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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

880
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...
880
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

437
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...
437
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

160
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...
160
Three-Winding Transformers01:19

Three-Winding Transformers

234
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...
234
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Types Of Transformers01:16

Types Of Transformers

983
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
983

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

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小样本建筑能源消耗预测使用对比变压器网络.

Wenxian Ji1, Zeyu Cao2, Xiaorun Li1

  • 1College of Electrical Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

由于数据有限,很难预测大型场馆的能源消耗. 一个新的对比变压器网络 (CTN) 使用自主监督学习来改善能源使用预测,即使数据稀缺.

科学领域:

  • 能源管理 能源管理
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 由于数据稀缺和使用模式波动,在大型曝光中心准确预测能源消耗是具有挑战性的.
  • 现有的方法往往在有限的数据集上扎,阻碍了有效的能源管理策略.

研究的目的:

  • 引入一种新的算法,即对比变压器网络 (CTN),用于预测大型曝光中心的能源消耗.
  • 解决能源预测模型中有限的数据集和波动的电力使用模式所带来的挑战.

主要方法:

  • 该研究使用基于自主监督学习的对比变压器网络 (CTN).
  • 对比式学习适用于跨时间和上下文维度.
  • 基于变压器的架构用于高效的特征提取.

主要成果:

  • 在预测能源消耗方面,CTN表现强,特别是在数据样本有限的场景中.
  • 在专有数据集上的实验验验证了CTN算法的有效性.
  • 该网络擅长捕捉庞大的结构中复杂的能源使用模式.

结论:

  • 对比变压器网络 (CTN) 为大型曝光中心的能源消耗预测提供了强有力的解决方案.
关键词:
相反的学习学习学习.预测能源消耗 预测能源消耗一个小样本的学习学习.

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  • 拟议的方法在改善这些设施的能源管理和效率方面具有显著的前景.
  • 自主监督学习与变压器架构相结合,对于使用稀缺数据进行能源预测是有效的.