Earticle

최근에 임베디드 환경은 하드웨어의 비약적인 발전과 다양한 전자 제품에서의 소프트웨어 제어로 인하여 소프트웨어 분야에서 많은 변화를 겪고 있다. 이러한 환경적 변화로 인한 요구사항의 증가는 임베디드 소프트웨어의 복잡도를 높여왔고 이는 기존 개발방법의 한계를 보여주었다. 모델 기반의 개발방법은 이미 오래전 제기되었던 범용소프트웨어 개발에서 발생했던 한계의 해결을 위하여 제시되고 지금까지 사용되어온 방법으로서 임베디드 환경에 닥친 한계의 극복을 위한 대안 중 하나로 꼽히고 있다. 이 논문에서는 이 모델 기반 개발 방법론을 임베디드 소프트웨어 적용하기 위한 모델 기반의 다이어그램 편집기와 이로부터 작성된 모델을 이용하여 자동적으로 코드를 생성하는 코드 자동생성기를 제안한다. 모델 정의에 사용된 다이어그램 편집기는 GMF를 이용하여 구현하며, 코드 자동생성기에는 임베디드 환경의 특징인 제한된 메모리와 동시적 병행성 문제를 해결하기 위한 코드 생성 기법을 추가한다. 아울러 생성된 코드의 검증을 위하여 기존의 코드를 대체하여 수행하는 방법을 사용한다.

Recently, embedded environment suffers a huge change, by growth of hardware and turning to be software-controlled. This has improved embedded software complexity. It also brought us the limit of the old development way to resolve the problem. Model-driven development is one solution to solve the limit common software development by previous way, and it became a one uses for embedded environment also. In this paper, we propose model based development approach for embedded software, witch consists of diagram editor and automatic code generator. The diagram editors are implemented by GMF, which include additional functions to solve memory restrictions and concurrent execution problems without OS environment to a automatic code generator. In order to verify the generated code, it will be tested in main control model of UAV by replacing existing module with generated one.

Purpose: This study aimed to create a Vietnamese version of both the Maslach Burnout Inventory- General Survey (MBI-GS) and Areas of Worklife Scale (AWS) to assess the burnout state of Vietnamese clinical nurses and to develop a causal model of burnout of clinical nurses. Methods: We conducted a descriptive design using a cross-sectional survey. The questionnaire was hand divided directly by nursing departments to 500 clinical nurses in three hospitals. Vietnamese MBI-GS and AWS were then examined for reliability and validity. We used the revised exhaustion þ1 burnout classification to access burnout state.We performed path analysis to develop a Vietnamese causal model based on the original model by Leiter and Maslach's theory. Results: We found that both scales were reliable and valid for assessing burnout. Among nurse participants, the percentage of severe burnout was 0.7% and burnout was 15.8%, and 17.2% of nurses were exhausted. The best predictor of burnout was “on-duty work schedule” that clinical nurses have to work for 24 hours. In the causal model, we also found similarity and difference pathways in comparison with the original model. Conclusion: Vietnamese MBI-GS and AWS were applicable to research on occupational stress. Nearly one-fifth of Vietnamese clinical nurses were working in burnout state. The causal model suggested a range of factors resulting in burnout, and it is necessary to consider the specific solution to prevent burnout problem.

Purpose: This study aimed to develop and test the effect of the information-motivation-behavioral skills(IMB) model?based online reproductive health (RH) promotion program for late adolescent women. Methods: This study used a nonequivalent control group pretest?post-test design. The study sampleincluded 39 late adolescent women (experimental group: 19 and control group: 20). The programconsisted of four sessions based on the IMB model (70 min, once per week) for four weeks. This studyintervention was provided as an online intervention. Data were a pretest before the program, a posttestupon program completion, and a follow-up test one month after the program ended. Data werecollected from November 1, 2021, to May 30, 2022. Data analysis was conducted using the t test, Chisquaretest, and Fisher’s exact test with statistical software. Results: The program significantly increased late adolescent women's RH promotion behaviors (posttestp < .001 and follow-up test p = .002), RH knowledge (posttest p = .006 and follow-up test p < .001), andsexual autonomy (posttest p = .044 and follow-up test p = .036). No significant differences in changes inattitudes toward RH promotion behaviors were reported between the groups (posttest p = .060 andfollow-up test p = .195). Conclusion: This study conducted an online intervention to increase access to RH promotion education. The program developed based on the IMB model effectively improves RH promotion behavior andknowledge of late adolescent women. The intervention program implemented in this study should beused widely in the RH education curriculum for late adolescent women

