임파워먼트에 대한 관심이 꾸준히 증가하고 있는 추세 속에 많은 기업의 관리자들이 구조적 임파워먼 트의 긍정적인 측면만을 바라보고 실제 자신들의 기업에 적용하고 있다. 이는 구조적 관점에서 의사결정 의 권한을 하위 부서로 이양하는 것이 불확실한 상황 속에서 혁신적인 결과물을 탐색하는 데 매우 효과적 일 것이라는 가정에서 비롯되었다. 그러나 조직이 처한 환경 속에서 이러한 구조적 임파워먼트가 실제로 효과적일까에 대한 연구는 거의 이루어지지 않고 있으며, 몇몇 연구에서조차 조직이 처한 다양한 상황적 요인들에 대해서는 전혀 고려되지 않고 있다. 그러므로 본 연구는 NK 모델을 통해 조직의 과업 상호의존 성과 부서의 업무역량에 따라 구조적 임파워먼트가 조직의 창의적 성과 탐색에 미치는 영향을 확인하고, 이에 따른 이론적/실무적 시사점을 제공하기 위해 실시하였다. 분석 결과 첫째, 부서 간 과업의 상호의존 성이 매우 높은 상황에서는 부서의 업무역량에 관계없이 구조적 임파워먼트가 창의적 조직 성과 탐색에 부정적으로 작용하였으며, 조직 차원의 의사결정에 대한 개입이 있을 시 성과가 개선되었다. 또한, 부서의 업무역량이 높을수록 일정 수준 이상의 조직의 개입(조직 수준의 의사결정)이 발생했을 때, 다시 창의적 조직 성과 탐색이 낮아짐을 확인하였다. 둘째, 과업의 상호의존성이 낮은 상황에서는 부서의 업무역량이 높을수록 구조적 임파워먼트의 창의적 조직 성과 탐색에 대한 효과성이 높은 것으로 나타났다. 또한, 구조 적 임파워먼트가 낮아질수록 부서의 업무역량에 관계없이 창의적 조직 성과 탐색도 같이 낮아지는 것을 확인하였다. 이러한 결과는 구조적 임파워먼트가 반드시 창의적 조직 성과 탐색에 긍정적인 영향을 미치 는 것이 아니라 조직이 처한 상황적 요인을 확인하여 실행할 필요가 있음을 시사하며, 구조적 임파워먼트 가 긍정적인 효과를 가져오기 위해서는 조직 차원의 의사결정 개입이 필요할 수 있음을 시사한다.

Written examination for driver’s license certification plays a critical role in promoting road safety by assessing the applicants' understanding of traffic laws and safe driving practices. However, concerns have emerged regarding structural biases in multiple-choice question (MCQ) formats, such as disproportionate answer placement and leading linguistic cues, which may allow test-takers to guess the correct answers without substantive legal knowledge. To address these problems, this paper proposes a prompt-driven evaluation framework that integrates structural item analysis with response simulations using a large language model (LLM). First, we conducted a quantitative analysis of 1,000 items to assess formal biases in the answer positions and option lengths. Subsequently, GPT-based simulations were performed under four distinct prompt conditions: (1) safety-oriented reasoning without access to legal knowledge, (2) safety-oriented reasoning with random choices for knowledge-based questions, (3) performance-oriented reasoning using all available knowledge, and (4) a random-guessing baseline model to simulate non-inferential choice behavior. The results revealed notable variations in item difficulty and prompt sensitivity, particularly when safety-related keywords influence answer selection, irrespective of legal accuracy. The proposed framework enables a pretest diagnosis of potential biases in the MCQ design and provides a practical tool for enhancing the fairness and validity of traffic law assessments. By improving the quality control of item banks, this approach contributes to the development of more reliable knowledge-based testing systems that better support public road safety.

This study examines a digital training model for the professional development of French language educators, focusing on module-based collaborative learning. It explores the theoretical foundations of teacher development, the concept and practice of digital training, and the educational implications of modular learning design. The case study analyzes a digital training program jointly operated by FEI and CNED in July 2020, which involved over 24,000 educators from 162 countries. Comprising eight modules, the program integrated digital content, real-time forums, and collaborative projects. Based on participant experiences, this study identifies key characteristics of digital training and essential factors for enhancing teacher competencies. Findings suggest that digital training fosters teachers’ digital skills and practical application while yielding positive outcomes in gamification strategies and sustainable online training models. However, challenges such as technological accessibility gaps, imbalances in program design, and cultural differences persist. To address these, the study proposes adopting a hybrid education model, strengthening practice-oriented module design, and expanding collaborative learning strategies to support an effective and sustainable training framework in the digital era.

