Departamento de Ingeniería Eléctrica

Laboratorio de Información y Decisión (Information and Decision Systems Group, IDS)

Enlaces relacionados
Sitio web Laboratorio

 Laboratorio de Información y Decisión

Descripción

El interés central de la iniciativa IDS son los problemas inversos en presencia de incertidumbres que puedan plantearse como instancias de tareas de decisión y estimación.

El grupo trabaja activamente tanto en investigación aplicada como en el desarrollo de nuevos métodos y resultados teóricos.

Lo integran  tres académicos con formación y experiencia demostrada en temas de estimación y decisión, teoría de información y aprendizaje estadístico, filtrado y predicción, procesamiento de señales, diseño de dispositivos y sensores.

Equipamiento e instrumentos

  • 6 estaciones de trabajo
  • 10 puestos de trabajo
  • 2 impresoras láser y otros equipamientos

Miembros permanentes

Académico responsable

Proyectos asociados

  • FONDECYT regular 1110145
  • FONDECYT regular 11110384
  • FONDECYT regular 1090138
  • FONDECYT regular  11070022
  • FONDECYT regular No 1110070
  • Innova-CORFO 11IDL1-10409
  • Advance Mining Technology Center (AMTC) internal funding suport

Publicaciones

  • Benjamín Olivares, Matías Cerda, Marcos Orchard, and Jorge F. Silva, “Particle-filtering-based Prognosis Framework for Energy Storage Devices with a Statistical Characterization of State-of-Health Regeneration Phenomena,” IEEE Trans. on Instr. & Measurement Decision, 2012, in press.
  • Orchard, M., Cerda, M., Olivares, B., and Silva, J., "Sequential Monte Carlo Methods for Discharge Time Prognosis in Lithium-Ion Batteries," International Journal of Prognostics and Health Management, Vol. 3 Issue 2 (010), pp. 1-12, 2012.
  • M. A. Diaz, M. Zettergren, J. L. Semeter and M. Oppenheim. Plasma parameter analysis of the Langmuir Decay process via Particle-In-Cell simulations. Ann. Geophys., August 2012.
  • H. Akbari, J. L. Semeter, H. Dahlgren, M. A. Diaz, M. Zettergren, A. Stromme, M. J. Nicolls, and C. J. Heinselman. Anomalous ISR echoes preceding auroral breakup: Evidence for strong Langmuir turbulence Geophys. Res. Lett.,  doi:10.1029/2011GL050288., February 2012.
  • Jorge F. Silva and Patricio Parada, “On the Convergence of Shannon Differential Entropy, and its Connections with Density and Entropy Estimation,” ELSEVIER Journal of Statistical Planning and Inference, vol 142, issue 7, pp. 1716-1732, July, 2012.
  • Eduardo Pavez and Jorge F. Silva, “Analysis and Design of Wavelet-Packet Cepstral Coefficients for Automatic Speech Recognition,”  ELSEVIER Speech Communication, January, 2012.
  • Jorge F. Silva and Shrikanth S. Narayanan, “On Signal Representations within the Bayes Decision Framework,” ELSEVIER Pattern Recognition, vol 45, issue 5, pp. 1853–1865, 2012.
  • Jorge F. Silva and Shrikanth S. Narayanan, “Complexity-Regularized Tree-Structured Partition for Mutual Information Estimation,” IEEE Transactions on Information Theory, vol. 58, no.3, pp.1940 – 1952 ,March, 2012.
  • Chen, C., Brown, D., Sconyers, C., Zhang, B., Vachtsevanos, G., and Orchard, Marcos., “An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems,” Expert Systems with Applications, vol. 39, Issue 10, pp. 90319040, August 2012.
  • Chen, C., Vachtsevanos, G., Orchard Marcos, “Machine Remaining Useful Life Prediction: an Integrated Adaptive Neuro-Fuzzy and High-Order Particle Filtering Approach,” Mechanical Systems and Signal Processing, vol. 28, pp. 597-607, April 2012.
  • Felipe Tobar  and Marcos Orchard., “Study of Financial Systems Volatility Using Suboptimal Estimation Algorithms,” Studies in Informatics and Control, vol. 21, Issue 1, pp. 5966, March 2012.
  • Chen, C., Vachtsevanos, G., Orchard, M., “Machine Condition Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering,” IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353-4364, September 2011.
  • Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., and Vachtsevanos, G., “A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2011-2018, May 2011.
  •  M. A. Diaz, M. Oppenheim, J. Semeter, and M. Zettergren. Particle-In-Cell Simulation of Incoherent Scatter Radar Spectral Distortions Related to Beam-Plasma Interactions in the Auroral Ionosphere. J. of Geophys. Res., Julio 2011.
  •  M. Zettergren, J. Semeter, C. Heinselman, and M. Diaz. Incoherent scatter radar estimation of F –region ionospheric composition during frictional heating events. J. Geophys. Res., January 2011.
  • Jorge F. Silva and S. S. Narayanan. “Information divergence estimation based on data-dependent partitions,” in Journal of Statistical Planning and Inference, Vol. 140, No. 11, pp. 3180-3198, November 2010.
  • Jorge F. Silva and Shrikanth S. Narayanan, “Non-Product Data-Dependent Partitions for Mutual Information Estimation: Strong Consistency and Applications,”  IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3497-3511, July, 2010.
  • Marcos Orchard, L. Tang, B. Saha, K. Goebel,  and G. Vachtsevanos, “Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices,” in Studies in Informatics and Control, Vol. 19, No. 3, pp. 209-218, September 2010.
  • B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, Marcos Orchard, and A. Saxena. “A novel blind deconvolution de-noising scheme in failure prognosis,” in Transactions of the Institute of Measurement Control, Vol. 32, No. 1, pp. 3-30, February 2010.
  • M. A. Diaz, J. L. Semeter, M. Oppenheim, and M. Zettergren. Analysis of beam plasma instability effects on incoherent scatter spectra. Ann. Geophys., December 2010.
  • M. Zettergren, J. Semeter, C. Heinselman, M. Diaz, and P.-L. Blelly. Dynamic variability in F-region ionospheric composition at auroral arc boundaries. Ann. Geophys., Feb 2010.
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