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相关概念视频

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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Sequence Networks of Rotating Machines01:24

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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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

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Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
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Network Function of a Circuit01:25

Network Function of a Circuit

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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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Block Diagram Reduction01:22

Block Diagram Reduction

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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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学位感知图 神经网络定量化

Ziqin Fan1, Xi Jin1

  • 1Institute of Microelectronics, Department of Physics, University of Science and Technology of China, No. 96 Jinzhai Road, Hefei 230026, China.

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|November 24, 2023
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概括
此摘要是机器生成的。

本研究引入了用于量化图形神经网络 (GNN) 的新方法,解决了网络灵活性和节点度变化的挑战,以提高GNN应用中的准确性.

关键词:
数据图表数据图表数据图表神经网络的神经网络网络定量化的网络量化.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 图形神经网络 (GNN) 在各种任务中取得了很大的成功.
  • 对于卷积神经网络的现有量子化方法在应用于GNN时面临挑战.
  • 这些挑战包括不灵活的参数缩放和不均的响应,由于不同的节点程度.

研究的目的:

  • 开发一个有效的GNN量化策略.
  • 克服固定尺度参数和GNN量子化中的节点度变化的局限性.
  • 提高量子化GNN的准确性和适用性.

主要方法:

  • 引入了可学习的尺度参数,与GNN一起优化.
  • 拟议的度意识正常化来处理不同的节点度.
  • 在各种任务,基线和数据集上进行实验.

主要成果:

  • 与现有的最先进的量子化技术相比,提出的方法显示出更高的性能.
  • 可学习的缩放参数在不同的GNN架构和任务中提供了灵活性.
  • 度意识正常化有效地解决由节点度变化引起的精度下降.

结论:

  • 开发的量子化方法显著提高了GNN的性能.
  • 该方法为GNN量子化提供了更强大,更适应性的解决方案.
  • 这项工作为在资源有限的环境中高效部署GNN铺平了道路.