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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.
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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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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Reducing Line Loss01:18

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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Norton's Theorem01:14

Norton's Theorem

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Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the...
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Propagation of Action Potentials01:23

Propagation of Action Potentials

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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双重目标对抗的例子是在图形神经网络上的逃避攻击中.

Hyun Kwon1, Dae-Jin Kim2

  • 1Department of Artificial Intelligence and Data Science, Korea Military Academy, Seoul, 01819, South Korea.

Scientific reports
|January 31, 2025
PubMed
概括

本研究介绍了图形神经网络 (GNN) 的双重目标对抗示例,使攻击同时对多个模型进行. 这进一步描绘了对抗性攻击,并强调了需要改进GNN防御的必要性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形神经网络 图形神经网络

背景情况:

  • 图形神经网络 (GNN) 越来越多地用于各种应用.
  • 现有的对抗性攻击主要集中在单一模型的错误分类上.
  • 需要更复杂的攻击,可以挑战多个GNN.

研究的目的:

  • 提出一种新的方法,用于在GNN中生成双重目标的对抗性示例.
  • 为了使对抗性攻击能够同时针对多个GNN模型,具有不同的错误分类目标.
  • 为了解决当前基于图形的对抗性攻击技术的局限性.

主要方法:

  • 开发一种新的方法来创建双重目标的对抗性示例.
  • 在各种 GNN 模型上严格评估拟议方法的有效性.
  • 使用Reddit和OGBN-Products等基准数据集对攻击影响的可视化.

主要成果:

  • 展示成功的双重目标对抗性示例生成.
  • 验证该方法能够影响具有不同目标的多个GNN的能力.
  • 经验证明了这些攻击对GNN性能具有破坏性的潜力.
关键词:
对抗性的例子.逃避攻击是一种逃避攻击.图表神经网络的神经网络机器学习是机器学习.节点的分类 节点的分类

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结论:

  • 拟议的双重目标对抗性攻击代表了基于图形的对抗性研究的重大进展.
  • 这些发现强调了GNN对复杂攻击的脆弱性.
  • 迫切需要为GNN开发强大和增强的防御策略.