离轴抛物面超精密加工轨迹时间序列控制方法

离轴抛物面超精密加工轨迹容易出现不可控轴与可控轴位置势函数不一致现象,影响加工次序,导致加工效果不理想。针对该问题,提出了离轴抛物面超精密加工轨迹时间序列控制方法。分析离轴抛物面超精密加工过程,计算点与点之间的旋转角度。以机床零点为基准,将笛卡尔坐标系中零件外形表达式转化到柱坐标系中,使工件方程的坐标系完成转换。将加工轨迹点时间间隔映射成0~1的数值,设定各个处理时间内时间权重,使得各个加工时间点间交互作用。检测基于轨迹点分布聚簇性的加工轨迹,分析加工轨迹聚集特性,消除各种轨迹之间的时间间距对轨迹的影响。采用PMAC的时基控制方式,保证不可控轴与可控轴位置势函数一致。以移动点更新速率为主要计算参量,将实时输入频率作为时基控制的输入频率,以非控制轴为输出频率,由此实现加工轨迹时间序列的时基控制。通过试验验证结果可知,该方法能够获取以(-150,300,0)为加工轨迹中心的完整超精密加工轨迹,且不会出现投影畸变现象,有效控制加工轨迹时间序列。

装备完好率混沌时间序列数据控制方法研究

以某类具有混沌特征的装备完好率时间序列数据为研究对象,分析了导致其混沌的系统因素,认为混沌时间序列源于混沌系统。用系统的观点讨论了对混沌时间序列进行控制的最小参数扰动方法和步骤,给出了几种控制策略。

水库岸坡岩体渗透变形的预测预报方法──自适应时间序列研究

研究了库岸边坡岩体渗透变形的动力学机制，并根据多参数的随机变化规律，建立了非线性ＣＡＲ模型，通过分析实例。

预测小波神经网络智能控制系统仿真研究

针对高危险高污染环境中控制系统参数在线整定和优化的需求,提出了一种结合向量时间序列预测方法和小波神经网络方法的控制参数在线整定智能控制系统,即通过增设小波神经网络作为控制系统的智能整定机制和自回归移动平均向量时间序列算法预测输出替代根据实时输出进行计算。采用理论分析和对多个方法进行计算机仿真对比实验等方式,验证了上述方法和智能控制系统具有可行性、工程应用稳定性、更好的快速性、更低的稳态误差等特性,给出了稳定性保证方法。

A test sequence generation method of zone controller based on timed automata

In order to solve the problem of artificial generation and low efficiency of test sequences for zone controller (ZC), a model-based automatic generation method of test sequence is proposed. Firstly, the timed automata model is established based on function analysis of the zone controller, and the correctness of the model is verified by UPPAAL. Then by parsing the timed automata model files, state information and transition conditions can be extracted to generate test case sets. Finally, according to the serialization conditions of test cases, the test cases are serialized into test sequences by using the improved depth first search algorithm. A case, the ZC controls the train running within its jurisdiction, shows that the method is correct and can effectively improve the efficiency of test sequence generation.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

An Empirical Study on the Relationship Between China's Maize Price and Maize Import and Export Trade

Based on the time series of China’s maize trade and domestic maize price,the article analyzes the relationship between domestic maize price and China’s maize international trade by using empirical methods.The results show that there is a connection between domestic maize prices and China's maize international trade,but the link is not very close.Domestic maize price is the cause of China’s maize international trade change,but its impact is very limited;China's maize international trade also can make some difference on the domestic maize price.Based on the results of research,the article brings three suggestion and expectation to adjust China’s maize international trade and the cost control reasonably in order to stabilize domestic maize price and maize yield,and to protect domestic grain security.

Warmer-Get-Wetter or Wet-Get-Wetter? A Criterion to Classify Oceanic Precipitation

