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相关概念视频

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.6K
Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

384
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
384
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

489
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
489
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

238
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
238
Aggregates Classification01:29

Aggregates Classification

963
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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相关实验视频

Updated: Jun 17, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

FLEX-SFL:一个灵活和高效的分裂联合学习框架,用于边缘异质性.

Hao Yu1,2,3, Jing Fan1,2,3, Hua Dong1,2,3

  • 1School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
概括

边缘系统中的联合学习 (FL) 面临着数据差异和缓慢连接等挑战. 通过优化模型分割,客户端选择和通信安排,FLEX-SFL提高了培训效率和可扩展性.

关键词:
不同步聚合的聚合.客户的选择,客户的选择.边缘异质性的异质性联合学习 (FL)分分学习 (SL)

相关实验视频

Last Updated: Jun 17, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

科学领域:

  • 边缘计算 边缘计算
  • 机器学习 机器学习
  • 分布式系统 分布式系统

背景情况:

  • 边缘环境中的联邦学习 (FL) 部署受到系统异质性,非IID数据和通信限制的阻碍.
  • 这些因素阻碍了边缘人工智能系统的培训效率和可扩展性.

研究的目的:

  • 介绍FLEX-SFL,一个灵活和高效的分割联合学习框架.
  • 为边缘环境共同优化模型分区,客户端选择和通信调度.

主要方法:

  • 设备意识的自适应细分策略,以减轻滞后者效应.
  • 数据代表性的透驱动客户端选择算法.
  • 层次的局部异步聚合,以提高吞吐量和降低延迟.

主要成果:

  • 与最先进的基线相比,FLEX-SFL显示出更高的模型精度,融合速度和资源效率.
  • 在FMNIST,CIFAR-10和CIFAR-100数据集上的实验验证了在高异质性条件下的性能.
  • 在凸设置下建立的理论收性质.

结论:

  • FLEX-SFL有效地解决了边缘FL中的挑战,提高了培训效率和可扩展性.
  • 该框架的协调机制对于现实世界的边缘智能系统来说是非常实用的.