In general, optimized pavement thickness design abilities and reliable construction procedures have been considered being crucial element for expressway in South Korea till millenium. However, after 2005, a proper management efforts on existing expressway became recognized as an important factor after 2,005. One of good example is rising attention of HPMS(Highway Pavement Management System). In HPMS, the crucial component is: surveying the existing expressway surface condition with reasonable, reliable and efficient procedure. Becasue of this reason, application of various advanced and sophisticated technologies on HPMS area were considered since 2010. During this time, many advanced technologies such as AI(Artificial Intelligence) techniques and corresponding physical equipment were considered to be applied. Through application of AI technologies in HPMS business area, two major outcomes can be achieved: first one is an automated pavement surface monitoring work system for maximized efficiency and second thing is saving current HPMS management budget through faster and more reasonable surveying results. In this paper, the feasibility of AI technology on actual pavement surface monitoring and analysis procedure was considered. As a result, AI based pavement surface monitoring and analysis approach succesfully provided reasonable results compared to the conventional human effort analysis approach. This findings provide a promising signal that more AI based technologies can successfully applied in HPMS business area in the next future. Morevoer, the achievement of automated HPMS can also be possible in the near future.

본 연구는 웹툰 제작 과정에서 배경 에셋제작을 자동화하기 위한 생성형 AI 기술 기반플랫폼 을 제안한다. 이 플랫폼은 서사에 중요하지 않지만, 웹툰에서 필수적인 요소인 배경과 관련된 에셋 제작을 자동화하여 웹툰 작가들의 고강도 노동을 경감시키고 제작 효율성을 높이는 것을 목표로 한다. 플랫폼은 React와 TypeScript를 기반으로 구현되었으며, Stable Diffusion 기 술과 LoRA 모델을 활용하여 웹툰의 배경 에셋을 생성한다. 연구 방법으로는 문헌 연구, 플랫 폼 개발, 실제 웹툰 적용 실험을 수행하였다. 연구 결과, 제안된 플랫폼은 프로그램 설치나 추 가 모델링 구매 없이 프롬프트 작성만으로 배경 생성이 가능하며, 이미지 생성과 보정을 동시 에 처리할 수 있어 작업 효율성이 높은 것으로 나타났다.그러나 근경 물체의 정확성과 인체 표현에서 한계를 보여, 기존 3D 모델링 도구와의 상호 보완적 사용이 효과적일 것으로 판단된 다.

전세계적으로 국제항공 부문의 탄소 감축에 대한 요구가 증가함에 따라 지속가능항공유 (Sustainable Aviation Fuel, SAF)의 사용 확대와 기술개발에 대한 투자가 촉진되고 있다. SAF는 기존 석유 계 항공유 연료와 유사하기 때문에 항공기의 엔진이나 연료 공급 시스템 및 공항의 연료공급 인프라의 구 조적 변경없이 그대로 사용 가능하며(drop-in fuel), 기존 항공유에 일정비율로 혼합하여 사용할 수 있는 장점이 있다. SAF는 전세계적으로 다양한 바이오매스 원료를 사용하여 제조된 바이오항공유(Bio-jet fuel) 와 포집 이산화탄소와 그린수를 사용하여 합성된 재생합성항공유(synthetic-SAF)로 구분할 수 있다. 또한 광합성을 하는 바이오매스를 기반으로 하는 바이오항공유는 전주기평가(life cycle assessment, LCA) 관점 에서 기존 석유계 항공유보다 이산화탄소를 약 80% 저감되며, 탄소중립연료로서 인정받고 있다. 본 논문 에서는 재생합성항공유의 생산을 위한 제조기술로 이산화탄소 포집기술, 역수성가스(Reverse Water-Gas Shift, RWGS) 전환기술, Fischer-Tropsch(F-T) 공정기술과 제조된 재생합성항공유의 연료특성과 제조규 격 및 제조기술 인증에 대한 연구사례를 분석하여 국내 기술개발 필요성과 방향을 제시하고자 한다.

