본 연구는 기술매체 시대 문화콘텐츠의 생산양식 발전에 기여하고 이 를 위한 인문학적 교육의 학문적 기반과 방향을 모색하는 데 있다. 이를 위해 기술매체 이론들의 흐름과 국내 문화콘텐츠학과 교과 과정 등을 살 펴보고 기술매체 시대 문화콘텐츠학의 학문적 지향을 제안하였다. 연구 결과는 다음과 같다. 첫째, 시몽동의 기술미학적 사유는 기술적 본질에 기반하는 새로운 미학의 정립이라고 볼 수 있다. 둘째, 2024년 현재 국 내 54개 대학의 문화콘텐츠학과는 독립학과, 단일 전공, 기존 학과의 명 칭 변경, 연계전공의 형태를 띠면서 예술, 비즈니스, 광고, 공학 그리고 인문학 기반으로 운영되고 있었다. 셋째, 이를 토대로 기술매체 시대 문 화콘텐츠학의 방향성 정립을 위한 제언을 하였다. 먼저, 인문학 본연으로 서 문화콘텐츠의 위상 정립이 필요하다. 다음으로 문화콘텐츠학의 절합 영역 확장이다. 무엇보다도 새로운 포지셔닝으로서 ‘기술매체시대의 문화 콘텐츠학’의 정립이 필요하다. 마지막으로 문화콘텐츠학 전문 교육기관에 서 ‘엔지니어’에 대한 학문적 접근을 담아야 한다. 요컨대, 문화콘텐츠학 은 기술매체 시대에서 기존의 학문 영역과의 차별성을 바탕으로 학문적 체계에 대한 사례연구가 진행될 수 있기를 기대한다.

기후변화로 인해 폭염 기간 증가, 하절기 극고온, 혹한기 극저온 현상이 두드러지면서 도로포장에 소성변형과 포트홀이 빈번하게 발생할 위험이 커지고 있다. 이로 인해 SMA(Stone Mastic Asphalt) 포장의 공용성능을 유지하기가 어렵다. SMA 포장은 골재 간 맞물림이 뛰어나 중차량 교통량이 많은 도로에서 내구성을 높이는 데 유리하지만, 이를 효과적으 로 활용하기 위해서는 혼합물 배합설계와 시공과정에서 다짐 품질을 엄격히 관리해야 한다. 국내 지침에서는 점도가 높은 개질 아스팔트 바인더(6% 이상)를 사용하는 SMA 혼합물이 다짐 시 타이어에 달라붙을 가능성이 커 타이어 롤러의 사용을 제한하고 있다. 그러나 적절한 부착방지제 사용, 타이어 예열, 시공 온도 확보 등을 통해 혼합물 부착 문제가 완화되고, 다짐도와 초기 공용성능을 높인 사례가 점차 보고되고 있다. 이는 타이어 롤러 특유 의 ‘반죽(kneading) 효과’로 인해 기존 철륜(머캐덤·탄뎀) 롤러 대비 하부층까지 균질하게 다져줄 수 있기 때문이다. 따라 서 이상기후 환경에서 SMA 포장의 균열·소성변형을 줄이기 위해서는 다짐도 증가에 따른 적절한 아스팔트 바인더 함량 결정이 필요하다. 더불어 시공 장비 및 혼합물 관리가 유기적으로 개선된다면 SMA 포장의 특성을 살린 적정능력이 발 휘될 수 있을 것이다. 본 연구에서는 SMA 포장 적정능력을 발휘하기 위한 기초연구를 수행하였다. 이를 위해 SMA 포장 시공 시 타이어 롤 러 장비 적용 효과, 혼합물 부착 방지 기술, 아스팔트 바인더 함량 조정 등을 국내·외 시공 사례와 문헌조사를 통해 고찰 하였다. 또한 타이어 롤러의 현장 적용성을 파악하기 위해 시험포장 구간에 대해 소형낙하하중시험(LFWD)을 실시하고, 표면처짐량과 역산 탄성계수를 산출하여 시공 품질 개선 가능성을 확인하였다.

