The sediment content of the Yellow River is resulted from the interactions of natural, economic, and social factors, so it includes some evolutive information of the Yellow River Basin system. Sediment contents from 1...The sediment content of the Yellow River is resulted from the interactions of natural, economic, and social factors, so it includes some evolutive information of the Yellow River Basin system. Sediment contents from 1952 to 2007 on Toudaoguai, Tongguan, Huayuankou and Lijin sections along the river are chosen as the study time series, and correlation dimensions （D2）, Kolmogorov entropies （K2）, and Hurst indexes （H） of the time series were calculated. Correlation dimensions on Toudaoguai, Tongguan, Huayuankou, and Lijin sections are 3.24, 5.69, 6.57 and 7.34 respectively, and the Kolmogorov entropies are 0.13, 0.37, 0.40 and 0.38 respectively, which indicates that the systems controlled by different sections along the Yellow River are chaotic systems and the chaotic degrees increase gradually from the upper to lower section. The average predictable period of the sediment contents is 8 years on Toudaoguai section and 3 years on the other sections with the reciprocals of the Kolmogorov entropies. The more obvious the chaotic degree is, the shorter the average predictable period is. Hurst indexes on the sections are above 0.5, with the maximum of 0.86 on Tongguan section and the minimum of 0.68 on Toudaoguai section, which indicates that the time series have persistent trends in the average predictable period. Eight state variables and two control parameters are necessary to construct the dynamic model of the Yellow River Basin system.展开更多
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) ...This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.展开更多
The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a...The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested.展开更多
We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments f...We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.展开更多
The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, ...The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available database which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite of bias in each individual reader’s measurements, statistical decisions on tumor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in future based on careful variability and measurement uncertainty analysis.展开更多
基金National Natural Science Foundation of China, No.40601105 Key Project of Science and Technology of Henan Province, No.0721021500
文摘The sediment content of the Yellow River is resulted from the interactions of natural, economic, and social factors, so it includes some evolutive information of the Yellow River Basin system. Sediment contents from 1952 to 2007 on Toudaoguai, Tongguan, Huayuankou and Lijin sections along the river are chosen as the study time series, and correlation dimensions （D2）, Kolmogorov entropies （K2）, and Hurst indexes （H） of the time series were calculated. Correlation dimensions on Toudaoguai, Tongguan, Huayuankou, and Lijin sections are 3.24, 5.69, 6.57 and 7.34 respectively, and the Kolmogorov entropies are 0.13, 0.37, 0.40 and 0.38 respectively, which indicates that the systems controlled by different sections along the Yellow River are chaotic systems and the chaotic degrees increase gradually from the upper to lower section. The average predictable period of the sediment contents is 8 years on Toudaoguai section and 3 years on the other sections with the reciprocals of the Kolmogorov entropies. The more obvious the chaotic degree is, the shorter the average predictable period is. Hurst indexes on the sections are above 0.5, with the maximum of 0.86 on Tongguan section and the minimum of 0.68 on Toudaoguai section, which indicates that the time series have persistent trends in the average predictable period. Eight state variables and two control parameters are necessary to construct the dynamic model of the Yellow River Basin system.
基金Project (Nos. 60174009 and 70071017) supported by the NationalNatural Science Foundation of China
文摘This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
基金Project supported by the Key Project of Ministry of Education of China (Grant No. 2010141)the National Natural Science Foundation of China (Grant No. 61203159)
文摘The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested.
基金Project supported by the National Natural Science Foundation of China ( Grant Nos. 61104148, 41174109, and 50974095)the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2011ZX05020-006)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110032120088)
文摘We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.
文摘The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available database which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite of bias in each individual reader’s measurements, statistical decisions on tumor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in future based on careful variability and measurement uncertainty analysis.