Reinforcement learning (RL) is successfully applied to various engineering fields. RL is generally used for structural control cases to develop the control algorithms. On the other hand, a machine learning (ML) is adopted in various research to make automated structural design model for reinforced concrete (RC) beam members. In this case, ML models are developed to produce results that are as similar to those of training data as possible. The ML model developed in this way is difficult to produce better results than the training data. However, in reinforcement learning, an agent learns to make decisions by interacting with an environment. Therefore, the RL agent can find better design solution than the training data. In the structural design process (environment), the action of RL agent represent design variables of RC beam. Because the number of design variables of RC beam section is many, multi-agent DQN (Deep Q-Network) was used in this study to effectively find the optimal design solution. Among various versions of DQN, Double Q-Learning (DDQN) that not only improves accuracy in estimating the action-values but also improves the policy learned was used in this study. American Concrete Institute (318) was selected as the design codes for optimal structural design of RC beam and it was used to train the RL model without any hand-labeled dataset. Six agents of DDQN provides actions for beam with, beam depth, bottom rebar size, number of bottom rebar, top rebar size, and shear stirrup size, respectively. Six agents of DDQN were trained for 5,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases that is not used for training. Based on this study, it can be seen that the multi-agent RL algorithm can provide successfully structural design results of doubly reinforced beam.

본 연구에서는 구조물의 응답 데이터를 기반으로 고유진동수, 감쇠비 등 동특성과 풍하중 모델의 파라미터를 동시에 추정할 수 있는 스펙트럼 백색화 기반 식별 기법을 제안하고, 이를 실제 40층 고층 구조물에 적용하여 실용성과 정확도를 평가하였다. 기존 연 구에서는 본 기법을 수치 시뮬레이션 및 풍동 실험에 적용하여 그 타당성을 입증한 바 있으나, 실계측 응답 데이터를 활용한 실구조물 적용에 대해서는 검증이 이루어지지 않았다. 본 연구는 이를 확장하여, 장기간 계측된 고층 건축물의 진동 응답을 분석하고, 각 주요 모드에 대해 백색화 처리를 수행함으로써 구조물 전달함수 및 풍하중 전달함수의 파라미터를 최적화 기반으로 동시 추정하였다. 특히 백색 잡음의 누적 파워 스펙트럼 길이를 목적함수로 설정함으로써, 기존 커브 피팅 기반 기법 대비 감쇠비 추정의 정확도와 안정성을 향상시켰다. 분석 결과는 전통적인 모달 식별 기법(예: SSI)과의 비교를 통해 제안 기법의 유효성을 입증하였으며, 풍하중 모델 파라미 터까지 포함하는 통합적 구조 해석 프레임워크로서의 가능성을 제시하였다. 본 연구는 향후 구조물의 풍응답 예측, 하중 생성 모델 구 축, 구조 건전도모니터링(SHM) 및 디지털 트윈 기반 해석 등 다양한 실무 응용에 기여할 수 있을 것으로 기대된다.

Blow-up in jointed concrete pavements refers to a type of distress caused by the excessive accumulation of compressive stress within concrete slabs, primarily resulting from internal expansion and elevated environmental temperatures. This phenomenon frequently leads to slab buckling and is challenging to predict in terms of both timing and location, thereby significantly threatening the long-term structural stability of the pavement. In the present study, the pavement growth and blow-up analysis (PGBA) model was employed to quantitatively predict the timing of blow-up events in jointed concrete pavements. The model estimates the maximum compressive stress within the slab throughout the pavement’s service life using input parameters such as reliability, climatic conditions, pavement structure, material properties, and expansion joint configurations. Subsequently, the model compares the estimated stress to the threshold stress associated with blow-up to determine the likely time of occurrence. A sensitivity analysis was performed on a range of design and environmental factors, including annual maximum temperature, annual maximum precipitation, coefficient of thermal expansion, ASR, pavement thickness, geometric imperfection, and expansion joint spacing and width. The influence of each factor on the predicted blow-up occurrence time was quantitatively evaluated. The analysis demonstrated that climatic conditions, pavement structure, material properties, and expansion joint characteristics, as considered in the PGBA model, collectively govern the timing of blow-up events. These findings offer critical insights for informing the design and maintenance strategies of jointed concrete pavements.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

Anomaly detection technique for the Unmanned Aerial Vehicles (UAVs) is one of the important techniques for ensuring airframe stability. There have been many researches on anomaly detection techniques using deep learning. However, most of research on the anomaly detection techniques are not consider the limited computational processing power and available energy of UAVs. Deep learning model convert to the model compression has significant advantages in terms of computational and energy efficiency for machine learning and deep learning. Therefore, this paper suggests a real-time anomaly detection model for the UAVs, achieved through model compression. The suggested anomaly detection model has three main layers which are a convolutional neural network (CNN) layer, a long short-term memory model (LSTM) layer, and an autoencoder (AE) layer. The suggested anomaly detection model undergoes model compression to increase computational efficiency. The model compression has same level of accuracy to that of the original model while reducing computational processing time of the UAVs. The proposed model can increase the stability of UAVs from a software perspective and is expected to contribute to improving UAVs efficiency through increased available computational capacity from a hardware perspective.

