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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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相关实验视频

Updated: Sep 20, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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QDNet:用于视觉流量知识图表生成的查询删除网络.

Yunfei Guo, Fei Yin, Xiao-Hui Li

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    |May 23, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了视觉交通知识图生成 (VTKGG) 的新方法,以全面了解交通场景. 查询删除网络 (QDNet) 提高了智能交通系统的准确性和稳定性.

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    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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    Published on: June 13, 2025

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    相关实验视频

    Last Updated: Sep 20, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

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    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 智能运输系统 智能运输系统

    背景情况:

    • 交通场景感知对于智能交通系统至关重要,但目前的方法缺乏全面的场景理解.
    • 现有的方法往往侧重于特定的元素,未能捕捉到交通环境的整体性质.

    研究的目的:

    • 解决视觉交通知识图生成 (VTKGG) 任务,旨在全面表示交通场景信息.
    • 开发一个端到端的框架,集成多个子任务,以有效地生成知识图表.

    主要方法:

    • 提议查询删除网络 (QDNet) 通过多种查询集成子任务,从而实现端到端生成视觉交通知识图.
    • 在培训期间采用查询否定策略,通过从噪音输入中恢复地面真相来提高模型的稳定性和性能.

    主要成果:

    • 通过消除中间步骤,QDNet有效地简化了视觉交通知识图的生成.
    • 查询-denoising方法显著提高了模型的准确性,稳定性和整体性能.
    • 实验证明了拟议框架在现有方法上的优越性,以及其在类似任务上的有效性,例如泛光场景图生成.

    结论:

    • 拟议的QDNet框架为视觉流量知识图生成提供了一种优越的方法.
    • 查询-denoising策略有效地提高了计算机视觉中的多任务学习模型的性能和稳定性.
    • 这项工作促进了对智能交通系统的交通场景的全面理解和表示.