Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

An Application of Markov Chain Analysis to the Game of Squash*

If the score in a squash game is tied late in the game, one player has a choice of how many additional points (from a prespecified set of possibilities) are to be played to determine the winner. This paper constructs a Markov chain model of the situation and solves for the optimal strategy. Expressions for the optimal strategy are obtained with a symbolic algebra computer package. Results are given for both international and American scoring systems. The model and analysis are very suitable for educational purposes. The resulting Markov chain is small enough that it can be easily presented in a classroom setting, yet the model is sufficiently complex that algebraic manipulation is nearly hopeless. The final results illustrate the power of the combination of mathematical and computer modeling applied to a problem of practical interest.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

Predatory purchasing practices in global apparel supply chains and the employment relations squeeze in the Indian garment export industry

While the literature has long acknowledged worker rights abuses in global apparel supply chains, less attention has been paid to the recent worsening of certain decent work deficits and its causes, which this article links to predatory purchasing practices arising from growing power asymmetries. These practices, illustrated here by the garment export industry in India, exert downward pressure on wages and incite non‐standard forms of employment and greater verbal and physical abuse as productivity demands rise. They can be addressed through pricing mechanisms that cover the cost of decent work, appropriate State regulation and worker involvement.     

基于家庭网络的数字融合及其影响分析

在描述家庭网络的重要性的基础上对基于家庭网络的数字融合及其影响进行分析和研究。随着信息技术和产品逐步由多标准向标准融合过渡,网络、通信、软件、硬件和消费电子产品等生产厂家通过成立联盟,推动家庭网络技术和产品相互兼容。于是产业融合得到促进,家庭网络市场逐步扩大。家庭网络推动服务融合和创新,促进形成新的价值链。在新的价值链中,新的商业模式促进网络运营、应用和服务的改变和融合,合作和协同不可或缺,并得到了政策和资源方面的支持。对于家庭网络业的发展来说,技术是基础,内容是源泉,应用是动力,协同是关键。     

具有延误休假的一个基本可修系统的可靠性分析

本文讨论了由两个同型部件和一个修理工组成的可修系统。假定修理工空闲时进行延误休假。部件的修理时间和修理工的假期均服从一般分布，休假前的延误期为指数分布。我们求得了这个系统的可用度、可靠度和故障频度。作为特例，我们还讨论了修理工进行不延误休假的情况。     

我国区域经济差距的空间演变趋势及其成因

文章采用空间马尔科夫链方法分析了我国区域差距和经济发展的空间效应及演变趋势,发现地理背景对经济类型转移具有显著影响,区域经济类型不会发生明显的跃迁,经济增长存在空间相关性和路径依赖性。然后,利用Kernel密度分布法提供了同时考察经济增长及区域差距的新分析框架,从经济增长分布演进的视角,分析了我国区域经济差距的原因。研究结果显示,中国经济差距扩散的动力来源于效率改进而非要素投入。     

双耦合腔链慢波系统的色散分析

采用模拟场方法对由二隙缝耦合腔链所构成的“捆扎式”双通道慢波系统进行了分析,得到该结构的色散方程及耦合阻抗的变化趋向,并与传统单耦合腔链慢波结构的色散特性进行了比较。