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.

본 연구는 다문화 청소년 지원 사업 중 후기청소년까지 수혜 대상을 포괄한 다톡다톡 프로젝트를 다차원 정책분석모델을 통해 분석하였다. 다톡다톡 프로젝트는 이주배경청소년을 대상으로 하여 사회적응과 정서 적 안정을 돕기 위해 2013년부터 2020년까지 운영된 민간 주도의 상담 및 심리치유 프로그램으로, 후기 청소년까지 포괄하여 다양한 연령대의 다문화 청소년에게 지원을 제공한 국내 유일의 사례이다. 본 연구는 규 범적, 구조적, 구성적, 기술적 차원의 네 가지 분석틀을 통해 다톡다톡 프로젝트의 정책적 가치와 목표, 제도적 구조와 집행 과정, 주요 이해관 계자 간의 상호작용, 그리고 실행 결과와 성과를 종합적으로 평가하였다. 규범적 차원에서는 프로젝트의 지향점과 다문화 청소년의 안정적 사회정 착을 위한 가치가 강조되었으며, 구조적 차원에서는 민간과 공공의 협력 을 통한 정책 추진 체계와 그 과정에서 나타난 한계가 분석되었다. 구성 적 차원에서는 청소년과 지역사회, 정부 기관 간의 상호작용이 프로젝트 의 중요한 구성 요소로 확인되었고, 기술적 차원에서는 심리적 지원과 직업훈련 등의 실질적 성과가 다톡다톡 프로그램의 효과를 뒷받침했다. 연구 결과는 다톡다톡 프로젝트가 다문화 청소년, 특히 후기 청소년을 위한 지속적이고 체계적인 지원이 이루어질 필요성을 시사한다.

This study focuses on the effectiveness of regional business support programs funded by South Korea's Balanced National Development Special Account, one of the policies designed to address regional imbalances and promote local autonomy. Using the analytical approach including DEA (Data Envelopment Analysis) methodology, This study analyzed the efficiency of 76 star companies in the Jeonbuk region based on their performance from 2018 to 2023. This study was designed to improve previous studies limitations, which only analyzed simple input-output efficiency in the short term, by using six years of mid-term data to comprehensively evaluate input variables in both R&D and Non-R&D sectors. The main purpose of this study is to analyze the effectiveness of the expiring Star Company Development Program by evaluating efficiency of supported company groups using DEA and to propose support models and policy suggestions for upcoming regional specialized industries support program by identifying the features of both optimal and inefficiency models. For this, employments along with financial indicators such as sales revenue, operating profit, and total assets were set as output variables, with R&D and non-R&D support amounts were set as input variables for analysis. According to the results, the optimal efficiency model group has strong intellectual property acquisition capabilities, and continuous R&D investment. It shows that continuous innovation activities are a key factor for improving the effectiveness of support. This study found that, from a mid․long term perspective, policy support programs should be customized by unique characteristics of each industry field, Based on this, it was suggested that upcoming regional specialized industry support programs in the Jeonbuk region should include policy planning and support program design to complement the weaknesses of each industry field.

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.

As digital transformation accelerates, platform business has become a core business model in modern society. Platform business has a network effect where the winner takes all. For this reason, it is crucial for a company's pricing policy to attract as many customers as possible in the early stages of business. Telecommunication service companies are experiencing stagnant growth due to the saturation of the smartphone market and intensifying competition in rates, but the burden of maintaining communication networks is increasing due to the rapid increase in traffic caused by domestic and foreign CSPs. This study aims to understand the dynamic characteristics of the telecommunications market by focusing on pricing policy. To this end, we analyzed how ISPs, CSPs, and consumers react to changes in pricing policy based on the prisoner's dilemma theory. The analysis of the dynamic characteristics of the market was conducted through simulation using the Agent-Based Model.

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 various types of products are produced in a single production system, it is important to determine a scheduling policy that selects one of the different types. In addition, the failure of processes in a line need to be considered due to machine failure, raw material supply and demand, quality issues, and worker absence, etc. Therefore, we studied production systems with various product types, dedicated buffers for each product type, Bernoulli equipment, and WIP-based scheduling or cyclic scheduling. To analyze such system exactly, we introduced a method to analyze the performance such as production rate, WIP level, blocking probability and starvation probability based on Markov chains and derived various characteristics. Especially, assuming that equipment does not need to select the type it just tried, the flow rate is no longer conserved and increasing buffer capacity does not guarantee increase production rate. The performance comparison between WIP-based and cyclic policy is studied as well.

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 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.

