In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a c...In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features.To solve this issue,a multi-timescale feature extraction method based on adaptive entropy is proposed.Firstly,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among them.Secondly,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the timescale.Thirdly,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph.The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.展开更多
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition（EMD） and a new wavelet entropy ratio with improving the accuracy of voice activity detection.With the EMD, the...A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition（EMD） and a new wavelet entropy ratio with improving the accuracy of voice activity detection.With the EMD, the noise signals can be decomposed into several intrinsic mode functions（IMFs）. Then the proposed wavelet energy entropy ratio can be used to extract the desired feature for each IMFs component. In view of the question that the method of voice endpoint detection based on the original wavelet entropy ratio cannot adapt to the low signal-to-noise ratio（SNR）condition, an appropriate positive constant was introduced to the basic wavelet energy entropy ratio with effectively improved discriminability between the speech and noise. After comparing the traditional wavelet energy entropy ratio with the proposed wavelet energy entropy ratio, the experiment results show that the proposed method is simple and fast. The speech endpoints can be accurately detected in low SNR environments.展开更多
基金the National Key Research and Development Program of China(2022YFB3305800-5)the National Natural Science Foundation of China(62125301,62021003)+2 种基金the Beijing Outstanding Young Scientist Program(BJJWZYJH01201910005020)the Natural Science Foundation of Beijing Municipality(KZ202110005009)Youth Beijing Scholar(037).
文摘In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features.To solve this issue,a multi-timescale feature extraction method based on adaptive entropy is proposed.Firstly,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among them.Secondly,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the timescale.Thirdly,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph.The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
基金supported by the Significant Projects of Anhui University of Technology under Grant No.14206003
文摘A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition（EMD） and a new wavelet entropy ratio with improving the accuracy of voice activity detection.With the EMD, the noise signals can be decomposed into several intrinsic mode functions（IMFs）. Then the proposed wavelet energy entropy ratio can be used to extract the desired feature for each IMFs component. In view of the question that the method of voice endpoint detection based on the original wavelet entropy ratio cannot adapt to the low signal-to-noise ratio（SNR）condition, an appropriate positive constant was introduced to the basic wavelet energy entropy ratio with effectively improved discriminability between the speech and noise. After comparing the traditional wavelet energy entropy ratio with the proposed wavelet energy entropy ratio, the experiment results show that the proposed method is simple and fast. The speech endpoints can be accurately detected in low SNR environments.