자전거 이용 증가는 도시 환경과 생활 환경에 긍정적인 영향을 미친다. 도시화, 인구 밀도 증가, 그리고 대기 오염과 같 은 문제들에 대응하기 위해 전 세계 도시들은 교통 패러다임을 자동차 중심에서 대중교통, 자전거, 보행 중심으로 전환 하고자 노력하고 있다. 본 연구는 이러한 변화가 개인의 활동적인 생활 방식을 촉진하고, 교통 혼잡 및 소음 문제를 완 화시켜 삶의 질을 향상시킬 수 있음에 주목한다. 특히 자전거는 단거리에서 중장거리 이동까지 모두 적용 가능한 이동 수단으로서, 필요한 주행 및 주차 공간이 적고, Door to Door 통행이 가능하여 매우 효율적인 교통수단이다. 또한 자전 거 이용자들은 승용차 운전자보다 더 자주 지역 내에서 소비하며, 한 번에 지출하는 금액은 적지만 더 자주 지출함으로 써 총 소비량은 승용차 운전자보다 더 크다. 이러한 이유로 자전거 이용은 지역 경제 활성화에도 기여할 수 있다. 본 연 구는 이 부분에 착안하여 자전거 이용을 활성화시키면서 지역 경제 활성화에 더 크게 기여할 수 있는 방안을 검토하고 자 한다. 본 연구는 국내외 여러 도시에서 운영 중인 공공 자전거 대여 서비스의 현황을 조사하여 자전거 이용 증진을 위한 기 반을 분석하였다. 국내에서는 서울의 따릉이, 대전의 타슈, 광주의 타랑께 등 다양한 지자체가 자전거 활성화를 위해 노 력하고 있음에도 불구하고, 자전거 이용자의 특성에 따라 기존의 자전거 기반 시설을 효율적으로 활용하여 자전거 이용 을 더욱 증진시킬 수 있는 방안을 탐구한 연구는 아직 미흡한 실정이다. 이에 본 연구의 저자는 서울시를 중심으로 자전 거 이용자의 소비패턴을 분석하고, 이를 활용하여 자전거 이용자를 활성화시키고, 지역 상권의 활성화에도 기여할 수 있 는 방안을 모색하고자 한다. 이를 위해 본 연구에서는 기존 연구의 분석 방법론을 검토하고, 분석에 필요한 데이터를 조 사하였으며, 향후 연구에서는 실질적인 자전거 활성화 방안을 제시할 예정이다.

중앙버스전용차로는 일반 도로 대비 높은 교통량과 반복적인 축하중이 작용하는 구간으로, 정차 및 출발 과정에서 발생 하는 국부적인 응력 집중으로 인해 포장 파손이 빈번하게 발생한다. 그러나 기존 도로 설계에서는 정적인 교통량을 기준 으로 축하중을 산정하여, 실제 교통 환경에서의 버스 유형별 차이, 재차 인원, 시간대별 하중 변화 등 동적인 요소를 충 분히 반영하지 못하는 한계가 존재한다. 이에 본 연구에서는 대중교통 빅데이터를 활용하여 중앙버스전용차로의 버스 유 형 및 시간대별 재차 인원을 반영한 새로운 축하중 산정 모델을 개발하였다. 이를 위해 서울시 열린 데이터 광장의 교통 정보를 활용하여 버스 유형 및 시간대별 재차 인원 데이터를 수집하고, 카카오맵 및 네이버 로드뷰 데이터를 이용해 결 측치를 보완하여 데이터셋을 구축하였다. 구축된 데이터셋을 활용하여 기존 ESAL(Equivalent Single Axle Load) 방식과 비교 분석한 결과, 새로운 축하중 모델에서는 기존 방식 대비 평균 111.8% 높은 축하중이 산정되었으며, 일부 구간에서 는 최대 128.9%까지 차이가 발생하는 것으로 나타났다. 이는 기존 포장 설계가 중앙버스전용차로의 실질적인 교통 하중 을 충분히 반영하지 못하고 있음을 시사하며, 추가적으로 버스 중하중의 가·감속의 영향을 고려한다면, 시간대별·노선별 실시간 축하중 변화를 보다 정밀하게 분석할 수 있으며, 이를 통해 과소 산정된 설계 하중을 보완하고 포장 공용성을 향 상시킬 수 있는 최적의 설계 및 유지보수 전략 수립이 가능할 것으로 기대된다.

Digital restoration of non-verbal expressions is difficult to trust unless the documentation. The purpose of this study is a new documentation methodology that can intuitively confirm the basis for restoration. The technical method utilized the BIM program function by referring to Italia's VRIM and Korea's HBIM cases. And the direction of documentation distinguishes between 'positivism' based on archaeological data and 'interpretivism' based on hypotheses. Specifically, it was applied to the 'Mireuksa Restoration Project' and tried to document it experimentally. This documentation proposed a framework for recording evidence according to sources based on the context of regions. Technically, the data organized in the Excel DB were directly input into the 3D model using the BIM program function. So, the user was able to intuitively review by matching the absence of the model and document information. The documenting method of this study is flexible to modify the restoration information whenever new evidence is found. And it has the advantage of being able to easily inform by converting it to IFC format.