Focusing high marble deposition, Hanwoo feedlot system uses high-energy diet over the prolonged fattening period. However, due to the individual genetic variation, around 40% of them are graded into inferior quality grades (QG), despite they utilized the same resources. Therefore, focusing on development of a nutrigenomic based precision management model, this study was to evaluated the response to the divergent selection on genetic merit for marbling score (MS), under different dietary total digestible nutrient (TDN) levels. Total of 111 calves were genotyped and initially grouped according to estimated breeding value (high and low) for marbling score (MS-EBV). Subsequently, managed under two levels of feed TDN%, over the calf period, early, middle, and final fattening periods following 2 × 2 factorial arrangement. Carcasses were evaluated for MS, Back fat thickness (BFT) and Korean beef quality grading standard. As the direct response to the selection was significant, the results confirmed the importance of initial genetic grouping of Hanwoo steers for MS-EBV. However, dietary TDN level did not show an effect (p > 0.05) on the MS. Furthermore, no genetic-by-nutrition interaction for MS (p > 0.05) was also observed. The present results showed no correlation response on BFT (p > 0.05), which indicates that the selection based on MS-EBV can be used to enhance the MS without undesirable effect on BFT. Ultimate turnover of the Hanwoo feedlot operation is primarily determined by the QGs. The present model shows that the initial grouping for MS-EBV increased the proportion of carcasses graded for higher QGs (QG1++ and QG1+) by approximately 20%. Moreover, there appear to be a potential to increase the proportion of QG 1++ animals among the high-genetic group by further increasing the dietary energy content. Overall, this precision management strategy suggests the importance of adopting an MS based initial genetic grouping system for Hanwoo steers with a subsequent divergent management based on dietary energy level.

Purpose: This study aimed to develop a mobile-based self-management health alarm (MSHA) program for modifying obese children's lifestyle based on the information-motivation-behavioral skills (IMB) model and to test its feasibility. Methods: A methodological study for the development of the MSHA program and pilot study with a one-group pretest-posttest design for feasibility testing was conducted. The MSHA program was designed to provide obesity-related information (I), monitor daily diet and exercise, provide motivational text messages (M), and enhance healthy diet and exercise skills (B) via a mobile-based web platform. In the feasibility test, six obese children participated in the 4-week program, and the number of days per week that they achieved their goals and differences in metabolic components were assessed. Data were analyzed using descriptive statistics and the Wilcoxon signed-rank test. Results: Participants successfully achieved their diet and exercise goals≥5 days per week. Body mass index (z=-1.99, p=.046), waist circumference (z=-2.20, p=.028), and triglyceride levels (z=-2.21, p=.027) significantly decreased. Conclusion: The MSHA program showed positive effects on health behaviors and metabolic syndrome risk. The program may be effective in improving metabolic syndrome in obese children by promoting self-health management behaviors.

Purpose: This study aimed to develop a predictive model for the early identification of patients at risk of sepsis, using routinely available clinical information and laboratory test results collected during the initial phase of patient care. Methods: This retrospective analysis included electronic medical records of 22,400 adult patients who presented with suspected infection to a tertiary care university hospital in Korea between January 2013 and May 2024. Patients were classified according to Systemic Inflammatory Response Syndrome (score ≥ 2) or Quick Sequential Organ Failure Assessment (score ≥ 2), in combination with sepsis-related International Classification of Diseases, 10th revision codes. Four different machine learning models were trained and validated using five-fold cross-validation. In addition, Shapley additive explanations analysis was performed to interpret the contribution and clinical relevance of key predictive variables. Results: Among the evaluated models, CatBoost demonstrated the strongest predictive performance. Notably, platelet distribution width, alveolar?arterial oxygen difference, procalcitonin, and the arterial/alveolar oxygen ratio consistently emerged as major predictors. Importantly, several variables that did not reach statistical significance in univariate analysis nevertheless contributed substantially to overall model performance, highlighting the importance of complex, multidimensional interactions among clinical factors. Conclusion: These findings indicate that a model based on simple, routinely collected clinical data can achieve high predictive accuracy and strong generalizability. Such a tool may support early clinical decision-making by multidisciplinary teams, including nurses, across diverse real-world care settings. Further prospective studies are warranted to validate its clinical utility and to assess its potential effects on patient outcomes.

