An explicit finite difference method for finite-time observers

In this paper we present a numerical method for estimating the current state of a nonlinear control system. We use finite differences to discretize a modified version of the finite-time observer equations in James. The discretized equations are simple and easily programmed. The convergence and accuracy of the scheme is proved, and the scheme enjoys a number of important properties: availability of rate of convergence estimates, good robustness characteristics, and the ability to handle certain types of discontinuities in the observations. The major disadvantage is that the number of grid points required increases exponentially with the number of state dimensions.     

A method of identifying latent human errors in work systems

Work system improvements are implemented in various manufacturing processes to prevent problems caused by human errors. However, they are almost always applied to problems which have already occurred. This paper examines a method of identifying latent human errors existing within the work systems beforehand. A procedure for applying failure mode and effect analysis to this identification problem was defined based on over 1000 empirical errors: a work system decomposition criterion and fundamental error modes for listing latent human errors, and then applied to three practical manufacturing processes in order to evaluate its effectiveness.     

Salt-water up-coning during extraction of fresh water from a tropical island

Rainwater can collect in a lens-shaped region within the rock of a tropical island, and may be separated from the underlying salt water by a sharp interface. This paper presents a nonlinear theory for determining the shape of this interface. The island is assumed to be saturated with rain, and provision is made for the outflow of rain-water through the sides of the island. The effect of a bore well on the shape of the interface is investigated, and the problem is solved using a spectral method. An integral-equation method is also presented for the case when the island has infinite width.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

低压直流供电电路中高压直流的产生

在由低压直流电源供电的电路中,往往电路的某些部位需要使用高于电源所供的电压,将电源所供的低压转换为较高的电压,常用的方法有3种:自举升压、电感升压、逆变升压。结合实际应用电路对各种升压方法从器件选择、升压原理、升压结果等诸方面进行分析、探讨,解决了总体供电电压不变时,局部高压产生的关键性问题。它对电子电器电路的设计人员有一定的启发作用,对优化电路、遴选器件有较强的参考价值。     

天津独流减河工程监控系统现地通信单元设计

提出一种配电网电容电流测量的新方法，在零序电压互感器的开口三角侧串联一个可调电感，通过注入一个变频恒流信号寻找配电网的谐振频率，改变可调电感的数值后寻找另一谐振频率，联立2个谐振方程求解电容电流，该方法消除了电压互感器漏抗对测量的影响。开发了配电网电容电流测量仪，经模拟试验和现场测试表明，该方法具有安全、快捷、准确等特点，适用于中性点不接地或经随调式消弧线圈接地配电网。     

Analysis of the Datagram Congestion Control Protocol’s connection management procedures using the sweep-line method

State space explosion is a key problem in the analysis of finite state systems. The sweep-line method is a state exploration method which uses a notion of progress to allow states to be deleted from memory when they are no longer required. This reduces the peak number of states that need to be stored, while still exploring the full state space. The technique shows promise but has never achieved reductions greater than about a factor of 10 in the number of states stored in memory for industrially relevant examples. This paper discusses sweep-line analysis of the connection management procedures of a new Internet standard, the Datagram Congestion Control Protocol (DCCP). As the intuitive approaches to sweep-line analysis are not effective, we introduce new variables to track progress. This creates further state explosion. However, when used with the sweep-line, the peak number of states is reduced by over two orders of magnitude compared with the original. Importantly, this allows DCCP to be analysed for larger parameter values. Somsak Vanit-Anunchai was partially supported by an Australian Research Council Discovery Grant (DP0559927) and Suranaree University of Technology. Guy Edward Gallasch was supported by an Australian Research Council Discovery Grant (DP0559927).