Özet:Bu çalışmada balina sürüsü algoritması kullanılarak ölü zamanlı sistemlerin denetlenmesinde kullanılan Smith Öngörücüsü için PID denetleyicisinin parametreleri belirlenmiştir. Ayrıca Smith Öngörücüsü için kullanılan Zeigler-Nichols veya AMIGO yöntemleri ile yapılan PID tasarımında kontrol işareti sınırlandırılmamaktadır. Bu kısıt aşımı ise kontrol işaretini oluşturan aktüatöre zarar verebilmektedir. Bu çalışmada balina sürüsü algoritması kullanılarak kontrol işaretinin sınırlandırılması yapılarak ölü zamanlı sistemler için ISE performans kriterine göre Smith Öngörücülü PID denetleyici tasarımı yapılmıştır. Yapılan denemeler ile balina sürüsü algoritması ile tasarlanan PID denetleyicisinin daha başarılı olduğu görülmüştür.
Abstract:In this study, parameters of PID controller for Smith predictor used to control time delay systems were determined using whale optimization algorithm. Furthermore, the control signal is not constrained in the PID design by Ziegler-Nichols or AMIGO methods used for Smith Predictor. This constraint violations can damage the actuator. PID controller was designed with Smith predictor according to ISE performance criteria for time delay systems by limiting the control signal by using the whale optimization algorithm. Experiments have shown that the PID controller designed with the whale optimization algorithm is more successful.
Deep learning is a powerful technique that has been applied to the task of stroke detection using medical imaging. Stroke is a medical condition that occurs when the blood supply to the brain is interrupted, which can cause brain damage and other serious complications. Detection of stroke is important in order to minimize damage and improve patient outcomes. One of the most common imaging modalities used for stroke detection is CT(Computed Tomography). CT can provide detailed images of the brain and can be used to identify the presence and location of a stroke. Deep learning models, particularly convolutional neural networks (CNNs), have shown promise for the task of stroke detection using CT images. These models can learn to automatically identify patterns in the images that are indicative of a stroke, such as the presence of an infarct or hemorrhage. Some examples of deep learning models used for stroke detection in CT images are U-Net, which is commonly used for medical image segmentation tasks, and CNNs, which have been trained to classify brain CT images into normal or abnormal.
The purpose of this study is to identify the type of stroke from brain CT images taken without the administration of a contrast agent, i.e. occlusive (ischemic) or hemorrhagic (hemorrhagic). Stroke images were collected and a dataset was constructed with medical specialists. Deep learning classification models were evaluated with hyperparameter optimization techniques. And the result segmented with improved Unet model to visualize the stroke in CT images. Classification models were compared and VGG16 achieved %94 success. Unet model was achieved %60 IOU and detected the ischemia and hemorrhage differences.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.