Engineered Barrier Systems (EBS) are a key element of deep geological repositories (DGR) and play an important role in safely isolating radioactive materials from the ecosystem. In the environment of a DGR, gases can be generated due to several factors, including canister corrosion. If the gas production rate exceeds the diffusion rate, pore pressures may increase, potentially inducing structural deterioration that impairs the function of the buffer material. Therefore, understanding the hydraulic-mechanical behavior of EBS due to gas generation is essential for evaluating the longterm stability of DGR. This study employed X-ray computed tomography (CT) technology to observe cracks created inside the buffer material after laboratory-scale gas injection experiments. After CT scanning, we identified cracks more clearly using an image analysis method based on machine learning techniques, enabling us to examine internal crack patterns caused by gas injection. In the samples observed in this study, no cracks were observed penetrating the entire buffer block, and it was confirmed that most cracks were created through the radial surface of the block. This is similar to the results observed in the LASGIT field experiment in which the paths of the gas migration were observed through the interface between the container and the buffer material. This study confirmed the applicability of high-resolution X-ray CT imaging and image analysis techniques for qualitative analysis of internal crack patterns and cracks generated by gas breakthrough phenomena. This is expected to be used as basic data and crack analysis techniques in future research to understand gas migration in the buffer material.

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the ‘Cheongmyeong Gaual’ variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37′ N 128°32′ E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

This study examines and proposes standardization approaches to address the heterogeneous issues of metadata in drone imagery within the agricultural sector. Image metadata comes in various formats depending on different camera manufacturers, with most utilizing EXIF and XMP. The metadata of cameras used in fixed-wing and rotary-wing platforms, along with the metadata requirements in image alignment software, were analyzed for sensors like DJI XT2, MicaSense RedEdge-M, and Sentera Double4K. In the agricultural domain, multispectral imagery is crucial for vegetation analysis, making the provision of such imagery essential. Based on Pix4D SW, a comparative analysis of metadata attributes was performed, and necessary elements were compiled and presented as a proposed standardization (draft) in the form of tag information.

Image mosaicking is one of the basic and important technologies in the field of application using images. The key of image mosaicking is to extract seamlines from a joint image. The method proposed in this paper for image mosaicking is as follows. The feature points of the images to be joined are extracted and the joining form between the two images is identified. A reference position for detection the seamlines were selected according to the joint form, and an image pyramid was created for efficient image processing. The outlines of the image including buildings and roads are extracted from the overlapping area with low resolution, and the seamlines are determined by considering the components of the outlines. Based on this, the seamlines in the high-resolution image was re-searched and finally the seamline for image mosaicking was determined. In addition, in order to minimize color distortion of the image with the determined seamline, a method of improving the quality of the mosaic image by fine correction of the mosaic area was applied. It was confirmed that the quality of the seamline extraction results applying the method proposed was reasonable.

본 연구는 썹 시술이 여성의 이미지 변화에 어떤 영향을 미치는지 그리고 속눈썹 시술 이 여성의 뷰티 행동에 어떤 변화를 가져오는지 살펴보고자 하였다 또한 여성의 속눈썹 시술과 뷰티행동에 대한 실태조사를 통해 속눈썹 시술이 여성의 이미지변화에 미치는 영향 과 뷰티행동에 미치는 영향을 분석하고 그 결과를 바탕으로 여성의 뷰티행동에 대한 시사 점을 제시함으로써 여성의 뷰티행동에 대한 이해를 높이고 여성의 뷰티행동을 개선하는 데 기여하고 속눈썹 시술의 시장성 및 발전 방향을 제시를 연구의 목적으로 하였다 본 연 구를 위하여 서울지역 속눈썹 시술 전문 샵 방문자를 대상으로 설문조사를 통해 자료를 수 집하여 대상자의 일반적 특성과 속눈썹 시술 이용행태 시술 관련 인식 현황을 살펴보기 위해 빈도분석 을 실시하였다 또한 속눈썹 시술경험이 있는 조사대상 자를 대상으로 조사대상자의 특성과 속눈썹 시술 이용행태에 따른 시술 비용 수준 인식 차 이 만족도 차이 시술 후 미적 주관적 자신감 증대 차이 지속적 속눈썹 시술 의향 차이를 살펴보기 위해 카이제곱 검정을 실시하였다 본 연구결과 속눈썹 시술이 여성 의 외모와 심리적 상태에 긍정적인 영향을 미칠 수 있고 여성의 외모를 변화시켜 자신감과 자존감을 높이는 데 도움이 될 수 있고 이는 속눈썹 시술이 여성의 외모와 심리적 상 태에 긍정적인 영향을 미칠 수 있음을 시사하는 것으로 나타났다

