A control approach for aircraft Starter/Generator (S/G) with Permanent Magnet Machine (PMM) operating in Flux Weakening (FW) mode is presented. The proposed strategy helps the previous approaches which are adopted for the Variable Voltage Bus (VVB) or Voltage Wild Bus (VWB) concept for an aircraft Electric Power System (EPS), to cover a wide speed range in motoring and generation modes. Compared to prior works, the proposed control approach adjusts the q-axis reference voltage with a single current regulator, and the maximum available voltage provided by the converter is used to evaluate the d-axis voltage. By adopting the proposed approach, the DC bus voltage can be fully utilized, increasing aircraft efficiency by allowing the S/G system to operate at a wide range of speeds. The results of the analytical design and the performance of the system were verified by time-domain simulations using MATLAB/Simulink and experiments and compared to the conventional method.

This paper deals with innovative multi-function battery controller with seamless transition between controllers for future MEA platforms. The battery controller performs different functions i.e. providing DC power, maintaining DC bus voltage, controlling battery voltage and battery current for charging and discharging process purposes. Due to its superiority and simplicity of implementation, the constant current-constant voltage technique is chosen for battery charging. Furthermore, the study provides a detailed analysis of the proposed approach and control challenges then proposes the corresponding solutions. The PI controller is adopted for all controllers and it was designed to provide a robust control system behaviour. Therefore, the battery terminal voltage and charging current are efficiently limited by the corresponding battery controllers. A robust Energy Management Strategy (EMS) to supervise the charging and

this paper proposes a control scheme for permanent magnet machine (PMM) based aircraft starter/generator (S/G) operated in flux weakening (FW) mode. In contrast to previous publications, the proposed control scheme uses a single current regulator to adjust the q-axis reference voltage, and the d- axis voltage is calculated considering the maximum available voltage from the converter. This allows the full utilization of DC bus voltage and improves the machine efficiency. Furthermore, it is more robust against with regards to machine parameters variation. The analytical design results and the system performance are confirmed by time domain simulation and compared against traditional approach.

Most of the existing methods used to estimate the cable resistance require the use of many hardware devices and the injection of perturbations to the system. Therefore, they are time-consuming, costly and prone to errors. In addition, the injection of perturbations has the potential of degrading the power quality of the system. In this paper, a new artificial neural network (ANN) aided cable resistance estimation approach is proposed. The ANN model is trained by simulation data. The trained ANN model can quickly and effectively map the current sharing ratios between the converters to the droop coefficients of the converters. In this way, the optimal droop coefficient combination that will yield the desired accurate current sharing ratio between the converters can be predicted by the trained ANN model. Subsequently, the optimal droop coefficient combination can be used in the estimation of the corresponding subsystem 

The DC/DC converter control presents non-minimum phase behaviour in boost mode as its control plant has a Right Hand Plane (RHP) zero. The RHP zero restricts the achievement of high dynamic performance and make the control design a challenging task. This paper investigates the use of classic PI controller and Model Predictive Control (MPC) to regulate the converter output voltage to achieve high dynamic performance. The DC/DC converter under study is a non-isolated bidirectional DC/DC converter interfacing supercapacitors energy storage device to a DC Micro Grid(DCMG). The MPC control problem is formulated as a current regulation problem, which enables using a short prediction horizon and hence less computational power. The controllers design are compared and performance are verified using MATLAB software.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.

Cost overruns are a common problem in the global construction industry, which affects the development of road construction, particularly in developing countries. Moreover, many risk factors in road construction might result in project cost overruns, particularly during COVID-19. So, the research aimed to look into the new risk variables' severity when a coronavirus infection was present. The primary goal of the study is to identify the most important risk variables affecting the construction of roads in Egypt, especially during COVID-19 to lessen the likelihood and impact of those risks. The likelihood and effects of the identified risks were determined by conducting a questionnaire survey on a set of 11 risk categories made up of 162 risks. The study's findings also indicate that the excessive and illegal loads on the roads, the fluctuating value of the Egyptian pound, and the accruing interest on loans to the contractor as a result of the work interruption caused by the coronavirus are the high-risk factors that have the greatest impact on cost overruns for road projects. In addition, the cost matrix has also been used to display risk factor levels as a road map for responding quickly to high risks.