In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.
This paper demonstrates the design of an algorithm to represent the design stages of fixturing system that serve in increasing the flexibility and automation of fixturing system planning for uniform polyhedral part. This system requires building a manufacturing feature recognition algorithm to present or describe inputs such as (configuration of workpiece) and built database system to represents (production plan and fixturing system exiting) to this algorithm. Also knowledge – base system was building or developed to find the best fixturing analysis (workpiece setup, constraints of workpiece and arrangement the contact on this workpiece) to workpiece.
Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Border
... Show MoreBackground: The adenomatoid odontogenic tumor is a relatively rare benign epithelial odontogenic tumor. It contains both epithelial and mesenchymal components. Few cases presented as an extrafollicular lesion or involve the mandible or associated with other odontogenic lesions. This paper represents a rare case of an extrafollicular AOT. Case presentation: A 24-year-old female had a painless swelling on the right side of the lower jaw since one-month duration. Intraorally there was a well defined fluctuant-blue swelling in the right alveolar premolar region measuring 1×2 cm obliterating the right lower buccal vestibule. Grade II mobility in the vital 44 and 45 teeth were observed. Panoramic radiographs showed a well-defined pear shaped
... Show MoreBackground: Bone regeneration in dehiscence and fenestration defect can be improved with the use of platelet rich fibrin (PRF) that provides a scaffold for new bone regeneration. This study was conducted to assess the effectiveness of PRF as a graft material and membrane in dehiscence and fenestration defects. Materials and Methods: This prospective clinical study included patients who received dental implants that demonstrated peri-implant defects which were augmented using Leukocyte- PRF (L-PRF) or Advanced-PRF (A-PRF). Twenty four weeks postoperatively the defect resolution and the density of regenerated bone were assessed by CBCT and re-entry surgery. The assessment also included measurement of primary and secondary implant stability
... Show MoreThe deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
... Show MoreThe research aims to recognize the impact of the training program based on integrating future thinking skills and classroom interaction patterns for mathematics teachers and providing their students with creative solution skills. To achieve the goal of the research, the following hypothesis was formulated: There is no statistically significant difference at the level (0.05) between the mean scores of students of mathematics teachers whose teachers trained according to the proposed training program (the experimental group) and whose teachers were not trained according to the proposed training program (the control group) in Pre-post creative solution skills test. Research sample is consisted of (31) teachers and schools were distribut
... Show MoreThis study investigates the impact of spatial resolution enhancement on supervised classification accuracy using Landsat 9 satellite imagery, achieved through pan-sharpening techniques leveraging Sentinel-2 data. Various methods were employed to synthesize a panchromatic (PAN) band from Sentinel-2 data, including dimension reduction algorithms and weighted averages based on correlation coefficients and standard deviation. Three pan-sharpening algorithms (Gram-Schmidt, Principal Components Analysis, Nearest Neighbour Diffusion) were employed, and their efficacy was assessed using seven fidelity criteria. Classification tasks were performed utilizing Support Vector Machine and Maximum Likelihood algorithms. Results reveal that specifi
... Show MoreThe study aims to show the correlation between inflation rates and the volume of private sector deposits in Jordan on one hand, and Amman Stock Exchange index for the period of 1999-2017. The study used the “Vector Auto Regression” model. Different types of tests are used such as: The “Johansen Co integration” test of joint integration, “Granger causality” test, the “Analysis of Variance Decomposition”, and “Impulse response Function” test.
The results showed there is a positive-one-way causal effect from Amman Stock Exchange index to inflation. Also, a one-way causal effect that comes from Amman Stock Exchange index to the size of private sec
... Show MoreThis research deals with the design and simulation of a solar power system consisting of a KC200GT solar panel, a closed loop boost converter and a three phase inverter by using Matlab / Simulink. The mathematical equations of the solar panel design are presented. The electrical characteristics of the panel are tested at the values of 1000 for light radiation and 25 °C for temperature environment. The Proportional Integral (PI) controller is connected as feedback with the Boost converter to obtain a stable output voltage by reducing the oscillations in the voltage to charge a battery connected to the output of the converter. Two methods (Particle Swarm Optimization (PSO) and Zeigler- Nichols) are used for tuning
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