Examinando por Autor "Montemayor, Antonio S."

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    Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study
    (Springer, 2022) Hernández-García, Sergio; Cuesta-Infante, Alfredo; Moreno-SanSegundo, José Ángel; Montemayor, Antonio S.
    Numerical optimization solves problems that are analytically intractable at the cost of arriving at a sufficiently good but rarely optimal solution. To maximize the result, optimization algorithms are run with the guidance and supervision of a human, usually an expert in the problem. Recent advances in deep reinforcement learning motivate interest in an artificial agent capable of learning to do the expert’s task. Specifically, we present a proximal policy optimization agent that learns to optimize in a real case study such as the modeling of the photo-fenton disinfection process, which involves a number of parameters that have to be adjusted to minimize the error of the model with respect to the experimental data collected in several trials. The expert spends an average of 4 h to find a suitable set of parameters. On the other hand, the agent we present does not require a human expert to guide or validate the optimization procedure and achieves similar results in 2:5 less time.
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    Ítem
    Synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning
    (Springer, 2023-06-25) Hernández-García, Sergio; Cuesta-Infante, Alfredo; Montemayor, Antonio S.
    Automated sperm sample analysis using computer vision techniques has gained increasing interest due to the tedious and time-consuming nature of manual evaluation. Deep learning models have been applied for sperm detection, tracking, motility analysis, and morphology recognition. However, the lack of labeled data hinders their adoption in laboratories. In this work, we propose a method to generate synthetic spermatozoa video sequences using Generative Adversarial Imitation Learning (GAIL). Our approach uses a parametric model based on Bezier splines to generate frames of a single spermatozoon. We evaluate our method against U-net and GAN-based approaches, and demonstrate its superior performance.