Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have signi...Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have significantly large absolute values across fine scale levels, the number of the jump points and locations where the jumps occur are estimated. The jump heights are also estimated. All estimators are shown to be consistent. Wavelet method ia also applied to the threshold AR(1) model(TAR(1)). The simple estimators of the thresholds are given,which are shown to be consistent.展开更多
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive predicti...A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.展开更多
Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significa...Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.展开更多
In the field of railway traction drive systems,voltage sensor intermittent faults can significantly impact the reliability and safety of the entire system.This paper proposes an online diagnosis method for detecting s...In the field of railway traction drive systems,voltage sensor intermittent faults can significantly impact the reliability and safety of the entire system.This paper proposes an online diagnosis method for detecting such faults using an Artificial Intelligence(AI)predictor based on a Nonlinear Autoregressive with eXogenous inputs(NARX)data structure.The model is trained efficiently using the Extreme Learning Machine(ELM)algorithm.The NARX model captures the dynamic characteristics of the voltage sensor data,enabling the AI predictor to learn complex nonlinear relationships.The ELM training method ensures rapid convergence and high accuracy.Through extensive experimental validation,the proposed method demonstrates high sensitivity to voltage sensor intermittent faults and robust performance under varying operating conditions.This approach offers a promising solution for enhancing the diagnostic capabilities of railway traction systems,ensuring timely fault detection and improving overall system reliability.展开更多
The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge...The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.展开更多
With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （C...With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.展开更多
A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods...A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.展开更多
Based on the entropy criterion and n-dimensional uniform distribution of nonlinear digital filter （NDF）, we present an efficient NDF-based pseudorandom bit generator （NDF-PRBG） for chaotic stream ciphers. The cryp...Based on the entropy criterion and n-dimensional uniform distribution of nonlinear digital filter （NDF）, we present an efficient NDF-based pseudorandom bit generator （NDF-PRBG） for chaotic stream ciphers. The cryptographic properties of the proposed NDF-PRBG are analysed, and some experiments are made. The results show that it has desirable cryptographic properties, and can be used to construct secure stream ciphers with high speed.展开更多
Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linea...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.展开更多
The study examines the asymmetric effects of renewable energy consumption on carbon emissions in seven South Asian nations.By employing data from 1990 to 2019,the study utilized a nonlinear panel autoregressive distri...The study examines the asymmetric effects of renewable energy consumption on carbon emissions in seven South Asian nations.By employing data from 1990 to 2019,the study utilized a nonlinear panel autoregressive distributed lag modeling framework to identify an asymmetric relationship between carbon emissions and shocks(positive and negative)in renewable energy consumption.The study revealed that there exists an asymmetric short-and long-term impact of shocks in renewable energy consumption on carbon emissions in South Asian economies.Furthermore,the study reported that in South Asian economies,a 1%rise in positive shocks in renewable energy consumption results in a 1.86%reduction in carbon emissions in the long term and a 1.50%reduction in the short term.Conversely,a 1%increase in negative shocks in renewable energy consumption leads to a 0.55%increase in carbon emissions in the long term and a 2.40%increase in the short term.Furthermore,the findings of the study suggest a few policy implications for South Asian countries,stressing the importance of promoting renewable energy consumption to achieve sustained carbon emissions reductions and to lessen the impact of negative shocks on emissions over both short and long terms.展开更多
This study revisits the Fisher effect using a different empirical method that considers a potential nonlinear relationship between interest rates(treasury bond rates)and inflation in China.The rising uncertainty and a...This study revisits the Fisher effect using a different empirical method that considers a potential nonlinear relationship between interest rates(treasury bond rates)and inflation in China.The rising uncertainty and asymmetric information in financial markets between bond holders and bond issuers suggest such a potential nonlinear relationship.To this aim,we apply Shin et al.'s(2014)nonlinear autoregressive distributed lag(NARDL)model with asymmetric dynamic multipliers for the sample period 2002M7-2018M4.The empirical findings reveal symmetric and asymmetric partial Fisher effects for all sample bond rates in China.Furthermore,we find that 20-year bond rates experience the lowest partial Fisher effect.展开更多
Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improv...Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improved the prediction of sugarcane crop yield on the local and global scales,but an additional effort on the plot scale prediction is required.Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop,the lack of high spatial resolution data during the critical growth stages,and the non-linear complexation of yield data.The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle(UAV)imagery along with three advanced ML techniques,namely Random Forest Regression(RFR),Support Vector Regression(SVR),and Nonlinear Autoregressive Exogenous Artificial Neural Network(NARX ANN)as a solution to the plot-level sugarcane yield prediction.An experimental sugarcane field containing 48 plots was selected,and UAV imagery was collected during the three consecutive cropping seasons’early and middle crop growth stages.Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved.The datasets of the first two cropping seasons were trained and tested using the three ML techniques,utilizing 10-fold cross-validation to avoid overfitting.The third cropping season dataset was then used to evaluate the reliability of the developed prediction models.The results show that the correlation of Vegetation Indices(VIs)with crop yield in the middle stage outperforms the early stage in all three ML models.Moreover,comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient(R^(2))of 0.96 and the lowest Root Mean Square Error(RMSE)of 4.92 t/ha.It was followed by the SVR(R^(2)=0.52,RMSE of 14.85 t/ha),which performed similarly to the RFR method(R^(2)=0.48,RMSE=11.20 t/ha).In conclusion,the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge(NDRE),which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.展开更多
文摘Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have significantly large absolute values across fine scale levels, the number of the jump points and locations where the jumps occur are estimated. The jump heights are also estimated. All estimators are shown to be consistent. Wavelet method ia also applied to the threshold AR(1) model(TAR(1)). The simple estimators of the thresholds are given,which are shown to be consistent.
