Departamento de Ingeniería Eléctrica

Laboratorio de Inteligencia Computacional

 Laboratorio de Inteligencia Computacional

Descripción

En el laboratorio de Inteligencia Computacional se realiza investigación en el área de reconocimiento de patrones usando técnicas de redes neuronales artificiales, computación evolutiva, big data, deep learning, machine learning y teoría de información.

Se desarrollan por una parte nuevos algoritmos y métodos (teoría) y, por otra parte, se aplican las técnicas de inteligencia computacional al procesamiento de imágenes y señales astronómicas, procesamiento de señales biomédicas, procesamiento de series de tiempo, diseño óptimo de empaquetamiento de baterías, big data, y data mining.

Equipamiento e instrumentos

  • 8 PCs Intel i7
  • 2 GPU NVIDIA TESLA C2070
  • 2 GPU NVIDIA TESLA K20

Miembros permanentes

Académico responsable

Proyectos asociados

    • Proyecto Fondecyt 1140816 "Advanced Machine Learning and Signal Processing Methods for Time Series Analysis: Applications to Astronomical Light Curves and Sleep EEG"
    • Instituto Milenio de Astrofísica, ICM-MINECOM, Cód.IC120009, MAS
    • Proyecto Conicyt DPI 20140090, "Big Data Based Real Time Astronomy Applications for the LSST Era"
    • Proyecto INNOVA 13IDL2-23589 "Monitoreo Automático de Puntos de Ventas para Productos en el Retail mediante Procesamiento Digital de Imágenes"
    • Proyecto INNOVA N° 12IDL2- 16296(Perfil I+D Aplicada) "Empaquetamiento Optimo de Baterías de Ión-Litio"
    • Proyecto FONDAP: Solar Energy
    • Proyecto Basal “Centro de Tecnología para la Minería”

      Publicaciones

      • [PE1] Reyes-Marambio, J., Moser F., Gana F., Severino B., Calderon-Muñoz, W.R., Palma-Behnke, R., Estevez, P.A., Orchard M., Cortes, M., “A fractal time thermal model for predicting the surface temperature of air-cooled cylindrical Li-ion cells based on experimental measurements”, Journal of Power Sources Vol 306, 2016, pp. 636-645.
      • [PE2] Protopapas, P.; Huijse, P.; Estévez, P.A.; Zegers, P.; Principe, J.C.; Marquette, J.B., “A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set”, Astrophysical Journal Supplement Series, Vol. 216: 25 (30pp.), Feb. 2015.
      • [PE3] Estévez, P.A.; Principe, J.C.; “Guest editorial, special issue on Advances on Self-organizing Maps”, Neurocomputing, Jan. 2015, pp. 1-2.
      • [PE4] Orchard, M.E.; Lacalle, M.S.; Silva, J.F.; Palma, R.; Estévez, P.A.; Severino, B.; Calderón-Muñoz, W; Cortés-Carmona, M. “Information-Theoretic Measures and Sequential Monte Carlo Methods for Detection of Regeneration Phenomena in the Degradation of Lithium-Ion Battery Cells”, IEEE Transactions on Reliability, Vol. 64, nº 2, June 2015, pp. 701-708.
      • [PE5] Huijse, P.; Estévez, P.A.; Protopapas, P.; Principe, J.C.; Zegers, P., “Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases”, IEEE Computational Intelligence Magazine, August 2014, pp. 27-39.
      • [PE6] Nova, D.; Estévez, P.A.; “A review of learning vector quantization classifiers”, Neural Computing & Applications, Vol. 25, Issues 3-4, September 2014, pp. 511-524.
      • [PE7] Severino, B.; Gana, F.; Palma-Behnke, R.; Estévez, P.A.; Calderón, W.; Orchard, M.; Cortés, M.; Reyes, J.; “Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms”, Journal of Power Sources, Elsevier, Vol. 267, Dec 2014, pp: 288-299.
      • [PE8] Vergara, J; Estévez P.A., “A review of feature selection methods based on mutual information”, Neural Computing & Applications, Vol. 24, N° 1, pp. 175-186, January 2014.
      • [PE9] Huijse, P.; Estévez, P.A.; Protopapas, P., Zegers, P.; Principe, J.C.; “An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves”, IEEE Transactions on Signal Processing, Vol. 60, October 2012, pp. 5145-5149.
      • [PE10] Perez, C., Tapia, J., Estévez, P., Held, C., “Gender Classification from Face Images Using Mutual Information and Feature Fusion”, International Journal of Optomechatronics, Vol. 6, N°1, 2012, pp. 92-119. ISI Citations: 5
      • [PE11] Huijse, P., Estévez PA., Zegers, P., Principe J.C., Protopapas P., “Period Estimation in Astronomical Time Series Using Slotted Correntropy” IEEE Signal Processing Letters, Vol. 18, N° 6, June 2011, pp. 371-374.
      • [PE12] Estévez, P.A., Hernández, R., Perez, CA., Held, C.M., “Gamma-filter self-organising neural networks for unsupervised sequence processing”, Electronics Letters, Vol. 47, N° 8, April 2011, pp. 494-496.
      • [PE13] Perez, C.A., Estévez, P.A., Vera, P.A., Castillo, L.E., Aravena, C.M., Schulz, D.A., Medina, L.E., “Ore Grade by Feature Selection and Voting Using Boundary Detection in Digital Image Analysis”, Int. J. Mineral Processing, vol. 101, pp. 28-36, 2011.
      • [PE14] Galdames, F.J, Perez C.A., Estévez, P.A., Held, C.M., Jaillet F., Lobo, G., Donoso, G., Coll, C., “Registration of Renal SPECT and 2.5D US images”, Computerized Medical Imaging and Graphics, Vol. 35, N°4, June 2011, pp. 302-314.
      • [PE15] Perez C.A., Aravena C.M., Vallejos J.I., Estévez P.A., Held C.M., “Face and Iris Localization Using Templates Designed by Particle Swarm Optimization”, Pattern Recognition Letters, Vo. 31, pp. 857-868, 2010.
      • [PE16] Perez C.A., Castillo L.A., Cament L.E., Estévez, P.A., Held C.M., “Genetic Optimisation for Illumination Compensation Methods in Cascade for Face Recognition”, Electronics Letters, Vol. 46, pp. 498-500, 2010.
      • [PE17] Causa, L., Held, C.M., Causa J., Estévez, P.A., Perez, C.A., Chamorro, R., Garrido M., Algarin C., Peirano, P., “Automated Sleep-Spindle Detection in Healthy Children Polysomnograms”, IEEE Transactions on Biomedical Engineering, Vol. 57, N°9, September 2010, pp. 2135-2146.
      • [PE18] Estévez, P.A., Tesmer, M., Perez, C.A., Zurada, J.M., "Normalized Mutual Information Feature Selection", IEEE Transactions on Neural Networks, Vol. 20, pp. 189-201, Feb. 2009. ISI Citations: 183. Google Scholar citations: 363
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