• Title/Summary/Keyword: Battery R&D

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Optimization and Application Research on Triboelectric Nanogenerator for Wind Energy Based High Voltage Generation (정전발전 기반 바람에너지 수확장치의 최적화 및 고전압 생성을 위한 활용 방안)

  • Jang, Sunmin;Ra, Yoonsang;Cho, Sumin;Kam, Dongik;Shin, Dongjin;Lee, Heegyu;Choi, Buhee;Lee, Sae Hyuk;Cha, Kyoung Je;Seo, Kyoung Duck;Kim, Hyung Woo;Choi, Dongwhi
    • Korean Chemical Engineering Research
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    • v.60 no.2
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    • pp.243-248
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    • 2022
  • As the scope of use of portable and wearable electronic devices is expanding, the limitations of heavy and bulky solid-state batteries are being revealed. Given that, it is urgent to develop a small energy harvesting device that can partially share the role of a battery and the utilization of energy sources that are thrown away in daily life is becoming more important. Contact electrification, which generates electricity based on the coupling of the triboelectric effect and electrical induction when the two material surfaces are in contact and separated, can effectively harvest the physical and mechanical energy sources existing in the surrounding environment without going through a complicated intermediate process. Recently, the interest in the harvest and utilization of wind energy is growing since the wind is an infinitely ecofriendly energy source among the various environmental energy sources that exist in human surroundings. In this study, the optimization of the energy harvesting device for the effective harvest of wind energy based on the contact electrification was analyzed and then, the utilization strategy to maximize the utilization of the generated electricity was investigated. Natural wind based Fluttering TENG (NF-TENG) using fluttering film was developed, and design optimization was conducted. Moreover, the safe high voltage generation system was developed and a plan for application in the field requiring high voltage was proposed by highlighting the unique characteristics of TENG that generates low current and high voltage. In this respect, the result of this study demonstrates that a portable energy harvesting device based on the contact electrification shows great potential as a strategy to harvest wind energy thrown away in daily life and use it widely in fields requiring high voltage.

Development of deep learning network based low-quality image enhancement techniques for improving foreign object detection performance (이물 객체 탐지 성능 개선을 위한 딥러닝 네트워크 기반 저품질 영상 개선 기법 개발)

  • Ki-Yeol Eom;Byeong-Seok Min
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.99-107
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    • 2024
  • Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.