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Xiaorui Cheng,Zhengbai Chang,Yimeng Jiang 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.8
In this paper, the influence of the specific area of balancing hole on the cavitation performance of high-speed centrifugal pump is studied by numerical method. The results show that in the initial cavitation stage, with the increase of the specific area, the head and efficiency of the pump decreases, and the shaft power increases in a small range. The specific area of the balance hole can change the magnitude and direction of the rotor axial force. With the increase of the specific area, the anti-cavitation performance of the pump is weakened, especially when the specific area reaches a certain value, the vortex flow appears in the balance hole, which causes serious distortion of the flow condition at the inlet of the centrifugal impeller. Meanwhile, cavitation also occurs in the balance chamber and is mainly concentrated near the hub of the centrifugal impeller.
A Tuberculosis Detection Method Using Attention and Sparse R-CNN
Xuebin Xu,Jiada Zhang,Xiaorui Cheng,Longbin Lu,Yuqing Zhao,Zongyu Xu,Zhuangzhuang Gu 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.7
To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.