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저층 경계면 연구용 Benthic chamber(BelcI) 개발
이재성,박경수,강범주,김영태,배재현,김성수,박정준,최옥인,Lee, Jae-Seong,Bahk, Kyung-Soo,Khang, Buem-Joo,Kim, Young-Tae,Bae, Jae-Hyun,Kim, Seong-Soo,Park, Jung-Jun,Choi, Ok-In 한국해양학회 2010 바다 Vol.15 No.1
소형선박에서 운영이 가능한 연안용 benthic chamber(BelcI)를 개발했다. 운영상에 유연성이 큰 BelcI는 연안 저층 경계면 연구에 폭넓게 이용될 수 있을 것으로 판단된다. BelcI는 몸체, 자동채수기, 교반기 및 전자제어부로 구성된다. 운영상에 유연성을 극대화하기 위해 몸체는 사각 셀 단위의 2단 구조로 설계했다. 센서신호의 증폭, 교반기 및 채수장치 제어회로를 초 전력 소모 회로로 구성하여 외부 전원장치를 제거했다. PIV(particle image velocimetry)기법으로 측정한 chamber 내부의 유체유통은 전형적인 radial-flow impeller의 특성을 나타냈다. chamber내 물의 혼합 시간은 약 30초로 추정되었으며, 바닥면에서 shear velocity($u^*$)는 약 $0.32\;cm\;s^{-1}$였다. 산경계층(DBL) 두께는 약 $180{\sim}230\;{\mu}m$였다. 현장에서 측정한 산소소모율은 약 $84\;mmol\;O_2\;m^{-2}\;d_{-1}$로 선상배양결과 보다 2배 이상 컸다. 저층 영양염 플럭스는 "질산+아질산"이 $0.18\;{\pm}\;0.07\;mmol\;m^{-2}\;d^{-1}$, 암모니움이 $2.3\;{\pm}\;0.5\;mmol\;m^{-2}\;d^{-1}$, 인산인이 $0.09\;{\pm}\;0.02\;mmol\;m^{-2}\;d^{-1}$, 규산규소가 $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$로 추정되 었다. We have developed an in-situ benthic chamber (BelcI) for use in coastal studies that can be deployed from a small boat. It is expected that BelcI will be useful in studying the benthic boundary layer because of its flexibility. BelcI is divided into three main areas: 1) frame and body chamber, 2) water sampler, and 3) stirring devices, electric controller, and data acquisition technology. To maximize in-situ use, the frame is constructed from two layers that consist of square cells. All electronic parts (motor controller, pA meter, data acquisition, etc.) are low-power consumers so that the external power supply can be safely removed from the system. The hydrodynamics of BelcI, measured by PIV (particle image velocimetry), show a typical "radial-flow impeller" pattern. Mixing time of water in the chamber is about 30 s, and shear velocity ($u^*$) near the bottom layer was calculated at $0.32\;cm\;s^{-1}$. Measurements of diffusivity boundary layer thickness showed a range of $180-230\;{\mu}m$. Sediment oxygen consumption rate, measured in-situ,was $84\;mmol\;O_2\;m^{-2}\;d_{-1}$, more than two times higher than on-board incubation results. Benthic fluxes assessed from in-situ incubation were estimated as follows: nitrate + nitrite = $0.18\;{\pm}\;0.07\;mmol\;m^{-2}\;d^{-1}$ ammonium $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$ phosphate = $0.09\;{\pm}\;0.02\;mmol\;m^{-2}\;d^{-1}$ and silicate = $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$.
적외선 영상 기반 신경망 구조와 군집 분석을 이용한 고속 피플 카운팅
권현송 (Hyun-Song Kwon),이종화(Jong-Whoa Lee),구호근(Ho-Geun Koo),이범주(Buem-Joo Lee),김영국(Young-Kuk Kim) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
"People Counting" is one of the main research areas of object detection and counting algorithms. This algorithm uses various electronic sensors or image cameras to automatically count the number of people passing by. In this paper, high—speed people counting algorithm using neural network structure in infrared video is proposed. The advantage of this method is that it is fast and can also be trained with classification data which is easy to build dataset. The proposed algorithm is performed in the order of detecting pedestrians using neural network and cluster analysis. After that it analyzes the movements of the detected pedestrians and counting the number of in—out people. Furthermore, the corresponding algorithm showed 3.03 times faster processing speed than the YOLOv3—tiny model which is specialized in high—speed operation of object detection.