论德勒兹的回忆-影像及非时序性时间

回忆-影像是德勒兹提出的(第一种时间-影像符号),它是将过去再现在当下,是一种再现的、当下的、现实的影像。将过去带回到现在的回忆-影像,它和现实影像是一种线性意义上的时序性时间关系。回忆-影像在德勒兹的影像分类里,是处于纯粹的晶体-影像之前的影像。它已经不以行动为中心,大量关涉记忆、幻觉,但最终还是没有摆脱掉感知-运动体制(现在)这个依托点。当然德勒兹作这番探讨,其方向是为了寻求生成的时间,即非时序性时间,它体现在晶体-影像和虚构叙事中,是一种生成的和创造的时间。电影不再仅仅是被观看的对象,影像真正成为了思维运作的舞台。这是德勒兹意义上的自由电影和创造性的电影。

多媒体技术在研究生教学中的应用探讨

随着计算机技术和网络技术的飞速发展,信息化社会对研究生能力的培养提出了更高的要求。本文通过探讨多媒体技术在研究生教学中的应用优势,提出正确处理多媒体技术与传统教学的关系有利于研究生教学效率的提高和教学方法的改革,更有利于研究生自学能力、研究能力和创新能力的培养。

A Cross-Reference Method for Nonlinear Time Series Analysis in Semi-Blind Case

In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.

Nonlinear Time Series Prediction Using Chaotic Neural Networks

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Nonlinear Analysis of Physiological Time Series

The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics.

Translating Linear Temporal Logic Formula s into Automata

To combat the well-known state-space explosion problem in Prop ositional Linear T emp o- ral Logic （PLTL） model checking, a novel algo- rithm capable of translating PLTL formulas into Nondeterministic Automata （NA） in an efficient way is proposed. The algorithm firstly transforms PLTL formulas into their non-free forms, then it further translates the non-free formulas into their Normal Forms （NFs）, next constructs Normal Form Graphs （NFGs） for NF formulas, and it fi- nally transforms NFGs into the NA which ac- cepts both finite words and int-mite words. The experimental data show that the new algorithm re- duces the average number of nodes of target NA for a benchmark formula set and selected formulas in the literature, respectively. These results indi- cate that the PLTL model checking technique em- ploying the new algorithm generates a smaller state space in verification of concurrent systems.

Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory

Previous studies have shown that year-to-year incremental prediction （YIP） can obtain considerable skill in seasonal forecasts. This study analyzes the mathematical deRnition of YiP and derives its formula in the nonlinear time series prediction （NP） method, it is shown that the two methods are equivalent when the prediction time series is embedded in one-dimensional phase space. Compared to previous NP models, the new one introduces multiple external forcings in the form of year-to-year increments. The year-to-year increments have physical meaning, which is better than the NP model with empirically chosen parameters. The summer rainfall over the middle to lower reaches of the Yangtze River is analyzed to examine the prediction skill of the NP models. Results show that the NP model with year-to-year increments can reach a similar skill as the YiP model. When the embedded number of dimensions is increased to two, more accurate prediction can be obtained. Besides similar results, the NP method has more dynamical meaning, as it is based on the classical reconstruction theory. Moreover, by choosing different embedded dimensions, the NP model can reconstruct the dynamical curve into phase space with more than one dimension, which is an advantage of the NP model. The present study suggests that YIP has a robust dynamical foundation, besides its physical mechanism, and the modified NP model has the potential to increase the operationaJ skill in short- term climate prediction.

Multi-scale Chaotic Analysis of the Characteristics of Gas-Liquid Two-phase Flow Patterns

Using the high-speed camera the time sequences of the classical flow patterns of horizontal gas-liquid pipe flow are recorded, from which the average gray-scale values of single-frame images are extracted. Thus obtained gray-scale time series is decomposed by the Empirical Mode Decomposition (EMD) method, the various scales of the signals are processed by Hurst exponent method, and then the dual-fractal characteristics are obtained. The scattered bubble and the bubble cluster theories are applied to the evolution analysis of two-phase flow patterns. At the same time the various signals are checked in the chaotic recursion chart by which the two typical characteristics (diagonal average length and Shannon entropy) are obtained. Resulting term of these properties, the dynamic characteristics of gas-liquid two-phase flow patterns are quantitatively analyzed. The results show that the evolution paths of gas-liquid two-phase flow patterns can be well characterized by the integrated analysis on the basis of the gray-scale time series of flowing images from EMD, Hurst exponents and Recurrence Plot (RP). In the middle frequency section (2nd, 3rd, 4th scales), three flow patterns decomposed by the EMD exhibit dual fractal characteristics which represent the dynamic features of bubble cluster, single bubble, slug bubble and scattered bubble. According to the change of diagonal average lengths and recursive Shannon entropy characteristic value, the structure deterministic of the slug flow is better than the other two patterns. After the decomposition by EMD the slug flow and the mist flow in the high frequency section have obvious peaks. Anyway, it is an effective way to understand and characterize the dynamic characteristics of two-phase flow patterns using the multi-scale non-linear analysis method based on image gray-scale fluctuation signals.