Model-driven engineering (MDE) is a new approach to information systems development, formalizing model representation and utilizing transformation technologies. It has been enthusiastically advocated as a means of increasing the productivity of development efforts and enhancing the quality of developed systems. But MDE technologies have turned out to be limited in capturing semantics of formalized models. Without adequate semantics it is hard to reuse modeled elements and automate transformations. Ontology engineering has recently received interest from members of both academia and industry since it has the potential to solve the semantic interoperability problems. This paper proposes a process model for software development integrating ontology engineering into MDE at a high-level abstraction.

Accurate classification of public policy is essential for effective policy analysis, design, comparison, and formulation across countries. However, manual classification by policy experts can lead to inconsistencies and human errors, highlighting the need for a more reliable and efficient approach. This study proposes a deep learning-based model to support policy classification using artificial intelligence. Leveraging Korean policy datasets, comprising administrative data (1988–2018), legislative data (1987–2018), and media data (1988–2020), previously curated by experts, we developed an AI model for automated policy classification based on the KoBERT language model. Designed as a supplementary tool for policy experts, this model enhances classification consistency, reduces decision-making time, and improves overall productivity. Moreover, the model enables the classification, comparison, and evaluation of diverse policies at both local and national levels, offering valuable support for strategic public policy development. The proposed model achieved a Top-1 accuracy of 62.4% and a Top-3 accuracy of 71.6%, outperforming traditional baselines and demonstrating its practical potential for real-world policy analysis.

모바일 SNS(Social Network Service) 메신저를 매개로 하며 네크워트를 통한 의사전달과 정보교 환이 커뮤니케이션 영역의 중심이 되고 있다. 모바일 SNS 메신저는 신체언어인 얼굴표정 및 몸 짓의 표현이 배제된 상태로 의사소통이 이루어지기 때문에 상대방의 미세한 감성을 정확히 인지 하는데 한계가 있다. 이를 극복하기 위한 방법 중 하나가 이모티콘을 사용한 감성 커뮤니케이션 이다. 사용자들의 이모티콘 사용이 증가함과 동시에 카카오톡, 라인 등의 회사뿐만 아니라, 개인 작가들을 통한 이모티콘 개발 또한 급격히 늘어나고 있다. 그럼에도 불구하고 적절한 디자인 가 이드라인 없이 제작된 이모티콘은 여전히 사용자마다 다른 의미로 해석되어 커뮤니케이션의 오 류가 발생되거나 감성 표현에 적절한 이모티콘을 찾는 것이 어렵다. 따라서, 본 연구는 모바일 SNS 환경에서의 커뮤니케이션에서 사용자들의 원활한 감성과 관련된 의사소통을 위하여 이모티 콘을 체계적으로 연구하여 디자인 가이드라인을 구축하고 이를 실제로 적용한 이모티콘을 개발 하고자 한다. 또한, 개발한 이모티콘에 대한 감성 커뮤니케이션의 효과를 검증하였다. 본 연구의 결과에서 정립한 이모티콘 가이드라인과 이를 적용한 감성 이모티콘은 앞으로 지속적으로 향상 될 이모티콘 디자인에 있어서 감성 표현의 효과를 높이기 위한 긍정적인 방법으로 활용될 수 있 을 것으로 기대된다.