基金Project supported by the Scientific Research Foundation for the Returned 0verseas Chinese Scholars of China （Grant No 2004.176.4） and the Natural Science Foundation of Shandong Province of China （Grant No Z2004G01）.
文摘A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.
文摘Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.
文摘In the field of railway traction drive systems,voltage sensor intermittent faults can significantly impact the reliability and safety of the entire system.This paper proposes an online diagnosis method for detecting such faults using an Artificial Intelligence(AI)predictor based on a Nonlinear Autoregressive with eXogenous inputs(NARX)data structure.The model is trained efficiently using the Extreme Learning Machine(ELM)algorithm.The NARX model captures the dynamic characteristics of the voltage sensor data,enabling the AI predictor to learn complex nonlinear relationships.The ELM training method ensures rapid convergence and high accuracy.Through extensive experimental validation,the proposed method demonstrates high sensitivity to voltage sensor intermittent faults and robust performance under varying operating conditions.This approach offers a promising solution for enhancing the diagnostic capabilities of railway traction systems,ensuring timely fault detection and improving overall system reliability.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51978589,51778544,and 51525804).
文摘The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.
文摘With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.
基金2022 Basic Scientific Research Project supported by Liaoning Provincial Education Department(No.LJKMZ20221686)。
文摘A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.
基金Supported by the National Natural Science Foundation of China under Grant No 60572027, the Programme for New Century Excellent Talents in University of China under Grant No NCET-05-0794, the Basic Research Foundation of Sichuan Province under Grant No 2006J13-110, and the 2006 Doctoral Innovation Fund of Southwest Jiaotong University.
文摘Based on the entropy criterion and n-dimensional uniform distribution of nonlinear digital filter （NDF）, we present an efficient NDF-based pseudorandom bit generator （NDF-PRBG） for chaotic stream ciphers. The cryptographic properties of the proposed NDF-PRBG are analysed, and some experiments are made. The results show that it has desirable cryptographic properties, and can be used to construct secure stream ciphers with high speed.
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150 and 10926197
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.
文摘The study examines the asymmetric effects of renewable energy consumption on carbon emissions in seven South Asian nations.By employing data from 1990 to 2019,the study utilized a nonlinear panel autoregressive distributed lag modeling framework to identify an asymmetric relationship between carbon emissions and shocks(positive and negative)in renewable energy consumption.The study revealed that there exists an asymmetric short-and long-term impact of shocks in renewable energy consumption on carbon emissions in South Asian economies.Furthermore,the study reported that in South Asian economies,a 1%rise in positive shocks in renewable energy consumption results in a 1.86%reduction in carbon emissions in the long term and a 1.50%reduction in the short term.Conversely,a 1%increase in negative shocks in renewable energy consumption leads to a 0.55%increase in carbon emissions in the long term and a 2.40%increase in the short term.Furthermore,the findings of the study suggest a few policy implications for South Asian countries,stressing the importance of promoting renewable energy consumption to achieve sustained carbon emissions reductions and to lessen the impact of negative shocks on emissions over both short and long terms.
文摘This study revisits the Fisher effect using a different empirical method that considers a potential nonlinear relationship between interest rates(treasury bond rates)and inflation in China.The rising uncertainty and asymmetric information in financial markets between bond holders and bond issuers suggest such a potential nonlinear relationship.To this aim,we apply Shin et al.'s(2014)nonlinear autoregressive distributed lag(NARDL)model with asymmetric dynamic multipliers for the sample period 2002M7-2018M4.The empirical findings reveal symmetric and asymmetric partial Fisher effects for all sample bond rates in China.Furthermore,we find that 20-year bond rates experience the lowest partial Fisher effect.
文摘Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improved the prediction of sugarcane crop yield on the local and global scales,but an additional effort on the plot scale prediction is required.Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop,the lack of high spatial resolution data during the critical growth stages,and the non-linear complexation of yield data.The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle(UAV)imagery along with three advanced ML techniques,namely Random Forest Regression(RFR),Support Vector Regression(SVR),and Nonlinear Autoregressive Exogenous Artificial Neural Network(NARX ANN)as a solution to the plot-level sugarcane yield prediction.An experimental sugarcane field containing 48 plots was selected,and UAV imagery was collected during the three consecutive cropping seasons’early and middle crop growth stages.Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved.The datasets of the first two cropping seasons were trained and tested using the three ML techniques,utilizing 10-fold cross-validation to avoid overfitting.The third cropping season dataset was then used to evaluate the reliability of the developed prediction models.The results show that the correlation of Vegetation Indices(VIs)with crop yield in the middle stage outperforms the early stage in all three ML models.Moreover,comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient(R^(2))of 0.96 and the lowest Root Mean Square Error(RMSE)of 4.92 t/ha.It was followed by the SVR(R^(2)=0.52,RMSE of 14.85 t/ha),which performed similarly to the RFR method(R^(2)=0.48,RMSE=11.20 t/ha).In conclusion,the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge(NDRE),which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.
