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相关概念视频

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
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

75
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...
75
Instrument Transformers01:23

Instrument Transformers

87
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
87
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
Basic Operations on Signals01:22

Basic Operations on Signals

388
Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
388
Differential Relays01:20

Differential Relays

142
Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
142

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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在时间序列数据中检测异常使用可逆实例规范异常变压器

Ranjai Baidya1, Heon Jeong2

  • 1Kpro System, 673-1 Dogok-ri, Wabu-eup, Namyangju-si 12270, Gyeonggi-do, Republic of Korea.

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

本研究介绍了可逆实例规范异常变压器 (RINAT) 用于异常检测. 通过分析正常化和非正常化数据关联,RINAT有效地识别了罕见的异常.

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 时间序列分析时间序列分析

背景情况:

  • 异常检测对系统完整性至关重要,但由于异常的不频繁性质,具有挑战性.
  • 现有的模型通常依赖于相邻数据点之间的关系,这对于罕见事件可能是不够的.

研究的目的:

  • 开发一种新的异常检测方法,利用异常的稀有性.
  • 介绍可逆实例规范异常变压器 (RINAT) 模型.

主要方法:

  • 瑞纳特是建立在异常变压器的基础上,结合了先前和序列协会.
  • 优先关联使用可学习的高斯内核来实现归纳偏差,而序列关联则在原始数据上采用自我注意.
  • 规范化数据用于序列关联,而非规范化数据用于先前的关联,以增强检测.

主要成果:

  • 拟议的方法通过战略性地使用规范化和非规范化数据,有效地增强模拟序列的关联.
  • 这种方法可以更好地检测关联差异,这对于识别异常至关重要.

结论:

  • 通过独特地利用数据规范化策略,RINAT提供了一种强大的异常检测方法.
  • 该模型能够捕获本地和全球数据模式,从而提高了罕见异常的识别能力.
关键词:
检测异常检测异常检测注意力机制注意力机制规范化的正常化.时间序列数据数据时间序列数据变压器的变压器是一个变压器.

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