Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in ...Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in aerospace engineering.The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency.This paper propose a new method using the Long Short-Term Memory(LSTM)networks for Continuously Variable Configuration Structures(CVCSs),which is an important subclass of TV structures.The configuration parameters are used to represent the time-varying dynamic characteristics by the‘‘freezing"method.The relationship between TV dynamic characteristics and vibration responses is established by LSTM,and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM.A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method.The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain.Besides,the average one-step estimation time of responses is less than the sampling interval.Thus,the proposed method is promising to on-line estimate the important responses of TV structures.展开更多
We aimed to study the association between sperm DNA fragmentation and recurrent pregnancy loss(RPL)in the Chinese population via a retrospective observational study of Chinese couples who had experienced RPL between M...We aimed to study the association between sperm DNA fragmentation and recurrent pregnancy loss(RPL)in the Chinese population via a retrospective observational study of Chinese couples who had experienced RPL between May 2013 and August 2018.The study population included 461 men from couples with RPL and 411 men from a control group(couples with clinical pregnancy via in v/tro fertiIization owing to female causes).Routine semen analysis,sperm chromatin analysis,and microscopic(high-power)morphological analysis were performed using semen samples.Semen samples were assessed for volume,sperm count,and motility.The sperm DNA fragmentation index(DFI)was calculated,and the median DFI was obtained.Men were categorized as having normal(37.8%;DFI<15.0%),moderate(33.6%;15.0%<DFI<30.0%),or severe(28.6%;DFI A30.0%)DNA fragmentation levels.The percentage of men with severe DNA fragmentation was significantly higher in the RPL(42.3%)group than that in the control group(13.1%),whereas the percentage of men with normal levels of DNA fragmentation was significantly lower in the RPL group(22.8%)tha n that in the control group(54.7%).Subsequent analysis also dem on strated that the sperm DNA fragmentation rate had a moderate reverse correlation with the sperm progressive motility rate(r=-0.47,P<0.001)and the total motile sperm count(r=-0.31,P<0.001).We found a positive correlation between RPL and sperm DNA fragmentation.The results suggest that increased sperm DNA damage is associated with RPL.展开更多
Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to...A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar, acidic, basic and secondary structure information and residue separation between two residues. In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0.29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.展开更多
An adaptive neural fuzzy (NF) controller is developed in this paper for active vibration suppression in flexible structures. A recurrent identification network (RIN) is developed to adaptively identify system dynamics...An adaptive neural fuzzy (NF) controller is developed in this paper for active vibration suppression in flexible structures. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the plant. A novel recurrent training (RT) technique is suggested to train the RIN so as to optimize nonlinear input-output mapping and to enhance convergence. The effectiveness of the developed controller and the related techniques has been verified experimentally corresponding to different control scenarios. Test results show that the proposed RIN can effectively recognize the time-varying dynamics of the plant. The RT-based hybrid training technique can improve the adaptive capability of the control system to accommodate different system conditions and enhance the training convergence. The developed NF controller is a robust and stable vibration suppression system, and it outperforms other related NF controllers.展开更多
We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United State...We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United States.In this study,we use the lat-est vintage of Divisia aggregates,maintained within CFS.We use monthly data,from January 1967 to December 2020,which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis.We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin.The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008.Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates,we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.展开更多
文摘Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in aerospace engineering.The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency.This paper propose a new method using the Long Short-Term Memory(LSTM)networks for Continuously Variable Configuration Structures(CVCSs),which is an important subclass of TV structures.The configuration parameters are used to represent the time-varying dynamic characteristics by the‘‘freezing"method.The relationship between TV dynamic characteristics and vibration responses is established by LSTM,and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM.A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method.The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain.Besides,the average one-step estimation time of responses is less than the sampling interval.Thus,the proposed method is promising to on-line estimate the important responses of TV structures.
基金This work was supported by grants from the National Natural Science Foundation of China(No 81100469,81671517)the Scientific Research Foundation of Shanghai Municipal Commission of Health and Planning(201840060).
文摘We aimed to study the association between sperm DNA fragmentation and recurrent pregnancy loss(RPL)in the Chinese population via a retrospective observational study of Chinese couples who had experienced RPL between May 2013 and August 2018.The study population included 461 men from couples with RPL and 411 men from a control group(couples with clinical pregnancy via in v/tro fertiIization owing to female causes).Routine semen analysis,sperm chromatin analysis,and microscopic(high-power)morphological analysis were performed using semen samples.Semen samples were assessed for volume,sperm count,and motility.The sperm DNA fragmentation index(DFI)was calculated,and the median DFI was obtained.Men were categorized as having normal(37.8%;DFI<15.0%),moderate(33.6%;15.0%<DFI<30.0%),or severe(28.6%;DFI A30.0%)DNA fragmentation levels.The percentage of men with severe DNA fragmentation was significantly higher in the RPL(42.3%)group than that in the control group(13.1%),whereas the percentage of men with normal levels of DNA fragmentation was significantly lower in the RPL group(22.8%)tha n that in the control group(54.7%).Subsequent analysis also dem on strated that the sperm DNA fragmentation rate had a moderate reverse correlation with the sperm progressive motility rate(r=-0.47,P<0.001)and the total motile sperm count(r=-0.31,P<0.001).We found a positive correlation between RPL and sperm DNA fragmentation.The results suggest that increased sperm DNA damage is associated with RPL.
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
文摘A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar, acidic, basic and secondary structure information and residue separation between two residues. In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0.29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.
文摘An adaptive neural fuzzy (NF) controller is developed in this paper for active vibration suppression in flexible structures. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the plant. A novel recurrent training (RT) technique is suggested to train the RIN so as to optimize nonlinear input-output mapping and to enhance convergence. The effectiveness of the developed controller and the related techniques has been verified experimentally corresponding to different control scenarios. Test results show that the proposed RIN can effectively recognize the time-varying dynamics of the plant. The RT-based hybrid training technique can improve the adaptive capability of the control system to accommodate different system conditions and enhance the training convergence. The developed NF controller is a robust and stable vibration suppression system, and it outperforms other related NF controllers.
文摘We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United States.In this study,we use the lat-est vintage of Divisia aggregates,maintained within CFS.We use monthly data,from January 1967 to December 2020,which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis.We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin.The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008.Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates,we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.