This study analyzed the selectivity of Octopus minor using the extended SELECT model in netpots. The data used for the analysis were collected from ten sea trials conducted between 2009 and 2010 using cylindrical octopus traps with six mesh sizes (16, 18, 20, 22, 24, and 26 mm). The selectivity analysis was performed using two models: the p-fixed split model and the p-estimated split model, depending on whether the encounter probability (split parameter) was estimated. The model fit was evaluated by comparing the model deviation and AIC values. The results showed that octopus catch decreased as mesh size increased, with a general tendency for larger individuals to be caught. The 16 mm trap, which had the smallest mesh size, accounted for 25.9% of the total octopus catch by number of individuals while the 22 mm trap, a commercial mesh size, accounted for 14.1%. The CPUE based on weight was highest for the 18 mm trap. The selectivity analysis results indicated that the p-estimated split model provided the best fit, and the 50% selection length for the 22 mm trap was 64.57 mm. In this study, reliability of various models was considered in the mesh selectivity analysis, and the findings are intended to serve as basic data for improving relevant regulations and deriving scientific research results.

The precast concrete (PC) method allows for simple assembly and disassembly of structures; however, ensuring airtight connections is crucial to prevent energy loss and maintain optimal building performance. This study focuses on the analytical investigation of the shear capacity of precast ultra-high-performance concrete (UHPC) ribs combined with standard concrete PC cladding walls. Five specimens were tested under static loading conditions to evaluate their structural performance and the thermal behavior of the UHPC rib shear keys. Test results indicated that the specimens exhibited remarkable structural performance, with shear capacity approximately three times greater than that of standard concrete. Numerical models were subsequently developed to predict the shear capacity of the shear keys under various loading conditions. A comparison between the experimental results and finite element (FE) models showed a maximum strength difference of less than 10% and a rib displacement error of up to 1.76 mm. These findings demonstrated the efficiency of the FE model for the simulation of the behavior of structures.

Purpose: This study aimed to explore user experience with UV-C disinfection devices in clinical settings to obtain critical information for the development of domestic devices to meet the increasing demand for efficient environmental disinfection in healthcare settings, particularly in terms of effective multidrug-resistant organism control. Methods: A qualitative approach was employed involving 21 participants (infection control nurses, staff nurses, and device managers). Data were collected through five sessions of focus group interviews, and conventional content analysis was undertaken. Results: Four categories and 13 subcategories were identified: (i) Introduction and usage status of UV-C disinfection devices, with ‘Surge in demand for rapid response to infectious diseases’ and ‘Diversified application and management of UV-C disinfection devices based on needs’ subcategories; (ii) Advantages of using UV-C disinfection devices, with ‘Significantly reduced disinfection time,’ ‘Easy and simple operation,’ and ‘Providing a sense of reassurance from a visible confirmation of disinfection’ subcategories; (iii) Limitations of current UV-C disinfection devices, including ‘Ambiguous disinfection range and presence of disinfection blind spots,’ ‘Lack of standards for disinfection efficacy verification and management,’ ‘Safety concerns regarding ultraviolet radiation exposure,’ and ‘Issues related to maintenance of UV-C disinfection devices’ subcategories; and (iv) Expectations for domestic UV-C disinfection devices, including ‘Minimization of disinfection blind spots,’ ‘Variety in device sizes,’ ‘Auxiliary devices for enhancing usage efficiency,’ and ‘Clear protocols for device usage and maintenance. Subcategories.’ Conclusion: UV-C disinfection devices offer valuable benefits for infection control; however, improvements are needed to enhance their efficacy and usability. Practical recommendations include developing standardized safety protocols, enhancing UV-C coverage, and improving device mobility and maintenance to meet diverse healthcare needs. Such advancements in UV-C technology can significantly support effective infection control and operational efficiency in healthcare settings.

본 연구는 급격히 변화하는 현대 사회에서 교회가 유가족에게 복음을 효과적으로 전할 방법을 탐구하기 위해, 다양한 이론적 접근을 통합하여 새로운 전도 전략 수립을 위한 이론적 배경을 제시하고자 한다. 매슬로우의 욕구 위계 이론을 중심으로, 퀴블러-로스의 애도 5단계 모델, 반 겐넵의 통과 의례 이론, 빅터 터너의 의례과정 모델을 통합하여 유가족의 복잡한 심리적, 정서적, 영적 필요를 심도 있게 분석했다. 이 이론들은 각각 유가족이 겪는 상실의 과정과 그에 따른 반응을 이해하는 데 중요한 틀을 제공하며, 교회가 유가족의 다양한 욕구를 충족시켜 복음을 효과적으로 전달하는 방법을 제시한다. 이 연구는 유가족 전도에 있어 이러한 다차원적 이론의 통합적 접근이 교회가 유가족을 더욱 깊이 이해하고, 그들에게 맞춤형 목회적 지원을 제공하며, 복음을 전달하는 데 중요한 역할을 할 수 있음을 제안한다. 유가족의 다양한 필요를 충족시키는 교회의 역할이 그들이 신앙을 받아들이고 영적 성장을 이루는 데 있어 얼마나 중요한지를 강조하며, 이를 통해 교회가 유가족 전도라는 중요한 사명을 충실히 수행할 수 있도록 방향을 제시한다.