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고분자 나노복합소재 내의 그래핀 나노입자 분산성에 대한 유변학적 평가
하진수,송예은,주양율,황정민,윤지선,이두진 한국섬유공학회 2021 한국섬유공학회지 Vol.58 No.1
The dispersibility of nanoparticles in polymer nanocomposites significantlyaffects the mechanical, thermal, and electrical properties of the final products. The objectiveof this study is to quantify the dispersibility of nanoparticles in polymer nanocomposites. Various amounts of graphene nanoparticles were introduced in polypropylene-basedand polylactic acid-based resins through melt compounding. They were then injectionmoldedto fabricate disc-type specimens for characterizing the rheological properties ofthe nanocomposites. To evaluate the dispersibility of the nanoparticles in the composites,the associated storage (G’) and loss (G’‘) moduli were analyzed for plotting the G’-G’‘ slopes. Furthermore, the Van Gurp-Palmen plot was employed for analyzing the increase in materialelasticity with respect to the base resins and nanoparticle compositions.
하진수,김현우,이두진 한국고분자학회 2021 한국고분자학회 학술대회 연구논문 초록집 Vol.46 No.2
For ceramic 3D printing, stereolithography (SLA) enables more precise fabrication of ceramic suspensions than other 3D printing methods. For densely-packed ceramic based 3D materials, the dispersion stability of ceramic particles in suspensions is important. As a simple method to improve dispersion stability in polymer matrix, surface modification of inorganic nanoparticles is widely used. The silane coupling agent which is a useful surface modifier has two functional groups, an inorganic group generates hydrolysis and forms a silane-functionalized surface in inorganic materials, and an organic group improves dispersion stability by forming the interpenetrating networks (IPNs). In this work, we modified several types of ceramic nanoparticles (NPs) with a vinyltriethoxysilane (VTES) and prepare ceramic nanoparticle reinforced nanocomposites. The enhanced dispersity was quantitatively evaluated using rheological methods, dynamic light scattering, and morphology observation.
야지환경에서의 딥러닝 기반 영상 깊이 추정 데이터셋 구축을 위한 플랫폼 연구
하진수,강현욱,이종현,박정희,이채현,김양곤,조기춘 한국자동차공학회 2024 한국 자동차공학회논문집 Vol.32 No.5
Accurate perception is critical for unmanned ground systems in unstructured outdoor environments. LiDAR sensors utilize laser beams to generate point-based 3D spatial information. However, they are vulnerable to external impact. Conversely, camera sensors, known for their affordability and durability, require indirect methods, such as stereo vision and deep learning, to generate spatial information. Creating datasets for deep learning in outdoor environments is challenging due to the absence of specialized data acquisition platforms and the time-consuming and costly process of generating manual annotations. To address these issues, this study proposes an outdoor data acquisition platform and a LiDAR-based technique to generate depth estimation datasets. The platform was employed to construct real outdoor datasets, and to conduct a qualitative evaluation of the constructed dataset. The dataset was then used to train and evaluate a depth-estimation network, validating the method's effectiveness. In conclusion, this study offers a comprehensive solution to acquire data in unstructured outdoor environments.