简介：摘要

简介：AbstractBackground:Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

简介：摘要：管状带式输送机具有运输距离长、运量大、速度快等特点，在长周期的连续运转中，胀管和扭管问题是管带机常见的问题，一旦发上对输送生产造成极大的影响。本文介绍了通过激光三角监控原理对胀管、扭管检测研究。

简介：摘要：管状带式输送机具有运输距离长、运量大、速度快等特点，在长周期的连续运转中，胀管和扭管问题是管带机常见的问题，一旦发上对输送生产造成极大的影响。本文介绍了通过激光三角监控原理对胀管、扭管检测研究。