Six circularly polarized patch antennas with electromagnetic band gap（EBG）arranged at different locations were studied.These EBG antennas were compared in terms of impedance bandwidth,axial ratio（AR）bandwidth and ...Six circularly polarized patch antennas with electromagnetic band gap（EBG）arranged at different locations were studied.These EBG antennas were compared in terms of impedance bandwidth,axial ratio（AR）bandwidth and radiation patterns.When the EBG cells were placed closer to the edge of the substrate,the EBG antenna had a larger front radiation and a narrower bandwidth.Integrating the EBG cells closer to the center of the patch resulted in a wider impedance bandwidth,a wider axial ratio bandwidth and a decreased front gain.展开更多
AIM: To characterize hydraulic right ventricle (RV) af- terload by pulmonary arterial pressure waveform analy- sis in an acute pulmonary hypertension (PH) model. METHODS: Pulmonary artery (PA) flow and pressure were r...AIM: To characterize hydraulic right ventricle (RV) af- terload by pulmonary arterial pressure waveform analy- sis in an acute pulmonary hypertension (PH) model. METHODS: Pulmonary artery (PA) flow and pressure were recorded in six anesthetized sheep. Acute iso- baric PH was induced by phenylephrine (active) and PA mechanical constriction (passive). We estimated the amplitude of the forward and reflected pressure waves according to the inflection point. In most cases the in- flection pressure was smooth, thus the inflection point was defined as the time at which the first derivative ofpulmonary arterial pressure reached its first minimum. We calculated the input and characteristic (Z C , time- domain Li method) impedances, the capacitance index (stroke volume/pulse pressure), the augmentation index (AI) (reflected pressure/pulse pressure), the frac- tional pulse pressure (pulse pressure/mean pressure) and the wasted energy generated by the RV due to wave reflection during ejection (E W ). RESULTS: Pulse pressure, fractional pulse pressure, AI and Z C increased and capacitance index decreased during passive PH with respect to control (P < 0.05). In contrast, Z C and the capacitance index did not change and E W and the AI decreased during active PH. Pulse pressure correlated with E W and Z C and the AI was cor- related with E W (r > 0.6, P < 0.05). CONCLUSION: PA pressure waveform analysis al- lows the quantification of the dynamic RV afterload. Prospective clinical studies will be necessary to validate this time-domain approach to evaluate the dynamic RV afterload in chronic PH.展开更多
Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the mo...Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.展开更多
基金Supported by the National Natural Science Foundation of China(61102022)the Fundamental Research Foundation of Beijing Institute of Technology of China(20120542014)
文摘Six circularly polarized patch antennas with electromagnetic band gap（EBG）arranged at different locations were studied.These EBG antennas were compared in terms of impedance bandwidth,axial ratio（AR）bandwidth and radiation patterns.When the EBG cells were placed closer to the edge of the substrate,the EBG antenna had a larger front radiation and a narrower bandwidth.Integrating the EBG cells closer to the center of the patch resulted in a wider impedance bandwidth,a wider axial ratio bandwidth and a decreased front gain.
基金Supported by Comisión Sectorial de Investigación Científica,Universidad de la República and Programa de Desarrollo de las Ciencias Básicas
文摘AIM: To characterize hydraulic right ventricle (RV) af- terload by pulmonary arterial pressure waveform analy- sis in an acute pulmonary hypertension (PH) model. METHODS: Pulmonary artery (PA) flow and pressure were recorded in six anesthetized sheep. Acute iso- baric PH was induced by phenylephrine (active) and PA mechanical constriction (passive). We estimated the amplitude of the forward and reflected pressure waves according to the inflection point. In most cases the in- flection pressure was smooth, thus the inflection point was defined as the time at which the first derivative ofpulmonary arterial pressure reached its first minimum. We calculated the input and characteristic (Z C , time- domain Li method) impedances, the capacitance index (stroke volume/pulse pressure), the augmentation index (AI) (reflected pressure/pulse pressure), the frac- tional pulse pressure (pulse pressure/mean pressure) and the wasted energy generated by the RV due to wave reflection during ejection (E W ). RESULTS: Pulse pressure, fractional pulse pressure, AI and Z C increased and capacitance index decreased during passive PH with respect to control (P < 0.05). In contrast, Z C and the capacitance index did not change and E W and the AI decreased during active PH. Pulse pressure correlated with E W and Z C and the AI was cor- related with E W (r > 0.6, P < 0.05). CONCLUSION: PA pressure waveform analysis al- lows the quantification of the dynamic RV afterload. Prospective clinical studies will be necessary to validate this time-domain approach to evaluate the dynamic RV afterload in chronic PH.
基金supported by the National Natural Science Foundation of China(No.62173281 and No.61801407)the Sichuan Science and Technology Pro-gram(No.2019YFG0427 and No.2023YFG0108)+1 种基金the China Scholarship Council(No.201908515099)the Fund of Robot Technology used for the Special Environment Key Laboratory of Sichuan Province(No.18kftk03).
文摘Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.