The research aims to estimate the role of SMAs implementation in impacting event image, commemorative product perceived value, and TBIs in the context of sports tourism events. Additionally, this study analyzes the effects of perceived value and event image on TBIs. This article focuses on the following research questions: (1) What is the SMAs' influence on TBIs? (2) What is the impact of SMAs on event image and commemorative product perceived value? (3) What is the role of event image and product perceived value in the relationships between SMAs and TBIs in the sports tourism context? The data collection was done during the Beijing Winter Olympic Games to address the research goals. The study examines the data from 315 valid questionnaires from tourists in the Chinese market by SEM (structural equation modeling).

Metaverse is a virtual world where users can engage in various activities, and its market is expected to grow rapidly in the coming years (Hwang, 2021). Generation Z, also known as digital natives, are heavily involved in social media and often share selfies, making them more sensitive to body image concerns (Ameen, 2022). In the metaverse, users can create customized avatars, allowing them to experience an ideal body image. This study applies the “extraordinary self” theory to avatar creation, a concept that explains how people create an ideal, fantastic, or transcendent self by consuming experiences that set them apart from their mundane real self (Procter, 2021). Based on the theory, this study examines how dissatisfaction with body image of Generation Z can lead to the creation of avatars that manifest an ideal body image, which in turn, can influence self-congruence with the avatar and self-esteem in the metaverse. This study also looks into the possible effects of enhanced self-esteem on user’s intention to purchase virtual products and their loyalty to a specific metaverse platform. Therefore, the following hypotheses are developed.

The main objective of this research is to investigate the impact of brand image fit on consumer’s perceived luxury, perceived symbolic value, perceived authenticity, and perceived novelty towards fashion brand collaboration. This study uses a qualitative approach to test the research hypotheses. The contribution of this study is twofold: (1) it conceptually advances Congruity Theory and Spillover effect into the area of brand collaboration within a fashion domain; (2) it provides insights into the efficacy of each consumer-related factors which can be used in the development of sound collaboration strategies, in addition to branding and pricing strategies of new fashion brand collaborations.

This study examines how brand-influencer, influencer-users, and user-brand congruences affect perceived fantasy about the influencer and closeness toward the brand when an Instagram ad is endorsed by celebrity (vs. non-celebrity) influencers. The study employed a between-subject online experiment by manipulating an advertisement endorsed by a celebrity and a non-celebrity influencer. The results suggest that congruence between brand-influencer matters in building effective relationships.

Fast-paced advancements in technology demand swift adaptation and presents new opportunities and challenges for the optimization of communication, especially for advertisers. Digitalization and new developments in ICT have brought significant changes to the ways in which information, especially promotional messages, is disseminated to consumers. Additionally, with explosive interests in anticipation of fully autonomous vehicles, this study identifies and addresses the potential to optimize communication in an under examined digital media environment – in-vehicle infotainment system. Therefore, this study proposes a text-image embedding method recommender system for the personalization of multimedia contents and advertisements for in-vehicle infotainment systems. Unlike most previous research, which focuses on textual-only or image-only analyses, the current study explores the understanding, development and application of text embedding models and image feature extraction methods simultaneously in the context of target advertisement research. Overall, this study highlights the need to adapt to the ever-evolving technological landscape to optimize communication in various digital media environments. With the proposed text-image embedding method, this study offers a unique approach to personalizing multimedia content and advertisements in the under-explored digital media environment of in-vehicle infotainment systems.