Mobile SNS(Social Network Service) messengers have been generally used communication as users communicate and exchange information through network. However, mobile SNS messengers have limitations in recognizing detailed emotions, as communication occurs without facial expressions and gestures. Emoticon is widely used for emotional communication as a way to overcome these limitations. For this reason, many users often use emoticons to express their specific emotions. As the usage of emoticons has increased, emoticons have been rapidly developed by individual artists as well as companies. However, emoticons without proper design guidelines has been interpreted differently by individual users, causing communication errors. It could make difficult to find the right emoticon for emotional expression. Therefore, this study researched emoticons for users' efficient emotional communication in mobile SNS environments to create design guidelines for emoticons and develop emoticons that following the guidelines. Then, the effects of emotional communication on emoticons verified by statistical analysis. Emoticon design guidelines created in the results of this study and emotional emoticons following the guidelines are expected to be used as positive ways to enhance the effectiveness of emotional expression.

대한민국은 OECD 회원국 중 자살률이 가장 높으며, 생애주기별로 10대를 제외한 전 연령층의 사망원인 1위를 차지하고 있다. 대한민국의 정부는 자살률 감소를 위하여 2011년「자살예방 및 생명존중문화 조성을 위한 법률」을 제정하고, 각 지자체별 특성과 상황에 맞는 자살예방대책 수립과 추진을 권장하고 있다. 본 연구에서는 국내· 외 자살예방 정책 동향 및 우수사례를 살펴보고, 김해시의 환경특성 분석, 행정안전부 지역안전지수, 자살사망자료 및 자살상담자료에 근거하여 자살 현안문제를 진단하고 개선방 안을 제안하였다. 또한, SWOT 분석과 기존의 김해시 자살예방 사업에 대한 비교 분석을 통하여 향후 김해시의 자살예방 정책 방향성 및 자살예방사업 모델을 도출하고, 5가지 자살예방 사업 추진 전략을 수립하였다. 마지막으로, 연구 결과를 바탕으로 8가지의 김해시 맞춤형 자살예방 사업을 도출하였다. 연구의 주요 결과는 향후 김해시의 사업 도출에 반영될 것이다.

South Korea has the highest suicide rate among OECD member countries, and suicide is the leading cause of death in all age groups except teenagers. In order to reduce the suicide rate, the South Korean government enacted the “Act on Suicide Prevention and Creation of a Culture of Respect for Life” in 2011, and has recommended the establishment and promotion of suicide prevention measures tailored to the characteristics of each local government. This study examined domestic and international suicide prevention policy trends and best practices, diagnosed current suicide issues based on an analysis of Gimhae City’s environmental characteristics, the Ministry of the Interior and Safety’s regional safety index, suicide death data, and suicide counseling data, and proposed improvement measures. In addition, the direction of Gimhae City’s suicide prevention policy and suicide prevention business model were derived through SWOT analysis. Based on the research results, eight customized suicide prevention projects for Gimhae City were derived. The main results of the research should be reflected in the future project derivation of Gimhae City.

핀테크에 적용되는 소프트웨어의 규모 추정은 매우 난해하며, 오랜 시간이 소요되는 작업이기에 발주 기업 과 개발 업체들이 정확하게 소프트웨어 개발 규모를 산정하기 어려운 상황이다. 일반적으로 프로젝트 관리자들의 경험과, 일반적인 기능 점수 방식에 의하여 규모를 추정하고 있다. 본 논문에서는 기능 점수 모형을 사용자 관점에 서 요구 기능들을 정량적으로 산정하고, 규모를 측정하여, 실시간으로 규모를 산정하여 핀테크 개발 프로젝트에 적용할 수 있는 모델을 제안한다. 제안 모델을 통하여 개발 전에 업무량을 예상하여 규모를 측정할 수 있으며, 레이어 별 프로그램 목록을 기준으로 M/M 및 견적 금액을 산출할 수 있다. 향후 연구에서는 기존 핀테크 프로젝 트의 규모 산정 데이터를 다량 확보하여 정확한 규모 산정에 대한 연구가 필요하다.

