Yazar "Yildirim, Ugur" seçeneğine göre listele

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    Bayesian Binary Hypothesis Testing Under Model Uncertainty
    (IEEE, 2020) Afser, Huseyin; Yildirim, Ugur
    We consider Bayesian binary hypothesis testing problem when there is only partial knowledge about one of the distributions, while the other distribution is fully known. Specifically, let P-1 and P-2 be the distributions under two hypothesis, where P-2 is known and P-1 is unknown. We propose a test and show that if the Chernoff distance between P-1 and P-2 is known to be larger than Phi, an error exponent Phi,- epsilon, epsilon > 0, can be achieved in the Bayesian setting. If the Chernoff distance between P-1 and P-2 is not known, but another distribution Q(1) known such that l(1) distance between P-1 and Q(1) is known the smaller than a, then the same test can be applied, and it coincides with the robust hypothesis testing methods existing in the literature.
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    Öğe
    Bayesian Binary Hypothesis Testing under Model Uncertainty
    (Institute of Electrical and Electronics Engineers Inc., 2020) Afser, Huseyin; Yildirim, Ugur
    We consider Bayesian binary hypothesis testing problem when there is only partial knowledge about one of the distributions, while the other distribution is fully known. Specifically, let P1 and P2 be the distributions under two hypothesis, where P2 is known and P1 is unknown. We propose a test and show that if the Chernoff distance between P1 and P2 is known to be larger than ?, an error exponent ?-?, ?>0, can be achieved in the Bayesian setting. If the Chernoff distance between P1 and P2 is not known, but another distribution Q1 known such that l1 distance between P1 and Q1 is known the smaller than ?, then the same test can be applied, and it coincides with the robust hypothesis testing methods existing in the literature. © 2020 IEEE.
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    Öğe
    Linear Methods for Predictive Maintenance: The Case of NASA C-MAPSS Datasets
    (MDPI, 2025) Yildirim, Ugur; Afser, Huseyin
    Predictive maintenance systems increasingly leverage diverse sensor modalities to improve failure prognostics and remaining useful life (RUL) estimation. However, integrating heterogeneous data types-vibration, temperature, acoustic, and visual sensors-typically requires complex fusion architectures. This paper proposes a unified linear classification-regression framework that addresses predictive maintenance through a shared measurement space approach. The developed method employs Linear Discriminant Analysis to establish hyperplane boundaries partitioning the measurement space into nominal, warning, and failure regions. By tracking each data point's signed distance to these learned boundaries, this research generates continuous RUL predictions through linear regression mapping. The framework's key innovation lies in seamlessly integrating heterogeneous sensor modalities without requiring separate preprocessing pipelines or complex fusion layers-each modality contributes to fault detection and RUL estimation based on its discriminative power in the joint feature space. While linear assumptions may simplify complex non-linear failure patterns, the proposed approach offers significant advantages in interpretability, computational efficiency, and deployment ease. Validation on the C-MAPSS turbofan engine degradation dataset demonstrates that while not achieving state-of-the-art performance, the framework provides a practical foundation accommodating data-driven, physics-based, and knowledge-based modeling paradigms within a unified architecture, making it valuable for industrial applications requiring transparent multi-modal integration.