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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

619
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
619
Associative Learning01:27

Associative Learning

283
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
283
Weighted Mean00:57

Weighted Mean

4.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.9K
Distributed Loads01:19

Distributed Loads

505
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
505
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

515
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
515
Machines: Problem Solving II01:30

Machines: Problem Solving II

288
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
288

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相关实验视频

Updated: May 28, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
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工业物联网中的高效协作学习使用联邦式学习和基于Shapley值的自适应权重.

Dost Muhammad Saqib Bhatti1, Mazhar Ali1, Junyong Yoon1

  • 1School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括

本研究介绍了Shapley基于价值的工业物联网 (IIoT) 联合学习 (FL) 方法,以提高AI模型的准确性和效率. 适应权重机制通过考虑数据多样性和降低计算成本来增强全球模型培训.

关键词:
沙普利的价值是什么意思深度神经网络是一个神经网络.分布式学习是一种分布式的学习.联合学习的联合学习.工业物联网工业物联网工业物联网

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 工业物联网的工业物联网.

背景情况:

  • 联合学习 (FL) 能够在保护数据隐私的同时进行协作AI模型培训.
  • 跨行业的数据多样性可能会阻碍FL全球模型培训的有效性.
  • 工业物联网 (IIoT) 集成为工业4.0中的安全,协作AI提供了潜力.

研究的目的:

  • 增强国际工业物联网联合学习全球模式培训的稳定性和准确性.
  • 解决数据多样性影响联合学习模型性能的挑战.
  • 为了减少与联合学习相关的计算开销.

主要方法:

  • 为全球模型培训提出了基于Shapley价值的自适应权重机制.
  • 将全球模型训练为一系列合作游戏,根据Shapley贡献,数据集大小和可变性调整客户权重.
  • 引入了一种量子化策略,以减轻沙普利值计算的计算成本.

主要成果:

  • 由于有效的重量分配,与现有方法相比,获得了最高的准确性.
  • 证明了可比的准确性,计算成本显著降低.
  • 在每个训练轮中减少了Shapley值计算的计算开销.

结论:

  • 拟议的Shapley基于价值的自适应权重机制提高了IIoT联合学习的全球模型性能.
  • 该方法有效地平衡了准确性和计算效率.
  • 这种方法为工业4.0环境中安全和协作的人工智能提供了一个有希望的解决方案.