This 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 performance and achieve the desired output. In addition, there is a minimization for the tracking voltage error to zero value of the Buck converter output, especially when changing a load resistance by 10%.
This work presents the use of laser diode in the fiber distributed data interface FDDI networks. FDDI uses optical fiber as a transmission media. This solves the problems resulted from the EMI, and noise. In addition it increases the security of transmission. A network with a ring topology consists of three computers was designed and implemented. The timed token protocol was used to achieve and control the process of communication over the ring. Nonreturn to zero inversion (NRZI) modulation was carried out as a part of the physical (PHY) sublayer. The optical system consists of a laser diode with wavelength of 820 nm and 2.5 mW maximum output power as a source, optical fiber as a channel, and positive intrinsic negative (PIN) photodiode
... Show MoreThe paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.
This paper presents a control system to make the robotic hand mimic human hand motion in real time and offline mode. The human hand tracking system is a wearable sensing arm (potentiometers) used to determine the position in space and to sense the grasping task of human hand. The maskable sensing arm was designed with same geometrical arrangement of robotic hand that needs to be controlled. The control software of a robot was implemented using Visual Basic and supported with graphical user interface (GUI). The control algorithm depends on joint to joint mapping method to match between the motions at each joint of portable sensing arm with corresponding joint of a robot in order to make the robot mimic the motion.
This research discusses the subject of emotional stimulation factors through the stages of product design interaction with the consumer and what they reflect in the formation of a self-consideration of the design in his emotional memory regarding the specific brand through the following question (What is the emotional stimulus in the design of certain products and its reflection on the consumer during the processes of receiving and using it?), The importance of the research comes in the connection of design elements with many sensory factors used and motivating to attract the consumer to the acquisition of a specific design and the demand for its experience, the study aims Detection and access to emotional stimulation in the design of th
... Show MoreThe investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o
... Show MoreThis paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
Moreover, the proposed controller i
... Show MoreThis study thoroughly investigates the potential of niobium oxide (Nb2O5) thin films as UV-A photodetectors. The films were precisely fabricated using dc reactive magnetron sputtering on Si(100) and quartz substrates, maintaining a consistent power output of 50W while varying substrate temperatures. The dominant presence of hexagonal crystal structure Nb2O5 in the films was confirmed. An increased particle diameter at 150°C substrate temperature and a reduced Nb content at higher substrate temperatures were revealed. A distinct band gap with high UV sensitivity at 350 nm was determined. Remarkably, films sputtered using 50W displayed the highest photosensitivity at 514.89%. These outstanding optoelectronic properties highlight Nb2O5 thin f
... Show MoreIn this work, multilayer nanostructures were prepared from two metal oxide thin films by dc reactive magnetron sputtering technique. These metal oxide were nickel oxide (NiO) and titanium dioxide (TiO2). The prepared nanostructures showed high structural purity as confirmed by the spectroscopic and structural characterization tests, mainly FTIR, XRD and EDX. This feature may be attributed to the fine control of operation parameters of dc reactive magnetron sputtering system as well as the preparation conditions using the same system. The nanostructures prepared in this work can be successfully used for the fabrication of nanodevices for photonics and optoelectronics requiring highly-pure nanomaterials.