In this study,the temporal and spatial variations of observed global oceanic precipitation during 1979-2010 are investigated.It is found that the global trend in precipitation during this period varies at a rate of 1.5％/K of surface warming while the rate is 6.6％/K during 2006-2010.The precipitation is highly correlated with Sea Surface Temperature （SST） in both the temporal and the spatial patterns since the strong 1997-98 E1 Ni（n）o event.Considering the distributions of precipitation and SST,seven oceanic regions are classified and presented using the observed Global Precipitation Climatology Project （GPCP） data and Extended Reconstructed Sea Surface Temperatures,version 3 （ERSST.v3） data.Further examining the mechanisms of the classified oceanic precipitation regions is conducted using the Tropical Rainfall Measuring Mission （TRMM） satellite,GFDL-ESM-2G model precipitation and SST data and Hadley Center sea ice and SST version 1 （HadISST1） data.More than 85％ of global oceanic precipitations are controlled by either one or both of the warmer-get-wetter mechanism and wet-get-wetter mechanism.It is estimated that a 0.5 SST signal-to-noise ratio,representing the trend of SST time series to the standard deviation,is a criterion to distinguish the mechanism of a region.When the SST ratio is larger than 0.5,the precipitation of this region is controlled by the warmer-get-wetter mechanism.SST,rather than the humidity,is the pivotal factor.On the other hand,when the SST ratio is less than 0.5,the precipitation is controlled by the wet-get-wetter mechanism.The SST variability is a significant factor contributing to the precipitation variation.

Concentration–discharge patterns of weathering products from global rivers

Quantifying the functional relationships relating river discharge and weathering products places key constraints on the negative feedback between the silicate weathering and climate. In this study we analyze the concentration–discharge relationships of weathering products from global rivers using previously compiled time-series datasets for concentrations and discharge from global rivers. To analyze the nature of the covariation between specific discharge and concentrations, we use both a power law equation and a recently developed solute production equation. The solute production equation allows us to quantify weathering efficiency, or the resistance to dilution at high runoff, via the Damkohler coefficient. These results are also compared to those derived using average concentration–discharge pairs.Both the power law exponent and the Damkohler coefficient increase and asymptote as catchments exhibit increasingly chemostatic behavior, resulting in an inverse relationship between the two parameters. We also show that using thedistribution of average concentration–discharge pairs from global rivers, rather than fitting concentration–discharge relationships for each individual river, underestimates global median weathering efficiency by up to a factor of ～10. This study demonstrates the utility of long time-series sampling of global rivers to elucidate controlling processes needed to quantify patterns in global silicate weathering rates.

Distributed consensus for multiple Euler-Lagrange systems: An event-triggered approach

Distributed consensus problems for multiple Euler-Lagrange systems are addressed on the basis of event-triggered information in this study. Distributed consensus protocols are first designed in terms of two event-triggered scenarios: a decentralized strategy and a distributed strategy. Sufficient conditions that guarantee the event-triggered consensus for multiple Euler-Lagrange systems are then presented, with the associated advantages of reducing controller update times. It is shown that the Zeno behavior of triggering time sequences is excluded for both strategies. Finally, multiple Euler-Lagrange systems that consist of six two-link manipulators are considered to illustrate the effectiveness of the proposed theoretical algorithms.

Fourier mode decomposition of PIV data.

The modal decomposition technique is one of the most effective methods for studying the flow dynamics in a complex flow. By rejuvenating the discrete Fourier transform(DFT), this paper proposes a Fourier mode decomposition(FMD) method for the time series of particle image velocimetry(PIV) data from the fluid field. An experimental case concerning the control of the flow around a circular cylinder by a synthetic jet positioned at the rear stagnation point is used to demonstrate the use of the FMD method. In the three different regimes where the natural shedding frequency and actuation frequency dominate respectively or simultaneously, it is found that the FMD method is capable of extracting the dynamic mode along with its amplitude and phase according to the selected characteristic frequency based on the global power spectrum. For the quasiperiodic flow phenomena presented in this particular case, the FMD method can reconstruct the original flow field using the zero-th mode and the selected mode corresponding to the characteristic frequency. Similarities and differences between the FMD method and the dynamical mode decomposition(DMD) and proper orthogonal decomposition(POD) methods are also discussed.

PREDICTING CHAOTIC TIME SERIES WITH IMPROVED LOCAL APPROXIMATIONS

In this paper, new approaches for chaotic time series prediction areintroduced. We first summarize and evaluate the existing local prediction models, then proposeoptimization models and new algorithms to modify procedures of local approximations. Themodification to the choice of sample sets is given, and the zeroth-order approximation is improvedby a linear programming method. Four procedures of first-order approximation are compared, andcorresponding modified methods are given. Lastly, the idea of nonlinear feedback to raise predictingaccuracy is put forward. In the end, we discuss two important examples, i.e. Lorenz system andRoessler system, and the simulation experiments indicate that the modified algorithms are effective.