안면 부 MRI 검사는 주변 조직에 대한 높은 대조도 와 해상력으로 해부학적 구조 파악과 질환 진단에 이용되고 있다. 최근 검사 시간을 줄이는 동시에 영상의 질을 향상하는 딥러닝이 주목받고 있다. 본 연구는 안면 부 MRI 검사에서 딥러닝 의 유용성을 알아보기 위해 34명의 환자를 대상으로 딥러닝 T2 강조 영상과 고식적인 T2 강조 영상의 축상면, 관상면 영상을 각각 획득하여, 무참조 영상 품질평가 기법인 NIQE와 NIMA를 통하여 정량적 평가하였고, 리커트 4점 척도를 통해 정성적 평가하였다. NIQE 결과에서 딥러닝 T2 강조 영상은 고식적인 T2 강조 영상보다 영상 품질이 우수하였고, NIMA 결과에서는 딥러닝 T2 강조 영상의 축상면은 통계적으로 유의한 차이가 없었고, 딥러닝 T2 강조 영상의 관상면에서는 통계 적으로 유의한 차이가 있었다. 정성적 평가지표에서는 입 인두, 후두 인두에서 질적인 이득이 있었다. 연구 결과를 통해 안면 부 영역 중 무의식적인 움직임이 많은 영역에서 딥러닝을 적용함으로써 고식적인 T2 강조 영상보다 높은 영상의 품질 을 제공하고, 상대적으로 움직임이 덜한 구조물에서도 품질을 유지하며 검사 시간을 2분 이상 단축하여 움직임에 의한 인공 물을 감소시킴으로써 응급 환자 및 비협조 환자의 진단에 유용하게 활용될 것으로 사료 된다.

근래 산업은 기계 자동화로 변화하고 있는 추세이며, 선박도 센서를 통해 기기 정보를 디지털 정보로 얻는다. 하지만 선박은 기기상태 점검을 위해 선원들이 정해진 시간마다 기관실을 순찰하며 기기들의 정보를 아날로그 게이지를 통해 확인하는데, 이는 순찰 중 에 선원에게 발생할 수 있는 모든 안전 위험은 물론 시간과 기회비용 또한 소모된다. 자율이동로봇을 이용한 기관실 순찰 방법은 선원의 안전 위험은 물론 시간과 기회비용도 소모되지 않기 때문에 해결책으로 활발히 연구 중이다. 자율이동로봇을 이용한 아날로그 게이지 판 독은 로봇이 게이지를 인식하기 위한 디지털화가 필요하다. 이를 위해 본 연구에서는 이미지 처리를 이용하였다. 아날로그 게이지 이미지 는 이미지 전처리를 통해 노이즈 제거 및 특징을 부각 시켰다. 이미지 전처리를 완료한 이미지는 이미지 처리를 통해 아날로그 게이지의 중심점, 지침점, 최소값 및 최대값을 검출하였다. 이 점들을 연결한 직선을 통해 최소값부터 최대값까지의 각도 및 최소값부터 지침점까 지의 각도를 획득하였다. 각도는 수식을 통해 현재 아날로그 게이지가 나타내고 있는 값을 디지털화하여 나타내었다. 실험을 통해 이미지 처리를 통한 아날로그 게이지의 디지털화가 잘되어 게이지의 현재 지시값을 근사하게 나타냄을 확인할 수 있었다. 본 알고리즘을 순찰로 봇에 적용한다면 기관실 순찰을 위한 선원의 안전 위험 및 시간과 기회비용까지 보전 할 수 있을 것으로 사료된다.

구조물의 안정성 및 이상 징후 판단에 있어 균열의 발생 여부와 진전 정도는 가장 기본적인 조사 항목이다. 본 연구 에서는 일반적인 스마트폰을 통해 균열 이미지를 촬영하고 이로 부터 균열 검출과 크기를 산정하는 균열 분석 시스템을 개발하 였다. 모폴로지 기법을 적용하되 투영변환 및 3차회선 보간, 히스토그램 기반의 명도 입계값 산정 기법을 적용함으로서 이미지 보정과 노이즈 제거 과정을 통한 효과적 균열 검출이 가능하였다. ArUco 마커를 통해 손쉽고 경제적인 균열 크기 산정이 가능 하였으며, 스마트폰 앱과 클라우드 서버 기반의 이원화 분석시스템을 통해 손쉬운 현장 적용성 및 처리 시간 단축, 세부 균열의 추적관리 가능성을 확인하였다. 개발 시스템을 이용한 실내 성능 평가를 수행한 결과, 균열 측정 오차는 0.03㎜ 미만으로 나타 났으며 조건별 다수 측정 결과에서 높은 재현성이 확인되었던바, 개발된 균열 분석 시스템의 정확성 및 현장 적용성을 예상할 수 있었다.