A Study on the change of the state regulation on higher education

학위논문(박사)--서울대학교 대학원 :교육학과 교육사회학전공,1998.Docto

저전력 WPAN (IEEE 802.15.4)을 위한 물리/매체접근제어 계층 프로토타입 시스템의 설계 및 구현

학위논문(석사) - 한국과학기술원 : 전기및전자공학전공, 2002.2, [ [iii], 82, [3] p. ]Recently LR-WPAN (IEEE 802.15.4) has developed the standard for a low-cost and low-power wireless network. In the thesis, the ultra-low power solution for the LR-WPAN, which MICROS (Micro Information and Communication Remote Object-oriented Systems Research Center) has developed, is considered, with emphasis on a mobile station. The PHY and MAC Iayer specifications of the LR-WPAN mobile station are proposed and analyzed. Moreover, the mobile station supporting the proposed PHY and MAC layer specifications is implemented using FPGA (Field Programmable Gate Array). Additionally, the PHY layer specifications for the LR-WPAN base station are also proposed and analyzed. Although the standardization process of the IEEE 802.15 TG4 is still in progress, the LRWPAN mobile station possibly supports the PHY and MAC layer functions for LR-WPAN. The thesis contains not only the PHY and MAC layer design considerations for LR-WPAN but also the implementation of the mobile station.한국과학기술원 : 전기및전자공학전공

Study on Map Building Performance Using OSM in Virtual Environment for Application to Self-Driving Vehicle

In recent years, automated vehicles have garnered attention in the multidisciplinary research field, promising increased safety on the road and new opportunities for passengers. High-Definition (HD) maps have been in development for many years as they offer roadmaps with inch-perfect accuracy and high environmental fidelity, containing precise information about pedestrian crossings, traffic lights/signs, barriers, and more. Demonstrating autonomous driving requires verification of driving on actual roads, but this can be challenging, time-consuming, and costly. To overcome these obstacles, creating HD maps of real roads in a simulation and conducting virtual driving has become an alternative solution. However, existing HD maps using high-precision data are expensive and time-consuming to build, which limits their verification in various environments and on different roads. Thus, it is challenging to demonstrate autonomous driving on anything other than extremely limited roads and environments. In this paper, we propose a new and simple method for implementing HD maps that are more accessible for autonomous driving demonstrations. Our HD map combines the CARLA simulator and OpenStreetMap (OSM) data, which are both open-source, allowing for the creation of HD maps containing high-accuracy road information globally with minimal dependence. Our results show that our easily accessible HD map has an accuracy of 98.28% for longitudinal length on straight roads and 98.42% on curved roads. Moreover, the accuracy for the lateral direction for the road width represented 100% compared to the manual method reflected with the exact road data. The proposed method can contribute to the advancement of autonomous driving and enable its demonstration in diverse environments and on various roads.FALS

Challenges of Lane Detection Using Deep Neural Networks in Severe Heavy Rain: A Synthetic Evaluation Dataset Based on the CARLA Simulator

Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicles to spread widely, it is important to properly deal with the safety issues on this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for lane de-tection algorithms during autonomous driving. To handle this problem, the importance of the generation of proper datasets is becoming more significant. In this paper, a synthetic lane dataset with sensor blockage is suggested in the format of lane detection evaluation. Rain streaks for each frame were made by an experimentally established equation. Using this dataset, the degradation of the diverse lane detection methods has been verified. The trend of the per-formance degradation of deep neural network- based lane detection methods has been analyzed in depth. Finally, the limitation and the future directions of the network-based methods were presented.FALSEkc