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.

Sequential zone picking is an order picking method designed to enhance warehouse efficiency by dividing the storage area into multiple zones and picking items in a sequential order across these zones. Picked items are often placed in dedicated totes and transported between zones using a conveyor system, which manages the picking flow but can occasionally result in inefficiencies during the process. This study presents a variant of the sequential zone picking system, called a dual-lane zone picking system (DZP), which consists of two parallel conveyor lanes without buffers between consecutive zones. This conveyor configuration allows the picker in each zone to alternate processing between the two lanes, thereby lessening the constraints of tote transitions between zones and improving both system throughput and picker utilization. We design and conduct a series of experiments using a discrete-event simulation model to evaluate the performance of DZPs. The experiment results indicate that DZP surpasses the original single-lane zone picking system by shortening the system’s mean flow time in low flow intensity scenarios and achieving a higher maximum throughput and worker utilization in high flow intensity scenarios. Additionally, we investigate the effects of the number of zones and order batching size on the performance of DZP to gain further insights into the system’s operational control.

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 is to develop a timely fall detection system aimed at improving elderly care, reducing injury risks, and promoting greater independence among older adults. Falls are a leading cause of severe complications, long-term disabilities, and even mortality in the aging population, making their detection and prevention a crucial area of public health focus. This research introduces an innovative fall detection approach by leveraging Mediapipe, a state-of-the-art computer vision tool designed for human posture tracking. By analyzing the velocity of keypoints derived from human movement data, the system is able to detect abrupt changes in motion patterns, which are indicative of potential falls. To enhance the accuracy and robustness of fall detection, this system integrates an LSTM (Long Short-Term Memory) model specifically optimized for time-series data analysis. LSTM's ability to capture critical temporal shifts in movement patterns ensures the system's reliability in distinguishing falls from other types of motion. The combination of Mediapipe and LSTM provides a highly accurate and robust monitoring system with a significantly reduced false-positive rate, making it suitable for real-world elderly care environments. Experimental results demonstrated the efficacy of the proposed system, achieving an F1 score of 0.934, with a precision of 0.935 and a recall of 0.932. These findings highlight the system's capability to handle complex motion data effectively while maintaining high accuracy and reliability. The proposed method represents a technological advancement in fall detection systems, with promising potential for implementation in elderly monitoring systems. By improving safety and quality of life for older adults, this research contributes meaningfully to advancements in elderly care technology.

This study analyzes the impact of ESG (Environmental, Social, and Governance) activities on Corporate Financial Performance(CFP) using machine learning techniques. To address the linear limitations of traditional multiple regression analysis, the study employs AutoML (Automated Machine Learning) to capture the nonlinear relationships between ESG activities and CFP. The dataset consists of 635 companies listed on KOSPI and KOSDAQ from 2013 to 2021, with Tobin's Q used as the dependent variable representing CFP. The results show that machine learning models outperformed traditional regression models in predicting firm value. In particular, the Extreme Gradient Boosting (XGBoost) model exhibited the best predictive performance. Among ESG activities, the Social (S) indicator had a positive effect on CFP, suggesting that corporate social responsibility enhances corporate reputation and trust, leading to long-term positive outcomes. In contrast, the Environmental (E) and Governance (G) indicators had negative effects in the short term, likely due to factors such as the initial costs associated with environmental investments or governance improvements. Using the SHAP (Shapley Additive exPlanations) technique to evaluate the importance of each variable, it was found that Return on Assets (ROA), firm size (SIZE), and foreign ownership (FOR) were key factors influencing CFP. ROA and foreign ownership had positive effects on firm value, while major shareholder ownership (MASR) showed a negative impact. This study differentiates itself from previous research by analyzing the nonlinear effects of ESG activities on CFP and presents a more accurate and interpretable prediction model by incorporating machine learning and XAI (Explainable AI) techniques.

As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

This study explores the utilization level of smart manufacturing systems in the value chain processes of manufacturing and empirically examines the effect of the utilization level of these systems on manufacturing competitiveness in SMEs. Smart manufacturing systems in the value chain processes are categorized into Sales, Purchasing, Production & Logistics, and Support systems. By analyzing the research model using structural equation modeling, this study identifies that Sales systems, Purchasing systems, Production & Logistics systems, and Support systems have a significant impact on manufacturing process efficiency. Additionally, Production & Logistics systems and manufacturing process efficiency positively and significantly influence manufacturing competitiveness. The findings suggest that the utilization of information is directly and positively related to manufacturing process efficiency, including reducing lead-time, decreasing work performance man-hours (M/H), and improving work accuracy. These improvements ultimately have a significant impact on manufacturing competitiveness. In conclusion, the use of smart manufacturing systems is becoming an integral part of the manufacturing industry. To gain a competitive edge, it will be necessary to introduce and utilize optimal smart manufacturing systems, taking into account the size of manufacturing firms.

