Yazar "Toptas, Murat" seçeneğine göre listele

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    Activated carbon from walnut shell for Reactive Blue 19 dye removal
    (Taylor & Francis Ltd, 2025) Toptas, Yeliz; Toptas, Murat
    This study investigates the potential of activated carbon (AC) derived from walnut shell powder as an effective adsorbent for the removal of Reactive Blue 19 (RB 19) dye from aqueous solutions. The textile industry is a significant contributor to water pollution, primarily due to the discharge of synthetic dyes, which pose serious ecological and health risks. The research focuses on optimising the adsorption process by evaluating various parameters, including initial dye concentration and temperature. The AC was characterised using BET surface area analysis, revealing a high surface area of 2854.25 m2/g, which enhances its adsorption capacity. The adsorption kinetics were analysed using multiple models, with the Pseudo Second Order model providing the best fit, indicating that the adsorption rate is primarily dependent on the availability of active sites on the AC surface. Additionally, the study employed various isotherm models, including Langmuir and Freundlich, to describe the adsorption behaviour, confirming the complex nature of the process. The findings highlight the effectiveness of walnut shell-derived AC in wastewater treatment, promoting sustainable practices by utilising agricultural waste for environmental remediation. This research contributes valuable insights into optimising dye removal processes, ultimately supporting efforts to mitigate water pollution and enhance environmental sustainability.
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    Characterization and performance analysis of eco-friendly solid rocket propellant from Prunus armeniaca L. agricultural residues
    (Springer Heidelberg, 2025) Toptas, Murat; Toptas, Yeliz
    The increasing demand for sustainable energy solutions has prompted the aerospace industry to explore alternative fuels that minimize environmental impact. This study investigates the potential of utilizing apricot waste, a by-product of the agricultural sector, as a feedstock for solid rocket propellant (SRP) production. Through innovative conversion processes, including sulfurization and caramelization, apricot waste was transformed into a viable propellant. The resulting SRP exhibited favorable chemical properties, including a high calorific value of 1726 cal/g, indicating its potential for efficient energy release during combustion. Elemental analysis revealed a composition that is rich in oxygen, enhancing its eco-friendliness compared to traditional propellants. The moderate burn rate exponent (n approximate to 0.602) suggests a balanced performance, making it suitable for applications requiring controlled thrust profiles. This research not only addresses the environmental challenges associated with conventional propellants but also highlights the importance of repurposing agricultural waste, thereby promoting sustainable resource utilization and waste reduction.
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    Comparative and experimentally validated surrogate machine learning framework for predicting airflow velocity in EHD thrusters
    (Elsevier Science Inc, 2026) Toptas, Murat; Yilmaz, Mehmet
    Accurate prediction of electrohydrodynamic (EHD) airflow is essential for advancing flow-control strategies, thermal management concepts, and low-power propulsion systems. In hybrid electric-magnetic EHD configurations, the induced flow field arises from a complex interaction among electric field gradients, charge transport, space-charge-driven momentum transfer, and Lorentz-force modulation. These nonlinear couplings are difficult for conventional analytical or numerical approaches to capture with sufficient fidelity. This study presents an experimentally validated and data-driven surrogate modeling framework for predicting airflow velocity in a multi-needle EHD thruster. A structured dataset was generated by systematically varying emitter voltage, emitter-collector spacing, and solenoid excitation. Four supervised regression models-Random Forest, Gradient Boosting, K-Nearest Neighbors, and ensemble-based techniques-were trained using standardized preprocessing and k-fold cross-validation. Among them, Gradient Boosting achieved the highest accuracy with an R2 of 0.8961, MAE of 0.0859, and MSE of 0.0115. To enhance physical interpretability, SHapley Additive exPlanations (SHAP) based analysis was performed. The results showed that emitter voltage dominates EHD-induced momentum transfer, while geometric spacing and magnetic forcing produce secondary modulation of the ion-driven flow. SHAP interactions further indicated that magnetic-field effects intensify at higher electric field strengths, aligning with expected multiphysics coupling mechanisms. Independent testing at previously unseen operating points confirmed strong agreement between predicted and measured velocities. The proposed framework provides a fast, transparent, and computationally efficient alternative to conventional simulations, offering new insight into coupled electro-fluidic behavior. Overall, the study demonstrates the potential of interpretable machinelearning-assisted modeling to accelerate the design and optimization of advanced thermo-fluidic and flowcontrol systems.
