This study explored how Korea's national image influences Malaysian consumers' attitudes toward Korean Home Meal Replacement (HMR) products and their purchase intentions, highlighting the mediating role of the Korean government's crisis response measures. Data were gathered through an online survey targeting Malaysian consumers aged 19 to 39 who had previously purchased Korean HMR. A total of 515 valid responses were analyzed, yielding a response rate of 90.48%. The analyses included exploratory factor analysis, correlation analysis, multiple regression analysis, and mediation analysis. The findings revealed that all three dimensions of national image—stability, democracy, and development—positively impacted perceptions of Korea's crisis response measures. Democracy had the strongest effect on policy-related perceptions, stability most significantly influenced management-related perceptions, and development was linked to administrative capability. Additionally, national image positively affected attitudes toward Korean HMR, with stability being the most influential factor. Positive perceptions of Korea's crisis response measures further enhanced product attitudes, indicating that trust established through government crisis management can influence product evaluations. Furthermore, positive attitudes toward Korean HMR were associated with higher purchase intentions. Mediation analysis confirmed that perceptions of Korea's crisis response measures partially mediated the relationship between national image and product attitudes.

본 논문은 아직 미술사학이나 관련 학문영역에서 충분히 논의되지 않은 조류충돌 사진을 시각문화 현상으로 간주한다. 그리고 이를 ’부재 의 이미지‘로 명명하면서, 다음 세 가지 연구를 수행하고자 한다. 첫 번째는 뱅시안 데스프레, 믹 스미스와 제이슨 영, 그리고 마리아 푸이그 드 라 벨라카사의 연구를 교차검토하면서 에토스로서의 돌봄 개념을 도출하는 것이다. 인간적인 윤리 너머의 에토스는 인간과 비인간 행위자 사이의 느슨한 관계, 온전히 소통할 수 없음, 그리고 무심함의 개념을 매개한다. 두 번째는 조르주 디디-위베르만이 주목한 네 장의 아우슈비 츠 캠프 학살 사진 분석을 중심으로, 그가 말하는 ‘찌르는 이미지’ 개념의 주요 논점을 검토하는 것이다. 이는 이미지를 ‘보기’의 문제로 재조 명하게끔 한다. 아울러 이미지를 지표, 표상, 또는 기호 너머의 열린 체제로 간주하면서 무엇을 어떻게 볼지에 대한 미학적이고 윤리적인 질문 을 촉발한다. 세 번째는 공적 플랫폼에 지속적으로 업데이트되는 조류충돌 사진에 대한 형식 분석을 수행하고, 이를 확장된 돌봄 개념 및 이미 지 비평과 연동시키는 것이다. 특히 새 이미지가 등장하지 않는 유리창 충돌 흔적에 주목하면서, 현전과 부재의 이분법 너머에서 인간-동물 관계 맺기의 다른 가능성을 탐색한다.

본 연구는 정조 시대 회화를 문화 자본화 과정으로 해석한다. 정조는 규장각 설치와 차비 대령 화원 제도 설립을 통해 회화를 정치· 문화적 기획의 핵심 자원으로 활용하였으며, 이러한 맥락에서 〈어진〉, 《화성능행 도병》, 『원행을묘정리의궤』는 국왕과 신하가 협력하여 생산한 회화적 네 트워크의 대표적 산물이다. 연구 방법은 『정조실록』, 『원행을묘정리의궤』 등 1차 사료 검토를 기반으로 하였으며, 문화 다이아몬드를 분석 틀로 하여 생산–분배–수용–지속의 네트워크 구조를 분석하였다. 또한 이미지 거버넌스 관점을 적용하여 회화가 권위를 시각화하고 질서를 제도화하며 집단 정체성을 관리하는 기능을 분석했으며, Bourdieu의 문화자본 이론 을 통해 전통 회화의 상징 자본이 현대의 문화자본으로 전화되는 과정을 해석하였다. 연구 결과, 정조대 회화는 생산 단계에서 국왕의 정치 기획과 규장각·도화서의 제도적 분업 체계를 통해 제작되었고, 분배 단계에서 어진 봉안·병풍 제작·의궤 인출 등을 통해 사회 전반으로 확산하였고, 수용 단계 에서 관료·사대부·백성의 공적 감정과 정체성을 강화하였고, 지속 단계에서 국가 의례의 기준·기억·상징 체계로 재맥락화되며 문화자본으로 전화되었 다. 이러한 고찰은 정조대 회화를 권력· 제도· 사회가 교차하는 복합적 장 (場)으로 재 위치시키며, 전통 회화가 지속 가능한 문화자산이자 미래 지향 적 문화자본으로 활용될 수 있는 전략적 가치를 지님을 시사한다.

This study proposes a mobile-based lightweight deep learning model (Lite-MCC) capable of reconstructing three-dimensional (3D) spatial structures from a single RGB image. Conventional 3D reconstruction models require multi-view inputs or point cloud data and depend on large-scale computational resources, which limits their real-time applicability in practical environments. To address this limitation, the proposed Lite-MCC model simplifies the existing Multiview Compressive Coding (MCC) architecture, enabling accurate 3D reconstruction using only a single image. The model adopts a parallel structure consisting of a Vision Transformer (ViT-Tiny) and a Geometry Encoder to extract visual and spatial features simultaneously, while a Transformer Decoder generates the corresponding 3D point cloud. Furthermore, depth map–based input transformation and ONNX-based optimization are employed to achieve efficient real-time inference on edge devices. Experimental results on the CO3D dataset demonstrate that Lite-MCC reduces computational cost by 87% and memory usage by 65%, while maintaining a Chamfer Distance of 0.045, comparable to the original MCC model. These results indicate that the proposed method provides a promising direction for lightweight AI models enabling low-cost, real-time 3D recording and visualization.

