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이재욱 ( Jaewook Lee ),고한얼 ( Haneul Ko ),백상헌 ( Sangheon Pack ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.2
네트워크 슬라이싱 기술은 5G 네트워크에서의 필수 기술 중 하나이다. 네트워크 망에서는 네트워크 슬라이싱을 통해 서로 다른 요구사항이 존재하는 다수의 서비스를 유연하게 처리 할 수 있다. 본 논문에서는 먼저 전망되는 5G 네트워크 구조와 기반 기술에 대해 소개하고,네트워크 슬라이싱 적용하기 위한 이슈를 정리한다. 끝으로 네트워크 슬라이스 선택 기능 설계 시 고려사항을 제안한다.
이재욱(Jaewook Lee),백윤아(Yoonah Paik),김창현(Chang Hyun Kim),이원준(Wonjun Lee),김선욱(Seon Wook Kim) 대한전자공학회 2019 대한전자공학회 학술대회 Vol.2019 No.11
Deep learning techniques have been applied to various fields, such as image recognition, natural language processing, computer vision, and so on. Therefore, their accelerators are getting attention due to the execution efficiency in terms of speed and power, and the deep learning compilers help programmers to develop optimized code. In this paper, we review TVM, an open-source deep-learning compiler, and analyze its performance by using GoogLeNet. Also, we compare the performance of VTA, the neural network accelerator based on TVM, with CPU by using the quantized ResNet-18.
수동변속기 차량의 클러치 특성과 차량 발진 성능에 관한 고찰
이재욱(Jaewook Lee),장호준(Hojun Jang),정문규(Moonkyu Jung) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
This paper is focuses on launch performance with clutch system specifications. Automotives are controlled clutch pedal for connecting or disconnection powertrain by users. Recently vehicles take an interest in fuel economic, and reduce IDLE RPM about 80% than old product. This is made of full optimization with idle condition as air-conditioner, alternator, steering system with hydroid, others pumps and electronic loads. This is taking an influence modulation zone of clutch pedal trace, and users get the feel with easy and difficult launches.
내장 그래픽 칩 기반 머신 러닝과 딥러닝의 알고리즘 융합을 활용한 실시간 교통 신호인지
이재욱(Jaewook Lee),백승범(Seungbeom Baek),최은지(Eunji Choi),문희창(Heechang Moon) 제어로봇시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.2
The recognition of traffic light in unmanned autonomous driving is one of the key functions. In general, to achieve this goal, deep learning and image processing technology are mainly used in autonomous driving industry. Both technologies have high recognition rates alone, but misrecognition can occur because of interference of the environment. misrecognitions are considered important problem because it may lead to big accidents. In this paper, we suggest using machine learning based on LabVIEW and deep running grounded in Python to increase recognition rate at intersection. By using both technologies, we can minimize the impact of the environment on traffic light recognition and reduce misrecognition.