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.

Republic of Korea is building a multi-layered missile defense system against North Korea’s growing ballistic missile threat. To maximize the intercept performance of a multi-layered missile defense system, it is important to develop an efficient engagement plan that considers the interceptable time/space of each interceptor system for ballistic missiles. To do so, it is necessary to predict the flight trajectory of the ballistic missile, which must be done within a short time considering the short battlefield environment and the speed of the ballistic missile. This study presents a model for rapid trajectory prediction of ballistic missiles using the kinetic characteristics of each flight phase(thrust phase, midcourse phase, and re-entry phase) of ballistic missiles, a method for estimating kinetic information from ballistic missile observation data(time and position), and a mathematical analysis of the equations of motion of ballistic missiles.

본 연구는 환경 요인을 바탕으로 절화용 국화 생장 예측을 위한 최적의 모델을 개발하는 것을 목표로 하였다. 이를 위해 13개의 모델(Linear Regression, Lasso Regression, Ridge Regression, ElasticNet Regression, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Neural Network, Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost, Stacking)의 성능을 R2, MAE, RMSE를 평가 지표 로 비교하였다. 단일 모델 중에서는 Decision Tree가 가장 우수한 성능을 보였으며, R2값은 0.90에서 0.91 사이였다. 앙 상블 모델 중에서는 CatBoost가 가장 높은 성능을 보였으며 (R2=0.90~0.92) Random Forest와 XGBoost 또한 유사한 성 능을 보였다. 전체적으로 트리 기반 앙상블 모델이 국화 생장 예측에 적합한 모델로 나타났다.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

This study proposes an improved method for updating finite element models (FEM) by incorporating the random field characteristics of concrete material properties in reinforced concrete structures. Traditional FEM often assumes homogeneous material properties, which can lead to significant discrepancies between predicted and actual dynamic responses, especially in structures where the Young’s modulus (E) of concrete varies due to factors like curing conditions, material composition, and construction methods. We employed a Gaussian random field model and a system identification (SI) technique to address this limitation to optimize sensor placement. We developed an FEM updating method that incorporates the spatial variability of concrete elasticity. This optimization allowed for a more accurate capture of dynamic properties across various structural locations, resulting in FEM updates that reflect concrete’s inherent heterogeneity. The proposed method was validated through numerical examples, comparing dynamic response accuracy in models before and after updating. Results demonstrated that error values, measured in terms of maximum value error and normalized root mean squared Error (NRMSE), were significantly reduced in the updated models compared to the pre-update model. This approach effectively addresses the limitations of homogeneous assumptions in FEM.

Our study develops a finite element analysis (FEA) model to evaluate the seismic performance of a two-story reinforced concrete (RC) school building and validates it through experiments. We developed a methodology that reflects failure modes from past experiments and validated it by comparing results at both the member (columns) and system (beam-column joints) levels. We created an FEA model for seismic-vulnerable RC moment frames using this methodology. This model incorporates bond-slip effects using three methods: Merged Nodes, Constrained Beam in Solid Penalty (CBISP), and Constrained Beam in Solid Friction (CBISF), which model the interaction between reinforcement and concrete. The approach provides a reliable tool for evaluating seismic performance and improves the accuracy of reinforced concrete frame evaluations.

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.

본 연구는 우즈베키스탄 자동차 산업 발전에 있어 리버스 엔지니어링 의 역할을 탐구하며, 특히 노나카와 타케우치의 지식 창출 모델(SECI Model)과의 통합에 중점을 두고 있다. 우즈베키스탄이 경쟁력 있는 국내 산업을 구축하고자 노력함에 따라, 리버스 엔지니어링은 해외 기술의 습 득, 적용, 그리고 국산화를 가능하게 했다. 본 연구는 정성적 사례 연구 접근법을 활용하여 기업 보고서, 정책 문서, 학술 문헌을 바탕으로 1996 년부터 2024년까지의 발전 상황을 분석한다. 연구 결과에 따르면 리버스 엔지니어링은 지식 이전과 혁신을 지원해 왔지만, 제한된 R&D 역량, 수 입 부품 의존도, 그리고 취약한 지식재산권 보호 등의 과제가 여전히 남 아 있다. 이러한 장벽을 극복하기 위해 본 연구는 전략적 정책, 국내 혁 신에 대한 투자 확대, 그리고 AI 기반 설계 프로세스 도입을 권고한다.