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Byeong-Uk Jeon,Kyungyong Chung 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.8
The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.
Dynamic Framerate SlowFast Network for Improving Autonomous Driving Performance
Byeong-Uk Jeon,정경용 대한전자공학회 2023 IEIE Transactions on Smart Processing & Computing Vol.12 No.3
Computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, deep learning algorithms that detect even the action of things require high computing performance. In addition, the computing performance of autonomous driving vehicles for processing such tasks is inferior. These classification processes are not used for recognizing and determining autonomous driving vehicles because it is impossible to process the classification of the action of autonomous driving vehicles in an autonomous driving vehicle on a real-time basis. This paper proposes a Dynamic Framerate SlowFast network for improving autonomous driving performance. Unlike pre-existing studies, the proposed model includes a cropping process through the YOLO model. In addition, it measures the similarity between unit frames through the SSIM and skips the input when the similarity exceeds a certain level. This process made it possible to reduce the number of frames entered into the model. Compared to the existing SlowFast Network, the performance evaluation compared the time required to analyze one image and the AUC of classification results when the number of input frames was reduced through similarity analysis techniques. The similarity analysis technique achieved the highest AUC when the SSIM was applied. The Dynamic Framerate SlowFast network proposed in this study achieved an AUC of 0.7126 and took an FPS of 0.7912 to analyze the entire verification video data. Compared to the pre-existing SlowFast network, which took an FPS of 0.5285 to achieve an AUC of 0.7531, the Dynamic Framerate SlowFast network achieved faster and more accurate results. Therefore, using the proposed technique, it is possible to achieve faster detection results while maintaining the object action detection AUC of the SlowFast network.
증례 : 호흡기 ; 색전술로 치료한 폐동맥 가성동맥류 1예
정병호 ( Byeong Ho Jeong ),이진희 ( Jin Hee Lee ),전준석 ( Jun Seok Jeon ),조아진 ( A Jin Cho ),송재욱 ( Jae Uk Song ),신성욱 ( Sung Wook Shin ),서지영 ( Gee Young Suh ) 대한내과학회 2011 대한내과학회지 Vol.80 No.2S
폐동맥 가성동맥류는 패혈성 폐렴의 드문 합병증으로 발생할 수 있으며 대량객혈로 이어질 위험성이 높아 치명적일 수 있다. 대량객혈을 동반한 폐동맥 가성동맥류의 치료는 색전술 또는 수술이지만, 색전술 시행 후 폐동맥 가성동맥류의 경과는 보고된 적이 없었다. 저자는 59세 남자가 패혈성 폐렴에서 회복 중 대량객혈을 하여 흉부 전산화단층촬영을 시행하였고, 폐동맥 가성동맥류를 발견하여 폐동맥 색전술로 치료하였다. 이후 외래 추적도중 시행한 단순흉부촬영상에서 색전물질에 의한 방사선 비투과성 병변은 수개월에 걸쳐 서서히 작아졌다. 결국, 색전술 후 14개월째 시행한 흉부 전산화단층촬영에서 폐동맥 가성동맥류와 색전물질이 완전히 사라졌음을 관찰하였다. Pulmonary artery pseudoaneurysm (PAP) is a rare complication of septic pneumonia. It is potentially fatal because of the risk of massive hemoptysis. Treatment of PAP involving massive hemoptysis is by embolization or surgery. However, the progression of PAP after embolization has not been reported. A 59-year-old male who was recovering from septic pneumonia experienced massive hemoptysis. Computed tomography (CT) revealed PAP of the right pulmonary artery with surrounding consolidation, suggesting active hemorrhage. The patient was successfully treated with embolization of the right pulmonary artery. During outpatient follow-up, the amount of radiopaque embolized material gradually decreased on chest radiography. At 14 months after embolization, both the PAP and embolized material had disappeared on chest CT. (Korean J Med 2011;80:S214-S219)
도로교통 이머징 리스크 탐지를 위한 AutoML과 CNN 기반 소프트 보팅 앙상블 분류 모델
전병욱,강지수,정경용,Jeon, Byeong-Uk,Kang, Ji-Soo,Chung, Kyungyong 중소기업융합학회 2021 융합정보논문지 Vol.11 No.7
겨울철 도로 결빙으로 인한 사고는 대부분 큰 사고로 이어진다. 이는 운전자가 도로의 결빙을 사전에 자각하기 어렵기 때문이다. 본 연구에서는 AutoML과 CNN의 앙상블 모델을 이용하여 도로교통 이머징 리스크를 정확하게 탐지하는 방법을 연구한다. 비정형 데이터인 이미지를 이용한 CNN 이미지 특징 추출 기반 도로교통 이머징 리스크 분류 모델과 정형 데이터인 기상 데이터를 이용한 AutoML 기반 도로교통 이머징 리스크 분류 모델을 각각 학습시킨다. 그 후 모델들에서 도출된 확률값을 입력하여 CNN 기반 분류 모델을 보완하도록 앙상블 모델을 설계한다. 이를 통해 도로교통 이머징 리스크 분류 성능을 향상하고 더 정확하고 빠르게 운전자에게 경고하여 안전한 주행이 가능하도록 한다. Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.