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Elektrik Elektronik Mühendisliği Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/410

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    Citation - WoS: 1
    Citation - Scopus: 2
    Dynamical System Parameter Identification Using Deep Recurrent Cell Networks Which Gated Recurrent Unit and When
    (Springer London Ltd, 2021) Akagunduz, Erdem; Cifdaloz, Oguzhan
    In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping factor identification is studied. By utilizing a six-layer deep neural network with different recurrent cells, namely GRUs, LSTMs or BiLSTMs; and by feeding input/output sequence pairs captured from a dynamical system simulator, we search for an effective deep recurrent architecture in order to resolve the damping factor identification problem. Our study's results show that, although previously not utilized for this task in the literature, bidirectional gated recurrent cells (BiLSTMs) provide better parameter identification results when compared to unidirectional gated recurrent memory cells such as GRUs and LSTM. Thus, indicating that an input/output sequence pair of finite length, collected from a dynamical system and when observed anachronistically, may carry information in both time directions to predict a dynamical systems parameter.
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    Citation - WoS: 8
    Citation - Scopus: 10
    Sparse Coding of Hyperspectral Imagery Using Online Learning
    (Springer London Ltd, 2015) Toreyin, Behcet Ugur; Ulku, Irem
    Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.