Jove
Visualize
联系我们

相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

135
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
135
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
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...
107
Reinforcement01:23

Reinforcement

186
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
186
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

631
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...
631
Multimachine Stability01:25

Multimachine Stability

143
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
143
Machines: Problem Solving II01:30

Machines: Problem Solving II

300
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.
300

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Ensemble Machine Learning- and Deep Learning-Driven Identification and Validation of Sennidin B as a Novel Dipeptidyl Peptidase-4 Inhibitor.

International journal of molecular sciences·2026
Same author

Mineral nutrients as regulators of plant flowering time: A molecular perspective.

Journal of integrative plant biology·2026
Same author

A Deep Learning-Based Method for Paddy Leaf Disease Detection and Growth Stage-Specific Treatment Recommendation.

Journal of visualized experiments : JoVE·2026
Same author

Adsorptive removal of methylene blue from wastewater using sawdust of Cedrus deodara and its composite with iron oxide nanoparticles: a comparative study.

Environmental science and pollution research international·2026
Same author

A hybrid deep learning framework for accurate N6,2'-O-Dimethyladenosine site prediction.

Biophysical chemistry·2026
Same author

Seasonal Dynamics of the Gut Microbiota of Ayu (<i>Plecoglossus altivelis</i>) Revealed by a Cross-Sectional Seasonal Survey in the Dajing Stream, Zhejiang Province, China.

Biology·2026
Same journal

Clinical crown height changes in mandibular anterior teeth retained with two types of fixed retainers over two years: findings from a randomized clinical trial.

Scientific reports·2026
Same journal

Rethinking water governance through indigenous systems: A comparative assessment of qanat and well irrigation productivity in Sabzevar County, Iran.

Scientific reports·2026
Same journal

Distributed Nash equilibrium seeking for second-order systems with finite/fixed-time convergence in the absence of velocity measurement.

Scientific reports·2026
Same journal

Determinants of pregnancy termination among ever-married women of reproductive age in Bangladesh.

Scientific reports·2026
Same journal

Occurrence and human health risk assessment of organochlorine pesticides in irrigated and non-irrigated agricultural soils of Wondogenet District, Ethiopia.

Scientific reports·2026
Same journal

High angular resolution diffusion imaging of neurodevelopment in children through data creation with deep learning.

Scientific reports·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 12, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

在多云环境中的高效的基于深度强化学习的任务调度器.

Sudheer Mangalampalli1, Ganesh Reddy Karri2, M V Ratnamani3

  • 1Department of CSE, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. ms.sudheer@manipal.edu.

Scientific reports
|September 19, 2024
PubMed
概括
此摘要是机器生成的。

适应性任务调度器 (ATSIA3C) 通过对任务进行细分和使用改进的异步优势演员关键算法来提高云计算效率. 这种方法可以显著降低产品种类,资源成本和能源消耗.

关键词:
云计算是一种云计算.深度强化学习的学习.这使得西班牙.资源成本 资源成本 资源成本任务安排 任务安排

更多相关视频

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.3K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K

相关实验视频

Last Updated: Jun 12, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.3K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K

科学领域:

  • 云计算 云计算 云计算
  • 人工智能的人工智能
  • 运营研究 运营研究

背景情况:

  • 云计算中的任务调度问题 (TSP) 由于动态,可变的任务负载和异质资源而带来了挑战,影响了性能指标,如产量,能源消耗和成本.
  • 传统的调度算法与动态云工作负载的复杂性和NP-hard性质作斗争.
  • 现有的元启发式和混合式方法提供了近乎最佳的解决方案,但无法完全解决云环境中的动态TSP.

研究的目的:

  • 为云计算环境开发有效的任务调度机制,以解决动态任务调度问题.
  • 通过降低产量,能源消耗和资源成本来提高云模式的性能.
  • 提出一种新的适应性任务调度器,利用先进的AI技术.

主要方法:

  • 制定了一个自适应任务调度器 (ATSIA3C),将传入任务分成子任务.
  • 模拟了使用改进异步优势演员关键 (IA3C) 算法的调度器,以实现有效的任务分割和调度.
  • 实施了两阶段的调度过程:任务细分和子任务分组,然后根据约束来分配VM.

主要成果:

  • 拟议的ATSIA3C与基线算法 (RATS-HM,AINN-BPSO,MOABCQ) 相比显示出显著的性能改进.
  • 在 makespan 中取得了 70.49% 的改进,这表明任务完成速度更快.
  • 在多云环境中,确保了资源成本提高77.42%,能源消耗减少74.24%.

结论:

  • ATSIA3C有效地解决了云计算中的动态任务调度问题.
  • 拟议的基于IA3C的自适应调度器为优化云资源利用和性能提供了卓越的解决方案.
  • ATSIA3C在产量,成本效益和节能方面提供了实质性的改进,优于现有方法.