Estimation on SW Sizing applied to fintech is very difficult, a task requiring long time, it is difficult for client companies and developer companies to accurately calculate the size of software development. The size is generally estimated based on the experience of project managers and the general functional scoring method. In this paper, propose a model that can be applied to fintech development projects by quantitatively calculating the required functions from the user's point of view, measuring the scale, and calculating the scale in real time. Through the proposed model, the amount of work can be estimated prior to development and the size can be measured, and the M/M and the estimated quotation amount can be calculated based on the program list by each layer. In future studies, by securing size computation data on existing the Fintech Project in mass, research on accurate size computation would be required.

Recently, many companies are developing platform business models by applying IoT, big data, and AI technologies as a part of their digital transformation (DT) strategies. Using qualitative research method, the purpose of this study is to investigate how manufacturing companies use IT vendor’s capabilities to expand, integrate, and reorganize their capabilities in pursuing the new platform business model. Case studies were conducted on manufacturing companies that are pursuing big data-based platform business as their DT strategies. It was founded that the roles of IT vendors include joint development of the technology required for the platform business, formation of business ecosystem for platform services, and co-selling/co-marketing of the services to the target customers. Since the cases are limited to manufacturing companies, there may be limitations in the generalization of research results. Thus, more studies across various industries are suggested for the future study.

The purpose of this study is to develop a model for evaluating urban safety using the concept of sustainable urban metabolism. In this study, the framework of sustainable metabolism to assess urban safety consists of five principles and urban safety assessment processes that should be sought in its development; 1) Development of safety assessment framework based on urban metabolism applying the components of sustainable development; 2) Development of urban safety assessment framework based on urban metabolism capable of grasping the relationships between economic, social, and environmental elements; 3) Development of the framework capable of understanding all the network relationships between the components; 4) Development of the framework considering the outside elements influencing sustainable development; and 5) Development of the framework using urban safety assessment as the final result. Based on these principles, the structures of sustainable urban metabolism and urban safety framework were established, and then three processes were set to assess urban safety. In addition, based on the DPSR structure, we developed a basic model of urban safety evaluation for structural equations and System Dynamics.

In order to propose a model of English teacher development in Asia, this study has investigated qualifications of primary, secondary and tertiary English teachers (including required degrees and English ability) and pre- and in-service education programs in 16 Asian nations including Korea, China, Indonesia, and Israel. The results of the survey illustrate that most of the Asian regions have strict controls on qualifications of primary and secondary English teachers, but not on those of tertiary teachers. A few regions have specific English proficiency requirements and not many regions have a test for teacher certification. Pre- and inservice English teacher education programs also vary with nations and school levels, but they share similar foci on English proficiency and teaching skills. On the basis of these results, the study suggests five constituents of English teacher qualifications as standards with a variation on their weighting and expected levels across regions, school levels, and teaching experiences. Finally, it proposes a model of English teacher development in Asia for progressive professional development which integrates top-down and bottom-up approaches to teacher education. A renewal system of teachers’ license is also suggested.

Analyzing and defining specific business process in applications such as CRM is complicated and changeable on demand as it has to support the enterprises to meet the business goals and requirements. Also, business process needs to be flexible enough to adapt the dynamic business strategies. MDA provides a standard way of defining business process meta-modeling and unifies the diverse business process containing well defined business rules. In this paper, we have designed the business process meta-model, which focuses on sales, marketing and service areas. The cross-functional relationship between the components within and across the CRM system shows the integrated business process. Thus, this paper provides a meta-modeling paradigm of business processes of the CRM system, and it is able to apply business rules of the domain to have more flexibility of the functional components and increase the system as well as the business value.

Predicting traffic accidents is a challenging task because taking into account uncertainty in modeling traffic accidents is not trivial. To address these issues, this article develops a hybrid modeling pipeline combining unsupervised and supervised learning to predict the level of hazardous road sites and explore the causality of accidents by controlling unobserved heterogeneity issues effectively. Traffic accident data for Won-ju province, Korea, from 2020 to 2021, and external factors affecting traffic accidents, such as average travel speed and weather information, are combined based on road links. Through the modeling pipeline, a clustering technique is adopted to capture unobserved heterogeneous information among roads. Since traffic accident data contains a wide variety of categorical and hierarchical features, ensemble methods such as boosting techniques were applied to handle heterogeneity issues among these features. To explore the relationship between the accident and determinant factors, are adopted to interpret the results of machine learning models. Model-agnostic methods, however, generally provide results based on images, this study also added a process that extracts texts from images to overcome compatible issues with existing road safety management systems.

