Until all vehicles are equipped with autonomous driving technology, there will inevitably be mixed traffic conditions that consist of autonomous vehicles (AVs) and manual vehicles (MVs). Interactions between AVs and MVs have a negative impact on traffic flow. Cloverleaf interchanges (ICs) have a high potential to cause traffic accidents owing to merging and diverging. Analyzing the driving safety of cloverleaf ICs in mixed traffic flows is an essential element of proactive traffic management to prevent accidents. This study proposes a comprehensive simulation approach that integrates driving simulation (DS) and traffic simulation (TS) to effectively analyze vehicle interactions between AVs and MVs. The purpose of this study is to identify hazardous road spots for a freeway cloverleaf IC by integrating DS and TS in mixed traffic flow. The driving behavior data of MVs collected through a DS were used to implement vehicle maneuvering based on an intelligent driver model in the TS. The driving behavior of the AVs was implemented using the VISSIM parameters of the AVs presented in the CoEXist project. Additionally, the market penetration rate of AVs, ranging from 10% to 90% in 10% increments, was considered in the analysis. Deceleration rate to avoid crashes was adopted as the evaluation indicator, and pinpointing hazardous spot technique was used to derive hazardous road spots for the cloverleaf IC. The most hazardous road spot was identified in the deceleration lane where greater speed changes were observed. Hazardous road spots moved downstream within the deceleration lane as traffic volumes increased based on level of service. The number of AVs decelerating stably increased as traffic increased, thereby improving the safety of the deceleration lane. These results can be used to determine the critical point of warning information provision for preventing accidents when introducing AVs.

한국 사회는 빠르게 초고령사회로 접어들고 있다. 초고령사회는 사회, 경제 및 문화 모든 분야에서 질적인 변화를 가져온다. 이에 대응하여 정부는 기존 도시들을 고령친 화도시로 변화시키는 계획을 추진하고 있다. 고령화에 따라 고령수형자도 꾸준히 증 가하여 전체 수용자의 약 17퍼센트에 이른다. 고령수용자에 대한 적정한 교정 처우방 안의 마련이 필요한 시점이다. 고령친화도시 조성과 연계하여 고령친화 교정타운을 건설할 필요성이 있다. 고령친 화 교정타운은 고령 및 장애인 수형자 등 돌봄과 치료 등 처우가 필요한 수형자들을 주로 수용하고 맞춤형 프로그램을 제공하는 복합 교정시설이다. 고령수형자 등은 교 정의 대상이자 돌봄의 대상인 특성이 있다. 고령친화도시는 고령자에 대한 복지, 의료 시설 및 다양한 프로그램을 갖추게 되므로 이를 고령수형자 등의 교정 처우에 활용하 는 것이 가능하다. 고령친화도시와 고령친화 교정타운이 연계 추진되면 고령수형자 등에 대한 적절한 교정처우를 할 수 있고 아울러 고령친화도시의 지역 경제도 활성화 되는 상생 구조가 된다. 고령친화 교정타운은 고령수형자 등의 특성을 고려할 때 민간 분야의 전문성을 활용할 필요가 있으므로 민관이 협력하여 운용하는 것이 바람직하다. 고령친화도시와 교정타운의 통합적 접근은 고령사회에서 미래 지향적 사회 모델을 제시한다. 초고령사회에서 고령자의 복지 향상과 인간다운 삶의 보장은 국가적 과제 라 할 것이다. ‘살던 곳에서 늙어가기’, ‘살던 지역공동체에서 늙어가기’는 노인복지의 핵심 명제라 할 수 있다. 고령수형자 등에게도 수형자로서의 지위에 상충되지 않는 한 이를 보장할 필요가 있다. 고령친화도시와 연계된 고령친화 교정타운의 건설은 이러 한 국가적 책무 달성에 효과적으로 부응하는 길이다.

본 논문에서는 초기 압축 성형 공정 조건들이 단섬유 강화 복합소재 구조물의 기계적 거동 특성에 미치는 영향을 효과적으로 반영 할 수 있는 압축 성형-구조 연계 해석 방안을 제안하였다. 압축 성형 해석을 바탕으로 초기 charge의 형상 및 배치에 따른 부위별 단섬 유 배향 특성을 분석하였으며, 평균장 균질화 이론을 통해 단섬유 배향 특성에 따른 등가 이방 물성을 도출하였다. 나아가, 단섬유 배 향 정보가 Mapping된 유한요소 모델을 기반으로 초기 공정 조건들에 의해 야기되는 부위별 거동 특성 변화를 고려할 수 있는 압축 성 형-구조 연계 해석을 진행하였다. 관련 수치 예제 검증을 통해 제시된 해석 방안은 압축 성형을 통해 제작된 단섬유 강화 복합소재 구 조물 설계 과정에서 효과적인 솔루션을 제공함을 확인하였다.

