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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

615
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...
615
Reducing Line Loss01:18

Reducing Line Loss

406
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

1.5K
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
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基于视觉变压器模型的剂量预测和光束角度优化,用于BNCT.

Yuliang Zong1, Changran Geng2, Gensheng Qian3

  • 1Department of Nuclear Science and Technology, Nanjing University of Aeronautics an d Astronautics, Nanjing University of Aeronautics an d Astronautics, Nanjing, 211106, China.

Physics in medicine and biology
|February 26, 2026
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概括

这项研究引入了一种新的深度学习模型,用于中子捕获疗法 (BNCT) 治疗计划. 人工智能准确预测辐射剂量,改善瘤向性和减少器官损伤,以获得更好的患者结果.

关键词:
在 BNCT 上,你会发现.贝叶斯的优化是贝叶斯的优化.剂量预测的预测剂量预测 视觉变压器 贝叶斯优化 BNCT BNCT视觉变压器 视觉变压器

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 人工智能在医学中的应用

背景情况:

  • 准确的剂量预测对于有效的中子捕获疗法 (BNCT) 治疗计划至关重要.
  • 目前的蒙特卡洛 (MC) 模拟提供了精度,但计算密集,限制了规划效率.
  • 开发更快,更准确的剂量预测方法对于优化BNCT计划至关重要.

研究的目的:

  • 开发一种先进的神经网络模型,以高效准确地预测BNCT剂量分布.
  • 将这个模型与贝叶斯优化进行集成,以选择最佳光束角度.
  • 通过改善瘤剂量输送和将风险器官的剂量降至最低来提高治疗规划.

主要方法:

  • 一个使用3D视觉转换器 (ViT) 和Mamba模块进行剂量预测的深度学习框架.
  • 纳入一个兴趣区域 (ROI) 引导的注意力机制,专注于瘤总体积 (GTV) 和皮肤.
  • 将预测剂量集成到贝叶斯光束角度选择的优化策略中.

主要成果:

  • 该模型在剂量预测方面取得了高准确性,平均绝对误差 (MAE) 在GTV下低于0.6 Gy,在皮肤下低于0.15 Gy.
  • 马传递率超过了90% (2mm/2%) 和97% (3mm/3%),表明与MC模拟的良好一致.
  • 治疗优化导致GTV最低剂量平均增加1.8 Gy,而没有增加风险器官 (OAR) 最大剂量.

结论:

  • 拟议的深度学习方法为BNCT提供了准确的剂量预测和高效的优化.
  • 结果与MC模拟进行验证,证明其临床潜力.
  • 这种方法可以促进自动化BNCT治疗规划和优化.