ANN (Artificial Neural Network) Controlled Virtual Laboratory Design for NdFeB Magnet Production

Magnets have an important place in electrical and electronic systems and applications nowadays. The developments in the field of magnets have also greatly expanded their usage areas. NdFeB magnets play active and important role in this development. In this study, design of virtual laboratory to be used for the production of nanocomposite NdFeB magnets has been realized. Maximum energy product (BHmax) is an important value for permanent magnets. The high BHmax value in small volume for the magnets is a desired criterion. In the study, mathematical functions were created from the data related to Br (permanent magnetism), Hc (magnetic coercivity), BHmax, Tc (Curie temperature) and density obtained in the researches on different NdFeB alloys in the laboratory. Additionally, Br functions were obtained by adding different additives (Co,Ti, Zr, Hf, V, Ta, Nb, Cr, W, Mo, Mn, Ni, Sb, Sn, Ge, Al, Bi) to the NdFeB magnets. A virtual laboratory is prepared with the created functions. The obtained results from the operation of the virtual laboratory system and the results obtained from Matlab Simulink and ANN (Artificial Neural Network) systems are compared. The designed and performed virtual laboratory system can be used both for industrial purposes and for educational purposes

The frequency of Duchenne muscular dystrophy/Becker muscular dystrophy and Pompe disease in children with isolated transaminase elevation: results from the observational VICTORIA study

IntroductionElevated transaminases and/or creatine phosphokinase can indicate underlying muscle disease. Therefore, this study aims to determine the frequency of Duchenne muscular dystrophy/Becker muscular dystrophy (DMD/BMD) in male children and Pompe disease (PD) in male and female children with isolated hypertransaminasemia.MethodsThis multi-center, prospective study enrolled patients aged 3–216 months with serum alanine transaminase (ALT) and/or aspartate transaminase (AST) levels >2× the upper limit of normal (ULN) for ≥3 months. Patients with a known history of liver or muscle disease or physical examination findings suggestive of liver disease were excluded. Patients were screened for creatinine phosphokinase (CPK) levels, and molecular genetic tests for DMD/BMD in male patients and enzyme analysis for PD in male and female patients with elevated CPK levels were performed. Genetic analyses confirmed PD. Demographic, clinical, and laboratory characteristics of the patients were analyzed.ResultsOverall, 589 patients [66.8% male, mean age of 63.4 months (standard deviation: 60.5)] were included. In total, 251 patients (188 male and 63 female) had CPK levels above the ULN. Of the patients assessed, 47% (85/182) of male patients were diagnosed with DMD/BMD and 1% (3/228) of male and female patients were diagnosed with PD. The median ALT, AST, and CPK levels were statistically significantly higher, and the questioned neurological symptoms and previously unnoticed examination findings were more common in DMD/BMD patients than those without DMD/BMD or PD (p < 0.001).DiscussionQuestioning neurological symptoms, conducting a complete physical examination, and testing for CPK levels in patients with isolated hypertransaminasemia will prevent costly and time-consuming investigations for liver diseases and will lead to the diagnosis of occult neuromuscular diseases. Trial RegistrationClinicaltrials.gov NCT04120168

Detecting spots on apricots due to coryneum beijerinckii disease with image processing methods

Bu tezde, görüntü bölütleme algoritmaları ile kayısılarda Yaprak Delen (Çil) Hastalığı sonucu meyve üzerinde oluşan lekeler (çiller) tespit edilmiş ve kayısıdaki kaliteyi yorumlamaya yönelik görüntü işleme teknikleri kullanılmıştır. Görüntü işleme tabanlı gerçek zamanlı çalışan bir sınıflandırma sistemi uygulanmıştır. Görüntü işleme kısmında, görüntü iyileştirme yöntemleri ve renk tabanlı bir bölütleme işlemi yapılmıştır. Bu tezde geliştirilen görüntü işleme yöntemi ile kayısının leke olmayan, olgunlaşmasından veya diğer sebeplerden dolayı oluşan kırmızılıkların, ikili görüntüde oluşturduğu yanılsama lekeler morfolojik işlemlerle en aza indirgenerek, morfolojik süzgeç çıkışındaki görüntüde daha yüksek doğrulukta tespit gözlemlenmiştir. Bilinen temel bir bölütleme yöntemi ile sonuçlar karşılaştırılmış ve daha iyi sonuçlar elde edilmiştir. Kayısıda yaprak delen hastalığı sebebiyle kayısı üzerinde oluşan lekeler (çiller) kayısı kalitesinde önemli bir yer tutmaktadır. Bu tezde geliştirilen yöntem ile lekelerin tespiti ve kayısı yüzeyinde kapladığı alan sonucu kayısıda kalite sınıflandırması yapılabilir ve daha doğru fiyatlandırma belirlenebilir.In this thesis, on apricots, spots emerged on fruits because of Coryneum beijerinckii disease are detected with image segmentation algorithms and image processing techniques are used for interpretation the quality of apricots. A real-time image processing-based classification system is implemented. Image enhancement methods and color-based segmentation processing are used in image processing section. With the image processing method which was developed in this thesis, misconcepted spots emerged from redness due to maturing or other reasons that are observed on the binary image are decreased by morphological processing at the morphological filter output, we increase the detection accuracy. The results were compared with a known basic segmentation method and better results were obtained. Spots emerged on apricots due to Coryneum beijerinckii disease have an important role in apricot quality. The method that was developed in this thesis, is able to detect the spots and the area that is covered by spots on the apricot skin, hence, it let us to perform a quality classification for determination of better pricing

Effect of Hilbert-Huang transform on classification of PCG signals using machine learning

Heartbeat sounds are biological signals used in the early diagnosis of cardiovascular diseases. Digital heartbeat sound recordings, called phonocardiogram (PCG), are used in the determination and automatic classification of possible heart diseases. Healthy and pathological PCG signals are non-stationary signals and conventional feature extraction methods are insufficient in classifying these signals. In this study, PCG signals in healthy and four pathological categories are decomposed into intrinsic mode functions (IMFs) by Hilbert-Huang transform. Mel-frequency cepstral coefficient (MFCC) features were extracted from each mode to investigate the effect of the modes obtained by Hilbert-Huang transform on the classification of PCG signals. Genetic algorithm was used as feature selection method and k-nearest neighbor (KNN), multilayer perceptron (MLP), support vector machine (SVM) and deep neural network (DNN) machine learning methods were used as classifier. We have implemented multi classifications of five PCG classes (healthy, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse) by using 5-fold cross validation and 10 × 5-fold cross validation Data Analysis Protocol (DAP) framework. The results show that the DNN model provides the highest classification performance with 98.9% precision, 98.7% recall, 98.8% F1-score and 98.9% accuracy using 5-fold cross validation, and Matthews correlation coefficient of 0.981 using the DAP method. © 2021 The Author