본 논문에서는 3D 프린팅 공정을 통해 제작된 단섬유 강화 복합소재 구조물의 기계적 거동을 효과적으로 예측하기 위한 AM 공정 연계 구조 해석 기법을 제안하였다. 복합소재 3D 프린터(Mark Two, Markforged)를 활용하여 다양한 노즐 경로를 갖는 인장 시편을 출력하였으며, 출력물에 대한 인장 시험을 진행하였다. 또한, 노즐 경로에 따른 부위별 이방 물성을 도출하기 위해 실험적 데이터를 기반으로 역공학 기법을 적용하였다. 제안된 AM 공정 연계 구조 해석 방안의 타당성을 검증하기 위해 실험 결과와의 비교/분석을 병 행하였으며, 부위별 이방 물성이 반영된 FE 모델을 바탕으로 AM 공정 연계 구조 해석을 수행함으로써 복합소재 3D 프린팅 출력물의 거동 양상을 정확하게 예측할 수 있음을 확인하였다.

위법행위를 저지른 발달장애인(이하 ‘범법 발달장애인’)은 형사사법 절차에 진입하 더라도 해당 범죄의 근본적 원인인 정신･행동 문제에 대한 치료적 개입이 이루어지지 않아 높은 재범률을 보이고 있다. 본 연구에서는 현 상황에 대한 하나의 대안으로, 형 사사법 절차의 과정 중에 범법 발달장애인을 지역사회의 적절한 의료･복지자원으로 연결하여 치료 및 재활 서비스를 제공하는 통합적 연계전환(diversion) 모형을 제안 하였다. 먼저 Ⅰ장에서는 범법 발달장애인의 재범 방지를 위한 적절한 치료 연계의 필요성 을 개괄하였다. Ⅱ장에서는 대표적인 ‘사회 내 처우’인 치료명령과 수강명령, 보호관찰 을 중심으로, 범법 발달장애인에 대한 치료 서비스 및 연계전환의 현황과 그 한계를 검토하였다. 그리고 이에 대한 대안으로써, ｢범죄 가･피해 발달장애인의 재범 방지를 위한 개별맞춤형 지원사업(PSRP)｣의 수평적 연계전환 모형과 발달장애인 거점병원･ 행동발달증진센터로의 수직적 전달체계 모형을 연계전환 체계로 활용할 수 있는 가능 성을 탐색하였다. 이어지는 Ⅲ장에서는 ‘시설 내 처우’ 중 치료감호시설을 중심으로, 범법 발달장애인에게 제공되고 있는 치료 서비스 및 연계전환의 현황과 그 한계를 검 토하였다. 마지막 Ⅳ장에서는 범법 발달장애인에 대한 형사사법 절차와 의료･복지자원 간의 연계전환 체계 모형을 구체적으로 제안하였다. (1) 검찰청과 발달장애인 지원센터, 장 애인복지관을 수평적으로 연결하는 PSRP 모형 및 (2) 발달장애인 거점병원･행동발달 증진센터로 이어지는 수직적 전달체계 모형을 통합하고, (3) 교정시설의 가용한 연계 전환 제도를 추가로 반영함으로써, 발달장애인 지원센터와 보호관찰소가 중심이 되는 범법 발달장애인에 대한 연계전환 모형을 도출하고자 했다.

This study aims to analyze cooperative autonomous driving by integrating two advanced simulation tools, UC-WinRoad and VISSIM. Cooperative autonomous driving refers to the interaction of autonomous vehicles (AVs) with human-driven vehicles, infrastructure, and other road users within a dynamic traffic environment. The integration of UC-WinRoad’s realistic 3D visualization capabilities with VISSIM’s detailed microscopic traffic modeling enables the simulation of complex traffic scenarios, providing a comprehensive analysis of autonomous and connected vehicle behavior. The necessity of this study arises from the growing interest in autonomous driving technologies and the need for reliable tools to evaluate their performance and impact on real-world traffic systems. Simulations offer a safe and cost-effective environment to test AV behavior in various scenarios, including extreme or hazardous conditions that are difficult to replicate in the real world. This study also provides valuable insights into AV-infrastructure interactions, offering data-driven recommendations for policy and infrastructure planning. The outcomes of this research include the development of a methodology for linking UC-WinRoad and VISSIM, simulation results demonstrating potential improvements in traffic flow, safety, and efficiency through cooperative autonomous driving, and the identification of challenges in integrating AVs into existing traffic systems. This research contributes to the advancement of autonomous driving technologies by providing a robust framework for analyzing cooperative driving scenarios, supporting AV and human-driven systems ahead of the fully autonomous traffic systems of the future.

