Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

服务台可修的多水平优先权排队(二)

文献[1]讨论了服务台可修的多水平优先权排队的排队指标。本文讨论该系统的可靠性指标。利用文献[3]中的随机序,获得了这些可靠性指标的界值。     

Bayesian analysis of discrete time warranty data

Summary.  The analysis of warranty claim data, and their use for prediction, has been a topic of active research in recent years. Field data comprising numbers of units returned under guarantee are examined, covering both situations in which the ages of the failed units are known and in which they are not. The latter case poses particular computational problems for likelihood-based methods because of the large number of feasible failure patterns that must be included as contributions to the likelihood function. For prediction of future warranty exposure, which is of central concern to the manufacturer, the Bayesian approach is adopted. For this, Markov chain Monte Carlo methodology is developed.     

会计信息可靠性与相关性的产权博弈分析

从产权经济学角度对会计信息的产权属性进行分析,把会计信息的产权主体定位为所有的信息使用者,认为会计信息的产权主体与会计信息质量特征之间存在密切关系。不同产权主体之间为有利于自身的信息进行博弈,可靠性和相关性孰轻孰重取决于各方在博弈中的力量对比。最后,文章提出重构会计信息市场、实现可靠性和相关性协调发展的若干对策。     

最少部件数的并联备份问题

如何快速提高系统的可靠度和最少部件数的并联备份问题,一直是系统可靠性问题中的研究热点。该文从系统的任意状态(k1,k2,L,kn)出发,讨论了上述问题,并证明了在子系统可靠度最小的位置上并联相同部件可使系统的可靠度提高最快,同时给出了最优算法和实例验证。     

浅谈类比法

首先给出了类比的定义 ,就是根据两个对象之间在某些方面的相似或相同而推出它们在其他方面也可能相似或相同的一种逻辑方法 ,并介绍了它的二个主要特点 ,即跳跃性和可靠程度低。然后结合一些成功的类比案例 ,对它的具体应用方式进行了探讨。最后给出了提高类比法使用可靠性的三个原则 ,一是所根据的相似属性要尽可能多 ;二是所根据的相似属性之间的联系要尽可能紧密 ;三是所根据的相似数学模型要尽可能精确     

Development of the Mental Illness and Disorder Understanding Scale

Abstract: Although administrative bodies such as community health centers have implemented various awareness campaigns to promote community understanding of mental health and to reduce the stigma associated with mental illness and disorder, there have been few reports that have quantitatively analyzed the effects of these campaigns in Japan. Therefore, this study was conducted for the purpose of developing the evaluative “Mental Illness and Disorder Understanding Scale (MIDUS)” for awareness campaigns and assessing its reliability and validity. A survey was conducted on 1 004 community residents using a self‐administered questionnaire. An assessment of the reliability and validity of the scale was then conducted. The Cronbach a coefficient of the MIDUS was 0.78, thus indicating adequate reliability. Factor analysis of the MIDUS revealed three factors: “Treatability of illness”, “Efficacy of medication” and “Social recognition of illness”. In addition, a significant correlation was observed between the MIDUS and existing scales, and fixed criterion‐based validity was demonstrated. The MIDUS utilizes items worded in the form of affirmative expressions so as to avoid promoting new bias. It was confirmed to be both reliable and valid, thereby suggesting its usefulness as an evaluative scale of awareness campaigns.     

无线传感器网络中覆盖性问题的研究

无线传感器网络由大量的具有低能量,短寿命并且不可靠的传感器节点组成,因此在设计无线传感器网络时一个主要的目标在于保证足够的覆盖率以及能量可靠性的前提下可以延长网络的寿命。本文介绍了几种减少能量消耗以延长网络寿命的节点调度方法,并比较了各种方法的优缺点,指出其应用环境和需要进一步研究的工作。最后在这几种算法的基础上提出了一种新的算法。     

Physiologically Based Liver Modeling and Risk Assessment

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.     

Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.