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.

CHARACTERISTIC FUNCTIONS OF BILINEAR TIME SERIES MODEL

Bilinear time series models are of importance to nonlinear time seriesanalysis.In this paper,the autocovariance function and the relation between linearand general bilinear time series models are derived.With the help of Volterra seriesexpansion,the impulse response function and frequency characteristic function of thegeneral bilinear time series model are also derived.

A Statistical Method for Determining the Fractal Dimension of Time Series

In this paper,we present a new method for determining the fractal dimension of time series and the algorithm of H index(Hurst index).

Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined （DSC） approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average （SARIMA） models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term （10-year） environmental planning and decision making.

Long-term deformation analysis of Shuibuya concrete face rockfill dam based on response surface method and improved genetic algorithm

Due to the size effects of rockfill materials, the settlement difference between numerical simulation and in situ monitoring of rockfill dams is a topic of general concern.The constitutive model parameters obtained from laboratory triaxial tests often underestimate the deformation of high rockfill dams.Therefore, constitutive model parameters obtained by back analysis were used to calculate and predict the long-term deformation of rockfill dams.Instead of using artificial neural networks (ANNs), the response surface method (RSM) was employed to replace the finite element simulation used in the optimization iteration.Only 27 training samples were required for RSM, improving computational efficiency compared with ANN, which required 300 training samples.RSM can be used to describe the relationship between the constitutive model parameters and dam settlements.The inversion results of the Shuibuya concrete face rockfill dam (CFRD) show that the calculated settlements agree with the measured data, indicating the accuracy and efficiency of RSM.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

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.

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

Research on Optimize Prediction Model and Algorithm about Chaotic Time Series

We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.

KINETIC MODELLING AND PERFORMANCE ANALYSIS OF THE FOUR-POST-FRAME LIFTING MECHANICAL SYSTEM

The kinetic model of the four-post-frame lifting mechanical system was established. The stiffness and damping matrices of differential equations of motion were obtained by using Lagrange’s equations. And the dynamic characteristics of system were analyzed by modal analysis method. Based upon this, the modifications of structural parameters which can improve dynamic performance were discussed. The low-level high-speed palletizer MDJ1200L was taken as a real case in the paper.

A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks

This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.

基于水光电力系统时序生产模拟模型的水光优化配比研究

基于考虑系统负荷、新能源发电出力等时序变化特性的时序生产模拟法,建立了水光电力系统时序生产模拟模型;从系统结构性、经济性、灵活性与可靠性方面综合确定最佳水光容量配比,并通过金沙江上游川藏段水光系统验证了模型的合理性。金沙江上游川藏段光伏电站与水电站出力具有较好的年内、日内互补特性;对于2030年水平年,考虑枯期水电留存,913.6万kW水电配套400万kW光伏的方案综合性能更优,其新增非水可再生电量约60亿kW·h。

考虑激励幅值慢变的卷绕头转子系统动力学建模

卷绕头转子系统结构之间的耦合振动和时频变激励使振动问题十分复杂,为了更准确地研究其动力学特性,根据转子动力学理论,使用有限元方法建立了考虑多盘质量慢变的卷绕头非线性转子系统整机动力学模型,运用Newmark-β数值积分方法对其动力学特性求解,得到仅存在初始不平衡量以及卷装不良带来的激励频率、幅值慢变下的非线性动力学行为。通过分析轴心轨迹、时域波形和幅频特性曲线等结果,发现卷绕头转子系统对因卷装不良引起的激励幅值慢变较为敏感,出现新的频率成分。进一步对比分析了不同节点处的振动频谱,其结果可为卷绕头转子系统动力学设计和故障识别提供一定理论支持。

密云水库流域降水径流非平稳特征识别及归因

为更准确揭示和认识变化环境下密云水库流域水文过程的非平稳特征,选取该流域1960—2019年降水和径流资料,采用多种时间序列分析方法综合诊断其趋势、突变点和周期等非平稳特征并进行成因分析。结果显示:该流域降水变化主要表现出较明显的随机特性,径流相比降水的下降趋势更显著,且在1979年发生了向下跳跃的强变异;相比气候变异影响,人类活动是密云水库流域径流减少的主导因素;1980—1998年流域水土保持措施与水利工程兴建对径流变化的贡献率为-111.40%,抵消了气候变异11.4%的增水效应;1999—2019年,塘坝建造和用地类型转变对径流变化的贡献率为-66.60%,同时叠加气候变异-33.40%的减水效应,导致近20年来该流域径流呈现显著减少的态势。研究结果可为密云水库安全运行、洪水调度以及流域水资源管理决策提供科学支撑。

基于时序参数分析的砂岩热储回灌特征变化研究——以山东临清市为例

为促进山东省临清市地热资源循环利用,开展了地热水回灌工程。针对采暖期前与采暖期两种情形的回灌情况,监测了开采井及回灌井的水量、水温、水位及井口压力等,并基于时序参数分析了回灌能力及最大回灌量、开采井温度场、水质变化和回灌堵塞程度。结果表明:本次施工的回灌井能够满足单井100%回灌,单井自然回灌量可达121~227 m^(3)/h;总体上开采井地热水温度表现为在供暖期上升、供暖末期下降的趋势;由于井位深度差别,在供暖期前后,开采井矿化度比回灌井分别高约45.34%和17.33%;经过一个供暖季的回灌,虽然在回扬过程中渗透系数有一定的改善,但仍由0.7699 m/s降至0.3254 m/s,有明显变小的趋势。

X波段双偏振雷达物理量时间—高度剖面的重构方法改进及应用研究

如何利用现有雷达体扫数据重构反射率或其它物理量的时间—高度剖面,提高雷达体扫垂直分辨率并使其适用于云微物理结构的分析,是近几年来雷达气象学的重点研究内容之一。本文基于分辨率更高的X波段双偏振雷达体扫数据,对目前最新的柱垂直廓线(Columnar Vertical Profile,简称CVP)重构算法从目标区范围的选取方面进行改进,使其能够应用于水平尺度较小的局地降水云以及发展演变迅速的对流云。结果显示:对于高原地区局地降水云个例,目标区选取5 km(径向范围)×10°(方位角范围)组成的较小扇形区域,与云雷达的对比显示,改进的CVP方法重构的基本反射率(ZH)垂直廓线体现了回波的垂直结构,尤其是中高层对流泡的结构特点,相应的时间—高度序列能够较好地反映回波顶高的变化以及中高层强度逐渐减弱、低层强度逐渐增加的特点;对于华北地区发展旺盛且局地水平不均匀的对流云个例,本文改进了原始的CVP重构目标区选取方法,对高、低仰角层采用变化的径向范围并调整插值参数,改进后重构的ZH垂直廓线有效避免了低层回波水平分布相对不均匀导致的重构分层结构,显示出高、低层回波特征以及不同阶段目标区云结构的转变。进一步对比改进前后CVP方法重构建立的各偏振量时间—高度序列,改进后准确显示了个例云系微物理特征及其随时间的变化,揭示了高原地区局地降水云中对流泡的形成及其播撒作用机制,华北地区对流云成熟阶段的各偏振量垂直分布特征及其演变。

基于Slepian序列信号字典的目标频段瞬态信号检测方法

提出一种基于Slepian序列信号字典的目标频段瞬态信号检测方法,该方法只关注目标频段的能量信息,是一种高效率的检测方式。首先,选取标准正交的Slepian序列组成信号字典,该字典能够表征目标频段内信号特征;然后,通过判断被观测信号样点与字典的匹配程度实现检测。对比实验表明,该方法的计算效率比短时傅里叶变换提升92%以上,比离散小波变换提升71%以上,比加窗Wigner-Ville分布提升35%以上。仿真实验使用时域稀疏的脉冲调制信号进行验证,结果表明了该检测方法的有效性。

基于同步提取变换的地空频率域电磁信号幅度提取方法

地空频率域电磁法探测信号为多频非平稳信号,为了解决应用传统傅里叶变换方法提取其幅度时分辨率较差的问题,本文提出了基于同步提取变换(SET)的地空频域电磁信号幅度提取方法。该方法对电磁数据进行SET,得到高分辨率时频图,并利用能量算子使电磁数据时频谱能量更为集中;采用贪心算法提取脊线,得到时频图的高能量带;通过自回归模型自适应地补充了脊线中的0值,解决了由窗函数引起的端点效应问题。根据脊线位置的时频图复数值,得到各频率分量幅度随时间的变化,研究了不同信噪比情况下基于SET的地空频率域电磁信号幅度提取结果的准确性。结果表明:当信噪比≥10 dB时,幅度提取结果的平均相对均方根误差均小于5%;当信噪比<10 dB时,幅度提取结果的平均相对均方根误差在10%以内。提取效果良好。将此方法应用于新疆霍拉山隧道工程地空频率域电磁法探测中,成功提取了多频电磁信号各频率分量的幅度,与采用傅里叶变换提取非平稳信号幅度的方法相比,该方法有效地提高了幅度提取结果的分辨率。

基于累积位移特征与时间序列组合模型的滑坡位移预测

分析滑坡累积位移演化特征对位移预测具有重要的意义,根据不同增长趋势的位移曲线构造合适的模型来预测位移,将有效提高预测结果的准确度。本文分析三峡库区阶跃型滑坡累积位移变化趋势,将其位移曲线划分为4类特征:等幅-阶跃型、减幅-阶跃型、增幅-阶跃型、复合型,并建立时间序列组合预测模型。以八字门滑坡监测点ZG111及白家包滑坡监测点ZG326为例,依据时间序列原理,采用变分模态分解法(VMD)将累积位移分解为趋势性位移、周期性位移、随机性位移;利用一元线性回归、幂函数非线性回归方法对趋势性位移进行建模分析,预测结果采用加权改进后的最小二乘法(WLS);用麻雀搜索算法(SSA)优化BP神经网络模型,并结合滚动预测的思想,预测周期性位移、随机性位移。最终得到的各位移预测值之和即累积位移预测结果,结果表明:趋势性位移预测MAPE分别为1.2%、0.77%;周期性位移、随机性位移拟合效果较好,预测结果能较好的符合位移整体变化趋势;累积位移预测MAPE在2%以内,预测结果与实际值具有良好的一致性。本文提出的预测模型满足预测精度的要求,能完成滑坡将来位移量的预测,具有较强的工程实用价值,为滑坡灾害预测和防治方面的研究工作提供指导。