Log-linear approaches to lag-sequential analysis when consecutive codes may and cannot repeat.

G. P. Sackett's (1979) lag-sequential analysis is recast in log-linear terms. Differing from articles by P. D. Allison and J. K. Liker (see record 1982-12033-001) and by D. Iacobucci and S. Wasserman (see record 1989-14205-001), sequences that, for logical reasons, do not allow consecutive events to be assigned the same code are considered. In addition, overlapped sampling, which the sequential analysis literature usually assumes, is contrasted with nonoverlapped sampling of sequences, which is also used and may seem more faithful to some statistical models. Several advantages of a log-linear approach to sequential problems are noted, including its ability to deal routinely with the structural zeros created when consecutive codes cannot repeat and its integration of sequential methods into an established and well supported statistical tradition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Record linkage for database integration using fuzzy integrals

Given two‐data databases, record linkage algorithms try to establish which records of these files contain information on the same individual. Standard record linkage algorithms assume that both files are described using the same attributes. In this article, we study the nonstandard case when the attributes are not the same. We apply aggregation operators for extracting relevant information for this purpose. We restrict to the case of numerical databases. © 2008 Wiley Periodicals, Inc.     

Container loading for nonorthogonal objects: an approximation using local search and simulated annealing

Container loading problems consist of finding an appropriate way to load objects into a container. A few alternative algorithms have been defined based on different optimization approaches. Different algorithms also depend on the types of objects considered. In most of the cases, however, the shape of the objects is restricted to be orthogonal (i.e., standard boxes). In this paper, we consider the case where the shape of the objects can be any polyhedron. We propose an algorithm using local search and simulated annealing, and we show that for standard boxes, it has a similar performance than previously established methods.     

Computationally intensive parameter selection for clustering algorithms: The case of fuzzy c‐means with tolerance

Parameter selection is a well‐known problem in the fuzzy clustering community. In this paper, we propose to tackle this problem using a computationally intensive approach. We apply this approach to a new method for clustering recently introduced in the literature. It is the fuzzy c‐means with tolerance. This method permits data to include some error, and this is modeled by moving data in a particular direction within a particular range when clusters are defined. The proper application of this approach needs the correct definition of the parameter κ. A value that might be different for each record and corresponds to the maximum shift allowed to the data. In this paper, we review this method and we study the definition of this parameter κ when the same value of κ is used for all data elements. Our approach is based on the analysis of sets of data with increasing noise and an exhaustive analysis of the behavior of the algorithm with different values of κ. The analysis is motivated in privacy preserving data mining. The same approach can be used for parameter selection in other clustering algorithms. © 2010 Wiley Periodicals, Inc.     

Optimal control as a graphical model inference problem

We reformulate a class of non-linear stochastic optimal control problems introduced by Todorov (in Advances in Neural Information Processing Systems, vol. 19, pp. 1369–1376, 2007) as a Kullback-Leibler (KL) minimization problem. As a result, the optimal control computation reduces to an inference computation and approximate inference methods can be applied to efficiently compute approximate optimal controls. We show how this KL control theory contains the path integral control method as a special case. We provide an example of a block stacking task and a multi-agent cooperative game where we demonstrate how approximate inference can be successfully applied to instances that are too complex for exact computation. We discuss the relation of the KL control approach to other inference approaches to control.     

Robust identification and fault diagnosis based on uncertain multiple input–multiple output linear parameter varying parity equations and zonotopes

We present a robust fault diagnosis method for uncertain multiple input–multiple output (MIMO) linear parameter varying (LPV) parity equations. The fault detection methodology is based on checking whether measurements are inside the prediction bounds provided by the uncertain MIMO LPV parity equations. The proposed approach takes into account existing couplings between the different measured outputs. Modelling and prediction uncertainty bounds are computed using zonotopes. Also proposed is an identification algorithm that estimates model parameters and their uncertainty such that all measured data free of faults will be inside the predicted bounds. The fault isolation and estimation algorithm is based on the use of residual fault sensitivity. Finally, two case studies (one based on a water distribution network and the other on a four-tank system) illustrate the effectiveness of the proposed approach.     

Effect of climate on the trophic structure of temperate forested streams. a comparison of Mediterranean and Atlantic streams

Climate affects many aspects of stream ecosystems, although the presence of riparian forests can buffer differences between streams in different climatic settings. In an attempt to measure the importance of climate, we compared the seasonal patterns of hydrology, input and storage of allochthonous organic matter, and the trophic structure (abundance of algae and macroinvertebrates) in two temperate forested streams, one Mediterranean, the other Atlantic. Hydrology played a leading role in shaping the trophic structure of both streams. Frequency and timing of floods and droughts determined benthic detritus storage. Inputs and retention of allochthonous organic matter were higher in the Atlantic stream, whereas chlorophyll concentration was lower because of stronger light limitation. Instead, light availability and scour of particulate organic matter during late winter favoured higher chlorophyll concentration in the Mediterranean stream. As a result, in the Mediterranean stream grazers were more prevalent and consumers showed a higher dependence on autotrophic materials. On the other hand, the Atlantic stream depended on allochthonous materials throughout the whole study period. The overall trophic structure showed much stronger seasonality in the Mediterranean than in the Atlantic stream, this being the most distinctive difference between these two types of temperate streams. The different patterns observed in the two streams are an indication that climatic differences should be incorporated in proper measurements of ecosystem health.     

Approximating fault detection linear interval observers using λ‐order interval predictors

Interval observers can be described by an autoregressive‐moving‐average model while λ ‐order interval predictors by a moving‐average model. Because an autoregressive‐moving‐average (ARMA) model can be approximated by a moving‐average model, this allows establishing the equivalence between interval observers and interval predictors. This paper deals with the fault detection application and focuses on the equivalence between the λ ‐order interval predictors and the interval observers from the point of view of the fault detection performance. The paper also proves that it is possible to obtain an equivalent λ  ? order interval predictor for a given interval observer with the same fault detection properties by the appropriate selection of the λ  ? order. A condition for selecting the minimal order that provides the λ  ? order interval predictor equivalent to a given interval observer is derived. Moreover, because the wrapping effect could be avoided by tuning properly the interval observer, we can find an equivalent λ  ? order interval predictor such that it also avoids the wrapping effect. Finally, an example based on an industrial servo actuator will be used to illustrate the derived results. Copyright © 2016 John Wiley & Sons, Ltd.