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Influence of heat treatment on the wear life of hydraulic fracturing tools
Chao Zheng,Yonghong Liu,Hanxiang Wang,Jie Qin,Yang Shen,Shihong Zhang 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.2
Wear phenomenon has caused severe damage or failure of fracturing tools in oil and gas industry. In this paper, influence of heat treatment on the mechanical properties and wear resistance of fracturing tool made of lamellar graphite grey cast iron were investigated. The surface composition and microstructure were characterized by X-ray diffraction (XRD) and metallographic microscope. Sliding wear tests were performed to study the tribological behavior. Tests results showed that wear rates of treated specimens decreased by 33 %. Besides, worn morphology and wear debris were analyzed using Scanning electron microscope (SEM) and Energy dispersive Xray spectra (EDS). Wear failure mechanisms of specimens were identified. Furthermore, on-site experiment results indicated that wear loss of treated samples decreased by 37.5 %. The wear life of hydraulic fracturing tools can be improved obviously by the heat treatment.
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환경변화에 강인한 딥러닝 기반의 터널 균열 측정 및 진단
L. Minh Dang,Chanmi Oh,Yanfen Li,Hanxiang Wang,Chang-Jae Chun,Hyeonjoon Moon 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.05
A tunnel is an essential public facility that enables uninterrupted transportation in crowded cities. Over time, various factors such as ageing and harsh environment could slowly damage the tunnel, leading to cracks and even human loss. There, the tunnel needs to be investigated regularly. Previous maintenance methods have primarily counted on the operators who directly monitor recorded videos to inspect the cracks and determine their seriousness. However, this is a time-consuming and error-prone process. Firstly, this paper introduces a huge tunnel cracks segmentation dataset that contains a total of 170,339 images. Next, a tunnel crack segmentation system that can automatically identify different types of cracks is suggested based on the collected data. The model uses the U-Net structure as the baseline model, with the encoder replaced by a pre-trained Resnet-152 model to improve the effectiveness of the feature extract process. Finally, additional measurements of the detected cracks, such as crack length and crack thickness, are computed.
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Wenqi Zhang,L. Minh Dang,Yanfen Li,Hanxiang Wang,Sujin Lee,Hyeonjoon Moon 한국방송·미디어공학회 2023 한국방송공학회 학술발표대회 논문집 Vol.2023 No.6
The dimensions of plants, including their length and width, as well as the size of their leaves, serve as crucial indicators for assessing their growth status. These factors provide essential data for studying plant conditions. This article introduces an algorithm for instance segmentation based on Solo, with the addition of a residual block module to enhance segmentation performance. The original rectified linear unit (ReLU) activation function is replaced with a functional ReLU (PReLU), and an open turnip segmentation dataset is proposed. Experimental results demonstrate that, in comparison to the original model, the average accuracy of the modified model reaches 87.6%, an improvement of approximately 2.0%. The improved algorithm accurately segments turnips and their leaves, exhibiting superior accuracy in measuring both turnip and leaf dimensions. Compared to manual measurements, the average accuracy for turnip length is 97.38%, turnip width is 95.46%, turnip leaf length is 97.79%, and turnip leaf width is 96.13%.
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남준영(Junyoung Nam),이혜민(Christine Lee),Asif Ashraf Patankar,Hanxiang Wang,Yanfen Li,문현준(Hyeonjoon Moon) 한국방송·미디어공학회 2019 한국방송공학회 학술발표대회 논문집 Vol.2019 No.11
도시화 과정에서 도시의 생활폐기물 문제가 빠르게 증가되고 있고, 효과적이지 못한 생활폐기물 관리는 도시의 오염을 악화시키고 물리적인 환경오염과 경제적인 부분에서 극심한 문제들을 야기시킬 수 있다. 게다가 부피가 커서 관리하기 힘든 대형 생활폐기물들이 증가하여 도시 발전에도 방해가 된다. 생활폐기물을 처리하는데 있어 대형 생활폐기물 품목에 대해서는 요금을 청구하여 처리한다. 다양한 유형의 대형 생활폐기물을 수동으로 분류하는 것은 시간과 비용이 많이 든다. 그 결과 대형 생활폐기물을 자동으로 분류하는 시스템을 도입하는 것이 중요하다. 본 논문에서는 대형 생활폐기물 분류를 위한 시스템을 제안하며, 이 논문의 4 가지로 분류된다. 1) 높은 정확도와 강 분류(roust classification) 수행에 적합한 Convolution Neural Network(CNN) 모델 중 VGG-19, Inception-V3, ResNet50 의 정확도와 속도를 비교한다. 제안된 20 개의 클래스의 대형 생활폐기물의 데이터 셋(data set)에 대해 가장 높은 분류의 정확도는 86.19%이다. 2) 불균형 데이터 문제를 처리하기 Class Weight VGG-19(CW-VGG-19)와 Extreme Gradient Boosting VGG-19 두 가지 방법을 사용하였다. 3) 20 개의 클래스를 포함하는 데이터 셋을 수동으로 수집 및 검증하였으며 각 클래스의 컬러 이미지 수는 500 개 이상이다. 4) 딥 러닝(Deep Learning) 기반 모바일 애플리케이션을 개발하였다.
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