Mobile phones are widely used nowadays, for different application such as wireless control and monitoring due to its availability and ease of use. The implemented system is based on "global system mobile (GSM)" network by using "short message service (SMS)". The design mainly contains a GSM modem and interfacing unit circuit with microcontrollers. This system could control up to eight different electrical devices such as light, Air conditioner, washing machine and many more applications which needed in daily life in different area (House, Office, or factory, etc.). The control is done by sending a specific SMS messages from traditional or smart phone. The controlling devices are restricted to a pre-defined phone number and are set in the software of the receiver. Also feedback status of eight devices can be requested in designed system. The designed receiver recovers syntax errors, like additional spaces, special symbols, and repeated letters.
The clutch system is essential for gently transmitting power from the engine to the gearbox, in addition to other tasks such as absorbing vibration, inertia reducing, and working as a protector against the overload for the motor and the components of power transmission. A new approach is developed for this research paper, to find a dry clutch system’s optimal design and operational parameters to improve performance and thermal behavior. Three design factors were selected to design the numerical tests based on the Taguchi method which are the friction material, the applied pressure function, and the dimensionless radius ratio R. A three-dimensional finite element model was developed, and the numerical cases were designed using the Taguchi
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An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to
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This research explores the obstacles teachers encounter in executing the smart schools initiative within the framework of Iraq, where educational facilities and digital preparedness are still at an early stage. Although worldwide trends reveal the growing use of smart technologies in education, Iraq has been hindered by systemic barriers, such as archaic curricula, restricted access to technologies, and an unqualified teaching staff. Data were collected using a validated questionnaire on 122 public school teachers working in Baghdad with a descriptive-analytical methodology. The study divided challenges into five areas: infrastructure, teacher preparedness, administrative support, curricular adaptation and cultural resistanc
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A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle
swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA)
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The modern steer-by-wire (SBW) systems represent a revolutionary departure from traditional automotive designs, replacing mechanical linkages with electronic control mechanisms. However, the integration of such cutting-edge technologies is not without its challenges, and one critical aspect that demands thorough consideration is the presence of nonlinear dynamics and communication network time delays. Therefore, to handle the tracking error caused by the challenge of time delays and to overcome the parameter uncertainties and external perturbations, a robust fast finite-time composite controller (FFTCC) is proposed for improving the performance and safety of the SBW systems in the present article. By lumping the uncertainties, parameter var
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This 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
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