Open Access
MATEC Web of Conferences
Volume 58, 2016
The 3rd Bali International Seminar on Science & Technology (BISSTECH 2015)
Article Number 02005
Number of page(s) 6
Section Industrial Engineering
Published online 23 May 2016
  1. Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63(1), 135-149. [CrossRef] [Google Scholar]
  2. Ardiansyah, F., Gunningham, N., & Drahos, P. (2012). An environmental perspective on energy development in Indonesia. In Energy and non-traditional security (NTS) in Asia (pp. 89-117). Springer Berlin Heidelberg. [CrossRef] [Google Scholar]
  3. Crespo A. Márquez, Benoit Iung. 2007. A Estructured Approach for the Assessment of System Availability and Reliability Using Montecarlo Simulation. Journal of Quality in Maintenance Engineering. 13 (2): 125-136. [CrossRef] [Google Scholar]
  4. Dhillon, B.S. 2005. Reliability, Quality, and Safety for Engineers, CRC Press, USA [Google Scholar]
  5. Díaz, V. G. P., & Márquez, A. C. (2014). On the Assessment and Control. In After–sales Service of Engineering Industrial Assets (pp. 175-211). Springer International Publishing. [Google Scholar]
  6. Fujiki Morii, Kazuko Kurahashi. 2009. Clustering Based on Multiple Criteria For LVQ and K-Means Algorithm, Journal of Advanced Computational Intelligence and Intelligent Informatics. 13: 360-370. [CrossRef] [Google Scholar]
  7. González Díaz, A. Crespo, P. Moreu, J. Gómez, C. Parra. 2009. Availability and reliability assessment of industrial complex systems: A practical view applied on a bioethanol plant simulation. Safety and Reliability for Managing Risk. Taylor & Francis Group: 687-695. [Google Scholar]
  8. Kate A. Smith, Frederick Woo, Vic Ciesielski and Remzi Ibrahim. 2001. Modeling the Relationship Between Problem Characteristics and Data Mining Algorithm Performance using Neural Networks. Intelligent Engineering Systems Through Artificial Neural Networks. 11: 356-362. [Google Scholar]
  9. Olden, Julian D., Michael K. Joy, and Russell G. Death. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Modelling 178 (3): 389-397. [CrossRef] [Google Scholar]
  10. Portugal-Pereira, J., & Esteban, M. (2014). Implications of paradigm shift in Japan’s electricity security of supply: A multi-dimensional indicator assessment. Applied Energy, 123, 424-434. [CrossRef] [Google Scholar]
  11. Robi Polikar. 2001. Learn++: An Incremental Learning Algorithm for Supervised Neural Networks. IEEE Transaction on System, Man, and Cybertics Aplications and Reviews. 31: 497-508., [Google Scholar]
  12. Sivanandam S. N., Sumathi and Deepa. 2006. Introduction to Neural Networks Using Matlab 6.0. Tata McGraw-Hill Education. [Google Scholar]
  13. Simões, M. G., Roche, R., Kyriakides, E., Miraoui, A., Blunier, B., McBee, K., and Ribeiro, P. (2011, September). Smart-grid technologies and progress in Europe and the USA. In Energy Conversion Congress and Exposition (ECCE), 2011 IEEE (pp. 383-390). IEEE. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.