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相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

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...
Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Three-Winding Transformers01:19

Three-Winding Transformers

Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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 rated...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Directional Relays01:25

Directional Relays

Directional relays, essential for managing unidirectional fault currents, enhance the safety and efficiency of power systems. On power lines equipped with directional relays, faults downstream (to the right) of the current transformer typically cause the fault current to lag the bus voltage by approximately 90 degrees, known as the forward direction. In contrast, upstream (left-side) faults may result in the fault current leading the bus voltage by nearly 90 degrees, termed the reverse...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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RTA-前任:反向变压器注意聚体细分

Zhikai Li, Murong Yi, Ali Uneri

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    概括

    这项研究介绍了RTA-Former,这是一种用于聚细分的新型深度学习网络. RTA-Former提高了边缘细分的准确性,改善了结直肠癌的早期检测和临床决策.

    科学领域:

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

    背景情况:

    • 聚细分对于预防结直肠癌和早期检测至关重要.
    • 深度学习有助于自动化聚体诊断,但精确的边缘细分仍然是一个挑战.

    研究的目的:

    • 引入RTA-Former,这是一个用于增强多片细分的新型网络.
    • 解决当前深度学习模型的局限性,以准确检测多边缘.

    主要方法:

    • 开发了基于变压器的网络RTA-Former.
    • 集成的反向注意 (RA) 具有变压器解码器阶段.
    • 在五个多片细分数据集上评估了性能.

    主要成果:

    • 在五个数据集中,RTA-Former实现了最先进的 (SOTA) 性能.
    • 在聚边缘细分方面表现出卓越的准确性.
    • 验证了新的反向注意力机制的有效性.

    结论:

    • RTA-Former显著提高了基于变压器的多片细分精度.
    • 增强的细分能力保证了更好的临床决策和患者的治疗结果.

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