Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent...Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent, and the Kolmogorov en- tropy, are estimated in the reconstructed phase space. The results indicate that the reverberation signals are nonlinear. The Volterra adaptive prediction method is introduced to model the oceanic reverberation signals. The reverberation time series can be predicted in short term with small prediction errors. A preliminary conclusion can be reached that the nonlinear low-dimensional dynamic sys- tem model is more suitable for modeling oceanic reverberation than the classical random AR model.展开更多
The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i...The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.展开更多
Ordovician limestone water is coal mines. In this paper, we analyze the the main source of water inrush in North China characteristic of three kinds of nonlinear seismic attributes, such as the largest lyapunov expone...Ordovician limestone water is coal mines. In this paper, we analyze the the main source of water inrush in North China characteristic of three kinds of nonlinear seismic attributes, such as the largest lyapunov exponent,fractal dimension and entropy and introduce their calculation methods. Taking the 81st and 82nd coal districts in the Xutuan coal mine as examples, we extract the three seismic attributes based on the 3D prestack migration seismic data of this area, which can display the Ordovician limestone fracture distribution in the mine. We comprehensively analyzed the three nonlinear seismic attributes and compared the results with transient electromagnetic exploration results and determined the possible Ordovician limestone aquosity distribution. This demonstrated that the nonlinear seismic attributes technology is an effective approach to predict the aquosity of Ordovician limestone.展开更多
This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of free...This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed for numerical analysis under increased radial clearances which are due to not only the scenarios of bearing grades but also gradual wear with bearing service lifetime. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics under different fault cases along with clearance changes. Numerical results show that the vibrations at fault characteristic frequencies exhibit clear inconsistency with common understandings for different cases of increased clearances. This study highlights that it has to take into account the clearance effect, especially for the inner race fault, in order to avoid the under-estimate of fault sizes which may be indicated by the feature amplitude reduction.展开更多
文摘Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent, and the Kolmogorov en- tropy, are estimated in the reconstructed phase space. The results indicate that the reverberation signals are nonlinear. The Volterra adaptive prediction method is introduced to model the oceanic reverberation signals. The reverberation time series can be predicted in short term with small prediction errors. A preliminary conclusion can be reached that the nonlinear low-dimensional dynamic sys- tem model is more suitable for modeling oceanic reverberation than the classical random AR model.
文摘The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.
文摘Ordovician limestone water is coal mines. In this paper, we analyze the the main source of water inrush in North China characteristic of three kinds of nonlinear seismic attributes, such as the largest lyapunov exponent,fractal dimension and entropy and introduce their calculation methods. Taking the 81st and 82nd coal districts in the Xutuan coal mine as examples, we extract the three seismic attributes based on the 3D prestack migration seismic data of this area, which can display the Ordovician limestone fracture distribution in the mine. We comprehensively analyzed the three nonlinear seismic attributes and compared the results with transient electromagnetic exploration results and determined the possible Ordovician limestone aquosity distribution. This demonstrated that the nonlinear seismic attributes technology is an effective approach to predict the aquosity of Ordovician limestone.
文摘This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed for numerical analysis under increased radial clearances which are due to not only the scenarios of bearing grades but also gradual wear with bearing service lifetime. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics under different fault cases along with clearance changes. Numerical results show that the vibrations at fault characteristic frequencies exhibit clear inconsistency with common understandings for different cases of increased clearances. This study highlights that it has to take into account the clearance effect, especially for the inner race fault, in order to avoid the under-estimate of fault sizes which may be indicated by the feature amplitude reduction.
