Based on the magnetic resonance coupling principle, in this paper a wireless energy transfer system is designed and implemented for the power supply of micro-implantable medical sensors. The entire system is composed of the in vitro part, including the energy transmitting circuit and resonant transmitter coils, and in vivo part, including the micro resonant receiver coils and signal shaping chip which includes the rectifier module and LDO voltage regulator module. Transmitter and receiver coils are wound by Litz wire, and the diameter of the receiver coils is just 1.9 cm. The energy transfer efficiency of the four-coil system is greatly improved compared to the conventional two-coil system. When the distance between the transmitter coils and the receiver coils is 1.5 cm, the transfer efficiency is 85% at the frequency of 742 kHz. The power transfer efficiency can be optimized by adding magnetic enhanced resonators. The receiving voltage signal is converted to a stable output voltage of 3.3 V and a current of 10 mA at the distance of 2 cm. In addition, the output current varies with changes in the distance. The whole implanted part is packaged with PDMS of excellent biocompatibility and the volume of it is about 1 cm3.
Urban public transit has been rapidly developed in recent years. However, given increases in travel volume, the level of service still needs to be improved to meet the satisfaction of passengers. Transit service providers and researchers have focused on improving transit devices, but the service level of public transit has not yet been effectively improved, so more and more research is interested in analyzing the travel patterns of passengers. Compared with traditional survey methods, smart card collection systems—which can collect spatial-temporal information about passengers’ trips—are convenient for the study of bus and subway passengers’ travel patterns. However, the data provided by smart cards have not yet been fully explored. Therefore, this paper proposed a multistep methodology to gather information on the travel patterns of bus and subway passengers in Beijing, China. We conducted statistical analyses and used an unsupervised clustering method to study and classify passengers based on travel patterns. Four groups have been identified: standard commuters, flexible commuters, and two types of low-frequency passengers. Then, a comprehensive analysis was conducted. We also discussed the changes of passengers’ travel time consumption before and after the implementation of customized bus for high-frequency passengers. The analyses indicated that passengers’ travel patterns can provide useful information for transit service providers and can help improve the level of service of urban public transit by promoting the promulgation of local public transport policies and the implementation of customized services.
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