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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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对复杂的网络系统进行深度学习弹性推断.

Chang Liu1,2, Fengli Xu1,2, Chen Gao1,2

  • 1Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China.

Nature communications
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习框架ResInf准确地从数据中推断出系统的弹性,克服了复杂网络传统分析方法的局限性.

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科学领域:

  • 复杂的网络分析.
  • 数据驱动的建模.
  • 机器学习用于系统科学科学.

背景情况:

  • 复杂的网络系统的弹性至关重要,在故障时能够实现功能.
  • 传统的弹性分析方法受到预定义方程和拓假设的限制.
  • 现实世界的系统需要更具适应性和基于数据的弹性评估.

研究的目的:

  • 引入ResInf,这是一个深度学习框架,可以直接从观测数据中推断系统弹性.
  • 克服分析方法在建模现实世界的网络动态和拓学的局限性.
  • 提供准确的弹性推断和可视化能力.

主要方法:

  • 开发了ResInf,这是一个结合变压器和图形神经网络的框架.
  • 从数据中学习了节点活动动态和网络拓的表示,而没有简化假设.
  • 对已建立的分析方法和尺寸缩小技术进行性能评估.

主要成果:

  • ResInf显著优于分析方法,F1得分得到了41.59%的改善.
  • 与光谱维度减小相比,表现出优异的性能 (14.32%的F1得分改善).
  • 展示了对未见的网络拓和动态的概括性,在干扰下具有强大的性能.

结论:

  • ResInf有效地解决了复杂的现实世界系统的弹性推断的差距.
  • 该框架为网络建模提供了数据驱动的方法,增强了弹性评估.
  • 深度学习集成为理解和预测系统弹性提供了强大的新视角.