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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

142
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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Neural Circuits01:25

Neural Circuits

1.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Support Reactions in Three Dimensions01:27

Support Reactions in Three Dimensions

1.1K
Support reactions in three dimensions help maintain the stability and equilibrium of various structures and systems. These reactions prevent the system from translating and rotating, ensuring the design can withstand external forces and perform its intended function efficiently and safely. Some of the supports providing support reactions in three dimensions are discussed below:
Ball and Socket Joint is one of the supports allowing free rotation about any axis. This freedom of rotation is...
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Vector Transformation in Rotating Coordinate Systems01:16

Vector Transformation in Rotating Coordinate Systems

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Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
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Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
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相关实验视频

Updated: Sep 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Published on: September 8, 2023

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对于3D图形数据的旋转和换等价量子图神经网络.

Wenjie Liu, Yifan Zhu, Ying Zha

    IEEE transactions on pattern analysis and machine intelligence
    |July 28, 2025
    PubMed
    概括

    我们介绍了一种新的旋转和换等价量子图神经网络 (RP-EQGNN),用于处理3D图数据. 该模型通过更好地利用几何和非几何信息,显著提高了图形回归和点云分类任务的性能.

    科学领域:

    • 人工智能的人工智能
    • 量子计算是一种量子计算.
    • 计算化学计算化学

    背景情况:

    • 现有的等同变量量子图神经网络 (EQGNN) 主要考虑 permutation 对称性,限制了它们与 3D 图数据的有效性.
    • 未能充分利用几何和非几何信息导致当前EQGNN模型在复杂的3D图形处理中表现不佳.

    研究的目的:

    • 开发一种新的量子图神经网络,该神经网络包含旋转和顺序等差,用于增强的3D图数据处理.
    • 通过更有效地提取几何和非几何特征来解决现有的EQGNN的局限性.

    主要方法:

    • 对于旋转和顺序等差的约束值的导出.
    • 一个新的旋转和换等价量子图神经网络 (RP-EQGNN) 的建议.
    • 设计用于几何信息提取的等价模块和用于非几何信息提取的卷积纠模块.
    • 实施边缘纠策略,以根据边缘异质性区分纠操作.

    主要成果:

    • 与Q3DGL和EQC相比,RP-EQGNN在QM9和OC20数据集上的图形回归中表现出卓越的性能,实现较低的平均绝对误差 (MAE).
    • 该模型的结果与EquiformerV2,Geoformer,SO3KRATES和HEGNN.等最先进的方法相美.
    • 在ModelNet40数据集上,RP-EQGNN在点云分类方面比sQCNN-3D和PI-QSVM等量子模型有优势.

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    结论:

    • RP-EQGNN提供了一种创新的方法来处理3D图形数据,通过有效利用旋转和变换对称性.
    • 开发的模型为未来研究图形神经网络中的对称性奠定了基础,特别是在复杂的3D应用中.