Person: YILDIRIM, ALPER
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YILDIRIM
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ALPER
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Publication Open Access Fuzzy decision based modeling of rheostatic brake system for autonomous land vehicles(2022-09-01) YILDIRIM, ALPER; Sünkün S., Parlak B. O. , Yıldırım A., Yavaşoğlu H. A.The most fundamental characteristic of autonomous vehicles (AVs) is their autonomy. However, due to the dynamic operating environment of the vehicle, their control algorithms may make imprecise, approximate, and unreliable decisions. Therefore, there is a need for the creation of more robust driving algorithms, notably consistent obstacle avoidance algorithms. Occasionally, the vehicle must come to a complete stop in order to avoid obstacles. In this situation, the engine brake control of the car can be engaged. In this study, a fuzzy model was proposed to effectively brake autonomous land vehicles, with an electrical braking system known as rheostatic braking. Since a rheostatic braking system (RBS) is employed, the input values of the fuzzy controller for this designed modeling are vehicle speed and ground slipperiness, and the output value is the rheostat resistance value. In the developed fuzzy controller, Mamdani inference and Aggregation methods were utilized. In addition to these two methods, the fuzzy controller also provides the output of the centroid, bisector, average of the maximum, smallest of the maximum and largest of the maximum sharpening methods to the user. Finally, using the Python programming language and the Tkinter library, the graphical user interface displays the linguistic expression and membership degree of the user\"s inputs, the final fuzzy output graph, and the exact outputs from all clarification methods (GUI).Publication Open Access Parameter estimation of magnetic growing rod with output error method(2023-10-11) YILDIRIM, ALPER; Yildirim A., Akgün G., Çokatar S., Demir U.This study focuses on a magnetic controlled growing rods (MCGR) used in the treatment of early-onset scoliosis. In this study, the Lyapunov direct method-based output error method, which is iterative adaptive by online, is used as a parameter estimation method to predict the velocity of the telescopic bar during the MCGR distraction process. The system parameters are estimated. by the proposed model. The main purpose is to minimize the error between the actual and desired states by the online configuration of the controller’s parameters. By using online models, we can continuously update and refine system parameters to improve prediction accuracy.Publication Open Access Design of a toolbox for kinematic analysis of jansen's linkage(2022-09-01) YILDIRIM, ALPER; Sünkün S., Parlak B. O. , Yıldırım A., Yavaşoğlu H. A.Utilizing industrial robots is an efficient method for addressing the labor crisis and advancing industrial technologies. As a result, industrial robots are becoming increasingly popular. Additionally, the widespread use of industrial robots will increase the interest in robot propulsion mechanisms. Legged robots should be primarily investigated because of their potential advantages. Among leg mechanisms, Jansen\"s linkage (JL) has gained popularity due to its organic walking motion, scalable design, and simple drive by rotary input. However, the highly nonlinear nature of JL makes its analysis challenging. The research provides a user-friendly toolbox design that visualizes the toe trajectory and simultaneously calculates the step height by performing a kinematic analysis of the JL using the user-supplied link lengths. In this way, the study contributes significantly to the design phase of legged robots and reduces the amount of time required.Publication Open Access Investigation of UWB-IMU sensor fusion for indoor navigation with DoE(2023-01-01) DEMİR, UĞUR; AKGÜN, GAZİ; YILDIRIM, ALPER; Durmus S., DEMİR U., AKGÜN G., YILDIRIM A.This study presents an evaluation of the optimal parameter configuration for Ultra-Wide Band (UWB) - Inertial Measurement Units - (IMU) based sensor fusion for indoor localization in Non-Line-of-Sight (NLOS) environments. The study employs the least squares method to predict position using UWB technology. Subsequently, sensor fusion techniques combining UWB and IMU are employed, utilizing the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms to enhance position estimation. To mitigate the effects of noise in IMU data, a high-pass filter is applied before feeding the data into the EKF and UKF. The experimental findings are then evaluated using Design of Experiment (DoE) techniques, and the optimal parameter configurations are analysed using linear regression. This study provides insight into the parameter settings that yield improved accuracy and robustness in UWB-IMU sensor fusion for indoor localization in NLOS scenarios.Publication Open Access The impact of the covid-19 pandemic on pediatric mental health emergency(2023-01-01) YILDIRIM, ALPER; Fındık O. T. P., Barin G. G., YILDIRIM A., Fiş N. P.