06.09.2021 | Publicaciones

The American Journal “IEEE Transactions on pattern analysis and Machine Intelligence” positioned in the web of science as the number one journal by citation indicator (JCI) and impact factor (JIF) in the area of computer science and artificial intelligence, interested in topics as computer vision, pattern recognition and machine learning, has accepted the article "Autoregressive Asymmetric Linear Gaussian Hidden Markov Models" presented by Aingura IIoT doctoral student Carlos Esteban Puerto at Universidad Politécnica de Madrid.

As the article’s abstract reads “In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component in the case of continuous variables, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model."[1]

In summary the article presents a new flexible statistical artificial intelligence model which can be used to understand, represent and learn dynamical processes and provide deeper insights in comparison to other state of the art dynamical statistical models. This new model is currently used for offline analysis of different kind of dynamical data. However, in collaboration with the Barcelona Supercomputer Center, the model is being incorporated and improved into the edge computer module Aingura Insights for online machine-tool surveillance and prognosis without using previous machine-tool signal.

More info: https://ieeexplore.ieee.org/document/9387117

[1] C. Puerto-Santana, P. Larranaga and C. Bielza, "Autoregressive Asymmetric Linear Gaussian Hidden Markov Models," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3068799.