This paper presents experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment from camera images. The experiments used two different attention mechanisms — saliency map and multi-scale Harris detector — and two different novelty detection mechanisms — Grow-When-Required (GWR) neural network and an incremental Principal Component Analysis (PCA...

Abstract. Groundwater recharge (GWR) is a strategic hydrologic variable, and its estimate necessary to implement sustainable groundwater management. This especially true in global warming context that highly impacts key winter conditions cold humid climates. For this reason, long-term simulations are particularly useful for understanding past changes GWR associated with changing climatic condit...

This paper examines the association between religious adherence and fertility rates across counties in the United States (1998-2002), controlling for other demographic and socioeconomic variables. Employing geographically weighted regression (GWR) analysis, this study finds that the relationship between religion and fertility differs remarkably over space, illustrating different spatial pattern...

Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform by assigning weights based on the geographical distance each observation location and assumption having heterogeneity. The result this an equation model whose parameter values apply only are different from other locations. However, when there outliers at location, more robust estimation needed. ...

Estimates of groundwater recharge are often needed for a variety of groundwater resource evaluation purposes. A method for estimating long-term groundwater recharge and actual évapotranspiration not known in the English literature is presented. The method uses long-term average annual precipitation, runoff, potential evaporation, and crop-yield information, and uses empirical parameter curves t...

This report presents the development of Geographically Weighted Regression (GWR) models for predicting public transit use for home-based work trip purpose. A large array of potential transit use predictors were considered, including demographic, socioeconomic, land use, transit supply quality, and pedestrian environment variables. The best predictors identified through model estimation include ...

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