경량 골재 콘크리트는 높은 다공성을 지닌 골재를 사용하여 제작되며, 이는 재료의 역학적 성질 및 내구성에 중대한 영향을 미친 다. 최근 콘크리트 분야에서는 내부 공극 구조를 비파괴적으로 분석할 수 있는 기술로서 micro-computed tomography(micro-CT)의 활 용이 활발히 이루어지고 있으며, 특히 경량 골재 콘크리트의 공극 구조를 정밀하게 포착하는 데 효과적인 것으로 나타났다. 그러나 경 량골재는 이질적인 밀도 분포와 내부 다공성으로 인해 영상 내 분할 과정에서 어려움을 유발하며, 이로 인해 골재가 공극으로 잘못 인 식되거나 경계가 명확히 구분되지 않는 문제가 발생할 수 있다. 이러한 한계를 극복하기 위해 본 연구에서는 경량 골재 콘크리트의 micro-CT 영상에서 골재를 정밀하게 식별할 수 있도록 고안된 향상된 분할 알고리즘을 제안하였다. 제안된 알고리즘의 성능은 기존 의 세분화 방법과의 비교 분석을 통해 평가되었으며, 더불어 제안 방식과 기존 방식 각각으로 생성된 3차원 micro-CT 데이터를 활용 하여 열전도도 시뮬레이션을 수행하였다. 그 결과, 제안된 알고리즘은 공극 및 골재 경계의 정확한 식별에 있어 기존 기법보다 향상된 정확도를 보였으며, 이는 LWAC의 미세구조 분석 및 거동 예측 모델링의 정밀도를 높이는 데 기여할 수 있는 가능성을 보여준다.

This study evaluated the perception of images and emotions of Korean food in nine countries. Intercultural patterns were identified through PCA and AHC of principal components. Korea showed a traditional-emotional orientation in which images such as “rich in fermented foods”, “nutritionally balanced”, and “various side dishes” were linked to emotions such as “like a mother’s home-cooked meal”, and “sharing affection”. On the other hand, the U.S., Australia, the U.K., Brazil, the United Arab Emirates, Malaysia, the Philippines, and Singapore emphasized sensory-practical aspects, such as “tastes well”, “comfortable”, and “colorful”, and linked them to emotions such as “comfort” and “healthy”. Cluster analysis placed these eight countries in separate clusters, along with Korea. These results highlight the cultural differences in imageemotional interactions and support customized globalization and marketing strategies.

The prediction of satisfactory orthodontic treatment outcomes can be very challenging owing to the subjectivity of orthodontists’ judgment, along with the inherent difficulties when considering numerous factors. Therefore, this study introduced a deep learning-based method for predicting orthodontic treatment outcomes based on the image-to-image translation of dental radiographs using the Pix2Pix model. This proposed method addresses the aforementioned issues using a Pix2Pix-based prediction model constructed from adversarial deep learning. Patient datasets and prediction models were separated and developed for extraction and non-extraction treatments, respectively. The patients’ radiographs were pre-processed and standardized for training, testing, and applying the Pix2Pix models by uniformly adjusting the degree of blackness for the region of interest. A comparison of actual with Pix2Pix-predicted images revealed high accuracy, with correlation coefficients of 0.8767 for extraction orthodontic treatments and 0.8686 for non-extraction treatments. The proposed method establishes a robust clinical and practical framework for digital dentistry, offering both quantitative and qualitative insights for orthodontists and patients.

This study investigates how non-experts learn to use generative AI image tools by comparing outcome-oriented tools (e.g., Midjourney, DALL·E) with process-oriented tools (e.g., ComfyUI). Outcome-oriented tools offer intuitive interfaces and immediate feedback, lowering initial cognitive load, while process-oriented tools provide advanced control but require higher effort to master. Using surveys with 15 participants and in-depth interviews with 6 users, this exploratory study examined cognitive load, sense of control, and motivation. Results show that outcome-oriented tools effectively engage beginners, whereas process-oriented tools foster sustained learning once early barriers are overcome. Based on these findings, a three-stage curriculum—Basic Exploration, Advanced Control, and Creative Application—is proposed to gradually reduce cognitive barriers and support long-term creative growth.

Tesla motors, as the most representative electric car brand today, has attracted the attention of global consumers, and has also been widely reported by the major auto portal websites in China. The automobile portal websites not only provide consumers with instant access to automobile product information, but also provide important publicity platforms for various automobile manufacturers. Therefore, the image positioning of automobile brands in the portal websites will provide an important reference for consumers' purchase intention. This study aims to analyze whether the brand image positioning of Tesla car is electric car or intelligent car, using python 3.11 version and PyCharm IDE platform to collect the data of "Qichezhijia", the most representative automobile portal website in China, for relevant analysis. The result shows that with the chronological and the development of automobile industry, attention to Tesla has been gradually increasing. Tesla's brand image positioning is dynamically changing as time goes by. The current brand image positioning of Tesla is still more of electric cars than intelligent cars, but the current brand image of intelligent cars in the Chinese portal has become clear.

This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

Overloaded and improperly loaded trucks cause serious road hazards, such as rollovers and cargo falls. Although automatic enforcement methods are being studied, they face challenges in accuracy and legal application. Thus, a technology for direct tracking and enforcement is needed. This study uses EfficientNet to extract features of vehicles and license plates, and applies cosine similarity to identify the same vehicle. Comparisons were divided into “same vehicle” and “similar vehicle,” with a threshold-based method and five classification types. Results showed that the average similarity of the same vehicle group was 0.11 higher than that of the similar vehicle group. The accuracy of correctly identifying the same vehicle was 84.54%. Integrating OCR or LPR is expected to further improve tracking performance.