The launcher of a hard-kill type APS (Active Protection System) requires rapid and precise driving to aim at incoming threats after detection. High angular acceleration is necessary for rapid driving, which demands high energy consumption. However, the capacity of the capacitor bank and power supply unit is limited due to weight and space constraints. If energy becomes insufficient during continuous operation, the voltage of the capacitor bank can drop below the minimum operating voltage of the drive motor, leading to problems such as torque deficiency. Therefore, it is necessary to determine an allowable angular acceleration that satisfies precision within the available energy and generate a driving profile accordingly. This paper proposes a method for deriving an allowable angular acceleration by analyzing the allowable energy and validates it through simulation. We examined the allowable energy by verifying the charged voltage of the capacitor bank, formulated equations for energy at the point of maximum consumption, and derived an equation for allowable angular acceleration through numerical analysis. By applying the proposed algorithm in simulations, we confirmed that the voltage of the capacitor bank did not drop below the minimum operating voltage of the driving motor during three consecutive operations. Therefore, it is expected that the stability of the APS launcher can be improved by applying the proposed algorithm, and continuous operation with limited performance is anticipated to be possible.

Automobiles are an essential means of transporting passengers and cargo, but traffic accidents are inevitable in their operation. These accidents can occur in various forms, such as front, rear, and side collisions. The resulting damage to the vehicle can also be seen similarly; it is inherently distinct: the complexity of repairing the car body makes a simple reliance on textbook knowledge insufficient. Successful correction of the damaged body largely depends on the experience of the practitioner. Discussions on body repair techniques should be based on empirical data reflecting current industry standards and associated costs. The variability of individual repair methodologies can result in significant time and financial expenditure in the field of automotive bodies. Application of new material technologies to vehicle fabrication requires continuous training and empirical research, especially on the body repair process involving new materials. In particular, since the left and right aprons and side members are made of different materials, such as aluminum and high-strength steel, careful restoration of these parts is required. Technical considerations are needed. Interest in safety and environmental impacts. In this study, SPR bonding technology analyzes experimental results.

본 연구의 목적은 그동안 한국 불교가 다문화 사회에서 이주민을 수용 하는 데에 어떠한 역할과 방향성을 제시하였는지에 대해서 연구적 성과 를 통해서 살펴보는 것이다. 이를 위하여 RISS에서 ‘불교’와 ‘다문화’를 키워드로 검색하여 국내 학술지에 등재된 연구 논문의 국문초록을 수집 하고 명사를 추출한 후 텍스트 마이닝 분석 방법 중 키워드 분석 방법과 언어 네트워크 분석을 실시하였다. 분석 결과, 불교와 다문화 관련 연구 들은 종교적 관점보다는 전통 한국 문화의 일부로서 불교적 요소를 기반 으로 하여, 이주민의 한국사회 통합을 모색하고자 하는 경향을 보였다. 특히 한국으로 유입한 이주민을 불교의 상생과 화합으로 포용하고자 하 는 것이 한국적 다문화를 실현하는 것으로 보았다. 이러한 연구적 성과 에도 불구하고, 한국 불교가 종교적 경계를 넘어서서, 한국사회문화 측면 에서 불교의 상생과 화합이 어떻게 구체화하여 실천할 것인지에 대한 논 의는 여전히 부족한 실정이었다. 따라서 향후 한국적 다문화를 실현하기 위한 학문적 논의가 구체적으로 진행될 필요가 있다.

Following the implementation of the Act on the Prevention of Light Pollution Due to Artificial Lighting in 2013, local governments designated lighting environment management zones and conducted assessments of the impacts of light pollution on the environment to ensure compliance with acceptable light emission standards. In addition, according to the Act on the Prevention of Light Pollution Due to Artificial Lighting, local governments conduct and manage light pollution assessments every three years. However, measuring and analyzing during nighttime requires a significant amount of time and labor. Therefore, this research aims to improve the current light pollution environmental impact assessment method by utilizing aerial information from satellite data and establishing a database of light pollution assessment methods, thereby laying the foundation for light pollution management. In this study, a reference light source was installed on the ground, and the luminance measurements of the installed reference light source and the advertising light sources on-site were analyzed to derive brightness values for ground light sources using the optical band (R, G, B) values from aerial information derived from satellite images. The analysis produced predictive equations for light pollution from upward lighting and general advertising lighting. When these equations were applied to residential and commercial areas in the lighting environment management area, the results indicated that the predicted rooftop upward lighting prediction brightness exceeded the acceptable standard of light emission of 800 cd/m2 in residential areas, and the advertisement lighting prediction brightness exceeded the standard of 1,000 cd/m2 in commercial areas.

The focus of this study was on the preparation of a clinoptilolite-based adsorbent, utilizing natural zeolite, to adsorb and remove ammonia (NH3) emitted from various environmental facilities, and to evaluate its performance. To create an adsorbent suitable for humid environments, hydrophobicity was introduced through HCl acid treatment. The impact of acid concentration and treatment time was analyzed to optimize the preparation conditions. As a result, the adsorbent treated with 0.5 M HCl for 2 hours demonstrated the highest NH3 adsorption performance. These findings suggest that the developed adsorbent could serve as an effective solution for controlling NH3 emissions in humid environments, contributing to the mitigation of environmental pollution and odor issues.

