Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11290

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Electromagnetic Interference Shielding Performance of Ionic Liquid Modified Carbon Black and Graphite in Polyvinylidene fluoride at Ku-Band
(Institute of Electrical and Electronics Engineers Inc., 2019-11) Zeynep Ertekin; Mustafa Seçmen; Mustafa Erol
In this study carbon black and/or graphite incorporated with polyvinylidene difluoride (PVDF) composites giving sufficiently high electromagnetic shielding properties at microwave frequencies are synthesized. Micro-scale carbon black and graphite particles are modified by ionic liquid to improve interference shielding properties of PVDF based composites. The negatively charged carbon black and graphite particles are interacted by positively charged imidazolium ring to form ion pairs being embedded in PVDF. The effects of nanoscale hematite magnetite maghemite and silver nanoparticles (AgNps) on carbon black incorporated PVDF graphite incorporated PVDF and performances of carbon black-PVDF/graphite-PVDF double-layer composites are investigated for the frequency range of 12-18.4 GHz at Ku-band. Among all the composite samples each having 4 mm thickness double-layer samples exhibit the strongest EMI shielding effectiveness by giving up to 35 dB at minimum in the given frequency range. © 2020 Elsevier B.V. All rights reserved.
Imaging the Cell Fate based on the Concept of Apoptogram
(Institute of Electrical and Electronics Engineers Inc., 2019-11) Gökhan Demirkıran
This study investigates cell fate selection in a single cell by carrying out theoretical analysis on a parametric 2-Dimensional model. The investigated model involves eight fixed parameters and two tuneable parameters r and m where the tuneable parameters are the determinants of cell fate decision. Using these tuneable parameters as the axes the concept of apoptogram where the exact values of r and m pairs that lead to a certain cell fate decision are portrayed as regions is introduced. The boundaries that depicts the regions are theoretically determined in a parametric fashion so that one can construct an apoptogram image given the values of fixed parameters of the model. The regions that correspond to a cell fate decision in the apoptogram provide valuable insights to investigate and manipulate cell fate decisions. Further studies may utilize such concept as a new tool to explore the complexity of cancer and cancer therapy. © 2020 Elsevier B.V. All rights reserved.
Investigation of Chaotic Mixing Performance on Characteristic Properties of Cake Batter
(Institute of Electrical and Electronics Engineers Inc., 2019-11) Ruhan Askin-Uzel; Tolga Tugay Izmir; Gökhan Demirkıran; Savaş Şahin; Cüneyt Güzeliş
Chaotification is the process of making an originally non-chaotic system being chaotic by applying a suitable control input. The aim of the study was to create a chaotic mixing mechanism using a kitchen type mixer and to test its performance on the quality characteristics of cake batter material. A prototype mixer that works originally with conventional method has been realized with hardware and software changes on a commercial kitchen type mixer. The results obtained at the end of the study showed that the kitchen type mixer was able to successfully switch from the classical mixing mode to the chaotic mixing mode and this mixing mode positively affected the structural and sensory characteristics of the cake batter samples. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Learning Feedback Linearization Based Stable Robust Adaptive NARMA Controller Design for Rotary Inverted Pendulum
(Institute of Electrical and Electronics Engineers Inc., 2019-11) Mehmet Uğur Soydemir; Savaş Şahin; Parvin Bulucu; Aykut Kocaoǧlu; Cüneyt Güzeliş; Bulucu, Panin; Kocaoglu, Aykut; Soydemir, Mchmet Ugur; Guzelis, Cuneyt; Sailin, Savas; Sahin, Savas
This paper presents a Learning Feedback Linearization (LFL) based Nonlinear Auto-Regressive Moving Average (NARMA) controller design for a ROTary inverted PENdulum (ROTPEN) plant. The proposed NARMA controller comprises of a linear controller and an LFL block. The LFL block concatenated with the nonlinear plant constitutes a linear closed loop system so that linear control is applicable. An online learning algorithm is used for the data-dependent identification of the linearized plant and then for the data-dependent design of the linear part of the NARMA controller. The identification of the linearized plant starts with the determination of the LFL block in a supervised way by exploiting the input and the corresponding state data obtained from the nonlinear plant. The linearized plant is then identified as an ARMA model by the data generated with the combination of the already learned LFL block and the nonlinear plant. Robustness of the linearized system model is obtained by employing the ϵ-insensitive loss function ℓ1 ϵ(••) as the identification error of the linearized system. The Schur stability of the overall closed loop system is ensured by the linear inequality constraints imposed in the minimization of the ℓ1 ϵ(••) tracking error for determining the linear controller parameters. The proposed LFL based NARMA controller is tested on ROTPEN model and its performance is compared with the Proportional-Derivative controller and Hammerstein based NARMA adaptive controller. © 2020 Elsevier B.V. All rights reserved.
Searching Optimal Values of Identification and Controller Design Horizon Lengths and Regularization Parameters in NARMA Based Online Learning Controller Design
(Institute of Electrical and Electronics Engineers Inc., 2019-11) Tugce Toprak; Savaş Şahin; Mehmet Uğur Soydemir; Parvin Bulucu; Aykut Kocaoǧlu; Cüneyt Güzeliş; Toprak, Tugce; Bulucu, Parvin; Kocaoglu, Aykut; Soydemir, M. Ugur; Guzelis, Cuneyt; Sahin, Savas
This paper presents an analysis on searching the optimal values of the system identification and tracking window lengths and regularization parameter for the online learning NARMA controller algorithm. Both window lengths and regularization parameter are generally determined with exhaustive searches by researchers. Although the estimation of plant and controller parameters plays the essential role in online learning control algorithms using non-optimal values of the window lengths and regularization parameter may deteriorate badly the estimation and so the performance of the controller. In the paper the effects of the window lengths and the regularization parameter on the tracking performance of the NARMA based online learning controller are analyzed with a search method. The considered NARMA based online learning control method is performed on a rotary inverted pendulum model. While the effect of the regularization parameter is examined in the batch mode the effects of identification and tracking error window lengths are studied for the online mode of the controller learning algorithm. The developed search method can provide the optimum values of the plant identification and tracking horizon lengths and regularization parameter when a sufficiently large class of possible input output and reference signals are taken into account in the search. The presented study may be extended as future research in the direction of developing intelligent control systems by determining the horizon window lengths and regularization parameter in an automatic way with efficient learning algorithms. © 2020 Elsevier B.V. All rights reserved.

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Browsing Scopus İndeksli Yayınlar Koleksiyonu by Journal "11th International Conference on Electrical and Electronics Engineering ELECO 2019"