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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

491
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
491

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Updated: Jan 9, 2026

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有效和准确的肺炎检测使用一种新的多尺度变压器方法.

Alireza Saber1, Amirreza Fateh2, Pouria Parhami1

  • 1Faculty of Computer Engineering, Shahrood University of Technology, Shahrood 36199-95161, Iran.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的AI方法,用于使用胸部X射线检测肺炎. 该方法提高了诊断的准确性和效率,为医学成像分析提供了宝贵的工具.

关键词:
这是分类分类的分类.多个尺度的多个尺度.肺炎是一种肺炎.细分化 细分化的细分化变压器变压器变压器变压器

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 肺炎是全球主要的健康问题,导致严重的发病率和死亡率.
  • 胸部X射线对于肺炎诊断至关重要,但由于图像的变化,它们面临解释挑战.
  • 自动化系统可以提高诊断的一致性,并支持肺炎检测的临床决策.

研究的目的:

  • 开发一种新的多尺度变压器方法,用于整合肺部细分和肺炎分类.
  • 通过胸部X射线提高自动肺炎检测的准确性和效率.
  • 创建一个计算效率高的模型,适合资源有限的临床环境.

主要方法:

  • 一个轻量级的变压器增强的TransUNet被用于精确的肺部细分 (95.68%的子得分).
  • 预先训练的ResNet模型 (ResNet-50,ResNet-101) 提取了用于分类的多尺度特征.
  • 一个卷积的剩余注意力模块和一个修改过的变压器处理了功能,以改善肺炎检测.

主要成果:

  • 拟议的方法在Kermany数据集上达到93.75%的准确性,在Cohen数据集上达到96.04%的准确性.
  • 肺部细分组件表现出高性能,使用的参数比传统变压器少.
  • 综合方法在肺炎检测准确性和计算效率方面超过了现有方法.

结论:

  • 新的多尺度变压器方法为肺炎检测提供了强大而高效的解决方案.
  • 针对细分和分类的统一框架提高了医学成像诊断的可靠性.
  • 这种人工智能驱动的方法有望改善患者的治疗结果,并支持肺炎诊断中的临床工作流程.