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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
Associative Learning01:27

Associative Learning

335
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...
335
Observational Learning01:12

Observational Learning

163
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...
163
Introduction to Learning01:18

Introduction to Learning

360
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...
360
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

642
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...
642
Machines01:19

Machines

268
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...
268

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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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联合学习启发了基于Antlion的编排,用于边缘计算环境.

Madhusudhan H S1, Punit Gupta2,3

  • 1Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

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概括
此摘要是机器生成的。

一个人工神经网络 (ANN) 启发的Antlion算法优化了边缘计算中的任务编排. 这种方法提高了资源利用率,并减少了物联网设备的能源消耗,提高了整体系统效率.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 分布式系统 分布式系统

背景情况:

  • 边缘计算架构对于处理更接近源头的物联网 (IoT) 数据至关重要.
  • 有效的资源管理和任务安排是雾边环境中的关键挑战.
  • 优化资源利用和能源消耗对于可扩展的边缘部署至关重要.

研究的目的:

  • 提出一个人工神经网络 (ANN) 启发了Antlion算法用于边缘计算环境中的任务编排.
  • 提高资源利用率,减少边缘和云层的能源消耗.
  • 评估拟议的算法的性能与现有方法相比,特别是对于医疗保健应用.

主要方法:

  • 人工神经网络 (ANN) 的开发启发了Antlion的任务调度算法.
  • 在边缘计算环境中实现算法,专注于资源配置.
  • 通过使用关键性能指标,使用模糊逻辑和圆形罗宾算法的比较分析.

主要成果:

  • 拟议的ANN-Antlion算法显著改善了边缘和云层的负载平衡.
  • 在云端能源消耗 (95.94%) 和边缘能源消耗 (16.79%) 中显著减少.
  • 在CPU利用率 (19.85%边缘,10.64%云),网络利用率 (23.33%) 和平均等待时间显著减少 (96%对比10.64%) 中取得了改进. 模糊的逻辑).

结论:

  • 受ANN启发的Antlion算法为边缘计算中的任务编排提供了一种优越的方法.
  • 该算法有效地平衡工作负载,从而提高效率并降低能源需求.
  • 拟议的方法比现有算法具有显著的优势,特别是在医疗保健等资源密集型应用程序中.