Detail publikace

Artificial intelligence based optimization for vibration energy harvesting applications

HADAŠ, Z. KURFÜRST, J. ONDRŮŠEK, Č. SINGULE, V.

Český název

Artificial intelligence based optimization for vibration energy harvesting applications

Anglický název

Artificial intelligence based optimization for vibration energy harvesting applications

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

en

Originální abstrakt

This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.

Český abstrakt

This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.

Anglický abstrakt

This paper deals with optimization studies based on artificial intelligence methods. These modern optimization methods can be very useful for design improving of an electromagnetic vibration energy harvester. The vibration energy harvester is a complex mechatronic device which harvests electrical energy from ambient mechanical vibrations. The harvester design consists of a precise mechanical resonator, electromagnetic converter and electronics. The optimization study of such complex mechatronic device is complicated however artificial intelligence methods can be used for set up of optimal harvester parameters. Used optimization strategies are applied to optimize the design of the electro-magnetic vibration energy harvester according to multi-objective fitness functions. Optimization results of the harvester are summarized in this paper. Presented optimization algorithms can be used for a design of new energy harvesting systems or for improving on existing energy harvesting systems.

Klíčová slova česky

Vibration energy harvester, energy harvesting, optimization, SOMA, artificial intelligence

Klíčová slova anglicky

Vibration energy harvester, energy harvesting, optimization, SOMA, artificial intelligence

Rok RIV

2012

Vydáno

07.02.2012

Nakladatel

Springer Berlin / Heidelberg

Místo

Berlín

ISSN

0946-7076

Ročník

18

Číslo

7-8

Strany od–do

1003–1014

Počet stran

12

BIBTEX


@article{BUT89532,
  author="Zdeněk {Hadaš} and Jiří {Kurfűrst} and Čestmír {Ondrůšek} and Vladislav {Singule},
  title="Artificial intelligence based optimization for vibration energy harvesting applications",
  year="2012",
  volume="18",
  number="7-8",
  month="February",
  pages="1003--1014",
  publisher="Springer Berlin / Heidelberg",
  address="Berlín",
  issn="0946-7076"
}