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相关概念视频

Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

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Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Updated: Feb 14, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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一种基于图形神经网络压缩在边缘的牛行为识别方法.

Hongbo Liu1, Ping Song1, Xiaoping Xin2

  • 1Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Animals : an open access journal from MDPI
|February 13, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个基于边缘的牛行为识别系统,使用图形神经网络 (GNN) 压缩. 这种可穿戴设备可以实时,低功耗监控,用于精确的畜牧管理.

关键词:
牛行为识别 牛行为识别嵌入式机器学习 嵌入式机器学习一个模型的压缩压缩.可穿戴设备可穿戴设备.

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科学领域:

  • 农业技术 农业技术
  • 机器学习 机器学习
  • 动物科学动物科学

背景情况:

  • 牛行为监测对健康和管理至关重要.
  • 目前基于服务器的识别导致高功耗和延迟.
  • 边缘计算为实时,低功耗的牲畜管理提供了解决方案.

研究的目的:

  • 开发一种基于边缘的牛行为识别方法.
  • 为了减少畜牧监测中的电力消耗和计算延迟.
  • 通过智能设备实现精确和科学的畜牧管理.

主要方法:

  • 使用高性能嵌入式微控制器集成数据采集和边缘推断的可穿戴设备.
  • 一种使用惯性测量单元 (IMU) 和位移数据进行特征提取的顺序剩余模型.
  • 用Actor-Critic模型进行图形神经网络 (GNN) 压缩,以在浮点运算 (FLOP) 约束下进行最佳修剪.

主要成果:

  • 拟议的方法有效地在边缘设备上实时分类牛的行为.
  • 在计算延迟和功耗方面实现了显著的减少.
  • 该系统在低功率,长期牛行为监测方面表现出有效性.

结论:

  • 基于边缘的GNN压缩方法可以有效和准确地识别牛的行为.
  • 实时边缘推断有利于减少畜牧管理中的延迟和功耗.
  • 开发的系统通过智能低功耗设备支持精确和科学的畜牧管理.