Defense Modeling & Simulation (M&S) techniques are used for developing the efficiency and economics of national defense at operational level so that it maintains interoperability and reusability in sustainability for the following process of the war simulation. However, the lack of conceptual models was one cause of limiting the interoperability and reusability in defense M&S areas. In this paper, the Conceptual Model of the Mission Space (CMMS) is studied as preliminary process for the defense M&S. The conceptual modeling framework called CMMS-K (Conceptual Model of the Mission Space-Korea) is suggested using a case example in consideration of the Korean Army specification and characteristics. The practicality of CMMS-K is evaluated through the ontology development for military scenarios. It is expected that the gap between the theoretical approach and the practical perspective of defense M&S can be diminished through the use of these approaches.

본 연구는 빅데이터 분석을 활용하여 국내 한류 연구의 동향을 파악하고, 향후 연구 방향을 제시하는 것을 목적으로 한다. 이를 위해 2014년부터 2024년까지 학술연구정보서비스(RISS)에 발표된 KCI 등재 학술논문 중 ‘한 류’를 검색어로 1,793편의 데이터를 수집하였다. 분석대상은 논문명, 주제어, 초록 등이며, 분석도구로는 소프트웨어 R을 활용하였다. 연구결과는 다음과 같다. 첫째, 주요 한류 소비국가에서의 콘텐츠 소비와 한국어 학습과의 상호 작용을 분석하는 것이 필요하다. 둘째, 한류 콘텐츠의 디자인과 제작 방식이 주목받으며, 이에 대한 저작권 이용 사례가 증가하고 있으므로 글로벌 시장 에서의 디자인 및 제작 전략을 정교화하기 위한 연구가 필요하다. 셋째, 콘 텐츠의 창의성이 한류의 핵심 경쟁력임을 고려하고, 시청자의 적극적 참여를 유도하는 확산 전략을 모색해야 한다. 넷째, 한류가 국가 브랜드 형성에 미 치는 영향을 분석하고, 특히 관광 및 화장품과 같은 소비재 산업과의 연계를 강화할 필요가 있다. 또한 한류 연구가 콘텐츠 및 시장 중심의 담론에서 정 책적, 제도적 관점의 담론으로 진화하고 있음을 감안하여 한류의 지속가능성 을 위해서는 경제적 관점과 제도적 관점을 분리하기보다 서로 상호보완적이 며 다학제적으로 접근하는 것이 필요하다. 마지막으로 제도적 관점에서의 한 류 연구에서 연구 간 데이터 및 지표 활용과 같이 정보흐름이 더욱 강화되 어야 한다. 본 연구는 한류 연구의 동향과 주요 연구주제를 분석함으로써 한 류연구의 현재와 향후 방향성을 제시하였다는 점에서 의의를 가진다.

This paper presents a finite-difference method (FDM)-based heat-transfer model for predicting black-ice formation on asphalt pavements and establishes decision criteria using only meteorological data. Black ice is a major cause of winter road accidents and forms under specific surface temperature and moisture conditions; however, its accurate prediction remains challenging owing to dynamic environmental interactions. The FDM incorporates thermodynamic properties, initial pavement-temperature profiles, and surface heat-transfer mechanisms, i.e., radiation, convection, and conduction. Sensitivity analysis shows the necessity of a 28-d stabilization period for reliable winter predictions. Black-ice prediction logic evaluates the surface conditions, relative humidity, wind speed, and latent-heat accumulation to assess phase changes. Field data from Nonsancheon Bridge were used for validation, where a maximum prediction accuracy of 64% is indicated in specific cases despite the overestimation of surface temperatures compared with sensor measurements. These findings highlight the challenges posed by wet surface conditions and prolonged latent-heat retention, which extend the predicted freezing duration. This study provides a theoretically grounded methodology for predicting black ice on various road structures without necessitating additional measurements. Future studies shall focus on enhancing the model by integrating vehicle-induced heat effects, solar radiation, and improved weather-prediction data while comparing the FDM with machine-learning approaches for performance optimization. The results of this study offer a foundation for developing efficient road-safety measures during winter.

