• Journal of the KSME
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• v.55 no.4
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• pp.39-42
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• 2015

이 글에서는 전자기형 진동 에너지 하베스터 개발을 위해 설계 시 고려해야 할 사항에 대해 소개하고자 한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.337-339
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.59-61
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.142-143
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.79-80
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.73-74
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• 2014

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.10
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• pp.809-816
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• 2014

This paper presents a systematic optimization process for designing an electromagnetic vibration energy harvester using FEA(finite element analysis) to improve computational accuracy and efficiency. A static FEA is used in the optimization process where trend analysis in a short period of time is rather important than precise computation, while a dynamic FEA is used in the verification step for the final result where precise computation is more important. An electromechanical transduction factor can be calculated efficiently by using an approach to use the radial component of magnetic flux density directly instead of an approach to compute the flux density gradient. The proposed optimization process was verified through a case study where simulation and experiment results were compared.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.2
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• pp.1-7
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• 2018

Recently, as interest in the internet of things has increased, a vibration energy harvester has attracted attention as a power supply method for a wireless sensor. The vibration energy harvester can be divided into piezoelectric types, electromagnetic type and electrostatic type, according to the energy conversion type. The electromagnetic vibration energy harvester has advantages, in terms of output density and design flexibility, compared to other methods. The efficiency of an electromagnetic vibration energy harvester is determined by the shape, size, and spacing of coils and magnets. Generating all the experimental cases is expensive, in terms of time and money. This study proposes a method to perform design optimization of an electromagnetic vibration energy harvester using an open source, big data platform.