Jove
Visualize
联系我们

相关概念视频

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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

Enhanced Optical Management in Organic Solar Cells by Virtue of Square-Lattice Triple Core-Shell Nanostructures.

Micromachines·2023
Same author

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation.

Journal of electrical engineering & technology·2023
Same author

Bio-Inspired Nanomembranes as Building Blocks for Nanophotonics, Plasmonics and Metamaterials.

Biomimetics (Basel, Switzerland)·2022
Same author

Investigation of Nonlinear Piezoelectric Energy Harvester for Low-Frequency and Wideband Applications.

Micromachines·2022
Same author

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method.

Micromachines·2022
Same author

Brochosome-Inspired Metal-Containing Particles as Biomimetic Building Blocks for Nanoplasmonics: Conceptual Generalizations.

Biomimetics (Basel, Switzerland)·2021
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
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: Jul 2, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.3K

对生物灵感优化算法的全面审查,包括微电子和纳米光子学中的应用.

Zoran Jakšić1, Swagata Devi2, Olga Jakšić1

  • 1Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia.

Biomimetics (Basel, Switzerland)
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

这篇评论探讨了生物启发的多参数优化算法,这对于推进人工智能应用至关重要. 它对方法进行了分类,并详细介绍了它们在微电子和纳米光子学中的应用.

关键词:
人工智能的人工智能是人工智能.生物启发的计算方法深度学习是一种深度学习.遗传算法 遗传算法听算法 (Metaheuristic Algorithms) 是一种算法,可以通过metasurfaces 是一个地表.微电子技术的微电子多参数优化多参数优化纳米电子学纳米电子学纳米光子学 纳米光子学

更多相关视频

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications
11:13

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Published on: February 19, 2017

9.7K
Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts
08:40

Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts

Published on: July 18, 2025

3

相关实验视频

Last Updated: Jul 2, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.3K
Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications
11:13

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Published on: February 19, 2017

9.7K
Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts
08:40

Bidirectional Electrical and Optoelectronic Interfaces in Healthy and Ischemic Ex Vivo Rat Hearts

Published on: July 18, 2025

3

科学领域:

  • 计算机科学,人工智能 人工智能
  • 工程,电气和电子工程.
  • 应用物理,应用物理.

背景情况:

  • 人工智能 (AI) 越来越多地融入日常生活.
  • 仿生/生物启发的算法对于各个领域的多参数优化至关重要.
  • 由于人工智能研究的快速发展性质,快速的进步需要更新的审查.

研究的目的:

  • 为多参数优化提供生物灵感算法的当前概述.
  • 为了分类现有的生物灵感优化方法,解决文献中的稀缺性和矛盾.
  • 详细介绍该领域的突出和最新的方法.

主要方法:

  • 生物启发的多参数优化技术的文献综述和分类.
  • 详细描述既有和新的算法.
  • 探索微电子和纳米光子学中的应用.

主要成果:

  • 生物启发的多参数优化方法的拟议分类.
  • 深入讨论关键算法及其最近的发展.
  • 分析微电子 (例如电路设计) 和纳米光子学 (例如反向设计) 中的算法实用性.

结论:

  • 生物启发的算法对于AI中复杂的优化任务至关重要.
  • 该综述提供了对这些方法及其应用的结构化理解.
  • 这项调查是研究人员和对人工智能和相关工程领域感兴趣的个人独立的资源.