Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three‐axiom‐based analysis partially validates the correctness and rationality of the proposed Bayesian network model.     

Social Networks of Old People in India

Summary

This article presents a comparative analysis of the available research on the social networks of older persons in India. Most of this research has been done in North Indian cities. The research foci of the available studies include network size, core networks and beyond, life course changes in networks, impacts of residency in old-age homes, gender differences, and joint and nuclear family residence. This research is discussed in terms of its policy implications. Because the research demonstrates that social networks are important for the welfare of older Indians, one can conclude that social policy that encourages the maintenance of robust networks throughout the life course may be worth pursuing. One aspect of policy is discussed. The analysis of the relationship between social network and gender suggests that current policies that can be seen as supporting gender inequality in terms of property may have a negative impact on the networks of older women.     

神经网络的滚动轴承故障诊断

文章通过旋转机械故障实验平台,采集旋转机械故障实验台轴承的3种工作状态分别是轴承正常、轴承内圈裂缝、轴承外圈裂缝的振动加速度信号.对信号进行零均值化处理后,选择频率成分幅值较大的频率进行信号重组,提取其时域量纲特征值,利用神经网络进行故障类型的识别；通过实验,取得了很好的诊断结果.     

Quadratic Assignment Problems and M/G/C/C/ State Dependent Network Flows

One of the most notorious network design problems is the Quadratic Assignment Problem (QAP). We develop an heuristic algorithm for QAPs along with an M/G/C/C state dependent queueing model for capturing congestion in the traffic system interconnecting the nodes in the network. Computational results are also presented.     

Flexible regression modeling

The author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications.     

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.     

Markov Chain Monte Carlo in small worlds

As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get trapped within one mode of a multi-modal space. Moreover, it may be difficult to determine if a chain is only sampling a certain area of the space or if it has indeed reached stationarity. In this paper, we show how a simple modification of the proposal mechanism results in faster convergence of the chain and helps to circumvent the problems described above. This mechanism, which is based on an idea from the field of “small-world” networks, amounts to adding occasional “wild” proposals to any local proposal scheme. We demonstrate through both theory and extensive simulations, that these new proposal distributions can greatly outperform the traditional local proposals when it comes to exploring complex heterogenous spaces and multi-modal distributions. Our method can easily be applied to most, if not all, problems involving MCMC and unlike many other remedies which improve the performance of MCMC it preserves the simplicity of the underlying algorithm.     

Social Support Networks of African-American Children Attending Head Start: A Longitudinal Investigation of Structural and Supportive Network Characteristics

This study examined the stability and coherence of African‐American children's social support networks. Participants included a total of 106 3‐ to 4‐year‐old children attending Head Start centers located in the southeast. Children completed a social network interview in two consecutive years at the Head Start centers. These interviews tapped multiple dimensions of the support network including social embeddedness, proportion of the network providing support, and perceived support. Analyses focused on both the rank‐order stability of children with respect to network characteristics as well as stability of network relationships (the same individuals included in the network at both time periods). Results indicated age‐related increases in network size for adult and child categories, network size across three support domains, proportion of support scores, and perceived support from adults. In addition to age‐related changes, analyses revealed considerable rank‐order stability with respect to structural network dimensions, but very little rank‐order stability in proportion of support and perceived support scores. In contrast, both structural and supportive components of the children's networks were shown to be coherent over a one‐year period when specific network member relationships were examined. Discussion highlights both continuities and discontinuities in young children's social networks, and how data obtained in this study contribute to theory building and the systematic examination of African‐American children's emerging social networks.     

WDM网双故障的共享子通路保护算法

研究了WDM网中双故障导致的多链路失效问题,提出了一种共享子通路保护算法。共享子通路保护为每条业务请求计算一条最短的工作路由,并为工作路由上每段子通路各计算两条共享风险链路组分离且代价最小的保护路由。仿真表明,共享子通路保护不仅能有效地保护双故障导致的多链路失效,而且能在资源利用率(阻塞率)和保护切换时间之间进行性能折衷。     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].