This study proposes a method to evaluate the publicity of real-time, demand-responsive, autonomous public-transportation systems. By analyzing real-time data collected based on publicity evaluation indicators suggested in previous research studies, this study seeks to establish a system that objectively assesses the publicity of public transportation. Thus, the introduction of autonomous public transportation systems is expected to contribute to solving problems in underserved transportation areas and enable more sophisticated public transportation operations. We reviewed evaluation indicators proposed in previous studies. Based on this review, publicity evaluation indicators were derived and specific criteria were selected to assess systematically the publicity of autonomous public transportation. An AHP analysis was conducted to assess the relative importance of each indicator by analyzing the importance of the selected indicators. Additionally, to score the indicators, minimum and maximum target values were established, and a method for assigning scores to each indicator was examined. The most important factor in the publicity evaluation of autonomous demand-responsive transport (DRT) was the “success rate of allocation to weak public transportation service areas,” with a significance level p of 0.204. This was analyzed as a key evaluation criterion because of the importance of service provision in areas with low-public-transportation accessibility. Subsequently, “Accessing distance to a virtual station” (p = 0.145) was evaluated as an important factor representing the convenience of the service. “Waiting time after allocation” (p = 0.134) also appeared as an important evaluation factor, as reducing waiting time considerably affected service quality. Conversely, “compliance rate of velocity” yielded the lowest significance (p = 0.017), as speed compliance was typically guaranteed owing to autonomous driving technology. This study proposed a specific evaluation method based on publicity indicators to provide a strategic direction for improving services and enhancing the publicity of autonomous DRT systems. These results can serve as a foundational resource for improving transportation services in underserved areas and for enhancing the overall quality of public transportation services. However, the study’s limitation was its inability to use real-time autonomous public transportation data, relying instead on I-MoD data from Incheon. This limitation constrained the ability to establish universal benchmarks because data from various municipalities were not included. Future research should collect and analyze data from diverse regions to establish more reliable evaluation indicators.

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.

In this study, the strength properties of recycled plastic materials using polypropylene, polyethylene, and high-density polyethylene were evaluated by measuring their compressive and flexural strengths, which are typically measured in cement-concrete pavements, to assess the feasibility of using recycled plastic materials as construction materials for modular pavements that can easily integrate advanced sensors, such as those for future autonomous driving. Two types of recycled plastic (composite resin and high-density polyethylene (HDPE)) and two types of inorganic materials (fly ash and limestone filler) were selected to evaluate the strengths of recycled plastic materials. Specimens for the compressive and flexural strength tests were prepared with four different recycled plastic contents (100%, 80%, 60%, and 40%). The compressive and flexural strengths of the recycled plastic specimens were measured according to the KSL ISO 679 and KSL 5105 methods, and the strength properties were analyzed based on the type and content of the recycled plastic and type of inorganic material used. Distortion and shrinkage problems were observed during specimen preparation using the 100% recycled plastic material. This indicated that inorganic materials must be incorporated to improve the flexural strength and facilitate specimen preparation. The compressive strength of plastic materials was comparable to the 28-day compressive strength of conventional cement-concrete pavements. The compressive strength of the composite resin was approximately twice that of HDPE. The flexural strengths of both the composite and HDPE were in the range of 15–25 MPa, suggesting their suitability as materials for the construction of modular pavement structures. Based on the limited strength test results, we can conclude that the strength properties of recycled plastic materials are similar to those of conventional cement-concrete paving materials. From the strength perspective, we confirmed that recycled plastic materials can be utilized as construction materials for modular pavements. However, further research should be conducted on factors such as molding methods for modular pavement structures based on different types of recycled plastic materials.

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.

This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.

In the case of Korean coastal fishing vessels primarily, it satisfies the fishing capacity and shifts in pace with trends. At the moment, speedy vessels with large load capacity and competitive hull forms are preferred since catch has decreased. However, in the design of Korean coastal fishing vessels, performance verification designers and related commercial programs are not utilized in various fields such as large vessels. Moreover, alleviated standards are applied, making securing and verifying the performance of fishermen’s preferred hull a must. To meet such demands, this research suggests a design system that the modules can be brought together as a fishing vessel model by AI; this would be a turnaround of coastal fishing vessel designing.

Purpose: This study aims to analyze the perceptions and experience of corporate managers on social value creation and explore how different types of cross-company collaboration can create new social value. Methods: In-depth interviews were conducted with 16 practitioners working on social value, and the data was analyzed using text analysis and topic modeling techniques. Results: A total of 11 topics emerged from the analysis, which revealed that to create social value, organizations need to build strong relationships with local communities, enhance job competencies, develop effective collaboration strategies, set clear goals, and continuously monitor performance. Conclusion: This study suggests that inter-organizational collaboration and a systematic approach to creating social value is effective and can lead to sustainable development and social responsibility. Future research should complement the findings of this study with various case studies and empirical analysis, and derive specific and practical strategies.

본 연구는 심리학적 이론을 활용한 선교 전략의 개발과 효과성을 탐구하는 것을 목적으로 한다. 21세기 선교 환경의 변화 속에서 심리학 과 선교의 융합은 선교의 효과성을 극대화하고 신자들의 영적 성장을 도모하는 데 필수적이다. 심리학적 이론은 인간의 신념과 행동을 분석 하고 예측하는 데 유용하며, 이를 통해 복음 전파의 효과를 높이고 신자들의 신앙생활을 강화할 수 있다. 본 연구는 심리학적 이론이 선교 전략에 어떻게 적용될 수 있는지, 심리학적 접근을 활용한 선교 전략의 장단점, 그리고 심리학적 이론을 활용한 선교 전략이 신자들의 영적 성장에 미치는 영향을 다룬다. 이를 통해 심리학과 선교의 융합이 현대 선교에 어떻게 기여할 수 있는지에 대한 통찰을 제공한다. 심리학 적 접근은 선교사들이 다양한 문화적 배경을 가진 사람들에게 효과적으 로 복음을 전하는 데 도움을 줄 수 있다. 문화 심리학을 통해 문화적 차이를 이해하고, 그에 맞는 선교 전략을 개발할 수 있다. 심리학적 이론을 활용한 선교 전략은 신자들의 신앙 동기를 강화하고, 심리적 치유를 통해 영적 회복을 도울 수 있다. 이러한 접근은 선교활동의 효과성을 높이고, 신자들의 영적 성장을 촉진하는 데 중요한 역할을 한다.