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相关概念视频

Orthogonal Trajectories01:26

Orthogonal Trajectories

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Histone Modification02:32

Histone Modification

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
The enzyme histone acetyltransferase adds acetyl group to the histones. Another enzyme, histone...
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Control Volume and System Representations01:16

Control Volume and System Representations

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
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Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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用于持续学习的输入数据矩阵表示与边缘设备上的直角重量修改的比较.

Ronald Mendez1, Andreas Maier2, Johannes Emmert1

  • 1Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Flugplatzstr. 75, 90768 Fürth, Germany.

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概括

人工神经双胞胎 (ANT) 与直角重量修改 (OWM) 结合,使智能工业设备中的自主学习成为可能. 费舍尔矩阵为大型AI模型提供了高效的解决方案,而NEig-OWM适合需要更多控制的较小设备.

关键词:
物联网的物联网,就是物联网.移动边缘计算 移动边缘计算人工神经双胞胎是什么意思持续的学习,持续的学习.分布式学习是一种分布式的学习.正角重量修改的正角重量修改

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科学领域:

  • 人工智能的人工智能
  • 事物的工业互联网 (IIoT)
  • 机器学习 机器学习

背景情况:

  • 工业过程越来越多地使用智能设备进行自动化和优化.
  • 工业物联网 (IIoT) 促进了设备通信,但缺乏先进的流程优化.
  • 物体检测传感器是智能工业应用中的关键组件.

研究的目的:

  • 探索人工神经双胞胎 (ANT) 作为工业过程的分布式优化工具.
  • 调查持续学习 (CL) 方法的集成,如用于自主设备学习的直角重量修改 (OWM).
  • 为了比较矩阵近似方法来降低资源受限设备上的CL算法中的计算复杂性.

主要方法:

  • 使用物体检测传感器作为ANTT和OWM的测试台.
  • 实现并比较了费舍尔矩阵,NEig-OWM和LoRA用于CL的矩阵近似.
  • 评估了计算成本,硬件要求和模型性能之间的权衡.

主要成果:

  • 费舍尔矩阵被证明是C.L.最便宜的近似计算方法.
  • 在大型AI模型中使用费舍尔矩阵用于CL时观察到微不足道的性能降低.
  • NEig-OWM证明适用于需要对CL过程进行更大的控制的较小模型.

结论:

  • 费舍尔矩阵是一种可行且具有成本效益的解决方案,可以在大型工业人工智能系统中实现持续学习.
  • 对于资源有限的微控制器来说,NEig-OWM提供了一种更受控的持续学习方法.
  • 与高效的CL矩阵近似相结合的ANT可以显著提升IIoT环境中的自主流程优化.