored by producing samples with various laser scanning speeds and laser power. It was found that under optimized conditions, samples with a density close to the theoretical density could be fabricated using PBF without any serious defects. The microstructure and mechanical properties of samples produced under optimized conditions were investigated and compared with a commercial alloy produced by the conventional method. The hardness, maximum tensile strength, and elongation of the samples were significantly higher than those of the commercially available cast alloy with the same chemical composition. Based on these results, it is expected to be possible to use the PBF technique to manufacture Cu-10Sn products with complex 3D shapes that could not be made using the conventional manufacturing method.

본 논문에서는 15차 bézier 곡선을 사용하여 기존의 연구보다 더 유연한 빔 형상을 설계하고, 더 넓은 설계 공간에서 최적 설계를 수 행하여 최적의 열전도도를 갖는 빔 형상을 설계한다. 설계 공간이 넓어지면 그 만큼 계산양이 증가하게 되는데, 고차원 변수 공간에서 효율적으로 작동하는 인공신경망을 사용하여 최적 설계를 가속화하여 계산 한계를 극복하였다. 더 나아가 최적의 탄성계수를 갖는 빔의 형상과 비교하였으며 열전도와 탄성학 사이의 수학적 유사성을 이용하여 빔 형상을 설명한다. 본 연구에서는 인공지능을 활용 한 형상 최적설계를 통해 기존의 한계를 뛰어넘는 격자구조의 빔 형상을 제안한다. 먼저, SC(Simple Cubic), BC(Body Centered Cubic) 격자 구조 빔 형상을 bézier 곡선으로 모델링하고 bézier 곡선의 제어점 좌표를 무작위로 설정하여 학습데이터를 확보하였다. NN(Neural Network) 및 GA(Genetic Algorithm)를 통해 우수한 유효 열전도도를 가진 빔 형상을 생성하여 최적의 빔 형상을 설계하였 다. 본 연구를 통해 추후 다양한 열 조건에서 격자구조의 적절한 구조적 해답을 제시할 수 있을 것으로 기대된다.