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.

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.

본 연구에서는 주민 체감형 수요자 중심의 녹지를 체감녹지(Public perception green spaces)로 정의하고 보다 실질적 으로 서비스권역 내 인구와 거주지에서 녹지까지의 접근성을 분석하였다. 이를 위하여 체감녹지를 산림녹지, 공원녹지, 시설녹지로 분류·선정하고, 통계 데이터의 최소기준인 집계구 단위를 조사구로 하여 QGIS를 사용하여 네트워크 분석 (Network Analysis)을 하였다. QGIS QNEAT3 알고리즘을 이용하여 체감녹지와 집계구 간 서비스권역 및 접근성 분석을 부산광역시 16개 구·군에 적용한 결과, 체감녹지로부터 도보 10분 내 서비스권역 인구비는 북구, 수영구, 기장군, 동래구 순서로 높은 비율을 나타냈고, 접근성 분석에서도 유사한 결과가 도출되었다. 본 연구는 기존의 녹지불평등 평가 방법을 보완하여 주민 체감형 수요자 중심의 녹지 현황을 파악하고, 향후 보다 적절한 곳에 공원과 녹지를 배치하 여 도시민의 녹지 이용 만족도를 높여줄 것으로 기대한다.

이 연구의 목적은 국내 대학의 유학생을 위한 한국어 관련 교과목의 강의 평가에서 평가 방법이 중국인 유학생의 평가 태도에 미치는 영향을 조사함으로써 평가의 문제점을 파악하고 이를 바탕으로 중국인 유학생 대상의 강의 평가 개선 방안을 제공하는 것이다. 이를 위해 현재 국내 대학에서 유학 중인 중국인 유학생을 대상으로 지면 설문조사를 실시한 후 그중 일부를 무작위 선정하여 면대면 개별 인터뷰를 진행하였다. 연 구 결과, 조사 방법에 따라 학습자의 강의평가 태도에 차이가 상당함을 확인하였다. 설문조사보다 인터뷰 방식으로 수집된 응답이 더 다양하고 구체적이며 응답 적극성도 높은 편이다. 향후 타당도 높은 강의 평가를 실시하기 위해서 학습자의 강의 평가에 대한 인식 개선, 강의 평가 능력 함양, 강의 평가의 실시 방식 다양화 등을 고려해야 할 것이다.

PURPOSES : This study was conducted to prevent slip accidents on manhole covers located on sidewalks and local roads as well as to propose reasonable slip resistance management standards for manhole covers. METHODS : Using field surveys, test groups were classified based on the patterns and wear amounts of the manhole covers. Standards for measuring the equipment and methods for slip resistance were established, and the slip resistance values were compared and analyzed for each manhole cover test group. RESULTS : According to the slip resistance test results, micro-protrusions on the non-slip manhole covers were found to be effective in improving slip resistance. However, in areas without microprotrusions, the improvement in slip resistance was minimal and yielded results similar to those of standard manhole covers. In addition, among the pattern types of standard manhole covers, the radial pattern was found to be the most susceptible to slipping. Under the current wear measurement standards, the change in slip resistance at different wear stages was found to be relatively small. Moreover, manhole covers had the lowest slip resistance among road surface structures, indicating the need to establish management standards for them. CONCLUSIONS : To prevent pedestrian slip accidents on sidewalks and local roads, it is necessary to ensure that the slip resistance standards of manhole covers are higher than those of sidewalks.

일반차량과 자율주행차량이 혼재하는 상황에서 발생가능한 미래 재난상황에 대한 관리방안 준비가 필요하다. 특히 재난 상황 중 안 개 발생 시 시야 확보가 어려운 일반차량 운전자와 센서기반 자율주행차량의 주행 특성이 다를 수 있다. 해당 상황에서의 문제점을 도출하고 이를 극복하기 위해 혼합교통류 관리 방안을 제안하고자 한다. 본 연구에서는 다양한 재난 상황 중 안개를 연구 대상으로 설정하였다. 과거 기상 상황별 일반차량을 주행 특성을 이력자료로 분석한 후, 안전한 교통흐름을 유지하기 위하여 자율주행차에게 정 보를 제공하는 방안을 제안한다.

본 연구에서는 비산먼지 농도를 평가하기 위한 영향 요인인 먼지부하량(Silt loading, sL)에 대한 연구로 노면에 쌓여있는 먼지 수집 시 효율적인 방법을 제시하기 위해 실험적 데이터 수집과 시각화를 통해 위치별 특성에 따른 먼지 분포량과 효율적인 먼지 수집 위치 를 분석하고자 하였다. 기존의 미국 EPA(Environmental Protection Agency)에서는 도로 전구간을 샘플링하기에 어려움이 있어 구간별 교 차로 길이(2.4km)를 기준으로 샘플링 위치를 제시하거나 1km 이하 구간에서는 2개를 샘플링하도록 제시하고 있다. 하지만 국내 실정 에 적용하기에는 교차로 사이 간격이 너무 넓거나, 샘플링 개수가 적은 등 한계점을 가지고 있다. 이에 본 연구에서는 청소기의 길이 0.3m에 따라 3m(0.3m X 10회) 샘플링 기법을 통해 25m와 100m 구간을 대표할 수 있는 위치를 제시해주는 것을 목표로 하고 있으며, 이때 시료를 채취하여 통계분석과 클러스터링 분석을 통해 샘플링 위치를 선정하고자 하였다. 또한 샘플링 위치에 따른 검증을 위해 서 도로 먼지 부하량과 비산먼지와의 상관관계를 정량적으로 평가하였다. 이때 먼저 sL의 양에 따른 비산먼지의 농도 측정은 도심부 제한속도에 따라 50km/h의 속도로 주행하는 조건에서 측정되었으며, 측정차량을 통해 수집된 GPS 좌표를 활용하여 도로 먼지 농도의 변화를 정량적으로 분석하였다. 분석 결과, 먼지 부하량(sL)이 농도가 높을수록 도로 먼지 농도가 증가하는 경향이 나타났으며, 이러한 상관관계는 먼지가 많을수록 공기중으로 비산되는 먼지의 양이 많은 것에 기인한 것으로 분석되었고 이때 측정한 전 구간에서 sL과 비산먼지 농도 간의 높은 상관 관계(상관계수 0.76)가 확인되었다. 추가적으로, 각 시료 채취 지점에서의 sL의 변화가 도로 먼지 농도에 미치는 영향을 평가하기 위해 K-평균 클러스터링 기법을 사용하였다. 클러스터링 결과, 최적의 샘플링 지점이 25m 구간 내에서는 3개, 100m 구간 안에서는 5개의 샘플링 위치로 대표값을 띄는 것으로 도출되었으며 비산먼지 농도의 변화와도 일치하는 것을 보였다. 이러한 방법을 통해 도로 먼지 샘플링의 신뢰성을 높일 수 있었으며, 도로 먼지의 특성을 보다 정확하게 분석할 수 있었고, 인력 수집에 따른 시간적, 공간적인 한계 를 해결할 수 있을 것으로 판단된다. 또한 이는 향후 비산먼지 측정 차량 제작 연구의 기초 자료로 활용될 수 있을 것이다.