A multi-criteria decision-making(MCDM) method allows the decision makers to systematically evaluate the alternatives based on a predefined set of decision criteria. The most commonly used MCDM methods include Analytic Hierarchy Process(AHP), Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS), Weighed Aggregated Sum Product Assessment(WASPAS), Preference Selection Index(PSI), etc. In MCDM Problems, it is common that performance ratings for different criteria are measured by different units. Normalization is thus used to convert performance ratings into commensurable data. There are many normalization techniques that can be used for MCDM problems. Much effort has been made for comparative studies on the suitability of normalization techniques used in MCDM methods. However, most studies present normalization methods suitable for specific MCDM problems based on specific data samples. Therefore, this study proposes the most suitable normalization method for each MCDM method under consideration using extensive data samples. A wide range of MCDM problems with various measurement scales are generated by simulation for comparative study. The experimental results show that vector normalization method is best suited for all MCDM methods considered in this study.

This study aimed to develop a comprehensive validation methodology for an Infra-guidance system, which is an infrastructure-based service aimed at enhancing the safety of autonomous driving. The proposed method includes quantitative techniques for validating both the Infra-guidance algorithm module and the guidance message module using each optimal indicator. In addition, a promising method is suggested to validate the entire system by applying a multicriteria decision methodology. The relative weight for the algorithm module was higher than relative weight for the message module. Moreover, the relative weight of the latency for the message module was slightly higher than weight of the packet error rate. The proposed methodology is applicable for validating the performance of infrastructure-based services for enhancing connected autonomous driving based on the comprehensive quantification of various factors and indicators.

The purpose of this study was to identify and evaluate hazardous road sections based on roadside friction. Using GIS mapping and clustering techniques, this study analyzed traffic accidents and roadside friction data based on latitude and longitude coordinates. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied, with parameters of MinPts = 5 and eps = 0.0001, determined through a K-nearest neighbor analysis. The data were separated based on traffic flow direction (uphill/ downhill), and clustering was performed separately in each direction to identify specific hazard zones. The DBSCAN clustering results revealed 18 clusters in traffic accident data and 44 clusters in roadside friction data. Traffic accident clusters include various types of accidents (e.g., vehicle-to-vehicle and vehicle-to-pedestrian accidents), identifying locations as high-accident zones. The clustering results from the roadside friction data highlighted areas with crosswalks, absence of curbs, and roadside parking zones as major risk sections. Future research should analyze the operational design domain (ODD) of autonomous vehicles on hazardous road sections and explore the integration of multiple data sources to establish a comprehensive safety management system for accident prevention in autonomous driving environments. Additionally, road hazard sections are categorized into stages (e.g., hazardous, cautious, and safe) to enhance the precision in assessing road conditions. This categorization, combined with a detailed analysis of ODD, serves as a foundation for future research aimed at improving the safety of autonomous driving environments.

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.

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.

Purpose: This mixed methods study aimed to investigate the anxiety and nursing satisfaction levels and experiences among users of a general health checkup center. Methods: A total of 152 participants completed a questionnaire to assess their pre-checkup anxiety and post-checkup nursing satisfaction levels. Additionally, 11 participants were individually interviewed to determine their pre-checkup anxiety and post-checkup nursing satisfaction experiences. Survey data were analyzed using SPSS software, while qualitative data were analyzed using content analysis. Results: The mean anxiety scores were 2.80±2.24 on the Visual Analog Scale for Anxiety and 44.06±9.55 on the State-Trait Anxiety Inventory (STAI) scale. Education level was a significant factor influencing the STAI scores. Participants with a college education or higher had significantly lower STAI scores(p<.005), indicating the association between higher education levels and lower STAI scores. The mean nursing satisfaction score was 36.49±8.84, with male participants reporting higher nursing satisfaction levels. The pre-health checkup anxiety experience included three themes: “contrasting expectations about checkup results,” “various emotions felt during the checkup process,” and “physical and mental reactions.” The health checkup nursing satisfaction experience included four themes: “satisfaction with nurses’ support and care,” “comfort during the checkup process,” “dissatisfaction due to nurses’ habitual responses,” and “expectations for nurses’ emotional support and communication.” Conclusion: Providing comprehensive nursing information is essential to reduce user anxiety and improve nursing satisfaction. Moreover, integrating advanced technologies, such as virtual reality, augmented reality, and metaversing, into information delivery can enhance educational effectiveness and better address the experiences and needs of checkup users.