Optimization of the Open Degree of Key Valves Based on Relative Entropy and Pipeline Leakage

Abstract

Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expertlevel accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.

Study on risk prevention and control of third-party construction damage of underground pressure pipelines

“…Optical fiber sensing monitoring technology [10] is to identify abnormal behaviors such as construction damage by analyzing and processing the collected signals when light wave propagates in optical fiber and its characteristic parameters will change under external influence. Acoustic signal/vibration signal monitoring [11][12] refers to laying sound sensor or vibration sensor on the pipeline, and identifying the pipeline damage characteristics through signal processing methods such as filtering technology for the collected acoustic signal/vibration signal. In these methods, although the video monitoring can identify abnormal behavior through AI (Artificial Intelligence) technology, the video imaging quality is sometimes affected by the environment.…”