Genetic algorithms (GAs) are used to optimize solutions to problems, particularly those that are analytically impossible to solve. As their name suggests, they are inspired by the biological concepts of genetics and evolution. Our work aims to study and model a silicon-based photovoltaic generator (PVG). Among the various models available is that of the diode. Modeling was used to approximate the PVG output (voltage, current) as a function of two inputs: temperature and irradiation. The parameters of our model were identified using a real coding algorithm, with the cumulative square error was used for selection. To test the effectiveness of our model, we carried out simulation tests on the power-voltage (P-V) and current-voltage (I-V) characteristics of a wide range of irradiation and temperature variations. This study demonstrates the effectiveness and accuracy of the proposed approach (GAs) and validates the parameters obtained and used in the single-diode electrical model. The results indicate that the GA technique is a better conventional parameter extraction strategy in terms of convergence. It provides globally optimal solutions.

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 developed a model to predict employee turnover intention using data from the 2022 Korean Labor & Income Panel Study (KLIPS) with 2471 participants. CopulaGAN and Isolation Forests were employed for data augmentation and variable importance. A logistic regression model using the augmented data achieved an accuracy of 0.80, precision of 0.60, recall of 0.72, and an F1-score of 0.65. Key variables included Job Satisfaction, Wage Satisfaction, Work Hours, Job Stability, and Job-Related Training. The study highlights the potential of these techniques for enhancing turnover prediction and aiding proactive HR strategies.

본 연구는 키워드 네트워크 분석과 토픽모델링을 활용하여 국내 학술 지에 게재된 위기개입·상담 관련 연구동향을 분석하는 데 목적이 있다. 이를 위하여 국내 등재(후보) 학술지에 게재된 137편의 논문을 분석하였 다. 논문 분석을 위하여 넷마이너(NetMiner) 4.0 프로그램을 활용한 키 워드 네트워크 분석과 토픽모델링을 수행하였다. 키워드 네트워크 분석 을 통해 키워드 빈도분석, 키워드 동시출현빈도분석과 함께 연결중심성, 근접중심성, 매개중심성 등의 중심성 분석을 실시하였고, 토픽모델링분석 을 통해 논문에 잠재된 토픽과 키워드를 도출하였다. 키워드 네트워크 분석을 통한 키워드 빈도분석에서는 자살, 상담자, 전문가, 학교, 청소년 등이 주요 키워드로 나타났고, 토픽모델링 분석에서는 토픽-1(외상 스트 레스 위기상담), 토픽-2(취약계층 위기개입), 토픽-3(위기청소년 가족통합 서비스), 토픽-4(위기가정 개입), 토픽-5(자살집단 위기대응) 등 총 5개의 토픽이 등장하였다. 분석 결과를 토대로 위기개입·상담에 관한 연구는 다 양한 위기집단을 대상으로 위기 후 외상 스트레스에 대한 개입과 대응에 관한 연구들이 주로 이루어진 것을 확인하였다. 본 연구의 결과는 향후 위기개입·상담 연구를 위한 기초자료를 제공하고, 위기개입·상담에 필요 한 방안 마련에 기여할 것으로 보여진다.

