WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 1A Comprehensive Analysis of Acoustic Emission Signals To Distinguish the Different Damage Types for Fiber-Reinforced Polymers: A Review(Wiley, 2025-12-03) Yilmaz, CagatayFiber-reinforced polymers (FRP) attract the attention of key industries, such as aerospace, wind energy, and automotive, as they can reduce the weight of structural components without compromising their mechanical properties. Due to FRP's anisotropic and non-homogeneous structure, their failure under different loading conditions and the corresponding failure mechanisms must be investigated. One method that progressively monitors the failure of FRP underload is Acoustic Emission (AE). AE can register the elastic stress waves in the form of digitized waveforms, released by the discontinuous events that occur in the FRP under load. These discontinuities can be clustered and identified as transverse cracking, fiber/matrix interface debonding, delamination, and fiber failure by analyzing the AE waveforms. Recently, numerous clustering approaches using machine learning algorithms, along with the varying features of AE waveforms, have been developed and are being used. These algorithms include supervised and unsupervised clustering, deep learning algorithms, and neural network methods, among others. While supervised algorithms require a training dataset to classify AE signals, unsupervised algorithms can perform clustering without training datasets. Deep learning and neural network algorithms can train themselves to cluster data, but they may require a significant amount of computer power when the dataset is large. This review paper provides comprehensive information on the clustering algorithm, along with the AE wave features, the range of features for different damage types, and the type of reinforcer.Article Citation - WoS: 52Citation - Scopus: 69Spectrum-Aware Bio-Inspired Routing in Cognitive Radio Sensor Networks for Smart Grid Applications(Elsevier Science Bv, 2017-03) Fadel, E.; Faheem, M.; Gungor, V. C.; Nassef, L.; Akkari, N.; Malik, M. G. A.; Akyildiz, I. F.Cognitive radio sensor networks (CRSNs) have been proposed to serve as a reliable, robust, and efficient communications infrastructure that can address both the existing and future energy management requirements of the smart grid. The existing and envisioned applications of CRSN-based smart grid include substation automation, overhead transmission line monitoring, home energy management, advanced metering infrastructure, wide-area situational awareness, demand response, outage management, distribution automation, asset management. To realize these applications, in this paper, honey bee mating optimization-based routing and cooperative channel assignment algorithms have been proposed. The developed framework significantly decreases the probability of packet loss and preserves high link quality among sensor nodes in harsh smart grid spectrum environments. The proposed approach performance has been evaluated in terms of packet delivery ratio, delay, and energy consumption demonstrating that it has successfully addressed the QoS requirements of most of the SG applications presented. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 10Citation - Scopus: 8Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission(Wiley-VCH Verlag GmbH, 2024-07-15) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi CagriAcoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes. This article presents a novel ensemble feature selection methodology to rank features relevant to damage modes on acoustic emission signals in carbon fiber-reinforced polymer sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, along with common features, other features, like partial powers, have a robust correlation with damage modes.image (c) 2024 WILEY-VCH GmbHArticle Citation - WoS: 53Citation - Scopus: 66Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data(MDPI, 2020-12-22) Yousef, Malik; Kumar, Abhishek; Bakir-Gungor, BurcuIn the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions.