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Bulent Ayhan

Researcher at North Carolina State University

Publications -  95
Citations -  2853

Bulent Ayhan is an academic researcher from North Carolina State University. The author has contributed to research in topics: Hyperspectral imaging & Fault detection and isolation. The author has an hindex of 22, co-authored 94 publications receiving 2307 citations.

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Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring

TL;DR: In this article, an ultrasonic guided wave structural health monitoring (SHM) system was developed for aircraft wing inspection, where small, low-cost and light-weight piezoelectric (PZT) discs were bonded to various parts of the aircraft wing, in a form of relatively sparse arrays, for simulated cracks and corrosion monitoring.
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Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors

TL;DR: Two fault-detection schemes for a broken-rotor-bar fault detection with a multiple signatureprocessing are described and it is demonstrated that the multiple signature processing is more efficient than a single signature processing.
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A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images

TL;DR: A generalization of the KRX algorithm, called cluster KRX (CKRX), which becomes KRX under certain conditions, which has comparable detection performance as KRX, but with much lower computational requirements.
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Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition

TL;DR: This paper focuses on anomaly detection in hyperspectral images (HSIs) and proposes a novel detection algorithm based on spectral unmixing and dictionary-based low-rank decomposition, which achieves high detection rate while maintaining low false alarm rate regardless of the type of images tested.
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On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods

TL;DR: This paper has demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken rotor bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods.