Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different metrics have been adopted to evaluate the proposed classifier's effectiveness: accuracy, precision, recall, Matthews Correlation Coefficient (MCC), and F1-Score. Compared with a convolutional neural network (CNN), the simulation results confirmed that the DSNN model could enhance traffic classification accuracy by 5%. The efficiency of the priority model also has been demonstrated in terms of Round Trip Time (RTT).
Active vibration control is the main problem in different structure. Smart material like piezoelectric make a structure smart, adaptive and self-controlling so, they are effective in active vibration control. In this paper piezoelectric elements are used as sensors and actuators in flexible structures for sensing and actuating purposes, and to control the vibration of a cantilever beam by using sliding mode control. The sliding mode controller (SMC) is designed to attenuate the vibration induced by initial tip displacement which is equal to 15 mm. It is designed based on the balance realization reduction method where three states are selected for the reduced model from the 24th states that describe the c
... Show More<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, t
... Show MorePosition control of servo motor systems is a challenging task because of inevitable factors such as uncertainties, nonlinearities, parametric variations, and external perturbations. In this article, to alleviate the above issues, a practical adaptive fast terminal sliding mode control (PAFTSMC) is proposed for better tracking performance of the servo motor system by using a state observer and bidirectional adaptive law. First, a smooth-tangent-hyperbolic-function-based practical fast terminal sliding mode control (PFTSM) surface is designed to ensure not only fast finite time tracking error convergence but also chattering reduction. Second, the PAFTSMC is proposed for the servo motor, in which a two-way adaptive law is designed to further s
... Show MoreThe drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the
Introduction: Infection control or hospital-acquired infections are the major concern of the health care system and agencies. Critical care nurses are on the first-line contact with the patients, so on, they are most vulnerable to acquired infections. It is really important to regularly check their knowledge and practices concerning infection control. Objectives: The study aims to identify the impact of years’ experience on nurses’ knowledge and practices concerning infection control in three hospitals and center (Baghdad teaching hospital, Ibn Al-Nafees hospital, and Ibn al-Bitar center) Methodology: Cross-sectional study was conducted, the study starting from 4th of July 2020 to 13th of November 2020. Non-probability (purposive) sampl
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The current research aims to demonstrate the relationship of correlation and influence between the independent variable strategic control through its dimensions represented by (organizational structure, human resources management, commitment to specialization, defining powers and responsibilities, values and integrity) and the dependent variable the performance of the insurance company, and the degree of arrangement of these dimensions according to their importance, as well as Detection of significant differences in the sample's response to the questionnaire paragraphs in the researched company, and the research problem
... Show MoreBanks face many of the various risks: which are of dangerous phenomena that cause the state achieved a waste of money and a threat to future development plans to be applied to reach the goals set by: prompting banks and departments to find appropriate solutions and fast: and it was within these solutions rely on Banking risk management and effective role in defining and identifying: measuring and monitoring risk and trying to control and take risks is expected to occur in order to encircle and make it in within acceptable limits: and try to avoid them in the future to reduce the losses that are likely to be exposed to the bank: and it began to emerge and dominate a lot of legislation that seeks to structure the year risk management and t
... Show MoreThis paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performan
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