본 연구는 YOLO(You Only Look Once)-Segmentation 기반 해양생물 탐지 모델의 성능 비교와 수중 이미지의 색상 왜곡 보정을 위한 딥러닝 모델 구축에 중점을 둔다. 탐지 모델 구축에는 Ultralytics에서 공식적으로 배포하는 YOLO의 버전별 객체분할 모델인 YOLOv5-Seg, YOLOv8-Seg, YOLOv9-Seg, YOLOv11-Seg를 활용하였으며, 22종의 해양생물 데이터셋을 사용해 동일한 학습 과정을 거쳤다. 이 를 통해 각 버전의 탐지 성능을 비교한 결과, YOLOv9c-Seg 모델이 정밀도(Precision) 0.908, 재현율(Recall) 0.912, mAP@50 0.943으로 가장 높 은 성능을 기록하며 최적의 모델로 선정되었다. 또한, 수중 환경에서 발생하는 색상 왜곡 문제를 해결하고 탐지 정확도를 높이기 위해 CLAHE, White Balance, Image Filter 등의 RGB 요소 변환 기법을 적용한 PhysicalNN 기반 이미지 보정 모델을 구축하였다. 선정된 탐지 모델 과 이미지 보정 모델을 이용해 수중영상 내 탐지된 생물의 위치를 정확히 파악하고, Monocular Depth Estimation(MDE) 알고리즘과 거리 및 크기 측정을 위한 가이드 스틱을 활용하여 대상 생물의 거리와 크기를 추정하였다. 이를 통해 단안 카메라 영상만으로도 3차원 공간의 해 양생물 크기와 이에 따른 체중을 간접적으로 추정하였으며, 향후 해양 생태계 모니터링에 활용할 수 있는 가능성을 시사한다.

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.

Within the framework of a project entitled “Development of Advanced Sweet Potato Cultivation Technology for Smallholder Farmers in Paraguay” implemented by KOPIA Paraguay Center (Korea Partnership for Innovation of Agriculture) in collaboration with Paraguayan Institute of Agricultural T echnology (I PTA) d uring the period 2021-2024, r esults o f four m ain e xperiments are described in this research: selection of suitable varieties, optimal planting and harvesting times, the use of ridges, and optimal chemical fertilization doses. In the selection of suitable varieties for Paraguay, 11 sweet potato varieties were evaluated in departments of San Pedro and Misiones. As a result, varieties Andaí, Jety Paraguay, and Chaco I showed the highest productivity in San Pedro, while varieties Jety Uruguayo, Chaco I, and Taiwanés showed higher productivity in Misiones. The other three experiments were carried out in San Pedro only. Optimal planting and harvesting times were determined with three varieties: Andaí, Pyta Guasu, and Jety Paraguay. For Andaí and Jety Paraguay varieties, they should be planted in December and harvested at 122 days post planting (DPP). For Pyta Guasu, it should be planted in October and harvested at 124 DPP. Regarding productivity response with soil preparation methods, the use of ridges showed higher yields in all planting methods, with the curved method planting being the most productive. Finally, optimal chemical fertilization doses were established in order to improve the total yield. The optimal nitrogen fertilizer dose (urea 45% N) was 40 kg/ha. The optimal phosphorus fertilizer dose (triple superphosphate 45% P2O5) was 80 kg/ha and the optimal potassium fertilizer dose (potassium chloride 60% K2O) was 120 kg/ha.

Wearable technology is expected to maintain continuous marketability and prospects, with its scope gradually expanding beyond the fashion sector to encompass fashion accessories. Meanwhile, the wedding industry is currently reflecting consumer preferences that emphasize individuality and emotional connection. As wedding trends evolve, there is a growing interest in unique and differentiated wedding styles. Therefore, the purpose of this study is to create high-value designs by integrating wearable LED technology into wedding accessories and dresses to meet the emotional needs of modern consumers. To achieve this, we analyzed the LED wedding accessories currently available in the market. Based on the findings, we designed and developed new such accessories and dresses through planning, development, and production processes. First, the study found out that LED wedding accessories are gaining attention as high-value products. Second, a survey of the domestic market for LED wedding accessories highlighted the needs for wedding dress designs that can be paired with LED hairpins. Third, we used Lilypad Arduino’s Lily Tiny to design and develop LED wedding hairpins and dresses through a production process. Finally, by styling LED wedding hairpins and dresses together, we demonstrated the potential in creating products that blend emotion and technology, in line with the current wearable technology trends. Overall, this study offers a fresh perspective on design development in wedding accessories.

We introduce the technology required todevelop a bracket process for installing and verifying FRT bumper sensors for passenger cars. Establish and demonstrate process automation through actual design and manufaturing. We conduct quality inspection of the production process using artificial intelligence and develop technology to automatically detect good and defective products and increase the reliability of the process