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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Fırat University Turkish Journal of Science & Technology
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1308-9080 - ISSN (Online) 1308-9099
Published by DergiPark Homepage  [187 journals]
  • A Substitution-Box Structure Based on Solar Panel Data

    • Authors: Esin TURAN; Mustafa Kemal ÖZDEMİR, Barış KARAKAYA, Fatih ÖZKAYNAK
      Abstract: The demonstration that the nonlinearity criterion of substitution box (s-box) structures based on the random selection principle can be improved through post-processing techniques has created a new research area. The necessity of obtaining sbox structures that can be given as input to these post-processing algorithms has emerged. In this study, a study was carried out on how to obtain sbox structures based on solar panel data. The cryptological properties of the obtained sbox structures were tested using five basic evaluation metrics and compared with similar studies in the literature. The successful results indicated that these outputs may have various practical applications in the future.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • The Effect of Imide Substituents on the Excited State Properties of
           Perylene Diimide Derivatives

    • Authors: Erkan AKSOY; Andrew DANOS, Chunyong Lİ, Andrew MONKMAN, Canan VARLIKLI
      Abstract: Solid state optical properties of fluorescent materials are important for many optical and electronic photonic devices such as organic light emitting diodes, frequency down-converters or luminescent solar concentrators. Perylene diimides (PDIs) represent one of the most popular organic semiconductors which find application in those phonic device applications. In this study, photophysical properties of two dibrominated PDI (DiBrPDIs), one of which contains a branched alkyl chain (2-ethylhexyl, 2-EH) and the other has an aromatic substituent (diisopropylphenyl, DIA) at the imide positions are comparatively studied. Besides their absorption and photoluminescence, lifetime and photoluminescence quantum yield (PLQY), photoinduced absorption properties (PIA) were also examined by fs-transient absorption spectroscopy. Due to their the same π conjugated system, DiBrPDI-DIA and DiBrPDI-2EH exhibited identical absorption and photoluminescence (PL) spectra in solution phase (λabs:527 nm and λPL:552 nm). However, in their film phases which were prepared at the same conditions, DiBrPDI-DIA (λPL-DIA:596 nm; PLQY:73.4%) presented a shorter PL wavelength with a higher PLQY than that of DiBrPDI-2EH (λPL-2EH:649 nm; PLQY:36.7%). 3-D investigations performed by using Chem3D pro software addressed the higher intermolecular distance between the perylene rings induced by the bulky DIA groups, as the main reason of this difference.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Mobile Robot Indoor Localization Using Color-Coded Beacons and a Depth
           Camera

    • Authors: Mawj MOHAMMED BASHEER; Mehmet ÇAVAŞ, Mohammed QASIM
      Abstract: This paper aims to design and implement an indoor localization method for mobile robots based on trilateration technique, Color Code signatures (CCs) as artificial passive beacons, and a depth camera. The artificial passive beacons are designed as three disks positioned one on top of the other’s with different sizes and colors (primary colors only, RGB). The three disks share the same center. The designed beacons are placed on the ceiling to be visible from most locations within the room. The robot's model, controller, and localization method are implemented and evaluated inside the CoppeliaSim environment. The simulation results show that the CCs detecting algorithm enables the robot to discover the designed beacons, achieve an accurate localization and reach the target
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Control Based on Feedback Linearization of a Mobile Manipulator Robot for
           Trajectory Tracking

    • Authors: Samir IKNI; Abdelyazid ACHOUR
      Abstract: The use of robotic systems has now become almost necessary in various fields. Of which, the realization of any hard or dangerous place requiring an implication of manipulation and locomotion, is necessarily entrusted to a mobile manipulator. In this work, we present a control approach that ensures the stability of the system, based on the linearization of feedback. We have determined the kinematic and dynamic models in order to apply the control with a high degree of accuracy. The approach is validated by simulation tests.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Comparison Photon Exposure and Energy Absorption Buildup Factors of CR-39
           and Trivex Optical Lenses