© 2023, AVES. All rights reserved.Objective: The aim of this study was to compare pre/post-coronavirus disease 2019 pandemic changes in mental health-related visits to the pediatric emergency department. Materials and Methods: We conducted a retrospective analysis of all mental health-related pediatric emergency department visits to a tertiary general hospital between June and September 2019, 2020, and 2021. We described pre/post-coronavirus disease 2019 changes in the use of pediatric emergency departments, such as timing of visits, sex discrepancies, diag-nostic distribution, discharge planning, and others. Results: Compared with the corresponding months before the pandemic (n = 187), mental health-related pediatric emergency department visits decreased by 20.8% in June–September 2020 (n = 148) and increased by 12.2% in 2021 (n = 210). The distributions of age, sex, timing of visits, reasons for presentations, hospitalization, and outpatient clinic appointment rates were not statistically significant between the years. Self-harm in females and aggression/violence in males were the most common reasons for presentation to pediatric emergency departments in each year. In the post-pandemic period, ambulance use and patients referred by other hospitals for psychiatric consultation increased, while the completion time of consultations decreased (P <.05). The frequency of attention-deficit hyperactivity disorder and depression decreased, but obsessive-compulsive disorder and anxiety disorders were more common in the post-pandemic period than in the corresponding months before the pandemic (P <.05). Conclusion: Our results suggest that the coronavirus disease 2019 pandemic resulted in a significant change in mental health-related visits to the pediatric emergency department. Those in the groups with reduced visits may be at risk for delayed access to treatment for their mental and behavioral difficulties.Publication Open Access EMG sinyallerinin derin öğrenme ile hareket sınıflandırması(2022-09-18) AKGÜN, GAZİ; YILDIRIM, ALPER; DEMİR, UĞUR; KAPLANOĞLU, ERKAN; Akgün G., Yıldırım A., Demir U., Kaplanoğlu E.Bu çalışmada EMG sinyalleri üzerinde öznitelikler hesaplanmıştır. Bu öznitelikler ile el hareketlerini sınıflandırmak için derin öğrenme algoritmaları kullanılmıştır. Bir zaman serisi olarak toplanan EMG sinyalleri üzerinde zaman alanında hesaplanan öznitelik vektörleri belirli boyutlarda simetrik matrisler olarak kaydedilmiştir. Yeniden oluşturulan ve resim dosyası formatında kaydedilen veri seti ile Evrişimsel Sinir Ağı eğitilmiştir. Bu eğitim sonucunda tüm veriler ile %93, test verileri ile %79 başarı ile hareket sınıflandırması gerçekleştirilmiştir.Publication Open Access Predisposing Variables in Children with Risk of Disruptive Mood: A Clinical Case–Control Study(2023-01-01) GÖKSU, MUHSİNE; YILDIRIM, ALPER; GÖKSU M., YILDIRIM A., Tüğen L. E.Disruptive mood dysregulation disorder (DMDD) is defined in the DSM-V as frequent, severe temper outbursts that significantly impair functioning in different environments. This was a second-stage of follow-up study, conducted to screen the frequency of DMDD in an elementary school. In the firststage of our study, 453 children between ages 7–11 were evaluated in terms of DMDD high-risk with Children Behavior Check List (CBCL). Of the children, 30 high-risk and 30 low-risk children for DMDD according to CBCL agreed to participate this clinical case–control study. Diagnoses of anxiety disorder, attention deficit and hyperactivity disorder (ADHD), and oppositional defiant disorder were more common among children in the high-risk group than the control group. Symptom Checklist-90-Revised (SCL-90-R) mothers’ interpersonal, anger, and paranoid subscale scores were higher in the DMDD highrisk group than the control group. Children in the DMDD high-risk group scored higher than the control group in all SRS subscales. In the Diagnostic Analysis of Nonverbal Accuracy (DANVA) test, the DMDD high-risk group had higher error rates for fearful and intense facial expressions. Multiple linear regression analysis showed that having a diagnosis of ADHD, high maternal SCL-90-R anger score, and presence of a paternal psychiatric diagnosis increased the high-risk of DMDD.Publication Open Access Neural network and IoT-based test maneuver deployment for 2 DoF vehicle simulator(2023-05-15) DEMİR, UĞUR; AKGÜN, GAZİ; YILDIRIM, ALPER; AKÜNER, MUSTAFA CANER; DEMİRCİ B., DEMİR U., AKGÜN G., YILDIRIM A., AKÜNER M. C., ÖZKAN M.This paper presents the driving scenarios deployment for 2 DoF (Degree of Freedom) vehicle simulator based on IoT (Internet of Things) and Neural Network. The controller structure is chosen as Neural Network-based controller is preferred as the transferring appropriate accelerations in 3 axes in the 2 DoF manipulator evokes a nonlinear problem. Due to the microcontroller used in the vehicle simulator to perform Neural Network calculations has limited processing capacity and speed, IoT-based computing and data transferring are chosen. Firstly, an open-loop measurement is performed to identify the vehicle simulator and to generate the training data for the neural network. Thereafter the acceleration data on the axes and the control signals are logged. Secondly, the neural network training is carried out with the logged data. Finally, the trained neural network was tested with various driving maneuvers. And the measurements are evaluated.Publication Metadata only Relationship between blood neutrophil amount and LDL and estrogen(2023-01-01) KAHRAMAN, MERVE MERİÇ; YEGEN, BERRAK; YILDIRIM, ALPER; ŞAHİN, ALİ; Sevim M., Altinoluk T., KAHRAMAN M. M., Akgun T., ŞAHİN A., Ozekici H. N., Fil A., Yildiz B., Yuceturk B., Lale E. N., et al.