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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

636
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...
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Cognitive Learning01:21

Cognitive Learning

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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...
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Associative Learning01:27

Associative Learning

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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...
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Introduction to Learning01:18

Introduction to Learning

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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...
355
Observational Learning01:12

Observational Learning

155
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Distributed Loads01:19

Distributed Loads

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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...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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一个可扩展的区块链支持的联邦学习架构,用于边缘计算.

Shuyang Ren1, Eunsam Kim2, Choonhwa Lee3

  • 1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.

PloS one
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PubMed
概括
此摘要是机器生成的。

本研究介绍了FLCoin,这是一个新的区块链和边缘计算的联合学习 (FL) 系统. 通过优化共识处理,FLCoin提高了物联网 (IoT) 网络的效率和可扩展性.

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科学领域:

  • * 分布式系统和人工智能
  • * 区块链技术和边缘计算

背景情况:

  • * 现有的深度学习和区块链解决方案用于边缘数据处理,往往忽视了物联网 (IoT) 环境中的区块链共识机制的重大资源需求.
  • *联合学习 (FL) 为分布式机器学习提供了一种保护隐私的方法,但需要有效地与底层网络基础设施集成.

研究的目的:

  • * 提出和评估FLCoin,这是一个集成区块链和联合学习的新系统,用于物联网网络中高效的边缘数据处理.
  • *通过优化共识处理和减少资源开销来解决目前基于区块链的联合学习方法的局限性.

主要方法:

  • * 开发一个针对联合学习 (FL) 处理的双层区块链架构.
  • * 引入一种新的以委员会为基础的共识机制,委员会成员通过FL过程选举.
  • *使用MNIST数据集进行实验验证,以训练卷积神经网络 (CNN) 模型.

主要成果:

  • *不论网络大小,FLCoin都显示出稳定的通信开销,确保了系统的可扩展性.
  • * 即使增加了参与节点,共识延迟仍低于3秒,导致整体训练时间减少.
  • * 与使用PBFT共识的类似系统相比,实现了90%的通信开支降低,培训时间成本降低35%.

结论:

  • * FLCoin提供了一种高效且可扩展的解决方案,用于在物联网边缘网络中集成区块链和联合学习.
  • * 拟议的架构有效地减少了共识处理的资源需求,使其适用于资源受限的物联网环境.
  • * FLCoin通过安全高效的分布式智能为开发先进智能物联网服务奠定了坚实的基础.