The management of pollutant emissions from industrial sites involves various crucial steps, including estimating emission quantities and assessing their impact on surrounding areas. While emissions from point sources, such as exhaust outlets, are relatively easier to manage, emissions from area sources, such as workshops and livestock facilities, are often challenging to measure due to various constraints. To address this issue, this study proposes a method for estimating emissions from area sources by utilizing data collected at site boundaries and applying a reverse modeling approach. Using data from actual livestock facilities, along with reverse modeling results, this study identified a strong correlation between the facility area and the number of livestock raised. Correlation analyses revealed positive relationships between the facility area and the average odor emission rate, as well as between the number of livestock and the average odor emission rate. In addition, the results of reverse modeling confirmed a significant correlation between odor emissions, the number of livestock, and the facility area. Based on these findings, this study developed an odor emission factor for livestock facilities using the number of livestock and the facility area as activity indicators. The odor emission factor is expressed in units of OU/s/pig/m², where “OU” represents odor units, “s” denotes seconds, “pig” corresponds to the number of livestock, and “m²” refers to the total facility area. By multiplying the number of livestock by the facility area, the total odor emission rate (OU/sec) can be calculated. Unlike traditional emission factors that rely solely on the number of livestock, this newly developed factor incorporates all facilities contributing to odor emissions within a livestock operation. This approach allows for the estimation of odor emissions using external measurement data and facility information, even in cases where direct measurements are impractical. The results of this study are expected to be effectively utilized for odor evaluation and management in livestock facilities.

본 연구에서는 중속 충돌하중을 받는 RC 벽체의 배면파쇄 영역을 모사할 수 있는 모델링 기법을 범용 유한요소해석 프로그램인 LS-DYNA을 통해 제안하였다. 충돌해석에 주로 사용되는 요소 삭제 기능이 발사체의 하중 전달에 영향을 미치고 이로 인해 배면파 쇄 영역이 과소평가 된다고 판단하였다. 따라서 충돌 위치 주변에는 요소 삭제 기능을 사용하지 않는 모델링 기법을 제안하였다. 제안 된 기법을 적용한 해석이 실험 결과를 근접하게 모사함에 따라, 제안한 기법이 연속적인 충돌에너지의 전달에 효과적임을 확인하였 다. 추가적으로 다른 충돌 조건에 대해 해당 기법의 적용성 검토를 진행한 결과, 대칭 조건을 사용하지 않고, 철근의 결속을 함께 모사 할 경우 RC 벽체의 파괴 거동을 더욱 근접하게 모사할 수 있음을 확인하였다.

In this work, the depth of the interphase in graphene polymer systems is determined by the properties of graphene and interfacial parameters. Furthermore, the actual volume fraction and percolation onset of the nanosheets are characterized by the actual inverse aspect ratio, interphase depth, and tunneling distance. In addition, the dimensions of graphene, along with interfacial/interphase properties and tunneling characteristics, are utilized to develop the power-law equation for the conductivity of graphene-filled composites. Using the derived equations, the interphase depth, percolation onset, and nanocomposite conductivity are graphed against various ranges of the aforementioned factors. Moreover, numerous experimental data points for percolation onset and conductivity are presented to validate the equations. The optimal levels for interphase depth, percolation onset, and conductivity are achieved through high interfacial conductivity and large graphene nanosheets. In addition, increased nanocomposite conductivity can be attained with thinner nanosheets, a larger tunneling distance, and a thicker interphase. The calculations highlight the considerable impacts of interfacial/interphase factors and tunneling distance on the percolation onset. The highest nanocomposite conductivity of 0.008 S/m is acquired by the highest interfacial conduction of 900 S/m and graphene length (D) of 5 μm, while an insulated sample is observed at D < 1.2 μm. Therefore, higher interfacial conduction and larger nanosheets cause the higher nanocomposite conductivity, but the short nanosheets cannot promote the conductivity.

This study analyzes the discourse of Korean internet users regarding patient clothing and identifies the changes to structure and content of clothing resulting from infectious disease outbreaks. The analysis draws on texts from Korean blogs, internet cafes, and news articles from 2011 to 2021 related to patient clothing. Using Ucinet 5 and NodeXL 1.0.1 programs, network density, centrality, and cluster analyses were conducted using the Wakita–Tsurumi algorithm. Additionally, Latent Dirichlet Allocation (LDA) topic modeling was applied using Python 3.7 to further explore thematic patterns within the discourse. Throughout the period of study, it was found that users consistently discussed the specific purpose and functionality of patient clothing. Following the outbreak of COVID-19, the distribution and influence of keywords related to the functional aspects of patient clothing, such as “hygiene and safety,” significantly increased. An increased focus was placed on elements such as functionality, activity, autonomy, hygiene, and safety during the pandemic as public health concerns grew. It can be seen that patients increasingly share their experiences online and hospitalization rates surge during health crises; this study provides valuable insights into how the design of patient clothing can be improved through various informatics techniques. It underscores the evolving perception of patient clothing as essential medical equipment during health emergencies. In addition, it offers practical guidance for enhancing designs that better reflect shifting societal concerns, particularly regarding health, safety, and patient comfort.

