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相关概念视频

Transformers01:26

Transformers

1.7K
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...
1.7K
Transformers in Distribution System01:27

Transformers in Distribution System

498
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...
498
Survival Tree01:19

Survival Tree

389
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
389
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

748
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
748
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K

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相关实验视频

Updated: Jan 17, 2026

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

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用生成性AI解释太阳能预测:一个两阶段的框架,将变压器和LLM结合起来.

Ayesha Siddiqa1, Nadim Rana2, Wazir Zada Khan1

  • 1Department of Computer Science, University of Wah, Wah, Pakistan.

PloS one
|September 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了SolarTrans,这是一个混合深度学习和大型语言模型 (LLM) 框架,用于准确的太阳能预测. 该模型通过提供可解释和精确的短期功率预测来增强光伏系统集成.

相关实验视频

Last Updated: Jan 17, 2026

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.4K

科学领域:

  • 可再生能源系统可再生能源系统
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 准确的太阳能预测对于将光伏 (PV) 系统集成到能源网中至关重要.
  • 当前的预测方法往往缺乏解释性,阻碍了信任和采用.

研究的目的:

  • 为准确和可解释的太阳能预测开发一种新的两阶段混合框架.
  • 加强光伏系统与现代能源基础设施的整合.

主要方法:

  • 基于变压器的编码器解码器架构 (SolarTrans) 用于短期直流功率预测.
  • 使用了多变量时间序列数据,包括天气和逆变器数据.
  • 一个生成的大型语言模型 (LLM),Flan-T5,被微调为生成预测的自然语言解释.

主要成果:

  • 太阳能运输模型在光伏电站数据集上表现出强大的预测性能,实现了低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE),以及高的R2得分.
  • 解释模块生成了高准确性,与域相关的自然语言解释,由强的ROUGE和BLEU分数证明.

结论:

  • 拟议的混合框架有效地提高了太阳能预测的准确性和可解释性.
  • 这种方法通过提供可靠和可理解的预测,促进了光伏系统的更好整合.