본 연구는 대학의 입장에서 갈수록 중요해지는 지역사회와 연계한 비 교과 프로그램 사례를 구체적으로 제시하고 분석하여, 대학에서 참고할 수 있는 기초자료를 제공하는데 연구의 목적이 있다. 이를 위해 충남 소 재 K대학을 대상으로, 대학 소재 지역 인구의 중요한 축인 다문화시민 관련 문제 탐색 및 해소 방안 마련을 위한 지역문제 해결형 비교과 프로 그램을 기획, 운영하였다. 선발된 4명의 참여 학생과 담당 교수는 지역 사회 다문화 관련 지자체, 산업체, 시민 등과 논의를 위하여 현장으로 직 접 찾아가 총 7회의 논의과정을 거쳤다. 이를 통해 다문화가정 아동, 청 소년 대상 멘토링의 필요성을 확인하였고, 기존 멘토링 프로그램의 한계 를 파악하였다. 이후 개선방안을 도출하여, 지역사회 다문화 관련 외부 전문가들의 자문을 2회 받아, 현장 적용 가능성을 높였다. 본 비교과 프 로그램의 만족도는 비슷한 시기에 운영된 다른 비교과 프로그램에 비해 높게 나타났다. 본 연구는 지역사회 연계 비교과 프로그램 연구가 부족 한 상황에서 사례를 제시한 의의가 있다.

In this study, the continuity of reading passages from high school mock College Scholastic Ability Test (CAST) English exams across grade levels was investigated using Coh-Metrix. A corpus consisting of 525 reading passages, evenly distributed with 175 passages from each high school grade level, was compiled from the 2017-2023 mock CSAT English exams administered by the Seoul Metropolitan Office of Education. Coh- Metrix measures included basic counts, word frequencies, word features, lexical diversity, personal pronouns, connectives, standard readability, syntactic complexity, coreference, and semantic cohesion indices. The analysis revealed significant differences among grade levels in the reading passages of the mock CSAT English exams in measures such as word counts, average word and sentence length, nouns, age of acquisition, second person pronouns, standard readability, and subject density indices. These findings highlight the potential for refining the design and construction of reading passages in mock CSAT exams to better prepare students for the linguistic challenges presented in the actual high-stakes CSAT.

PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

PURPOSES : This study defines private and public service providers connected to a public data hub in a smart city and examines the information that should be exchanged between them. The information exchange scheme covers data exchange at a minimum level. METHODS : First, we reviewed the entities participating in the emergency charging service and designed the scope of information linkages between the entities. Second, we diagnosed the main information linkages according to a service flowchart. Third, we reviewed the basic information requirements linked to actors participating in the service. Finally, we derived and presented information linked to the subjects. RESULTS : In addition to the basic requirements, the number of data-exchange information sets specified was four, which was the scope of the aforementioned study. We defined and analyzed an efficient information exchange system between various actors involved in emergency charging services. Data were defined based on interactions between service users, operators, providers, and data hubs. Each set had a different scope and purpose. CONCLUSIONS : Information collected and provided by emergency charging service providers in connection with a data hub that manages urban energy was proposed.

PURPOSES : This study aimed to develop a transportation-energy linkage model and performance evaluation indicators to improve the sustainability operation and technology of smart city transportation-energy services. METHODS : This study derived a new transportation-energy linkage system model for 15 services designated by the national pilot city. Evaluation indicators for energy-oriented transportation services in smart cities were selected, and a methodological framework was proposed for selecting quantitative evaluation indicators based on text mining and importance-performance analysis (IPA). RESULTS : Twenty indicators, confirmed as crucial for successful transportation-energy linkage in smart cities, were selected. These covered data linkage between services, IoT-based information linkage driving rate, and network and energy efficiency indicators. The proposed quantitative methodological framework can complement expert subjective evaluation by identifying meaningful implications in research literature that experts may have missed. The methodology can consistently derive indicators even when new services are added, aiding policymakers’ decisions. CONCLUSIONS : The methodological framework can contribute to minimizing operational risks in smart city transportation-energy expansion. It can also be used to prioritize service investment in smart cities by estimating benefit effects through quantitative indicators.

