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사용자 참여형 웨어러블 디바이스 데이터 전송 연계 및 딥러닝 대사증후군 예측 모델
이현식(Hyunsik Lee),이웅재(Woongjae Lee),정태경(Taikyeong Jeong) 한국산업정보학회 2020 한국산업정보학회논문지 Vol.25 No.6
본 논문은 최근 다양한 종류의 웨어러블 디바이스가 헬스케어 도메인에 급증하여 사용되고 있는 상황에서 최신 첨단 기술이 실제 메디컬 환경에서 개인의 질병예측이라는 관점을 바라본다. 사용자 참여형 웨어러블 디바이스를 통하여 임상 데이터와 유전자 데이터, 라이프 로그 데이터를 병합하여 데이터를 수집, 처리, 전송하는 과정을 걸쳐 딥뉴럴 네트워크의 환경에서 학습모델의 제시와 피드백 모델을 연결하는 과정을 제시한다. 이러한 첨단 의료 현장에서 일어나는 메디컬 IT의 임상시험 절차를 걸친 실제 현장의 경우 대사 증후군에 의한 특정 유전자가 질병에 미치는 영향을 측정과 더불어 임상 정보와 라이프 로그 데이터를 병합하여 서로 각기 다른 이종 데이터를 처리하면서 질병의 특이점을 확인하게 된다. 즉, 이종 데이터의 딥뉴럴 네트워크의 객관적 적합성과 확실성을 증빙하게 되고 이를 통한 실제 딥러닝 환경에서의 노이즈에 따른 성능 평가를 실시한다. 이를 통해 자동 인코더의 경우의 1,000 EPOCH당 변화하는 정확도와 예측치가 변수의 증가 값에 수차례 선형적으로 변화하는 현상을 증명하였다. This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.
시맨틱 세그멘테이션 기반의 P&ID 심볼 탐지에 대한 연구
오상진(Sangjin Oh),이현(Hyun Lee),이정규(Jeongkyu Lee),이영환(Younghwan Lee),정은경(Eunkyung Jeong),이현식(Hyunsik Lee) (사)한국CDE학회 2020 한국CDE학회 논문집 Vol.25 No.4
Although the Fourth Industrial Revolution comes to reality rapidly, construction industry is going through a difficult time to adopt new technologies. Also, improving productivity is one of the most urgent issues for major construction companies. However, reading information and digitizing them from imaged drawings takes much time and it becomes a reason for low productivity. Thus, in this paper, we propose a method to recognize symbols in P&ID (piping and instrumentation diagram) using neural networks for Semantic Segmentation. First, crop a drawing into small patches and label on them 8 classes of symbol. Then, Train U-net and FCN with 2,500 patches with annotation. After training, results of recognition are displayed with color code on imaged drawings. Finally, we run tests with 5 new P&ID drawings and scored the performance of our recognition models.
P&ID의 파이프라인 인식 향상을 위한 라인 검출 개선에 관한 연구
오상진,채명훈,이현,이영환,정은경,이현식,Oh, Sangjin,Chae, Myeonghoon,Lee, Hyun,Lee, Younghwan,Jeong, Eunkyung,Lee, Hyunsik 한국플랜트학회 2020 플랜트 저널 Vol.16 No.4
For several decades, productivity in construction industry has been regressed and it is inevitable to improve productivity for major EPC players. One of challenges to achieve this goal is automatically extracting information from imaged drawings. Although computer vision technique has been advanced rapidly, it is still a problem to detect pipe lines in a drawing. Earlier works for line detection have problems that detected line elements be broken into small pieces and accuracy of detection is not enough for engineers. Thus, we adopted Contour and Hough Transform algorithm and reinforced these to improve detection results. First, Contour algorithm is used with Ramer Douglas Peucker algorithm(RDP). Weakness of contour algorithm is that some blank spaces are occasionally found in the middle of lines and RDP covers them around 17%. Second, HEC Hough Transform algorithm, we propose on this paper, is improved version of Hough Transform. It adopted iteration of Hough Transform and merged detected lines by conventional Hough Transform based on Euclidean Distance. As a result, performance of Our proposed method improved by 30% than previous.
向精神性 藥物이 흰쥐의 Prolactin分泌에 미치는 影響에 關한 組織化學的 硏究
李懸式 中央醫學社 1976 中央醫學 Vol.31 No.4
The effects of several psychotropic drugs on the prolactin release in rats were studied histologically and immunohistochemically. Estradiol primed virgin rats were administered with perphenazine(1mg and 8mg/day), inzipramine(1mg and 8mg/day) and reserpine (0.1mg and 0.2mg/day) respectively- for 5 days. Paraffin sections of pituitary glands were stained with anti-prolactin and antiRGB-peroxidase immunohistochemically and other organs were studied by ordinary H-E stain. Through the prolactin- cell counts in pituitary glands and other histological observation, the following results were obtained. Administration of perphenazine and reserpine induced noticeable decrease on prolactin cell counts as well as reduction of staining intensities. While imipramine, injected rats had little effects on prolactin release. The most high cell counts were found in control groups that would be an evidence of intact hormone status. No obvious differences appeared between low doses and high doses administration in each groups. Marked effects on mammary glands after administration of perphenazine and reserpine were resulted while that of control groups revealed no evidence of development in duct system and acini. In ovaries of experimental groups, apparent features of well developed corpus luteum appeared by the injection of psychotropic drugs with the exception of animals in group III which gave no altered features to that of control group. There were also significant increase in weight of pituitary glands, ovaries And adrenal glands in reserpine treated animals. The above results strongly suggest that the injection of psychotrogic drugs can induce higher level of prolactin secretion even in virgin rats and consequently it effect to the mammary glands and ovaries for lactogenesis and luteo tropic fiction respectively.