Recent Submissions

  • Performance of predicting surface quality model using softcomputing, a comparative study of results 

    Correa Valencia, Maritza; Flores, Víctor; Quinonez, Alma Yadira (Springer, Cham, 2017-05-27)
    This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data ...
  • Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data 

    Pulgarin Giraldo, Juan Diego; Alvarez Meza, Andres Marino; Ruales Tores, A. A.; Castellanos Dominguez, German (Springer, Cham, 2017-05-27)
    This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution ...
  • Analysis of the alignment angles and flexion angle in women with patellofemoral pain syndrome 

    Agredo Rodríguez, Wilfredo; Pulgarin Giraldo, Juan Diego; Vinasco Isaza, Luz Elena; Díaz Martínez, N. F. (Springer, Singapore, 2017-04-07)
    Patellofemoral pain syndrome (PFPS) is one of the most com-mon disorders in the knee that occurs with a higher incidence in women than men. Q-angle and A-angle as well as alignment and flexion angle of bending were considered ...
  • Heterogeneity-aware data placement in hybrid clouds 

    Mondragon Martínez, Oscar Hernan; Márquez, Jack D.; González, Juan D. (Springer, Cham, 2019-06-14)
    In next-generation cloud computing clusters, performance of data-intensive applications will be limited, among other factors, by disks data transfer rates. In order to mitigate performance impacts, cloud systems offering ...
  • Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning 

    Pulgarin Giraldo, Juan Diego; Álvarez Meza, Andrés Marino; Santamaría, Ignacio; |Van Vaerenbergh, Steven; Castellanos Dominguez, Germán (Springer, Cham, 2019-03-03)
    A framework is presented to carry out prediction and classification of Motion Capture (MoCap) multichannel data, based on kernel adaptive filters and multi-kernel learning. To this end, a Kernel Adaptive Filter (KAF) ...