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36. Low-frequency vibration energy harvesting: a comprehensive review of frequency up-conversion approaches

Author

Tang, Hao, Hua, Chunrong, Huang, Heng, Liu, Weiqun, Yang, Zhengbao, Yuan, Yanping, Zhang, Zutao, Tang, Hao, Hua, Chunrong, Huang, Heng, Liu, Weiqun, Yang, Zhengbao, Yuan, Yanping, and Zhang, Zutao

Abstract

Along with the rapid development of low-power electronics and the Internet of Things, vibration energy harvesting is attracting increasingly attention from both industry and academia. However, most reported energy harvesters are inefficient in real applications where excitation sources mostly are unpredictable and low frequency. To harness low-frequency vibration energy, the concept of frequency up-conversion is proposed and a variety of designs and analysis has been reported. This article presents a critical review on frequency up-conversion vibration energy harvesters (FUCHs) based on two classification principles: (a) interaction mechanisms as contact and noncontact types; and (b) excitations as type of inertial vibration and type of direct loading. Furthermore, we illustrate the applications of FUCHs designed for harvesting energy from the low-frequency human motions and propose the feasible future research directions of FUCHs. © 2022 IOP Publishing Ltd.

Published
2022

42. Vigilance detection method for high‐speed rail using wireless wearable EEG collection technology based on low‐rank matrix decomposition

Author

Xiang Zhou, Xiaoliang Zhang, Di Yao, Hongye Pan, Zhu Xin, Zhang Zutao, Qi Lingfei, Miankuan Zhu, and Wang Yuan

Subjects
050210 logistics & transportation, Warning system, Computer science, business.industry, Mechanical Engineering, media_common.quotation_subject, 05 social sciences, Real-time computing, Wearable computer, Transportation, 02 engineering and technology, Matrix decomposition, ALARM, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Wireless, 020201 artificial intelligence & image processing, Detection theory, business, Law, Robust principal component analysis, General Environmental Science, Vigilance (psychology), media_common
Abstract

With the development of rail transit, driver vigilance is increasingly important in railway safety. A vigilance detection method based on high-speed rail (HSR) is presented in this study. The proposed method includes three main parts: (i) a wireless wearable electroencephalography (EEG) collection module; (ii) HSR driver's vigilance detection module; and (iii) an early warning module. Drivers’ vigilance is monitored using eight EEG channels. A low-rank matrix decomposition (also called robust principal component analysis) algorithm is used to classify EEG signals which are collected through wireless wearable EEG collection technology. The warning module will sound an alarm and the early warning begins to message the train control centre if the driver is judged as fatigue. The method was tested on driving EEG data from ten different drivers and reached 99.4% correct classification in a 9 s time window. The feasibility of the proposed vigilance-detecting method for HSR safety is demonstrated through simulation and test results.

Published
2018

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