Published in Information Sciences, 2017
This paper proposes and analysis a fully neural search engine architecture.
Recommended citation: T. A. Nakamura, P. H. Calais, D. C. Reis, A. P.Lemos. "An anatomy for neural search engines", Information Sciences (2019), Volume 480, 339-353. https://www.sciencedirect.com/science/article/pii/S0020025518309952
Published in Journal of the Franklin Institute, 2017
This paper proposes an adaptive fault detection and diagnosis methodology using parsimonious faussian mixture models (PGMM) trained with distributed computing techniques.
Recommended citation: T. A. Nakamura, R. M. Palhares, W. M. Caminhas, B. R. Menezes, M. C. M. M. de Campos, U. Fumega C. H. de M. Bomfim, A. P.Lemos. "Adaptive Fault Detection and Diagnosis Using Parsimonious Gaussian Mixture Models Trained with Distributed Computing Techniques", Journal of the Franklin Institute (2017), Volume 354, Issue 6, 2543-2572. http://akionakamura.github.io/files/2017-04-01-paper-parsimonious-gaussian-mixture-fdd.pdf
Published in Evolving and Adaptive Intelligent Systems, 2014
This paper proposes a batch-incremental method using mixtures of probabilistic principal components analysis (PCA) for process fault detection and diagnosis.
Recommended citation: T. A. Nakamura, A. P. Lemos. "A batch-incremental process fault detection and diagnosis using mixtures of probabilistic PCA", EEE Conference on Evolving and Adaptive Intelligent Systems (2014), 1–8. http://akionakamura.github.io/files/2014-06-02-paper-batch-incremental-process.pdf