Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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
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
Transformers in Distribution System01:27

Transformers in Distribution System

470
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...
470
Deconvolution01:20

Deconvolution

520
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
520
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

490
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...
490
Three-Winding Transformers01:19

Three-Winding Transformers

648
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...
648

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Sustainable closed-loop supply chain network design under uncertainty using a fuzzy multi-objective optimization framework for the battery industry.

Scientific reports·2026
Same author

Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.

MethodsX·2026
Same author

Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.

Frontiers in radiology·2026
Same author

A Lightweight Sequential AI Framework for Real Time Intrusion Detection in Dynamic Vehicular Networks.

Scientific reports·2026
Same author

Critical impact of automobile industry with advanced decision support system and Aczél-Alsina Hammy mean operators.

Scientific reports·2026
Same author

Evaluating blockchain-based waste management investments in smart cities using a multi-criteria decision support framework.

Scientific reports·2026
Same journal

Facile synthesis of model polystyrene nanoparticles for nanoplastics research.

MethodsX·2026
Same journal

Effectiveness of a posture education program in high school students: A randomized controlled trial protocol.

MethodsX·2026
Same journal

Development and characterization of silicone-based testosterone propionate implants for sustained androgen delivery in juvenile castrated male pigs.

MethodsX·2026
Same journal

Machine learning assisted multi-criteria decision-making approaches for site selection: A systematic review.

MethodsX·2026
Same journal

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery.

MethodsX·2026
Same journal

Decision tree and reinforcement learning for contextual electricity consumption forecasting in buildings.

MethodsX·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

2.2K

使用变压器解码器绘制注意力地图的视觉可解释性方法用于图像标题.

Meena Kowshalya1, Suchitra2, Rajesh Kumar Dhanaraj3

  • 1Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India.

MethodsX
|December 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种可解释的图像标题框架,使用卷积神经网络编码器和变压器解码器. 它提高了人工智能决策的透明度,以确保可靠的应用.

关键词:
卷积神经网络是一种卷积神经网络.可解释的人工智能图片标题图片标题图片标题变压器模型变压器模型视觉注意力地图 视觉注意力地图

相关实验视频

Last Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

2.2K

科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 自然语言处理自然语言处理.

背景情况:

  • 图像标题模型经常充当黑子,在决策过程中缺乏透明度.
  • 在视觉语言模型中需要可解释的AI (XAI),以建立信任和可靠性.

研究的目的:

  • 开发一个新的可解释的图像标题框架,整合视觉可解释性.
  • 解决当前视觉语言模型中的透明度差距.

主要方法:

  • 使用一个卷积神经网络 (CNN) 编码器和一个变压器解码器架构.
  • 集成的基于注意力的热图,为生成的标题提供视觉解释.
  • 在MS COCO数据集上使用标准指标 (BLEU,METEOR,CIDER,SPICE) 评估性能和可解释性.

主要成果:

  • 拟议的框架为图片标题的决策过程提供了透明度.
  • 基于注意力的热图有效地突出了影响标题生成的视觉特征.
  • 该方法平衡了标题质量与增强的可解释性.

结论:

  • 开发的框架提高了人工智能系统的可信度和透明度.
  • 这种方法适用于医疗保健,教育,安全和预测等关键应用.
  • 它通过在视觉语言模型中弥合性能和可解释性来促进可解释AI的进步.