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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

891
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...
891
Survival Tree01:19

Survival Tree

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

Transformers with Off-Nominal Turns Ratios

164
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...
164
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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...
11.4K
Transformers in Distribution System01:27

Transformers in Distribution System

109
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...
109
Types Of Transformers01:16

Types Of Transformers

991
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...
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相关实验视频

Updated: Jul 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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一个基于变压器的深度学习框架来预测员工的消耗.

Wenhui Li1

  • 1School of Information Science and Engineering, Shandong Normal University, Shandong, China.

PeerJ. Computer science
|October 9, 2023
PubMed
概括
此摘要是机器生成的。

使用先进的变压器神经网络预测员工磨损,可显著提高准确性并降低业务成本. 这种数据驱动的方法通过提供可靠的员工流动洞察力来增强战略决策.

关键词:
人工智能的人工智能是人工智能.磨损预测的预测数据科学是数据科学.深度学习是一种深度学习.机器学习 机器学习

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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相关实验视频

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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科学领域:

  • 业务分析 业务分析
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 员工消耗对企业利管理产生负面影响.
  • 预测建模提供了一个解决方案,以减轻与员工流动相关的成本.
  • 现有的模型缺乏现实世界的评估和融入决策支持系统.

研究的目的:

  • 为了评估基于变压器的神经网络的员工磨损预测.
  • 评估预测模型在业务决策支持系统中的整合.
  • 确定准确的消耗预测对战略业务决策的影响.

主要方法:

  • 基于变压器的神经网络模型的实施.
  • 使用上下文嵌入来适应表格数据.
  • 应用到IBM HR员工磨损数据集中的应用.

主要成果:

  • 变压器模型显示,与最先进的模型相比,预测效率显著提高.
  • 该研究验证了拟议的计算技术的有效性.
  • 深度学习,特别是变压器网络,对表式和不平衡数据显示出希望.

结论:

  • 基于变压器的神经网络对于预测员工磨损非常有效.
  • 这些模型可以集成到战略商业规划的决策支持系统中.
  • 深度学习为分析复杂,现实世界的业务数据提供了强大的方法.