An Analysis of the Application of Cognitive Linguistics in English Vocabulary Teaching

This paper aims to analyze the application strategies of Cognitive Linguistics in English vocabulary teaching. In the 1970s and 1980s, Cognitive Linguistics emerged as a prominent theoretical framework, providing new theoretical support for English teaching. This paper first introduces the basic concepts and principles of Cognitive Linguistics. Then, it discusses how Cognitive Linguistics can be applied to promote vocabulary acquisition, including the use of prototype, conceptual metaphor and image schema. Based on the literature review and analysis, this paper further examines the significance of context and collocation in vocabulary learning. Finally, it summarizes the advantages and challenges of the application of Cognitive Linguistics in English vocabulary teaching. This paper highlights the significant role of Cognitive Linguistics in vocabulary teaching and provides new insights and methods for future English teaching

Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to construct the temporarily changed drivable area and occupancy grid map (OGM) based on crowdsourcing trajectories. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.Comment: Presented at TRBAM. Journal version in progres

非貴金属系の多金属電気化学触媒の開発及び水分解反応への応用

九州工業大学博士学位論文（要旨）学位記番号：生工博甲第445号 学位授与年月日：令和4年9月26