Browsing by Author "Gabbouj M."

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Citation - Scopus: 1
Classification of Holter Registers by Dynamic Clustering Using Multi-Dimensional Particle Swarm Optimization
(2010) Kiranyaz S.; İnce, Türker; Pulkkinen J.; Gabbouj M.
In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system. © 2010 IEEE.
Citation - Scopus: 20
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
(Institute of Electrical and Electronics Engineers Inc., 2024) Kiranyaz S.; Devecioglu O.C.; Alhams A.; Sassi S.; Ince T.; Avci O.; Gabbouj M.
Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been widely neglected. As a result, no large-scale benchmark motor fault dataset exists with both sound and vibration data. The novel and significant contributions of this study can be summarized as follows. This study presents and publically shares the Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions. Then, we focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration signals over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. This study publicly shares the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations with the research community. Authors
Incremental Evolution of Collective Network of Binary Classifier for Content-Based Image Classification and Retrieval
(2011) Kiranyaz S.; Uhlmann S.; Pulkkinen J.; İnce, Türker; Gabbouj M.
In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be evolved incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. This design further allows such scalability that the CNBC can dynamically adapt its internal topology to new features and classes with minimal effort. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its efficiency and accuracy for scalable CBIR and classification. © 2011 IEEE.