简介:为混合雷达扫描的传统的算法使用贡献雷达的数字举起模型(DEM)数据和纬度,经度和高度驻扎的标准地面。当雷达车站地点信息经常是不精密的时,由于树,大楼,和另外的表面对象表明阻塞没在DEM数据被包括。因此,用这个传统的算法导出的混合扫描举起对错误敏感。这里,反射率气候学数据(反射率的出现的频率)被用来为混合扫描改进算法。三个参数被介绍,然后用一种模糊逻辑技术为每雷达箱适用于信号阻塞的评估。这个新算法为混合扫描提供最低解块的举起的一颗改进决心。新算法被在案例研究检验计算混合扫描反射率的范围和连续性验证,并且这个基于气候学的算法的表演相对传统的基于地面的算法被评估。基于气候学的混合扫描然后被用来检验运作的天气雷达网络在西藏的高原上提供的空间范围。结果显示基于地面的混合扫描算法介绍了在混合扫描反射率引起了明显的塑造V的差距的错误。由对比,基于气候学的混合扫描算法更精确地决定了最低解块的举起并且减少了阻塞的影响。范围地图在西藏的高原上说明天气雷达网络的限制。这些限制禁止雷达数据的实用性。另外的雷达或观察数据被需要充满这些差距并且最小化信号阻塞的影响。
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.