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RISS 인기검색어
- 검색키워드
- 저자 : ( Jiantao Qu ) (검색결과 2 건)
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Optimization of a piezoelectric wind energy harvester with a stepped beam
Jiantao Zhang,Dong Qu,Zhou Fang,Chang Shu 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.11
A galloping-based piezoelectric energy harvester using the stepped cantilever beam is proposed and investigated. Transverse galloping is induced with the square cross sectioned bluff body. When the wind speed exceeds the critical wind speed, the self-excited oscillation of the harvester occurs and more output power is generated. To obtain the optimal design of the energy harvester, the sequential quadratic programming (SQP) and the evolution strategy (ES) are employed to determine the optimal solution. The finite element method is used to calculate the output voltage of the harvester. After optimization, the output voltage of the optimal harvester is significantly improved in comparison with that of the initial one. Two prototype harvesters based on the initial and optimal dimensions were fabricated and measured experimentally. An open-circuit rms voltage of 36 V and an output power of 0.52 mW were obtained at the wind speed of 14 m/s for the optimal harvester. They are about 8.3 times and 4.73 times of that of the initial harvester. The validity of the optimal design is verified with the experimental results.
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Road Damage Detection and Classification based on Multi-level Feature Pyramids
( Junru Yin ),( Jiantao Qu ),( Wei Huang ),( Qiqiang Chen ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2
Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.
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