    • Authors: Sevim BİLİCİ; Ahmet BİLİCİ, Fatih KÜLAHCI
      Abstract: In the present study, Energy Absorption Buildup Factor (EABF) and Exposure Buildup Factors (EBF) of the CR-39 and Trivex optical lenses are calculated by using the Geometric Progression (GP) fitting method based on ANSI/ANS-6.4.3 database. The study analyses comprehensively for different penetration depths within the energy range of 0.015 - 15 MeV up to 40 mfp. The buildup factors are calculated in the examined materials depending on the photon energy that arrives, the penetration depths, and the chemical composition of the material reach at maximum values in the energy region where inconsistent scattering interaction probabilities are intensive. The results show that the CR-39 optical lens had better radiation shielding performance. The suitability of the results is compared with the powerful software tools (EPICS2017 and Phy-X/PSD), which are preferred frequently in the literature to calculate radiation shielding parameters. It is found that the relative changes between the EPICS2017 and Phy-X/PSD software compared with the results of this study are about 8% and 9% for the CR-39 and Trivex optical lens, respectively. This indicates that the results from the study are in good agreement.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Fault Detection from Images of Railroad Lines Using the Deep Learning
           Model Built with the Tensorflow Library

    • Authors: Abdullah ŞENER; Burhan ERGEN, Mesut TOĞAÇAR
      Abstract: A means of transportation is the way in which an object, person, or service is transported from one place to another. Rail transportation occupies an important place in terms of cost and reliability. Most train accidents are caused by faults in railroad tracks. Detecting faults in railroad tracks is a difficult and time-consuming process compared to conventional methods. In this study, an artificial intelligence based model is proposed that can detect faults in railroad tracks. The dataset used in the study consists of defective and non-defective railroad images. The proposed model consists of foldable neural networks developed using the Tensorflow library. Softmax method was used as a classifier. An overall accuracy of 92.21% was achieved in the experiment.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Investigation of an Outer Rotor In-Wheel Switched Reluctance Motor for
           Electric Vehicles by Finite Element Method

    • Authors: Zeki OMAÇ
      Abstract: The demand of fossil fuel vehicles has decreased due to carbon emissions. Recently, electric vehicles (EV) with electric motor propulsion have gotten attention due to their zero carbon emissions and high efficiency. In this study, an outer rotor in wheel switched reluctance motor (IWSRM) with 18 poles on the stator and 24 poles on the rotor, designed for the propulsion of electric vehicles, is investigated in two-dimensional Finite Element Method (FEM). The magnetic field distributions of IWSRM for different rotor positions at nominal current are obtained. Then, the torque and phase inductance are calculated. As a result, the designed IWRSM provided low torque.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • MUSIC Based Microwave Imaging of Nonlinear Point-Like Scatterers

    • Authors: Cüneyt UTKU
      Abstract: Narrowband localization of point-like nonlinear scatterers in a homogeneous background medium is investigated. A theoretical framework is provided based on Multiple Signal Classification (MUSIC) imaging, formerly developed for time-reversal imaging of point-like targets in cluttered environment. Numerical simulations are provided to assist in understanding the relations between various aspects of the imaging method. Numerical evidence shows that for the same signal to noise ratio, higher order harmonics (second and third harmonics) resulting from nonlinear scattering, have better imaging resolutions compared to the fundamental harmonic corresponding to linear scattering.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • A Lung Sound Classification System Based on Data Augmenting Using
           ELM-Wavelet-AE

    • Authors: Berna ARI; Ömer Faruk ALÇİN, Abdülkadir ŞENGÜR
      Abstract: The method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Multiplicative Conformable Fractional Differential Equations

    • Authors: Sertaç GÖKTAŞ
      Abstract: In this study, multiplicative conformable fractional differential equations are presented. Wronskian concept, linear dependence-independence concepts are defined on multiplicative conformable fractional calculus and some theorems and results are given among them. Finally, some examples are solved by giving some methods for finding general solutions of multiplicative conformable fractional differential equations.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Efficient Text Classification with Deep Learning on Imbalanced Data
           Improved with Better Distribution

    • Authors: Beytullah YILDIZ
      Abstract: Technological developments and the widespread use of the internet cause the data produced on a daily basis to increase exponentially. An important part of this deluge of data is text data from applications such as social media, communication tools, customer service. The processing of this large amount of text data needs automation. Significant successes have been achieved in text processing recently. Especially with deep learning applications, text classification performance has become quite satisfactory. In this study, we proposed an innovative data distribution algorithm that reduces the data imbalance problem to further increase the text classification success. Experiment results show that there is an improvement of approximately 3.5% in classification accuracy and over 3 in F1 score with the algorithm that optimizes the data distribution.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Production and Characterization of AlNiOZnOp-SiAl Composite Photodiodes
           for Solar Energy Tracking Systems

