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相关概念视频

Line Loss01:10

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
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Directing Proteins to the Rough Endoplasmic Reticulum01:34

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The organelle-specific signaling sequences direct proteins synthesized in the cytosol to their final destination like ER, mitochondria, peroxisomes, etc. Some of the proteins directed to ER are then trafficked via vesicles to other organelles within the cell or the extracellular environment through the Golgi complex. For example, the rough ER synthesizes soluble proteins for transportation to the lysosomes or secretion out of the cell. It can also synthesize transmembrane proteins that can...
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Reducing Line Loss01:18

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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.
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
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In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
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相关实验视频

Updated: Jan 27, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于深度学习的精确医学图像细分的模糊粗略设置损失.

Mohsin Furkh Dar1, Avatharam Ganivada2

  • 1School of Computer Science, UPES, Dehradun, 248007, India.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|January 25, 2026
PubMed
概括

一个新的Fuzzy Rough Set (FRS) 损失函数通过增强边界灵敏度和处理不确定性来改善医疗图像细分. 这种方法显著提高了各种数据集的准确性,为精确的细分任务提供了强大的解决方案.

关键词:
边界检测检测 边界检测检测深度学习是一种深度学习.模糊的粗设置模糊的粗设置.损失函数是一个损失函数.医疗图像细分 医疗图像细分

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 准确的医学图像细分对于诊断和治疗规划至关重要.
  • 挑战包括模两可的损伤界限,阶级不平衡和复杂的解剖学.
  • 现有的方法在精确的边界划分和数据不平衡方面扎.

研究的目的:

  • 引入一种新的 Fuzzy Rough Set (FRS) 损失函数,用于增强医疗图像细分.
  • 为了应对模两可的边界和阶级不平衡的挑战.
  • 为了提高细分模型的准确性和稳定性.

主要方法:

  • 开发了一种灵感来自 Fuzzy Rough Set (FRS) 的损失函数,整合了模糊相似关系和边界不确定性模型.
  • 使用模糊的下/上近似值和成员权重用于边界不确定性模型.
  • 采用凸组合方法将模糊相似性和边界不确定性组成部分合并.

主要成果:

  • 实现了优越的细分性能,与基线方法相比,平均2.1%的子得分有所改善.
  • 在所有评估指标 (p < 0.001) 上显示了统计学上显著的改善.
  • 显示了强度到中度的类失衡,并保持了计算效率 (0.075-0.12s推断时间,4.5MB内存).

结论:

  • FRS损失函数为精确的医疗图像细分提供了一个强大的和可解释的框架.
  • 它有效地处理模两可的界限和适度的阶级不平衡.
  • 该方法显示了改善诊断和治疗规划准确性的巨大潜力.