ROMP-based polymer composites and biorenewable rubbers

This research is divided into two related topics. In the first topic, the synthesis and characterization of novel composite materials reinforced with MWCNTs by ring-opening metathesis polymerization (ROMP) is reported for two ROMP based monomers: dicyclopentadiene (DCPD) and 5-ethylidene-2-norbornene (ENB). Homogeneous dispersion of MWCNTs in the polymer matrices is achieved by grafting norbornene moieties onto the nanotube surface. For the DCPD-based system, the investigation of mechanical properties of the composites shows a remarkable increase of tensile toughness with just 0.4 wt % of functionalized MWCNTs (f-MWCNTs). To our knowledge, this represents the highest toughness enhancement efficiency in thermosetting composites ever reported. DMA results show that there is a general increase of thermal stability (Tg) with the addition of f-MWCNTs, which means that covalently bonded f-MWCNTs can reduce the local chain mobility of the matrix by interfacial interactions. The ENB system also shows significant enhancement of the toughness using just 0.8 wt % f-MWCNTs. These results indicate that the ROMP approach for polyENB is also very effective. The second topic is an investigation of the biorenewable rubbers synthesized by the tandem ROMP and cationic polymerization. The resin consists of a norbornenyl-modified linseed oil and a norbornene diester. Characterization of the bio-based rubbers includes dynamic mechanical analysis, tensile testing, and thermogravimetric analysis. The experimental results show that there is a decrease in glass transition temperature and slight increase of elongation with increased diester loading

Learning Equi-angular Representations for Online Continual Learning

Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we propose an efficient online continual learning method using the neural collapse phenomenon. In particular, we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space so that the continuously learned model with a single epoch can better fit to the streamed data by proposing preparatory data training and residual correction in the representation space. With an extensive set of empirical validations using CIFAR-10/100, TinyImageNet, ImageNet-200, and ImageNet-1K, we show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios such as disjoint and Gaussian scheduled continuous (i.e., boundary-free) data setups.Comment: CVPR 202

ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments

Simulated virtual environments have been widely used to learn robotic agents that perform daily household tasks. These environments encourage research progress by far, but often provide limited object interactability, visual appearance different from real-world environments, or relatively smaller environment sizes. This prevents the learned models in the virtual scenes from being readily deployable. To bridge the gap between these learning environments and deploying (i.e., real) environments, we propose the ReALFRED benchmark that employs real-world scenes, objects, and room layouts to learn agents to complete household tasks by understanding free-form language instructions and interacting with objects in large, multi-room and 3D-captured scenes. Specifically, we extend the ALFRED benchmark with updates for larger environmental spaces with smaller visual domain gaps. With ReALFRED, we analyze previously crafted methods for the ALFRED benchmark and observe that they consistently yield lower performance in all metrics, encouraging the community to develop methods in more realistic environments. Our code and data are publicly available.ECCV 2024 (Project page: https://twoongg.github.io/projects/realfred

인과관계 및 상관관계에 따른 주식시장 분석 프레임워크 및 그 응용

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2022.8,[iv, 59 p. :]Correlation coefficient has been the major dependency measure of detecting similar movements between stocks and financial markets in the field of financial research. However, due to the statistical assumptions whilst calculating the correlation, a more general dependency measures have been proposed: mutual information, Granger causality and transfer entropy. This study aims to compare the dependency measures by analysing how the measures imply the structural properties of the stock market, utilizing the graph theory. According to the analysis, we were able to find that relationship based measures like correlation and mutual information depicted graphs more closer to a random graph compared to the causality measures. Especially, during the financial crisis induced by COVID-19, we were able to detect that relationship based measures were not able to efficiently identify the structural properties. Using the properties of each dependency measures, this study constructed a portfolio based on the measures and analyzed the performances during the COVID-19 period. According to the performance results, the causality measures displayed better returns and a lower maximum drawdown. The correlation measure having the worst performances, we were able to conclude that causality measures are a better approach in explaining the stock market structure compared to the relationship measures.한국과학기술원 :산업및시스템공학과

ROMP-based polymer composites and biorenewable rubbers

This research is divided into two related topics. In the first topic, the synthesis and characterization of novel composite materials reinforced with MWCNTs by ring-opening metathesis polymerization (ROMP) is reported for two ROMP based monomers: dicyclopentadiene (DCPD) and 5-ethylidene-2-norbornene (ENB). Homogeneous dispersion of MWCNTs in the polymer matrices is achieved by grafting norbornene moieties onto the nanotube surface. For the DCPD-based system, the investigation of mechanical properties of the composites shows a remarkable increase of tensile toughness with just 0.4 wt % of functionalized MWCNTs (f-MWCNTs). To our knowledge, this represents the highest toughness enhancement efficiency in thermosetting composites ever reported. DMA results show that there is a general increase of thermal stability (Tg) with the addition of f-MWCNTs, which means that covalently bonded f-MWCNTs can reduce the local chain mobility of the matrix by interfacial interactions. The ENB system also shows significant enhancement of the toughness using just 0.8 wt % f-MWCNTs. These results indicate that the ROMP approach for polyENB is also very effective. The second topic is an investigation of the biorenewable rubbers synthesized by the tandem ROMP and cationic polymerization. The resin consists of a norbornenyl-modified linseed oil and a norbornene diester. Characterization of the bio-based rubbers includes dynamic mechanical analysis, tensile testing, and thermogravimetric analysis. The experimental results show that there is a decrease in glass transition temperature and slight increase of elongation with increased diester loading.</p