Isochronous Explicit Time Integration Framework: Illustration to Thermal Stress Problems Involving Both First- and Second-Order Transient Systems

In this article, an isochronous explicit time integration framework (or the explicit iIntegrator) for solving thermal stress problems is illustrated. Similar to the implicit case, the same adaptation process of the isochronous integration is valid for the explicit case. That is, the adaptation process endows the explicit version of the generalized single step family of algorithms for second-order systems (explicit GS4-2 family of algorithms) with the applicability to first-order systems, and the explicit version of the GS4 family of algorithms for first-order systems (explicit GS4-1 family of algorithms) is automatically generated. Two illustrative thermal stress dynamic applications are shown to demonstrate the practicality and convenience of the explicit iIntegrator.     

Correlation between Chinese and international energy prices based on a HP filter and time difference analysis

To establish a reasonable system and mechanism for Chinese energy prices, we use the Granger causality test, Hodrick–Prescott (HP) filter and time difference analysis to research the pricing relationship between Chinese and international energy prices. We find that Chinese and international crude oil prices changed synchronously while Chinese refined oil prices follow the changes of international oil prices with the time difference being about 1 month to 2 months. Further, Australian coal prices Granger causes Chinese coal prices, and there is a high correlation between them. The U.S. electricity price is influenced by the WTI crude oil price, the U.S. gasoline price and the HenryHub gas price. Due to the unreasonable price-setting mechanism and regulation from the central government, China′s terminal market prices for both electricity and natural gas do not reflect the real supply–demand situation. This paper provides quantitative results on the correlation between Chinese and international energy prices to better predict the impact of international energy price fluctuations on China′s domestic energy supply and guide the design of more efficient energy pricing policies. Moreover, it provides references for developing countries to improve their energy market systems and trading, and to coordinate domestic and international energy markets.     

电渣重熔M2高速钢共晶碳化物控制研究

采用普通电渣重熔工艺和新型抽锭式电渣重熔工艺制备M2高速钢,研究了熔池深度与局部凝固时间等参数对高速钢铸锭共晶碳化物形貌和类型的影响.结果表明,电渣重熔工艺不改变碳化物类型,两种工艺制备的M2铸锭都存在M6C、MC、M2C三种类型的碳化物.抽锭式电渣重熔工艺通过有效控制电渣锭直径(D=120 mm)、熔速(11 mm/min)及熔池深度(h=50 mm),减少局部凝固时间,促进平直层片状共晶碳化物转变为离异棒状、粒状碳化物,碳化物平均晶粒尺寸小于50 μm.加快冷却速度会降低组织中共晶碳化物的质量分数,同时也会降低共晶碳化物中强碳化物形成元素如W、Mo、V的含量,使更多碳化物及合金溶于基体中.     

Comments on Kurtz-Link-Tukey-Wallace Paper

Part I of this paper was published in the previous issue of Technometrics, (Vol. 2, No. 1, February 1960). In Part I Dr. Epstein describes several graphical and analytical procedures for testing the assumption that the underlying distribution of life is exponential. This portion of the paper contains numerical examples illustrative of these procedures.     

Neural Networks in Applied Statistics

Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the physical and chemical sciences, as well as in other fields. We introduce the principles of the multilayer feedforward network that is among the most commonly used neural networks in practical problems. The relevance of neural network models for the applied statistician is considered using a time series prediction problem as an example. The multilayer feedforward neural network uses a nonlinear function of the predictors to obtain predictions for future time series values. We illustrate the considerations involved in specifying a neural network model and evaluate the accuracy of neural network models relative to the accuracy obtained using other computer-intensive, nonmodel-based techniques.     

Editor's Report

Bootstrap resampling methods have emerged as powerful tools for constructing inferential procedures in modern statistical data analysis. Although these methods depend on the availability of fast, inexpensive computing, they offer the potential for highly accurate methods of inference. Moreover, they can even eliminate the need to impose a convenient statistical model that does not have a strong scientific basis. In this article, we review some bootstrap methods, emphasizing applications through illustrations with some real data. Special attention is given to regression, problems with dependent data, and choosing tuning parameters for optimal performance.     

Extreme Values from a Lognormal Law With Applications to Air Pollution Problems

In this paper an attempt is made to show how certain empirical relationships observed in an analysis of air pollution data can be interpreted using extreme-value theory. In order to accomplish this, it was necessary to determine the limiting distribution of the maximum term of a random series from the lognormal law. Since an explicit development of this limiting distribution including a determination of the normalizing constants, is not available in the literature to the knowledge of this author, an appendix has been devoted to it. Potential applications of the extreme value theory to problems of air quality are pointed out.     

Maximum likelihood estimation of higher‐order integer‐valued autoregressive processes

Abstract. In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher‐order integer‐valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004) , we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) specification with binomial thinning and Poisson innovations, we examine both the asymptotic efficiency and finite sample properties of the ML estimator in relation to the widely used conditional least squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justified, there are substantial gains to be had from using ML especially when the thinning parameters are large.     

Time averaged temperature calculations in pulse electrochemical machining, part II: numerical simulation

Simulation of the temperature distribution and evolution during pulse electrochemical machining can be a computationally very expensive procedure. In a previous part of the work [Smets et al. J Appl Electrochem 37(11):1345, 2007] a new approach to calculate the temperature evolution was introduced: the hybrid method, which combines averaged and pulsed calculations. The averaged calculations are performed by time averaging the boundary conditions and the bulk heat sources of the system. The timesteps used during the averaged calculations are then no longer dictated by the pulse characteristics. Using this approach, computationally very cheap, yet satisfactory results can be obtained. The analysis in the previous part of the work was obtained from analytical solutions on simplified models. In this part, the more general case is solved numerically. Multiple geometries are simulated and analyzed and methods are compared. Very satisfactory, yet cheap results are obtained.