Jove
Visualize
联系我们
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

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
79
Numerical Calculations01:24

Numerical Calculations

377
In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
377
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

726
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
726
Arithmetic Mean01:08

Arithmetic Mean

14.5K
The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points...
14.5K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

667
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
667
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

83
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
83

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

Mitigating IAPA mortality in the ICU: is it time for personalized immunotherapy?

Critical care (London, England)·2026
Same author

Decomposing multisource uncertainties in ecosystem service assessment: A quantitative framework and application to water conservation in the Three-River-Source Region of China.

Water research·2026
Same author

Identification of a Four-Biomarker Panel for the Diagnosis of Tuberculous Pleural Effusion Using Olink Proteomics.

Journal of inflammation research·2026
Same author

Refining the Clinical Utility of Plasma Microbial Cell-Free DNA Sequencing in High-Risk Population of Infection: A Narrative Review.

Infection and drug resistance·2026
Same author

Voltage-controlled topological spin textures in the monolayer limit.

Nature communications·2026
Same author

Central venous catheter rupture following undiluted etoposide administration in a patient with multiple myeloma: A case report.

The journal of vascular access·2026
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

一个多重机制增强的算术优化算法用于数值问题的算法.

Sen Yang1, Linbo Zhang1, Xuesen Yang1

  • 1College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China.

Biomimetics (Basel, Switzerland)
|August 25, 2023
PubMed
概括
此摘要是机器生成的。

数学优化算法 (AOA) 变体ASFAOA通过整合新的策略来增强优化,以提高趋同性和准确性. 在基准测试和无线传感器覆盖问题上,ASFAOA表现出卓越的性能.

关键词:
算术优化算法算法的算术优化算法勘探和开采,以及开采使用.全球优化全球优化的元启发式算法.无线传感器的覆盖范围

更多相关视频

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

604
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K

相关实验视频

Last Updated: Jul 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

604
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K

科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 超启发式计算 超启发式计算

背景情况:

  • 数学优化算法 (AOA) 面临复杂问题的停滞,降低了趋同性和准确性.
  • 现有的元启发式算法需要改进,以便更好地进行本地利用和全球探索.

研究的目的:

  • 提出一种改进的AOA变种,ASFAOA,具有增强的本地开发和全球勘探能力.
  • 在复杂的优化场景中解决原始AOA的局限性.

主要方法:

  • 整合一个双相反的学习机制,以增强人口多样性.
  • 纳入金枪鱼群优化的螺旋搜索策略,以逃避局部优化.
  • 对指导个体进化的偏移分布估计策略的应用.
  • 为平衡的勘探和开发开发,开发了修改后的等号加速函数.

主要成果:

  • 在2017年CEC基准函数上,ASFAOA表现优异.
  • 该算法在趋同,准确性和稳定性方面显示出显著的改进.
  • ASFAOA有效地解决了跨不同维度的无线传感器覆盖问题.

结论:

  • ASFAOA显著超过了原来的AOA和其他最先进的算法.
  • 提议的改进可以在理论和实际的优化任务中提高性能.
  • ASFAOA是一个有前途的技术,用于解决复杂的现实世界的优化挑战.