This study attempted to provide implications by analyzing the impact of business Owner’s safety commitment on industrial accidents and examining the mediating role of management supervisors’ safety leadership and worker participation. Analysis was conducted on 2,067 manufacturing sites with 20 to 50 employees in the 10th Occupational Safety and Health Survey data. SPSS waw used to secure the reliability of the measurement variable. Hypothesis vertification was carried out after securing the suitability and validity of the structural model using AMOS. The direct impact of three latent variables on industrial accidents was confirmed: the business owner’s safety commitment, the management supervisor’s safety leadership, and the worker participation. The employer’s safety will and the management supervisor’s safety leadership do not directly affect industial accidents, but it has been verified that worker participation has a diret impact on industrial accident reduction. In addition, it has been confirmed that the safety leadership and worker participation of the management. Supervior have a complete mediating effect on the reduction of industrial accidents by mediating with the safety leadership of the management supervior and the participation of the workers. This study analyzed the impact on industrial accidents by dividing the stakeholders constituting the workplace into three classes: business owners, superviors, and workers, but the results suggest that employers and all workers inside the workplace may be organically linked to achieving the goal of reducing industrial accidents. Therefore, in order to establish an autonomous safety management system for safety and health at workerplaces, efforts are needed to reduce industrial accidents in their respective location by forming an organic community among internal stakeholders.

In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.

This study used optical and scanning electron microscopy to analyze the surface oxidation phenomenon that accompanies a γ'-precipitate free zone in a directional solidified CM247LC high temperature creep specimen. Surface oxidation occurs on nickel-based superalloy gas turbine blades due to high temperature during use. Among the superalloy components, Al and Cr are greatly affected by diffusion and movement, and Al is a major component of the surface oxidation products. This out-diffusion of Al was accompanied by γ' (Ni3Al) deficiency in the matrix, and formed a γ'-precipitate free zone at the boundary of the surface oxide layer. Among the components of CM247LC, Cr and Al related to surface oxidation consist of 8 % and 5.6 %, respectively. When Al, the main component of the γ' precipitation phase, diffused out to the surface, a high content of Cr was observed in these PFZs. This is because the PFZ is made of a high Cr γ phase. Surface oxidation of DS CM247LC was observed in high temperature creep specimens, and γ'-rafting occurred due to stress applied to the creep specimens. However, the stress states applied to the grip and gauge length of the creep specimen were different, and accordingly, different γ'-rafting patterns were observed. Such surface oxidation and PFZ and γ'-rafting are shown to affect CM247LC creep lifetime. Mapping the microstructure and composition of major components such as Al and Cr and their role in surface oxidation, revealed in this study, will be utilized in the development of alloys to improve creep life.

Bioreactors are devices used by sewage treatment plants to process sewage and which produce active sludge, and sediments separated by solid-liquid are treated in anaerobic digestion tanks. In anaerobic digestion tanks, the volume of active sludge deposits is reduced and biogas is produced. After dehydrating the digestive sludge generated after anaerobic digestion, anaerobic digested wastewater, which features a high concentration of organic matters, is generated. In this study, the decomposition of organic carbon and nitrogen was studied by advanced oxidation process. Ozone-microbubble flotation process was used for oxidation pretreatment. During ozonation, the TOC decreased by 11.6%. After ozone treatment, the TOC decreased and the removal rate reached 80.4% as a result of the Ultra Violet-Advanced Oxidation Process (UV-AOP). The results with regard to organic substances before and after treatment differed depending on the organic carbon index, such as CODMn, CODCr, and TOC. Those indexes did not change significantly in ozone treatment, but decreased significantly after the UV-AOP process as the linkage treatment, and were removed by up to 39.1%, 15.2%, and 80.4%, respectively. It was confirmed that biodegradability was improved according to the ratio of CODMn to TOC. As for the nitrogen component, the ammonia nitrogen component showed a level of 3.2×102 mg/L or more, and the content was maintained at 80% even after treatment. Since most of the contaminants are removed from the treated water and its transparency is high, this water can be utilized as a resource that contains high concentrations of nitrogen.