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    Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space
    (Springer, 2022) Toptas, Buket; Toptas, Murat; Hanbay, Davut
    Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.
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    Improving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Toptas, Murat; Toptas, Buket; Hanbay, Davut
    Cell images play a vital role in biological research and medical diagnoses, as they provide valuable information about the structure and function of cells. Specifically, accurate segmentation of cell images is critically important for the detection of abnormal cells and the early diagnosis of various diseases. This paper introduces a transformative approach that integrates the Isotropic Undecimated Wavelet Transform into the input layer of established deep learning architectures such as U-Net, SegNet, and FCN, thereby enhancing their ability to accurately delineate cell boundaries without the need for data augmentation or intervention in the depth of network architectures. The proposed method significantly enhances the contrast between cells and the background, which is crucial for reliable segmentation. Extensive experiments conducted on two datasets demonstrate that the preprocessing with Isotropic Undecimated Wavelet Transform significantly boosts the performance of these architectures. On Dataset1, the U-Net model enhanced with Isotropic Undecimated Wavelet Transform achieved a global accuracy of 0.988, a mean Intersection over Union of 0.972, and a mean Dice coefficient of 0.971, outperforming all other metrics. On Dataset2, the SegNet model enhanced with Isotropic Undecimated Wavelet Transform achieved up to a global accuracy of 0.976, a mean Intersection over Union of 0.905, and a mean Dice coefficient of 0.959, showcasing the best performance across all metrics. The method's consistent success in improving segmentation across different datasets and architectures has been empirically validated through experimental studies.
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    Investigating the effects of Lorentz forces on electrohydrodynamic flow generated by corona discharge in a multi needle-to-cylinder configuration
    (Elsevier, 2025) Toptas, Murat; Yilmaz, Mehmet
    This study investigates the enhancement of electrohydrodynamic (EHD) flow velocity in a multi needle-tocylinder configuration using an electromagnetically assisted system under atmospheric conditions. An experimental setup was developed to measure airflow velocity, incorporating a corona discharge emitter, solenoid, and precise instrumentation. The impact of emitter voltage, solenoid voltage (magnetic field strength), and needle-tocylinder distance on airflow velocity was evaluated using factorial analysis. The results highlight the role of the solenoid-generated magnetic field in enhancing EHD flow velocity via Lorentz forces. The maximum air velocity of 2.10 m/s was achieved with a maximum emitter voltage of 20.63 kV, emitter distance of 18 mm, and solenoid voltage of 30 V. Applying Lorentz force increased air speed by 4.9-56.7 % for different emitter voltages and distances compared to zero solenoid voltage. With a solenoid voltage of 15 V, the increase ranged from 4.9 % to 35.5 %, and with 30 V, it ranged from 8 % to 56.7 %. The average velocity increase was 18.63 % for 15 V and 39.94 % for 30 V. At a fixed emitter voltage and distance, increasing the solenoid voltage enhanced velocity, demonstrating the influence of Lorentz forces on ion acceleration and momentum transfer to air molecules. Pareto analysis confirmed that both solenoid and emitter voltages significantly contribute to flow enhancement. These results highlight the importance of Lorentz forces in enhancing EHD flow and suggest that optimizing solenoid voltage could improve the performance of EHD-based technologies in applications like heat exchangers, cooling systems, and microfluidic devices.
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    Smoke Detection Using Texture and Color Analysis in Videos
    (Ieee, 2017) Toptas, Murat; Hanbay, Davut
    The delays in the detection of fire in fire detection systems continue to be a life threatening problem for living things. Techniques based on image processing have been developed in order to remove this problem and minimize the detection period. This study also focused on the smoke image that appeared before the flame at the time of the fire. Smoke detection can provide earlier notification than flame detection. In the first step of the proposed method, smoke zone was detected with YUV color space. After than the Gray Level Co-Occurrence Matrix (GLCM) was used to extract the features that represent the smoke images. At last, these features are used to classify the smoke and non-smoke space by using Support Vector Machines (SVM).