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Air data inertial reference system (ADIRS) parameters are very important for the safe flight of an aircraft. The ADIRS consists of air data inertial reference units (ADIRUs) and the air data reference (ADR) part of the ADIRU, which provides the air data [altitude (ALT), angle of attack (AOA), airspeed, and temperature information] examined in this study. ADR is essential to continuously ensure accurate and precise information to the flight management guidance computers (FMGCs), electronic flight instruments system (EFIS), and other systems on the aircraft for reliable and safe flight operation. This study estimated the ADR parameters (altitude, angle of attack, airspeed, and temperature) to obtain a synthetic air data system for data continuity in the event of any sensor failure on the aircraft using correlated data. According to correlation analysis, the angle of attack, computed airspeed (CAS), and static air temperature (SAT) data have the highest correlation with the stabilizer position (STAB), whereas the altitude data have the highest correlation with the low-pressure engine spool rotational speed (N1). The AOA, CAS, SAT, and ALT parameters were estimated by decision tree, support vector machine, and Gaussian process regression models using real flight data collected from a local airline. The Gaussian process regression model was better at generalizing the data set for data estimation than were the other machine learning methods used in this study. MATLAB version R2023a software was used in all operations. [ABSTRACT FROM AUTHOR]

The Transformer Rectifier Unit (TRU) in aircraft is DC power supplies. Three-phase AC power supplies are available as long as they work. DC power distribution, on the other hand, provides DC power directly to all DC users, providing input power to the emergency AC distribution system so that the static inverter can generate AC power. To the voltages produced by TRUs to be less oscillating and of higher quality, multi-pulse ones are studied and recommended for aircraft systems. In this study, Zener diode filtered multi-pulse rectifier, which can further reduce the oscillations in the output voltage, is recommended for aircraft. Three-phase half-wave rectifier design and analysis are presented to better demonstrate the effectiveness of Zener diode regulation in multi-pulse TRUs. First, the parts of the power system, which includes the rectifier circuit of a two-engine aircraft and fed by this rectifier, are given. The circuit structure and working order of the rectifier are given with mathematical explanations. In the application phase, low oscillating output voltages and currents are obtained by creating currents and voltages on the load for different Zener voltages. Conventional rectifiers and the proposed rectifier are tested on the same loads, and comparisons are done. [ABSTRACT FROM AUTHOR]

In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT and US. Among these imaging techniques, is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer-aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R-CNN architectures, which are common CNN-based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R-CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1-score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans. [ABSTRACT FROM AUTHOR]

This article explores the tensile deformation and failure characteristics of a hybrid composite where microscale shape memory alloy (SMA) fibers and nanoscale graphene nanoplatelets (GNPs) were used to reinforce a ductile epoxy matrix. First, the GNPs were incorporated into epoxy system using a hybrid dispersion technique at various weight percentages ranging from 0.25 to 2.50 wt%. The mechanical properties of GNP composites were investigated using monotonic uniaxial tensile tests to failure and were compared to neat epoxy. The GNP concentration that resulted in highest enhancement in tensile properties of the epoxy was identified. Then, the GNP/SMA/epoxy composites were fabricated using a vacuum assisted hand lay‐up technique. The composites were reinforced with SMA fibers at 50% volume ratio and an optimal GNP concentration was added. The tensile response of the developed composites was characterized using full‐field strain measurements captured by a digital image correlation system, while simultaneously the thermal response of the composites was monitored through measurements of an infrared camera. The tensile properties, superelastic response, and failure mechanisms of the developed composites were discussed. Results shows that the developed GNP/SMA/epoxy composites exhibit excellent superelastic characteristics with a maximum strength of 533 MPa and failure elongation of 12.5%. [ABSTRACT FROM AUTHOR]