Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 5Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks(Springer, 2022) Al-Qamaji, A.; Atakan, B.Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency. © 2021, The Author(s).Article Citation - WoS: 56Citation - Scopus: 67Characterization of Concrete Matrix/Steel Fiber De-Bonding in an Sfrc Beam: Principal Component Analysis and K-Mean Algorithm for Clustering Ae Data(Elsevier, 2018) Tayfur, Sena; Alver, Ninel; Abdi, Saeed; Saatçi, Selçuk; Ghiami, AmirSteel fibers have been used in concrete structures to increase the tensile strength and ductility of concrete. Fibers bridging cracks reduce micro cracking and improve post-cracking strength in concrete. Propagation of damage in a fiber reinforced concrete member occurs by concrete matrix cracking and widening of these cracks, which is accompanied by de-bonding of steel fibers from the concrete matrix. Fiber de-bonding is the main factor affecting the post-peak behavior of these members. Therefore, distinguishing the matrix cracking and fiber de-bonding mechanisms is important in nondestructive structural health monitoring methods. This study is focused on characterizing steel fiber/matrix de-bonding events apart from concrete matrix cracking sources in acoustic emission (AE) method. Two reinforced concrete beams, one of which included steel fibers within the concrete matrix, were tested under three point bending and monitored by AE. Afterwards, Principal Component Analysis (PCA) was applied to AE data and the failure mechanisms were clustered for characterization of steel fiber/matrix de-bonding. Finally, different AE features of these clusters were evaluated and applicable AE parameter distributions, which are useful to clarify steel fiber de-bonding mechanisms, were revealed.Conference Object Citation - Scopus: 4Survey: Running and Comparing Stream Clustering Algorithms(CEUR Workshop Proceedings, 2018) Ahmed, Rowanda D.; Dalkılıç, Gökhan; Erten, MuratRecently, clustering data streams have become an incredibly important research area for knowledge discovery as applications produce more and more unstoppable streaming data. In this paper we introduce clustering, streams and data streaming clustering algorithms, as well as discussions of the most important stream clustering algorithms, considering their structure. As an additional contribution of our work and differently from review and survey papers in stream clustering, we offer the practical part of the most known stream clustering algorithms, namely: (i) CluStream; (ii) DenStream; (iii) D-Stream; and (iv) ClusTree, showing their experimental results along with some performance metrics computation of for each, depending on MOA framework.Article Citation - WoS: 15Citation - Scopus: 21Tracking Fast Moving Targets in Wireless Sensor Networks(Institution of Electronics and Telecommunication Engineers, 2010) Alaybeyoğlu, Ayşegül; Erciyeş, Kayhan; Kantarcı, Aylin; Dağdeviren, OrhanWe propose a dynamic distributed algorithm for tracking objects that move fast in a sensor network. In the earlier efforts in tracking moving targets, the current leader node at time t predicts the location only for time t + 1 and if the target moves in high speed, it can pass by a group of nodes very fast without being detected. Therefore, as the target increases its speed, the probability of missing that target also increases. In this study, we propose a target tracking system that predicts future k locations of the target and awakens the -corresponding leader nodes so that the nodes along the trajectory self organize to form the clusters to collect data related to the target in advance and thus reduce the target misses. The algorithm first -provides detection of the target and forms a cluster with the neighboring nodes around it. After the selection of the cluster leader, the coordinates of the target is estimated using localization methods and cooperation -between the cluster nodes under the control of the leader node. The coordinates and the speed of the target are then used to estimate its trajectory. This information in turn provides the location of the nodes along the estimated trajectory which can be awaken, hence providing tracking of the moving object. We describe the algorithm, analyze its efficiency and show by simulations that it performs well to track very fast moving objects with speeds much higher than reported in literature.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.Article Citation - Scopus: 1Annealing-Based Model-Free Expectation Maximisation for Multi-Colour Flow Cytometry Data Clustering(Inderscience Enterprises Ltd., 2016) Köktürk, Başak Esin; Karaçalı, BilgeThis paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models.Conference Object Citation - WoS: 13Citation - Scopus: 12Distributed Algorithms To Form Cluster Based Spanning Trees in Wireless Sensor Networks(Springer Verlag, 2008) Erciyeş, Kayhan; Özsoyeller, Deniz; Dağdeviren, OrhanWe propose two algorithms to form spanning trees in sensor networks. The first algorithm forms hierarchical clusters of spanning trees with a given root, the sink. All of the nodes in the sensor network are then classified iteratively as subroot, intermediate or leaf nodes. At the end of this phase, the local spanning trees are formed, each having a unique subroot (clusterhead) node. The communication and data aggregation towards the sink by an ordinary node then is accomplished by sending data to the local subroot which routes data towards the sink. A modified version of the first algorithm is also provided which ensures that the obtained tree is a breadth-first search tree where a node can modify its parent to yield shorter distances to the root. Once the sub-spanning trees in the clusters are formed, a communication architecture such as a ring can be formed among the subroots. This hybrid architecture which provides co-existing spanning trees within clusters yields the necessary foundation for a two-level communication protocol in a sensor network as well as providing a structure for a higher level abstraction such as the γ synchronizer where communication between the clusters is performed using the ring similar to an α synchronizer and the intra cluster communication is accomplished using the sub-spanning trees as in the β synchronizers. We discuss the model along with the algorithms, compare them and comment on their performances.