Search for disappearing tracks in proton-proton collisions at root s=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.

A formalized optimum code search for q-ary trellis codes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.

Ecological optimization of an irreversible Brayton cycle with regeneration, inter-cooling and reheating

A mathematical model is developed for an irreversible Brayton cycle with regeneration, inter-cooling and reheating. The irreversibility are from the thermal resistance in the heat exchangers, the pressure drops in pipes, the non-isentropic behavior in the adiabatic expansions and compressions and the heat leakage to the cold source. The cycle is optimized by maximizing the ecological function, which is achieved by the search for optimal values for the temperatures of the cycle and for the pressure ratios of the first stage compression and the first stage expansion. The advantages of using the regenerator, intercooler and reheater are presented by comparison with cycles that do not incorporate one or more of these processes. Optimization results are compared with those obtained by maximizing the power output and it is concluded that the point of maximum ecological function has major advantages with respect to the entropy generation rate and the thermal efficiency, at the cost of a small loss in power.

Reconfiguração de sistemas de distribuição de energia elétrica utilizando uma metodologia multipartida

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)