Genetic programming-based chaotic time series modeling

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.

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.

Stability Analysis of Cylindrical Tanks under Static and Earthquake Loading

Large thin walled cylindrical above ground tanks have become more susceptible to failure by buckling during earthquakes. In this study, three different geometries of tanks with H/D （height to diameter） ratios of 2.0, 0.56, 1.0, and D/t （depth to thickness） ratios of 960.0, 1,706.67 and 640.0 respectively were analyzed for stability when subjected to the E1 Centro earthquake at the base. The Budiansky and Roth procedure was used to find the buckling loads when the tanks were empty and when they were filled with liquid up to 90% of their depth. Also, nonlinear time history analysis using ANSYS finite element computer program was performed. Analysis results show that the dynamic buckling occurs for empty tanks at very high PGA （peak ground accelerations） which are unrealistic even for major earthquakes. Furthermore, when the tanks filled with water up to 90% of its height, analysis results show that when the H/D ratio reduced by two times （i.e., from 2 to 1）, the PGA for the buckling increased by six times （increase from 0.25g to 1 .Sg）. Hence, H/D ratio plays an important role in the earthquake stability design of over ground steel tanks.

The Research of Fractal Characteristics of the Electrocardiogram in a Real Time Mode

The article presents the results of recent investigations into Holter monitoring of ECG, using non-linear analysis methods. This paper discusses one of the modern methods of time series analysis--a method of deterministic chaos theory. It involves the transition from study of the characteristics of the signal to the investigation of metric （and probabilistic） properties of the reconstructed attractor of the signal. It is shown that one of the most precise characteristics of the functional state of biological systems is the dynamical trend of correlation dimension and entropy of the reconstructed attractor. On the basis of this it is suggested that a complex programming apparatus be created for calculating these characteristics on line. A similar programming product is being created now with the support of RFBR. The first results of the working program, its adjustment, and further development, are also considered in the article.

The stationarity and invertibility of a class of nonlinear ARMA models

We investigate some probabilistic properties of a new class of nonlinear time series models. A sufficient condition for the existence of a unique causal, strictly and weakly stationary solution is derived. To understand the proposed models better, we further discuss the moment structure and obtain some Yule-Walker difference equations for the second and third order cumulants, which can also be used for identification purpose. A sufficient condition for invertibility is also provided.

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

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.

Engineering practice of seismic isolation and energy dissipation structures in China

The concepts of seismic isolation and energy dissipation structures emerged in the early 1970s.In China,the first seismic isolation structure was finished in 1993,and the first energy dissipation structure was built at about the same time.Up to 2007,China had more than 600 seismic isolation and about 100 energy dissipation building structures.In 2008,the huge Wenchuan earthquake hit the southwest of China,which triggered a bloom of new seismic isolation and energy dissipation structures.This paper presents the development history and representative applications of seismic isolation and energy dissipation structures in China,reviews the state-of-the-practice of Chinese design,and discusses the challenges in the future applications.Major findings are as follows:Basic design procedures are becoming standardized after more than ten years of experiences,which mainly involve determination of design earthquake forces,selection of ground motions,modeling and time-history analyses,and performance criteria.Nonlinear time-history analyses using multiple ground motions are the characteristic of the design of seismic isolation and energy dissipation structures.Regulations,standardization and quality control of devices,balance between performance and cost,comparison with real responses,and regular inspection are identified as the issues that should be improved to further promote the application of seismic isolation and energy dissipation structures in China.

Measuring Nonlinear Dependence Between Time Series Based on Correlation Dimension

In this paper, it is proved that the correlation dimension estimate of a nonlinear dynamical system with its multivariate observation series is the same as that with its univariate observation series. Based on this result, an inference method is presented, and the Nonlinear Dependence Coefficient is defined. This method is designed for testing nonlinear dependence between time series, and can be used in economic analysis and forecasting. Numerical results show the method is effective.

A NEW TEST FOR NORMALITY IN LINEAR AUTOREGRESSIVE MODELS

A nonparametric test for normality of linear autoregressive time series is proposed in this paper.The test is based on the best one-step forecast in mean square with time reverse.Some asymptotic theory is developed for the test,and it is shown that the test is easy to use and has good powers.The empirical percentage points to conduct the test in practice are provided and three examples using real data are included.