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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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使用边缘计算框架进行建筑工地图像分类.

Gongfan Chen1, Abdullah Alsharef2, Edward Jaselskis1

  • 1Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种高效的边缘计算框架,用于建筑工地实时图像分类. 该系统可以在没有互联网的情况下实现AI应用,改善项目监控和安全.

关键词:
边缘 TPU TPU 的使用拉斯伯派 (Raspberry Pi) 是一款非常有价值的小米电脑.建筑图像分类,建筑图像分类.边缘计算是一种边缘计算.材料的分类材料的分类.定量化定量化是什么安全检测检测安全检测检测转移学习转移学习

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科学领域:

  • 建筑技术 建筑技术 建筑技术
  • 人工智能的人工智能
  • 边缘计算 边缘计算

背景情况:

  • 建筑工地实时图像分类对于项目监控至关重要,但受到现场计算资源和连接性差的限制.
  • 现有的解决方案与缺乏电信支持或经历信号减弱的远程站点作斗争.

研究的目的:

  • 为实时构建人工智能应用提出一个高效的边缘计算支持的图像分类框架.
  • 开发一个能够克服现场计算和互联网依赖性局限性的系统.

主要方法:

  • 开发了一种轻量级的二进制图像分类器,使用MobileNet传输学习和定量化.
  • 组装了一个完整的边缘计算硬件模块 (树派,边缘TPU,电池).
  • 集成了一个多式联网软件模块 (视觉,文本,音频数据) 用于智能分类.

主要成果:

  • 该框架成功地同步了多式联运数据,用于零延迟分类.
  • 在没有互联网连接的情况下,在材料分类和安全检测 (例如,识别危险的钉子) 中表现出有效性.
  • 该系统保持了准确性,同时通过量子化减少了模型大小.

结论:

  • 拟议的边缘计算框架可以在建筑工地实时实现人工智能应用,即使在偏远的地点.
  • 施工经理可以利用该系统进行集中管理,提高准确性和安全性,而无需额外投资.
  • 这项研究促进了未来建筑的边缘智能,促进了人与技术的互动,而无需高速互联网.