http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
남효현(Hyohyun Nam),박준식(Junsik Park),송규하(Kyu-Ha Song),박정동(Jung-Dong Park) 대한전자공학회 2018 전자공학회논문지 Vol.55 No.4
본 논문은 압축센싱을 위한 광대역 수신기를 0.13μm SiGe 프로세스를 사용하여 집적화 구현한 것이다. 제안 된 수신기는 광대역 능동 발룬으로 구성된 2-18 GHz 대역 구동 증폭기, 이중 평형 수동 믹서, 그리고 출력 구동 버퍼를 포함하는 700 MHz 4 단 가변 이득 증폭기 (VGA)로 구성된다. 광대역 버퍼는 -10dB 미만의 반사 손실로 20GHz 이상의 대역폭을 달성하기 위해 RF 입력단에서 의사 전송선로를 이용하였다. IF VGA는 15.49 dB에서 34.74 dB의 가변 이득을 조정할 수 있다. 구현 된 수신기는 통합된 64 비트 SPI 스캔 체인 회로에 의해 제어된다. 서브-나이퀴스트 샘플링 기능은 LO 신호용 외부 PRBS 생성기를 통해 검증되었다. 제작된 수신기의 크기는 0.5 × 0.77㎟이고, 2.5V 전원에서 약 27mA의 DC 전류를 소비한다. In this paper, an integrated broadband receiver capable of the compressed sensing is implemented using 0.13μm SiGe process. The proposed receiver consists of a 2-18 GHz driving amplifier configured as the broadband active balun, a double-balanced passive mixer, and a 700-MHz four stage variable gain amplifier (VGA) including an output driving buffer. The broadband buffer utilizes the pseudo transmission-line at the RF input to achieve the more than 20 GHz of the bandwidth with the return loss less than -10 dB. The IF VGA is capable of varying the gain from 15.5 dB to 34.7 dB. The implemented receiver is controlled by an integrated 64-bit SPI scan-chain block. The sub-Nyquist sampling operation has been verified with an external PRBS generator as the LO signal. The receiver chip size is 0.5 × 0.77㎟, and consumes around 27 mA of DC current under 2.5V supply voltage.
무인수상정 경로점 추종을 위한 강화학습 기반 Dynamic Window Approach
허진영,하지수,이준식,유재관,권용진,Heo, Jinyeong,Ha, Jeesoo,Lee, Junsik,Ryu, Jaekwan,Kwon, Yongjin 한국군사과학기술학회 2021 한국군사과학기술학회지 Vol.24 No.1
Recently, autonomous navigation technology is actively being developed due to the increasing demand of an unmanned surface vehicle(USV). Local planning is essential for the USV to safely reach its destination along paths. the dynamic window approach(DWA) algorithm is a well-known navigation scheme as a local path planning. However, the existing DWA algorithm does not consider path line tracking, and the fixed weight coefficient of the evaluation function, which is a core part, cannot provide flexible path planning for all situations. Therefore, in this paper, we propose a new DWA algorithm that can follow path lines in all situations. Fixed weight coefficients were trained using reinforcement learning(RL) which has been actively studied recently. We implemented the simulation and compared the existing DWA algorithm with the DWA algorithm proposed in this paper. As a result, we confirmed the effectiveness of the proposed algorithm.