Julio Rojas-Mora

Analyzing Transient Phenomena in the Time Domain Using the Feature Selective Validation (FSV) Method

The increasing application of simulation tools to increasingly complex problems makes the use of validation tools essential to improve confidence in the veracity of those simulation results. IEEE Standard 1597.1 is the first true standard for the validation of computational electromagnetics method. This standard uses the feature selective validation (FSV) method as the key quantification tool. However, despite its many advantages, there have been some interesting issues surrounding the validation of transients. This paper presents a new approach to the validation of a set of generally representative transient types using the FSV method and shows how the previously experienced limitations can be overcome. In order to analyze the main parameters associated with transient comparison, a survey which included 20 experts was conducted. This information was used to identify the significant regions that need to be taken into account in the transient comparison. Finally, using the statistics obtained by the experts, a new solution was defined and its improvement over the existing approach was demonstrated.

A Review on the Drawbacks and Enhancement Opportunities of the Feature Selective Validation

The growing application of simulation tools to increasingly complex problems makes the use of validation techniques essential to improve confidence in the veracity of those simulation results. The feature selective validation (FSV) method is widely used today, because of its versatility and simplicity. However, despite its many advantages, some problems have been identified in the standardized FSV method. Those drawbacks can produce misleading results for the validation process and constitutes enhancement opportunities for further research on FSV improvement. This paper presents a review that summarizes the major problems of the FSV and their possible solutions as a justification to reform the FSV method and also looking towards the forthcoming update of the standards IEEE 1957.1/2.

Spatial Function of Influence on Center Optimal Location Based on \(L_{\textit{p}}\)-Norms

We propose a sensitivity analysis using generalized (L_{textit{p}})-norm (Minkowski distance) applied on center optimal location (1 facility). The results show that there exists in one dimension an underlying (log)linear relation between influence and distance of the demand points on the center. New (L_{textit{p}})-norms are emphasized with interesting properties in statistics (e.g. with p=3) although they are not used in location optimization. The law we enhance is of interest in both statistics and and spatial analysis domains and highlights in a new way the impact of the metrics choice on the center location, through the induced spatial influence function, those metrics aiming at spatial equity ((L_{infty })), equality ((L_{2})) or efficiency ((L_{1})).

Modeling and Simulation of Mobility of Crowds

Mobility models studied in the networking community usually assume independence between the movement of individuals. While this may well model sparse networks, there are many scenarios that might not follow this assumption. In contrast, within other communities, such as road traffic engineering, biology and computer graphics, models of mobility usually take into account the dependence of the mobility pattern of an individual with respect to that of its neighbors. Our goal in this paper is to study how this dependence impacts the performance measure from the networking point of view. In particular, we implement a bio-inspired model for mobility of crowds and, by simulation, we study how mobility influences the performance measures of a distributed network. We perform statistical analysis on the samples obtained through simulations. In particular, we study the distribution of the message delivery time and show that it is light tailed, with exponential tail distribution.