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相关概念视频

Spherical and Cylindrical Capacitor01:26

Spherical and Cylindrical Capacitor

A spherical capacitor consists of two concentric conducting spherical shells of radii R1 (inner shell) and R2 (outer shell). The shells have equal and opposite charges of +Q and −Q, respectively. For an isolated conducting spherical capacitor, the radius of the outer shell can be considered to be infinite.
Conventionally, considering the symmetry, the electric field between the concentric shells of a spherical capacitor is directed radially outward. The magnitude of the field, calculated by...
Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.

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相关实验视频

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Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
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原始单端电容传感器用于对变形结构的连续形状估计.

Lala Ray1, Daniel Geißler2, Bo Zhou2,3

  • 1German Research Center for Artificial Intelligence (DFKI), Embedded Intelligence, Kaiserslautern, Germany. lala_shakti_swarup.ray@dfki.de.

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|July 29, 2024
PubMed
概括

我们开发了FxC,这是一种新的变形电容传感方法,使用原木结构来跟踪形状. 这种方法通过分析嵌入于原木的单端电容传感器的变化来准确检测结构运动,达到高达95%的R平方相关性.

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科学领域:

  • 机器人与机械工程 机器人与机械工程
  • 传感器技术 传感器技术
  • 材料科学 材料科学 材料科学

背景情况:

  • 传统的形状跟踪方法经常与复杂的,变形结构作斗争.
  • 现有的电容传感技术通常涉及双板电容器,限制了设计灵活性.
  • 需要新的传感方法,能够实时重建动态,可折叠结构的形状.

研究的目的:

  • 引入FxC,一种新的单端变形电容传感方法,用于准确的形状跟踪.
  • 为了证明将折叠式原木结构与用于运动检测的电容传感器相结合的有效性.
  • 从传感器数据中重建动态结构几何学的强大管道.

主要方法:

  • 通过将导电材料集成到原木结构中作为单端电容传感贴片来开发FxC.
  • 利用3D几何模拟和基于物理的推断来理解变形单端电容器的操作原理.
  • 创建了一个软件管道,使用深度神经网络回归来从传感器信号和视觉跟踪数据中进行动态形状重建.
  • 验证了使用各种原创折叠模式 (协奏曲,雪佛龙,太阳光,V折叠) 与基于纸张和基于织品的材料的方法.

主要成果:

  • 传感器信号显示与变形结构运动相一致的变化.
  • 基于物理的模拟证实了对单端电容器行为的实验观测.
  • 深度学习模型在预测的几何原体和视觉基础真相之间实现了强烈的相关性 (R平方高达95%).
  • 在检测补丁时,显示了6.5mm的低跟踪误差.

结论:

  • FxC提供了一种独特的解决方案,通过利用原木的机械性能和电容传感来跟踪形状.
  • 该方法通过模拟和机器学习的协同方法实现了准确的动态形状重建.
  • 集成与原木的单端电容传感为先进的结构监测和机器人技术提供了一个有前途的途径.