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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
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...
497
Cognitive Learning01:21

Cognitive Learning

1.0K
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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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.
647
Machines: Problem Solving I01:22

Machines: Problem Solving I

689
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...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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相关实验视频

Updated: Jan 17, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

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在使用机器学习的雾计算环境中部署经验意识应用程序的质量.

P Jenifer1, J Angela Jennifa Sujana2

  • 1Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

能源智能组件配置 (ESCP) 算法优化了边缘设备上的人工智能 (AI) 工作负载,提高了效率并降低了能源消耗. 这个框架确保了实时应用程序的服务质量和体验.

关键词:
云计算是一种云计算.边缘情报 边缘情报是指边缘情报.雾边缘设备作为一种服务.长期短期记忆 长期短期记忆机器学习是机器学习.一个元启发式的元启发式.移动网络-V3 移动网络经验的质量经验的质量.服务质量服务质量.在XGBoost中使用.

相关实验视频

Last Updated: Jan 17, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

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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
  • 边缘计算 边缘计算

背景情况:

  • 边缘智能对于实时传感器数据处理至关重要,但面临带宽,延迟和数据隐私方面的挑战.
  • 现有的解决方案难以有效地在资源有限的边缘设备上部署人工智能 (AI) 工作负载.
  • 对于在云雾边缘环境中保证服务质量 (QoS) 和体验质量 (QoE) 的动态架构的需求正在增长.

研究的目的:

  • 引入能源智能组件配置 (ESCP) 算法,以优化AI在雾边缘设备上的工作负载部署.
  • 为雾边设备作为服务 (FEdaaS) 开发可靠和动态的架构,确保QoS和QoE.
  • 提高人工智能边缘系统的能源效率和性能.

主要方法:

  • 开发了用于雾设备 (FCMN,FN) 的能源智能组件放置 (ESCP) 算法,用于分配模块和禁用不活跃的设备.
  • 实现了一个元启发式调度器,将 eXtreme Gradient Boosting (XGB) 结合起来,用于即时的 QoS 评分和长短期内存 (LSTM) 用于节点拥堵预测.
  • 设计了一个框架,通过无服务器云,雾和极端边缘层透明地分配压缩的神经工作负载.

主要成果:

  • 与仅使用云计算的基线相比,ESCP使带宽利用率提高了5.2%,可扩展性提高了3.2%,能源消耗提高了3.8%,响应时间提高了2.1%.
  • 保持了0.4%的预测准确度,同时满足了QoE目标,例如为低资源AI边缘设备提供250ms的延迟和24小时的电池寿命.
  • 通过自适应性框架演示了编排AI边缘设备的可行性,以满足严格的应用程序级 QoS 和 QoE 需求.

结论:

  • ESCP算法和自适应框架有效优化边缘设备上的AI工作负载,提高性能和能源效率.
  • 拟议的FEdaaS架构为跨云,雾和边缘层部署AI服务提供了可靠的解决方案.
  • 未来的工作包括探索隐私的联合学习,并在实时重症监护和智能城市应用中验证架构.