Aluminum-based composites are in high demand in industrial fields due to their light weight, high electrical conductivity, and corrosion resistance. Due to its unique advantages for composite fabrication, powder metallurgy is a crucial player in meeting this demand. However, the size and weight fraction of the reinforcement significantly influence the components' quality and performance. Understanding the correlation of these variables is crucial for building high-quality components. This study, therefore, investigated the correlations among various parameters—namely, milling time, reinforcement ratio, and size—that affect the composite’s physical and mechanical properties. An artificial neural network model was developed and showed the ability to correlate the processing parameters with the density, hardness, and tensile strength of Al2024-B4C composites. The predicted index of relative importance suggests that the milling time has the most substantial effect on fabricated components. This practical insight can be directly applied in the fabrication of high-quality Al2024-B4C composites.

본 논문은 모바일 폰 사진을 활용하여 도시 공간에서 전력선과 건물 사이의 거리를 편리하게 모델링하고 측정하는 방법에 중점을 두고 있으며, 이에 대한 정확성을 평가하였다. 모바일 장 치용 2뷰 지오메트리를 사용한 전력선과 건물의 3D 재구성 방법을 설계하여 어디에서나 즉시 측정할 수 있도록 하였다. 특징점을 사용하여 전력선과 건물 사이의 거리를 계산하고 건물 정 보를 활용하여 게임 가상 공간에 전력선과 전주를 배치할 수 있다. 본 논문에서는 대전시 내 10개의 위치에서 실험을 실시하고, 상업용 시스템과 LiDAR가 있는 경우와 없는 경우를 포함 한 이전 방법과의 성능을 평가하였다. 우리의 방법을 스마트폰에 구현했기 때문에 장점은 휴 대성과 저렴함이다.

The health and working conditions of employees have become increasingly important issues in modern society. In recent years, there has been a continuous rise in problems related to the deterioration of workers’ alth, which seriously affects their safety and overall quality of life. Although existing research has investigated various factors affecting workers’ health and working conditions, there is still a lack of studies that scientifically analyze and identify key variables from the vast number of factors. This study employs the Lasso (Least Absolute Shrinkage and Selection Operator) technique to mathematically analyze the key variables influencing workers’ health status and satisfaction with their working environment. Lasso is a technique used in machine learning to identify a small number of variables that impact the dependent variable among a large set of variables, thereby reducing model complexity and improving predictive accuracy. The results of the study can be utilized in efficiently improving workers’ health and working environments by focusing on a smaller set of impactful variables.

Line observations of young stellar objects (YSOs) at (sub)millimeter wavelengths provide essential information of gas kinematics in star and planet forming environments. For Class 0 and I YSOs, identification of Keplerian rotation is of particular interest, because it reveals presence of rotationally-supported disks that are still being embedded in infalling envelopes and enables us to dynamically measure the protostellar mass. We have developed a python library SLAM (Spectral Line Analysis/Modeling) with a primary focus on analyses of emission line data at (sub)millimeter wavelengths. Here, we present an overview of the pvanalysis tool from SLAM, which is designed to identify Keplerian rotation of a disk and measure the dynamical mass of a central object using a position-velocity (PV) diagram of emission line data. The advantage of this tool is that it analyzes observational features of given data and thus requires few computational time and parameter assumptions, in contrast to detailed radiative transfer modelings. In this article, we introduce the basic concept and usage of this tool, present an application to observational data, and discuss remaining caveats.