To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） s...To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.展开更多
This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, ...This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.展开更多
Dimension reduction and manifold learning are the two most popular feature extraction methods.The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimens...Dimension reduction and manifold learning are the two most popular feature extraction methods.The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data,but no bases or features are more spatially localized than the original image pixels.So,adaptive image combination is presented to represent a class by a combined sample.The combined sample is a linear combination of original samples in the same class.Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance.Experimental results show that AIC is effective.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.20576071)the Natural Science Foundation of Shanghai Municipality (Grant No.08ZR1409800)
文摘To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.
基金supported by the National Natural Science Foundation of China(61172159)
文摘This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.
基金the Science and Technology Program of Shanghai Maritime University (Nos.20100095,20100068 and 20080474) the Innovation Program of Shanghai Municipal Education Commission (No.11ZZ143)
文摘Dimension reduction and manifold learning are the two most popular feature extraction methods.The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data,but no bases or features are more spatially localized than the original image pixels.So,adaptive image combination is presented to represent a class by a combined sample.The combined sample is a linear combination of original samples in the same class.Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance.Experimental results show that AIC is effective.