    • Authors: Ezgi GÜRGENÇ; Aydin DİKİCİ, Fehmi ASLAN
      Abstract: In present study, NiO:ZnO thin films in molar ratios of 1:0, 0:1, 3:1, 1:1 and 1:3 were formed on p-Si layers with aluminum (Al) bottom contact. Dynamic sol-gel spin coating method was used as coating method. Al top contacts were deposited on thin films and Al/NiO:ZnO/p-Si/Al photodiodes were fabricated. The structural and morphological properties of the photodiodes were determined by X-ray diffraction (XRD), emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectroscopy (EDX). The photoresponse and electrical properties of the produced photodiodes were investigated by current–voltage (I–V) and capacitance-voltage (C-V) measurements. Al/NiO:ZnO/p-Si/Al photodiodes were successfully produced. It was determined that the thin films formed were composed of nanostructures. All photodiodes were found to be sensitive to light. It was seen that the photosensitivity of composite photodiodes was higher than the pure photodiodes and photosensitivity decreased as the ZnO ratio increased. It was determined that the most sensitive photodiode to light was the composite photodiode with a NiO:ZnO ratio of 3:1, and the highest photosensitivity was measured as 3.12 x 103 at 100 mW/cm2 light intensity in this photodiode. In all photodiodes, the capacitance values decreased as the frequency increased. The results show that by changing the NiO:ZnO ratio, the photoresponse and electrical parameters of the photodiodes can be controlled and the produced photodiodes can be used as a photosensor in solar tracking systems and optoelectronic applications.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • Vegetable and Fruit Image Classification with SqueezeNet based Deep
           Feature Generator

    • Authors: Mehmet BAYĞIN
      Abstract: Automatic classification of food products according to their types is one of the most common problems in computer vision. In this paper, 15 different types of vegetables were automatically classified through transfer learning in deep learning. The dataset used in the study is large and consists of 21,000 vegetable images. These images in the dataset are divided into 3 groups as training, testing and validation. Within the scope of the study, all of these groups were combined and a large dataset was obtained. SqueezeNet architecture is used for feature extraction in the developed deep learning-based machine learning model. In addition, the ReliefF method was used for feature selection and the most significant features were determined by eliminating negative features. In the classification phase of the developed application, Linear Discriminant Analysis (LDA) method was preferred. In this study, Hold-Out and 10-fold cross-validation techniques were used. Approximately 99% accuracy value was obtained in both validation techniques. The obtained results of the study show that the proposed method can be used successfully in automatic vegetable classification.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
  • A new dataset for EEG abnormality detection MTOUH

    • Authors: İrem TAŞCI; Burak TASCİ, Sengul DOGAN, Türker TUNCER
      Abstract: Elektroensefalogram (EEG), beyindeki elektriksel aktivitenin izlenmesi için yaygın olarak kullanılmaktadır. EEG sinyallerinin hekimler tarafından incelenmesi yorucu ve zaman alıcıdır. Bu nedenle, algılama doğruluğunu artırmak için makine öğrenme teknikleri kullanılabilir. Bu çalışmada 35 kanal, 10575x15 saniye normal ve 11240x15 saniye anormal EEG sinyalinden oluşan 2 sınıflı veri seti oluşturulmuştur. Bu very seti Turgut Özal Üniversitesi Malatya Eğitim Araştırma Hastanesi’ ne 2021 yılında başvuran hastaların EEG sinyalleri incelenerek elde edilmiştir. Çalışmada istatistiksel özellik çıkarımı tabanlı bir model önerilmiştir. Önerilen modele komşu bileşen analizi kullanılarak öznitelik vektörü indirgemesi yapıldıktan sonra destek vektör makineleri kullanılarak sınıflandırma yapılmıştır. 35 kanaldan en yüksek doğruluk P4O2 kanalında elde edilmiştir. P4O2 kanalı için doğruluk, duyarlılık, özgüllük, kesinlik ve f-skoru sırasıyla %81.3,%78.9, %83.7, %82.0 ve %80.4 olarak elde edilmiştir.
      PubDate: Sun, 20 Mar 2022 00:00:00 +030
       
 
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