The accuracy of flowmeter of the first circuit coolant sampling system in nuclear power plant affects the measurement of the Boron meter. If the fault treatment cannot be completed in time, the reactor state needs to be withdrawn, which will affect nuclear safety and cause great economic losses. In this paper, a feasible improvement scheme is proposed by analyzing the causes of instrument failure, researching the optimization direction, and adopting measures such as seismic analysis, prototype verification, split design, and interference suppression, which effectively improves the reliability of nuclear sampling flow measurement in nuclear power plants.
Kidney tumors are among the ten most common tumors in humans. Precise resection of renal tumors has become an essential means of tumor treatment. Accurate kidney segmentation in CT images is a prerequisite for surgery, and segmenting kidneys and kidney tumors is challenging. At present, most segmentation methods use traditional convolutional neural networks. This paper uses a visual transformer to replace the encoder part of the neural network and innovatively adds a new attention mechanism, encoder-decoder transformer (EDformer), to the skip connection to learn local features. We also adopted a new type of skip connection to integrate low-level semantic features with high-level semantic features as much as possible. I named our method TAU-Net3+. Based on the experimental results of CT images of 300 patients, our proposed method can detect kidney and renal tumors with the highest accuracy. The mean dice coefficients of kidney and kidney tumors obtained by this method are 0.9885 and 0.8638, respectively, which are higher than the other three advanced segmentation methods.
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