In this study, the Analytic Hierarchy Process (AHP) methodology is implemented to provide decision criteria in selection, planning, and development of electric power generation projects from renewable energy sources Caribbean region Colombia. Six energy; biomass combustion; anaerobic digestion biomass; biogas landfills; waste incineration; Photovoltaic solar thermal radiation were considered st...

This research tends to development of the requirements elicitation methodology with regard to operational nature and hierarchical analysis for complex systems and also, regarding available technologies. This methodology applies Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) to ensure traceability of planned qualitative and quantitative data from requirements to available te...

urban parks have essential role in public welfare. their role could be observed in personal, socio-economic, environmental and mental wellbeing. provision of socio-mental needs of citizens and enhancement of social cohesion are contributed to the way of using urban open and green spaces which such issue requires elaborated planning and design. equal access to parks is an important issue in dist...

In fitting the process-dissociation model (L. L. Jacoby, 1991) to observed data, researchers aggregate outcomes across participant, items, or both. T. Curran and D. L. Hintzman (1995) demonstrated how biases from aggregation may lead to artifactual support for the model. The authors develop a hierarchical process-dissociation model that does not require aggregation for analysis. Most importantl...

In this work, we focus on the analysis of process schemas in order to extract common substructures. In particular, we represent processes as graphs, and we apply a graph-based hierarchical clustering technique to group similar sub-processes together at different levels of abstraction. We discuss different representation choices of process schemas that lead to different outcomes.

We consider the problem for which K different types of data are collected to characterize an associated inference task, with this performed for M distinct tasks. It is assumed that the parameters associated with the model for data type (modality) k may be represented in the form of a mixture model, with the M tasks representing M draws from the mixture. We wish to simultaneously infer mixture m...

Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving mul...