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고혈압백서의 신장 Renin Heterogeneity에 관하여
전창렬(Jeon, Chang-Yeal),최병수(Choi, Byung-Soo),김선희(Kim, Suhn-Hee),조경우(Cho, Kyung-Woo) 대한생리학회 1988 대한생리학회지 Vol.22 No.2
It has been well known that the renal cortical blood flow rate was much higher than that of the medulla and the renal blood flow distribution was affected by hemorrhage, volume expansion or salt-loading. The existance of the heterogeneities of glomerular filtration rate and nephron has also been reported. In order to understand the regulations and physiological roles of the heterogeneities, studies on the intrarenal renin-angiotensin system have been focused. Although it is well known that the granularity of iuxtaglomerular cells and renal renin content are more marked in superficial than in the deep glomeruli, their physiological significance is not quite clear. This study was therefore undertaken to clarify changes in renin response and isoelectric ronin profile to TMB-8 in outer, mid and inner cotices of normotensive and hypertensive rats. The basal rate of renin release was highest in outer cortex of Sprague-Dawley rat (SDR), Wistar rat (WR) and spontaneously hypertensive rat (SHR). The basal renin release from outer and inner cortex of SHR was significantly lower than that from those of SDR. The reponse of renin release to TM8-8 was highest in mid cortex and the increase of renin release in response to TMB-8 from inner cortex of SDR was significantly higher than that in SHR. In dehydrated rats, the basal renin release from renal cortical slices of SDR was increased but that from WR and SHR was not. The response of renin release to TMB-8 from mid and inner cortex of dehydrated WR tended to increase. In dehyrated SHR, increase of renin release from inner cortex was significantly higher than that in euhydrated SHR. No significant differences in the isoelectric renin profile were found both in different cortical areas and strains. In dehydrated rats, the percentage of renin form 2 was decreased and those of renin form 5 and 6 were increased. These results suggest that the heterogeneity of renin release from cortical area of euhydrated and dehydrated rats in response to TMB-8 may be related to the changes of renal blood flow and/or calcium metabolism in cortical area. These data also suggest that the renin forms with different isoelectric points may have an physiological significance.
자율 주행 차량 후방 카메라 오염 탐지를 위한 에너지 기반 학습 외 분포 데이터 탐지 네트워크와 확산 모델 기반 데이터셋
문승훈,전창렬,이정훈,정동혁,염석주,강석주 대한전자공학회 2023 전자공학회논문지 Vol.60 No.11
자율 주행 상황에서 차량 후방 카메라의 오염 여부를 감지하는 것은 중요한 문제이다. 오염 물질을 도로 위 객체로 오탐지하여 자율 주행이 원활하게 이루어 질 수 없기 때문이다. 하지만 이러한 문제를 해결하기 위한 연구는 오염 탐지 특화 데이터셋의 부재로 인해 활발히 이루어지지 않고 있으며 실제 자율 주행 상황에 적용하기 충분한 성능을 보이지 않고 있다. 본 논문은 이러한 한계점을 해결하기 위해 에너지 기반 학습 외 분포 데이터 탐지 네트워크와 확산 모델을 기반한 오염 탐지 특화 데이터셋을 제안한다. 실험 결과 에너지 기반 학습 외 분포 데이터 탐지 네트워크는 baseline 대비 15.52% 개선된 94.73%의 오염과 비오염 상황 분류 정확도를 기록하였다. 또한 제안한 확산 모델 기반 오염 탐지 특화 데이터셋을 통한 학습은 상황 분류 성능을 95.24%까지 개선하였다. 마지막으로 사용한 네트워크를 모바일 기기에 임베딩하여 백본별 오염 및 비오염 상황 분류 성능과 추론 시간의 트레이드오프를 분석하는 실험을 진행하여 자율 주행 상황에서의 실시간 동작 가능성을 검증하였다. It is a crucial problem in autonomous driving scene to detect whether a rear-view camera of a vehicle is soiled. The false detection of soiled contaminants as road objects makes it hard to operate autonomous driving. However, due to the absence of a soiling detection-specific dataset, recent research to solve the issue is not actively conducted while they show insufficient performance for real-world applications. To address these limitations, we propose an energy-based out-of-distribution (OOD) detection network and diffusion model-based task-specific dataset. In the experimental results, the proposed network reaches 94.73% of soiling detection accuracy, which is a 15.52% improvement compared to the baseline. In addition, training procedure with the proposed task-specific dataset further impoved the soiling detection accuracy to 95.24%. Finally, we embedded the proposed network onto a mobile device and conducted experiments to validate its real-time capability in autonomous driving scenarios. These experiments analyzed the trade-offs between the backbone-specific soiling detection performance as well as inference time.
자율 주행 차량용 카메라 이미지 오염 감지를 위한 확산 모델 에너지 기반 이상 탐지 네트워크
문승훈(Seunghun Moon),전창렬(Chang-Ryeol Jeon),이정훈(Junghoon Lee),정동혁(donghyuk Jeong),염석주(Seokju Yeom),강석주(Suk-Ju Kang) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Detecting whether a vehicle’s rearview camera is soiled is a crucial problem in the autonomous driving scene. However, the absence of a soiling detection task-specific dataset which leads to low detection performance makes it hard for real-world driving scene applications. We propose a deep learningbased anomaly detection network and diffusionbased soiling detection-specific dataset to handle this limitation. We adopted an energy-based outof-distribution(OOD) detection network to differentiate between soiled and non-soiled contexts. The proposed method has achieved 95.24% accuracy in classifying soiled and nonsoiled rearview driving scenes, which is a 16.03% improvement compared to the baseline.