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Article Citation - WoS: 1Application of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir(Centre Environment Social & Economic Research Publ-ceser, 2007) Tuzun, Ilhami; Soyupak, Selcuk; Ince, Ozlem; Basaran, GokbenAn Artificial Neural Network (ANN) modelling approach has been shown to be successful in calculating time and space dependent dissolved oxygen (DO) concentration profiles in Kapulukaya Dam Reservoir using limited number of input variables. The variation of inflow to the reservoir with respect to time was significantly high. The reservoir operational levels were relatively stable. The Levenberg-Marquardt algorithm was adopted during training. Preprocessing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Different configurations of Multilayer perceptron neural networks were designed by selecting different combinations of number of hidden layers (single and double) and number of neurons within each of the hidden layers. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The conventional model criteria of correlation coefficient (R) and mean square errors (MSE) were adopted to compare model performances. The correlation coefficients between neural network estimates and field measurements were as high as 0.96 for daily and monthly data respectively with experiments that involve double layer neural network structure with 31 neurons within each hidden layer. The study results revealed that the data sizes effect model performances up to a certain level.Article Citation - WoS: 11Citation - Scopus: 11Modified Criteria for Global Robust Stability of Interval Delayed Neural Networks(Elsevier Science inc, 2009) Singh, VimalTwo simple criteria for global robust stability of Hopfield-type interval neural networks with delay are presented. The criteria turn out to be modified versions of an earlier criterion due to Cao, Huang, and Qu. Examples show the effectiveness of the modified criteria. Numerical simulations are carried out to confirm the applicability of the modified criteria. (C) 2009 Elsevier Inc. All rights reserved.Article Citation - WoS: 15Citation - Scopus: 17Improved Global Robust Stability of Interval Delayed Neural Networks Via Split Interval: Generalizations(Elsevier Science inc, 2008) Singh, VimaldThe problem of global robust stability of Hop field-type delayed neural networks with the intervalized network parameters is revisited. Recently, a computationally tractable, i.e., linear matrix inequality (LMI) based global robust stability criterion derived from an earlier criterion based on dividing the given interval into more that two intervals has been presented. In the present paper, generalizations, i.e., division of the given interval into m intervals (where m is an integer greater than or equal to 2) is considered and some new LMI-based global robust stability criteria are derived. It is shown that, in some cases, m = 2 may not suffice, i.e., m > 2 may be needed to realize the improvement. An example showing the effectiveness of the proposed generalization is given. The paper also provides a complete and systematic explanation of the "split interval" idea. (c) 2008 Elsevier Inc. All rights reserved.Conference Object Citation - Scopus: 2Comparison of Gross Calorific Value Estimation of Turkish Coals Using Regression and Neural Networks Techniques(2012) Ozbayoglu,A.M.; Ozbayoglu,M.E.; Ozbayoglu,G.Gross calorific value (GCV) of coals was estimated using artificial neural networks, linear and non-linear regression techniques. Proximate and ultimate analysis results were collected for 187 different coal samples. Different input data sets were compared, such as both proximate and ultimate analysis data, and only proximate analysis data and only ultimate analysis data. It was observed that the best results were obtained when both proximate analysis and ultimate analysis results were used for estimating the gross calorific value. When the performance of artificial neural networks and regression analysis techniques were compared, it was observed that both artificial neural networks and regression techniques were promisingly accurate in estimating gross calorific values. In general, most of the models estimated the gross calorific value within ±3% of the expected value.Article Citation - WoS: 19Citation - Scopus: 19Identification of Materials With Magnetic Characteristics by Neural Networks(Elsevier Sci Ltd, 2012) Nazlibilek, Sedat; Ege, Yavuz; Kalender, Osman; Sensoy, Mehmet Gokhan; Karacor, Deniz; Sazh, Murat HusnuIn industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.Article Citation - WoS: 11Citation - Scopus: 16New Lmi-Based Criteria for Global Robust Stability of Delayed Neural Networks(Elsevier Science inc, 2010) Singh, VimalSome novel, linear matrix inequality based, criteria for the uniqueness and global robust stability of the equilibrium point of Hopfield-type neural networks with delay are presented. A comparison of the present criteria with the previous criteria is made. (C) 2010 Elsevier Inc. All rights reserved.Article Citation - WoS: 16Citation - Scopus: 23A New Criterion for Global Robust Stability of Interval Delayed Neural Networks(Elsevier Science Bv, 2008) Singh, VimalA novel criterion for the global robust stability of Hopfield-type interval neural networks with delay is presented. An example showing the effectiveness of the present criterion is given. (C) 2007 Elsevier B.V. All rights reserved.Article Citation - WoS: 18Citation - Scopus: 19Performance Analysis of Modular Rf Front End for Rf Fingerprinting of Bluetooth Devices(Springer, 2020) Uzundurukan, Emre; Ali, Aysha M.; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) could provide an efficient solution to address the security issues in wireless networks. The data acquisition system constitutes an important part of RFF. In this context, this paper presents an implementation of a modular RF front end system to be used in data acquisition for RFF. Modularity of the system provides flexible implementation options to suit diverse frequency bands with different applications. Moreover, the system is able to collect data by means of any digitizer, and enable to record the data at lower frequencies. Therefore, proposed RF front end system becomes a low-cost alternative to existing devices used in data acquisition. In its implementation, Bluetooth (BT) signals were used. Initially, transients of BT signals were detected by utilizing a large number of BT devices (smartphones). From the detected transients, distinctive signal features were extracted. Then, support vector machine (SVM) and neural networks (NN) classifiers were implemented to the extracted features for evaluating the feasibility of proposed system in RFF. As a result, 96.9% and 96.5% classification accuracies on BT devices have been demonstrated for SVM and NN classifiers respectively.
