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    An overview of FOPID controller design in v-domain: design methodologies and robust controller performance
    (Taylor & Francis Ltd, 2023) Tufenkci, Sevilay; Alagoz, Baris Baykant; Senol, Bilal; Matusu, Radek
    The complex v-plane is an emerging design domain for fractional order control system design. Recently, several works demonstrated the advantages of tuning FOPID controllers in v-plane. These approaches essentially perform the minimum angle pole placement to a target angle within the stability region of the v-plane and facilitate fractional order control system design tasks because of inherently guaranteed stabilisation of fractional order transfer functions. Accordingly, the optimal FOPID controller tuning problem can be simplified to placement of minimum angle system pole to a target point within the stability region of the v-plane. After reviewing previous v-domain design works, authors investigate prominent target points that can result in improved FOPID control performance for the v-domain design task. The consideration of target points in polar coordinates can provide two design parameters (angle and magnitude), which can convey essential system knowledge associated with the stability and control performance of FOPID control systems. In this perspective, effects of minimum angle pole positions on control performance indices are investigated in detail, and some prominent target points to manage FOPID design in v-domain have been reported. The v-domain design examples are illustrated to reveal the effects of the sampled pole positions on the robust control performance.
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    Classification of computerized tomography images to diagnose non-small cell lung cancer using a hybrid model
    (Springer, 2023) Demiroglu, Ugur; Senol, Bilal; Yildirim, Muhammed; Eroglu, Yesim
    Lung cancer arises from the abnormal and uncontrolled reproduction of parenchymal cells. Among all cancer cases, lung cancer is one of the prevailing types. Prevalence and death rates of the cancer increases day by day. From this point of view, early diagnosis and treatment of this cancer increases survival times and rates. The main idea in the development of the method presented in this publication is to increase the rate of early diagnosis. Computerized Tomography (CT) is the major screening method when encountered with suspicious symptoms. The cancer can be determined with CT and besides subtyping can be done. Diagnosing the disease with the human eye can sometimes lead to the emergence of deficiencies. This is one of the problems faced today. In this direction, the study in this paper presents a hybrid method to predict and diagnose the lung cancer from CT images to minimize potential human errors. Using the method, feature maps of the CT images of the dataset are obtained using the previously trained DarkNet-53 and DenseNet-201 deep model architectures. DarkNet-53 and DenseNet-201 architectures were chosen because they gave the best feature extraction results among 7 different architectures. The purpose of using the two architectures is to combine two high-performance models to create a hybrid classification method with high accuracy. Feature concatenation is applied to increase the diagnosis accuracy. To optimize the performance and computation cost of the proposed method, the Neighborhood Component Analysis (NCA) optimization method is used in determining and analysis of the features with more information. Therefore, features with less contribution in the accuracy are eliminated. Next, new feature maps are achieved by grading all features upon their weights and applying an elimination using a threshold value. The new feature maps are classified using Classical Machine Learning (CML) classifiers. Classification accuracies on DarkNet-53 architecture were calculated as 69.11% with SoftMax and 96.25% with Ensemble Classifiers and Nearest Neighbor Classifiers respectively. Similarly, accuracies on DenseNet-201 architecture were calculated as 68.29% with SoftMax and 97.39% with Ensemble Classifiers and Nearest Neighbor Classifiers respectively. With the proposed hybrid model, the Ensemble Classifiers reached the accuracy of 98.69% and the highest accuracy is achieved by using k-Nearest Neighbor Classifier (kNN) with the value of 98.86%. The results are supported with detailed illustrations.