This study aims to develop a predictive model for student counseling support by identifying key individual factors encountered in university life, such as academic performance, financial conditions, psychological factors, and campus environment, as explanatory variables. Using logistic regression (logit model), the model was constructed to predict academic persistence and dropout, set as binary response variables, and validated for consistency through the support index. Additionally, the consistency of the dropout prediction model based on the logit model was cross-validated using a classification model with CART (Classification and Regression Tree) to determine which model or approach provided superior results. Modeling and comparing 2,000 simulated data points yielded similar results. All explanatory variables, except gender, were significant (p < .05), and, considering odds ratios, the number of leaves of absence (1.7818) and academic warnings (1.2921) had strong positive effects on dropout likelihood. Conversely, academic year, number of enrolled semesters, and GPA showed a negative effect on dropout probability. From a practical perspective for system implementation and operation, the logit model proved more efficient for constructing and applying the prediction model.

In the context of economic and trade globalization and the rapid development of China’s Internet technology, China’s cross-border e-commerce is booming. Since the global financial crisis in 2008, with the slowdown of global economic and trade development, China’s cross-border e-commerce transactions have continued to expend, accounting for an increasing proportion of China’s total GDP. From the perspective of cross-border e-commerce development, this paper uses the volume of cross-border e-commerce transactions from 2008-2020 and the quantity of mobile phone subscriptions and fixed broadband subscriptions to establish a VAR model analyzing the influence of China’s cross-border e-commerce infrastructure on cross-border e-commerce development. Based on the results of our empirical analysis, this paper proposes to improve China’s cross-border e-commerce infrastructure to ensure the long-term development of the country’s cross-border e-commerce.

토양수분은 가뭄 발생과 해소를 매개하는 핵심 인자로서, 대기-지표-지하수로 이어지는 수문순환의 연결 고리 역할을 한다. 이처럼 가뭄 연구 및 분석을 위한 토양수분 자료 관측 센서를 설치하여 측정하고 있으나, 기상 및 통신 장애로 결측이 발생하여 자료 활용에 불편함을 겪고 있다. 본 연구에서는 해남·예산 지역에 설치된 토양수분 모니터링 시스템의 토양수분 결측 자료를 보간하기 위하여 먼저 강수 자료를 보간하고, 강수 특징변수로 머신러닝의 학습자료를 5개로 구축하여 학습 및 평가자료 정확도 결과를 비교·분석하였다. 연구 결과, 지연, 누적, 시계열 특징변수로 구성한 D, E 학습자료 기반 머신러닝 모형이 훈련·검증자료 정확도가 우수하였다. 평가자료 정확도는 D, E 학습자료 기반 XGB 모형이 우수하였으나, E 학습자료 기반 XGB 모형은 다른 조합 대비 더 많은 경우에서 우수한 정확도를 보였다. 따라서 E 학습자료 기반 XGB 모형을 활용하여 해남·예산 지역에 설치된 토양수분 모니터링 시스템의 10·20cm 깊이 토양수분 결측 자료를 보간하는게 적절하다고 판단하였다.

Soil moisture is a key variable governing drought onset and recovery and a critical link in the hydrological cycle connecting the atmosphere, land surface, and groundwater. Missing observations frequently occur in soil moisture monitoring systems due to meteorological and communication failures, limiting data usability. In this study, missing soil moisture data from monitoring systems in Haenam and Yesan were gap-filled by first correcting precipitation data and constructing five machine-learning training datasets using precipitation-based features. Model performance was evaluated using training, validation, and evaluation datasets. Results indicate that Training D and E datasets, incorporating lagged, accumulated, and time-series precipitation features, combined with the XGB algorithm, showed superior performance. The D– and E–XGB combinations also achieved high accuracy in the evaluation dataset, with the E–XGB model outperforming others in more cases. Therefore, the E-dataset XGB model is suitable for gap-filling 10- and 20-cm soil moisture data in the Haenam and Yesan regions.