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相关概念视频

Cognitive Learning01:21

Cognitive Learning

237
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
237
Machines01:19

Machines

267
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
267
Introduction to Learning01:18

Introduction to Learning

359
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
359
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

639
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...
639
Control Systems: Applications01:25

Control Systems: Applications

599
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
599
Machines: Problem Solving I01:22

Machines: Problem Solving I

315
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
315

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相关实验视频

Updated: Jun 21, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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面向学习的联邦边缘计算框架用于IIoT.

Xianhui Liu1, Xianghu Dong1, Ning Jia1

  • 1CAD Research Center, Tongji University, Shanghai 201800, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

本研究介绍了一个新的以人工智能为重点的边缘计算框架,用于工业物联网 (IIoT). 拟议的系统大大减少了人工智能模型训练时间和IIoT边缘设备的能源消耗.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 工业物联网的工业物联网.

背景情况:

  • 现有的工业物联网 (IIoT) 边缘计算框架面临的挑战包括硬件-软件合,多样化的协议,AI模型部署困难,有限的边缘设备功能,以及对延迟和能源消耗的敏感性.
  • 人工智能 (AI) 在边缘计算中的整合对于推进工业技术至关重要.

研究的目的:

  • 提出一个软件定义的,以人工智能为导向的三层IIoT边缘计算框架.
  • 设计和实施一个以人工智能为导向的边缘计算系统,支持设备访问,基于云的AI模型部署和端到端边缘处理.
  • 开发一种基于时间序列的方法,用于在联合学习中选择设备和计算卸载,以最大限度地减少训练延迟和能源使用.

主要方法:

  • 开发一个软件定义的,三层的IIoT边缘计算框架.
  • 实现面向AI的边缘计算系统,以实现无的AI模型集成和处理.
  • 建议基于时间序列的算法用于智能设备选择和计算卸载在联合学习.

主要成果:

  • 拟议的以人工智能为导向的边缘计算框架和系统有效地支持从云端访问设备和部署人工智能模型.
  • 基于时间序列的设备选择和计算卸载方法显著减少了联合学习培训延迟.
  • 实验结果显示,与随机选择方法相比,模型训练时间减少了30%至50%,训练能耗减少了35%至55%.
关键词:
人工智能的人工智能是人工智能.边缘计算是一种边缘计算.联合学习的联合学习工业物联网的工业物联网.

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结论:

  • 开发的软件定义的以AI为导向的IIoT边缘计算框架解决了当前系统的关键挑战.
  • 拟议的联合学习卸载战略通过减少培训时间和能源消耗来提高效率.
  • 实施的系统证明了AI驱动的边缘计算在工业应用中的可行性和有效性.