Partial Duplication of Response Surface Designs

Continuing the author's earlier work [6] a method is described which requires that certain experimental runs of a central composite, second-order, response surface design be repeated, thereby providing a more general estimate of the experimental error, at the same time providing more reliable estimates of the effects.

The partial duplication of the factorial portion as well as the partial duplication of the star portion has been considered and described. The response surface designs with the star portion duplicated seem to have more potential than the designs with their factorial portions duplicated or partially duplicated.     

Discussion

The use of Stein estimation in multiple linear regression is considered. Tables and graphs are presented that compare the prediction mean squared errors of positive-part James-Stein, preliminary-test, reduced, and full-model least squares estimates. The appropriateness of using Stein contraction on possibly extraneous variables is emphasized, and a procedure is presented for evaluating the likely savings in using Stein estimation on the problem at hand. An example is given.     

Prediction of ground motion parameters using randomized ANFIS (RANFIS)

In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies.     

Predicting the Initiation of Thermoset De-Bonding

Napkin ring adhesion tests over a broad range of experimental conditions suggested a de-bonding mechanism for glassy thermosets associated with “run-away” nonlinear viscoelasticity. Finite element analyses of these tests using a high fidelity, nonlinear constitutive equation were used to identify a single, scalar metric that consistently predicted the initiation of de-bonding, a critical value of the maximum principal strain in the “interphase” zone. In principle, such a de-bonding metric enables evaluation of design margins in practical components.     

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5^°×2.5^° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.     

底水气藏气井见水时间预测方法

基于Sobocinaki-Cornelius方法对底水油藏水锥突破时间的计算公式,通过数值模拟计算,得到了底水气藏的无因次锥高与无因次时间的相关关系式,利用该方法可以较为准确的预测底水气藏水锥突破的时间。胜利油田某气藏的计算结果表明,该方法合理可靠,对指导底水气藏的开发具有重要意义。     

A system for destination and future route prediction based on trajectory mining

Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward–Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin–destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80% and 60% accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user’s historical movement data and predict the destination and future route.     

基于多代理人模拟的上海市域零售业中心体系研究

在上海市域人口向郊区疏解的背景下,零售业空间随之重构。应用基于多代理人模拟技术的零售业空间结构模拟系统,模拟消费者与商业中心之间的互动,并呈现商业中心体系的空间布局,为市域商业体系规划提供新的方法和思路。对2000年上海市域零售业中心体系进行实证,检验了模拟系统对实际、大规模零售业中心体系的解释和再现能力,获得关键模型参数。在此基础上,推演上海未来可能情景下的零售业中心体系。发现市级中心以及地区级中心发展的重点分别位于中心城区和郊区,北外滩、世纪大道具有成为顶级市级中心的潜力,有考虑网购影响的必要性。     

无底柱分段崩落法开采沉陷预测研究

针对无底柱分段崩落法的采矿特点，把概率积分法引入金属矿山开采沉陷预测中，并通过模型修正提高预测精度。利用GIS强大的图形表现力和空间分析能力实现开采沉陷预测的可视化，能够直观掌握开采沉陷对周围环境的影响程度及范围。通过将预测结果与地表观测数据及实际情况的对比分析，验证了此预测方法的适用性。