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相关概念视频

Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Transformers in Distribution System01:27

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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...
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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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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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MMTransformer:用于多组件应用的多变量时间序列资源预测模型.

Guangzhang Cui1,2, Tao Hu2, Wei Zhang2

  • 1State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310012, China.

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PubMed
概括
此摘要是机器生成的。

MMTransformer通过考虑组件间的依赖关系,增强了多组件应用程序的资源预测. 这种多变量时间序列模型显著提高了比传统方法更准确的预测准确性.

关键词:
MM变压器 变压器 变压器多组件应用程序的多组件应用程序.多尺度编码器解码器多阶段的注意力注意力.多变量时间序列资源预测基于分段的嵌入方式

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 在复杂的多组件应用程序中,高效的资源预测是具有挑战性的,原因是组件之间的依赖性和资源相互作用.
  • 现有的单变量和单步预测模型未能捕捉到这些系统的复杂动态,导致性能低于最佳.
  • 需要先进的多变量时间序列预测模型来考虑系统复杂性,这对于有效的资源管理至关重要.

研究的目的:

  • 介绍MMTransformer,一种专门为多组件应用设计的新型多变量时间序列预测模型.
  • 通过结合组件间的依赖关系和动态信息变化来解决现有方法的局限性.
  • 在复杂的应用环境中显著提高资源预测的准确性.

主要方法:

  • 开发了一种多变量时间序列预测模型MMTransformer.
  • 实施了针对有效的序列特征捕获的细分嵌入策略.
  • 利用多阶段的注意力机制来建模变量间的依赖关系.
  • 采用多级编码解码器结构,以适应本地和全球信息动态.

主要成果:

  • 与传统模型 (LSTM,GRU,RNN) 相比,MMTransformer表现出显著的改进,平均平方误差 (MSE) 减少了42.15%,平均绝对误差 (MAE) 减少了35.37%.
  • 与最先进的模型 (Fedformer,Autoformer,Informer) 相比,MMTransformer实现了MSE的平均减少27.14%,MAE的平均减少25.55%.
  • 关于课程制作和数字人视频创建系统的实验结果验证了该模型在资源预测方面的卓越性能.

结论:

  • MMTransformer有效地捕获了序列特征和变量之间的依赖关系,这对于在多组件应用中准确预测资源至关重要.
  • 该模型的多尺度架构允许适应动态信息变化,从而提高预测准确性.
  • MMTransformer代表了复杂计算系统的多变量时间序列预测的重大进步.