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Morelia, Mexico

The Morelia Institute of Technology , also known as Morelia Tech , is a public university in Morelia, Michoacán, México founded in 1964. Wikipedia.


Medina V.,Universidad Michoacana de San Nicolas de Hidalgo | GARCiA J.M.,Morelia Institute of Technology
ACM Computing Surveys | Year: 2014

In the virtualization area, replication has been considered as a mechanism to provide high availability. A high-availability system should be active most of the time, and this is the reason that its design should consider almost zero downtime and a minimal human intervention if a recovery process is demanded. Several migration and replication mechanisms have been developed to provide high availability inside virtualized environments. In this article, a survey of migration mechanisms is reported. These approaches are classified in three main classes: process migration, memory migration, and suspend/resume migration. © 2014 ACM. Source


Gutierrez-Alcaraz G.,Morelia Institute of Technology
IEEE Power and Energy Society General Meeting | Year: 2011

Electricity deregulation makes it more feasible to apply differential rates across customers. Dynamic prices can be used to reflect the changes in marginal energy costs of a power system. Some dynamic pricing pilot projects reveal that dynamic prices can actually reduce or shift electricity usage. However, system-load peaks and local-area load peaks could occur at different times. When both peaks are coincident, two objectives can be achieved simultaneously by applying dynamic pricing: total system demand and local demand reduction. However, when these peaks are non-coincident, local demand requires investment. We propose and extended dynamic pricing scheme by taking area- and time-specific marginal distribution capacity costs into account for addressing locational investment deferral. Elasticity model which uses actual test data from pilot projects is adopted to estimate load reduction due to dynamic pricing. © 2011 IEEE. Source


Salinas-Melgoza A.,New Mexico State University | Salinas-Melgoza V.,Morelia Institute of Technology | Wright T.F.,New Mexico State University
Biological Conservation | Year: 2013

Behavioral plasticity is a strategy employed by many species to cope with both naturally occurring and human-mediated environmental variability. Such plasticity may be especially important for long-lived and wide-ranging species, such as parrots, that likely face great temporal and spatial variation within their long lifespans, and are often disproportionately affected by anthropogenic habitat change. We used radio-telemetry and roost counts to assess ranging patterns, habitat usage, and roosting behaviors of the Yellow-naped Amazon (Amazona auropalliata) at two sites in northern Costa Rica with different degrees of anthropogenic habitat alteration. We compared behaviors for residents at the two sites and for experimentally translocated individuals to test the hypothesis that this species would employ behavioral plasticity in response to habitat differences. We found that individuals in the region with dispersed vegetation recorded ranging movements and communal roosting behavior ten times larger than the region with concentrated vegetation. Translocated individuals showed flexibility in these behaviors and matched the behavioral patterns of resident birds at the release site rather than maintaining behaviors characteristic of their capture site. Our results illustrate a generalized rapid plastic response to human-induced changes in habitat for a number of behavioral traits in the Yellow-naped Amazon. Such plasticity is directly relevant to reintroduction efforts that are commonly employed as a conservation tool in parrots. Our study provides an example of how behavioral plasticity may allow some wild populations to withstand anthropogenic change. © 2012 Elsevier Ltd. Source


Su S.-Y.,Taiwan Semiconductor Manufacturing Company | Lu C.-N.,National Sun Yat - sen University | Chang R.-F.,Kao Yuan University | Gutierrez-Alcaraz G.,National Sun Yat - sen University | Gutierrez-Alcaraz G.,Morelia Institute of Technology
IEEE Transactions on Smart Grid | Year: 2011

There is increasing requests for noncontrollable distribution generation (DG) interconnections in the medium and low voltage networks. Many studies have suggested that with proper system planning, DG could provide benefits such as reliability enhancement, investment deferment, and reduced losses. However, without network reinforcements, the allowable interconnection capacity in a network is often restricted due to fault current level, voltage variation, and power flow constraints. This paper aims to address the issue of optimizing network operation and use for accommodating DG integrations. A new DG interconnection planning study framework that includes a coordinated feeder reconfiguration and voltage control to calculate the maximum allowable DG capacity at a given node in the distribution network is presented. A binary particle swarm optimization (BPSO) technique is employed to solve the discrete nonlinear optimization problem and possible uncertainties associated with volatile renewable DG resource and loads are incorporated through a stochastic simulation approach. Comprehensive case studies are conducted to illustrate the applicability of the proposed method. Numerical examples suggest that the method and procedure used in the current DG interconnection impact study should be modified in order to optimize the existing grid operation and usage to facilitate customer participation in system operation and planning. © 2010 IEEE. Source


Sheble G.B.,INESC Porto | Gutierrez-Alcaraz G.,Morelia Institute of Technology
European Transactions on Electrical Power | Year: 2012

This paper explores the use of genetic algorithms (GAs) in the development of the bidding strategies used by generation companies under two different market clearing mechanisms, uniform pricing and pay-as-bid pricing. The bidding strategies are represented by two modifications of a classical data processing structure known as finite-state automata. Semi-fixed fitness function and co-evolutionary fitness function were incorporated in our GA. A third simple representation to obtain a comparison baseline for the other two representations, showing how their behaviors compare with a "standard" solution, was also incorporated. The strategies developed by our method were adaptive, and all GA types were based on maximizing profit in a competitive bidding situation. Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd. Source

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