In a further development Ghodsypour and O'Brien (2001) presented a mixed integer non-linear programming model to solve the multiple sourcing problems.
Especially their ability to reject interfering periodic components is remarkable and important as it leads to a natural decoupling of the fundamental frequency estimation problem for multiple sources, a problem that otherwise involves multi-dimensional non-linear optimization.
The widely used dispersion model to compute pollutant concentration profiles is the Gaussian plume model for single or multiple source pollution problems.
Typically, the solutions to both single and multiple source localization problems are based on estimations of the Time Difference Of Arrival (TDOA) at different microphone pairs, which are obtained by means of Generalized Cross-Correlation PHAse Transform (GCC-PHAT) [3], also known as Crosspower-Spectrum Phase (CSP) [4].
The aim of the present work is to apply the Water Sources Diagram methodology, as well as a proposal variation, in multiples contaminants and multiple water sources problems, to compare its results with more complex approaches presented in literature.
Chevron spokesman Jim Edmisson said the nearest hydrocarbon pool is 500 feet from the El Porto site and that there may be "multiple sources" of the problem.
Moreover, the framework developed in this work can also be extended, for example, to the multiple-source localization problem, which can be regarded as a special case of the problem at hand in which all sources have the same transmit ID.
Typically this means researching multiple sources of information, solving problems, distilling and synthesizing the material and developing a conclusion.
In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper.
Mathematical background results for the problem from multiple sources are summarized.
