Many studies have been done in cognitive radios to maximize the network efficiency. There is always a trade-off between sensing and transmission time which has been addressed fully in the literature. There is also som...Many studies have been done in cognitive radios to maximize the network efficiency. There is always a trade-off between sensing and transmission time which has been addressed fully in the literature. There is also some missed idle times during the waiting phase when secondary user finds the channel busy. Therefore, the longer the transmission time is, the higher the successfully delivered bits would be and the higher the missed idle times in the waiting stage would be expected. In this work, we formulate these missed idle times to addressed this trade-off. Furthermore, interference due to PU re-occupancy is modelled in successful transmitted time and in collision probability. Moreover, we derive secondary user's packet delay distribution using phase type model at which retransmission due to collision results from both sensing error and PU re-occupancy is considered. Thanks to derived delay distribution, any statistical moments of delay could be found as a closed form mathematical expression. These moments can be used as constraints for applications with delay sensitive packets. Numerical results are given to clarify the network metrics. Finally, the optimized values for sensing and transmission time are computed using genetic algorithm in order to maximize access efficiency.展开更多
Reliable and timely detection of an internal short circuit(ISC)in lithium-ion batteries is important to ensure safe and efficient operation.This paper investigates ISC detection of parallel-connected battery cells by ...Reliable and timely detection of an internal short circuit(ISC)in lithium-ion batteries is important to ensure safe and efficient operation.This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation(i.e.,no independent current sensors for individual cells in a parallel string).To characterize ISC-related signatures in battery string responses,an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC.By analyzing the data generated from the electro-thermal model,the distribution of surface tem-perature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations.A convolutional neural network(CNN)is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs.Based on the estimated ISC resistance from CNN,the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery.The algorithm is evaluated in the presence of signal noises in terms of accuracy,false alarm rate,and missed detection rate,verifying the effectiveness and robustness of the proposed approach.展开更多
基金supported by Islamic Azad University,Boroujerd Branch,Iran
文摘Many studies have been done in cognitive radios to maximize the network efficiency. There is always a trade-off between sensing and transmission time which has been addressed fully in the literature. There is also some missed idle times during the waiting phase when secondary user finds the channel busy. Therefore, the longer the transmission time is, the higher the successfully delivered bits would be and the higher the missed idle times in the waiting stage would be expected. In this work, we formulate these missed idle times to addressed this trade-off. Furthermore, interference due to PU re-occupancy is modelled in successful transmitted time and in collision probability. Moreover, we derive secondary user's packet delay distribution using phase type model at which retransmission due to collision results from both sensing error and PU re-occupancy is considered. Thanks to derived delay distribution, any statistical moments of delay could be found as a closed form mathematical expression. These moments can be used as constraints for applications with delay sensitive packets. Numerical results are given to clarify the network metrics. Finally, the optimized values for sensing and transmission time are computed using genetic algorithm in order to maximize access efficiency.
文摘Reliable and timely detection of an internal short circuit(ISC)in lithium-ion batteries is important to ensure safe and efficient operation.This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation(i.e.,no independent current sensors for individual cells in a parallel string).To characterize ISC-related signatures in battery string responses,an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC.By analyzing the data generated from the electro-thermal model,the distribution of surface tem-perature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations.A convolutional neural network(CNN)is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs.Based on the estimated ISC resistance from CNN,the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery.The algorithm is evaluated in the presence of signal noises in terms of accuracy,false alarm rate,and missed detection rate,verifying the effectiveness and robustness of the proposed approach.
