저 주파수 진동환경에서의 고 신뢰성 압전기반 에너지 하베스터 제작 및 특성평가

학위논문(석사) - 한국과학기술원 : 신소재공학과, 2020.2,[iv, 60 p. :]Energy harvesting is the conversion of energy that is thrown away into usable electrical energy. Recently, with the rise of the Internet of Things (IoT) technology, various types of sensors have been applied to systems such as mobile, wearable devices, and unmanned equipment. Since it is difficult to supply all the energy required by many sensors with wired power, many studies have been conducted to overcome through energy harvesting. At present, the most optimized method for harvesting vibration energy is piezoelectric energy harvesting, which is suitable for long life design because of its simple structure and reliable device design. However, solving the relatively low power yield and the degradation of the energy harvesting device due to fatigue caused by tens of millions of vibrations are major challenges. In this study, we optimized the structure of cantilever and verification weight of piezoelectric energy harvester to obtain high harvesting power to operate wireless communication sensor. In addition, we have optimized the harvesting in the low frequency range of 30 Hz band which are in low harvesting efficiency. In order to minimize vibration fatigue, energy harvesters made of flexible FR4 are used instead of traditional silicon substrates. Piezoelectric energy harvester modules were integrated with optimum proof mass to ensure long lifetime in the low frequency region. In addition, piezoelectric fatigue was visualized in microscopic region, and degradation characteristics were evaluated in the microscopic region using piezoresponse force microscopy (PFM).한국과학기술원 :신소재공학과

소재 조성 도출용 라이브러리 생성 장치 및 방법

The present invention provides an apparatus and a method for constructing a library for deriving a material composition using empirical result. Which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction