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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
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Radiation Pressure: Problem Solving01:09

Radiation Pressure: Problem Solving

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The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
The average value of the rate of momentum transfer divided by the absorbing area represents the average force...
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Two-Dimensional Force System01:20

Two-Dimensional Force System

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A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
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Three-Dimensional Force System01:30

Three-Dimensional Force System

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In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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基于超维计算的增强耐噪声压力系统

Fatemeh Asgarinejad1,2, Xiaofan Yu1, Danlin Jiang1

  • 1Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括
此摘要是机器生成的。

我们使用高维计算 (HDC) 开发了一种具有成本效益的压力系统,用于人类活动识别 (HAR). 与传统方法相比,这种新的系统提供了更好的准确性和对噪声的弹性,能耗显著降低.

关键词:
人类活动的认可 人类活动的认可超维的计算超维的计算.压力传感器 压力传感器

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科学领域:

  • 传感器技术 传感器技术
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 传统的室内压力传感和人类活动识别 (HAR) 系统通常使用昂贵的,高分辨率的子和计算要求高的神经网络 (NN) 模型.
  • 这些传统方法容易受到噪音的影响,这影响了它们在现实应用中的可靠性和效率.

研究的目的:

  • 为HAR设计和评估一个具有成本效益和耐噪声的压力系统.
  • 为了提高性能,利用Velostat的智能压力传感器和一款全新的超维计算 (HDC) 分类器来提高性能.
  • 引入一个专门为噪声弹性设计的新HDC算法.

主要方法:

  • 使用Velostat进行智能压力传感的压力系统的开发.
  • 实施一个新的超维计算 (HDC) 分类器用于HAR.
  • 收集两个数据集以捕捉静态和连续的人类运动.
  • 在各种噪声条件下分析系统的性能 (内存/通信,输入,传感器,高斯式).

主要成果:

  • 基于HDC的分类算法实现了93.19%的准确性,超过了最先进的卷积神经网络 (CNN) 的9.47%.
  • 与传统方法相比,该系统可以减少85%的能源消耗.
  • 拟议的HDC算法表现出对内存/通信,输入和传感器噪声的增强弹性.
  • 在高斯噪声的存在下,该系统实现了92.15%的准确性 (静态数据为97.51%),与CNN相比显著改善.

结论:

  • 提出的具有HDC的成本效益的压力系统为HAR提供了优质的替代方案,提供了更高的精度和对噪声的强度.
  • 新的HDC耐噪声算法在具有挑战性的环境中显著提高了系统可靠性.
  • 这种方法为低功耗,高性能HAR系统提供了一个有希望的方向.