Majlesi Journal of Electrical Engineering
Volume:17 Issue: 4, Dec 2023

Switched Boost Inverter (SBI) operates in buck-boost mode with an extensive spectrum of output voltage obtainable from a specific source voltage. Also, this structure provides superior protection against Electromagnetic Wave Interference (EMI) juxtaposed with the traditional impedance source inverter. The number of energy storage elements in this structure has been reduced, which increases the efficiency of the converter in low-power applications, reducing cost, size, and weight. In this paper, this inverter is analyzed and investigated in different operating modes and steady-state. Also, three different pulse width modulation (PWM) control techniques are presented for this inverter. Simulation results are presented in PSCAD-EMTDC software to validate the calculated relationships and confirm their performance in three different switching methods.

Over the past few years, many electrical vehicle manufacturers have focused on developing enhanced controller efficiency for Four-Wheel Drive (FWD) systems. The control of the speed and direction of the FWD is crucial for safe and efficient operation, particularly in challenging maneuvers. The FWD system movements in straight routes and during maneuvers, turning all four wheels right and left, have not been well covered. Therefore, a robust control design that is capable of controlling the FWD system at an optimal time of operation is highly required. In this research, a Finite Time Control (FTC) is designed, implemented, and simulated to improve the robustness and performance of the FWD system during challenging maneuvers. The proposed FTC controls both the speed and direction of all wheels of FWD according to the route situations. The proposed FTC is compared with an FWD system that is controlled by a traditional Proportional, Integral, and Derivative (PID) controller during straight moving and maneuvers. The comparison is based on controlling parameters such as settling time, maximum overshoot, and speed error values. The results showed that the proposed FTC has a much faster settling time, significantly less maximum overshoot, and much lower error values than the PID controller. These factors are considered the main features of the contribution of any controller system that aims for optimal and robustness and FTC proved to have them adequately.

The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as “pay-as-per-usage”. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN). The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration.

In this paper, an adaptive controller is presented to control a quadrotor, whose parameters are extracted from the genetic algorithm optimization method. The advantage of this method is that based on the system states, the control coefficients are calculated online. For this purpose, a function between system states-space and control coefficients is obtained. From the database collected from the genetic algorithm optimization method, the parameters of the control coefficient function are obtained using the least squares method. The stability of the proposed controller is proved by the Lyapunov method. Finally, the performance of the proposed controller is compared with the PID controller, which is widely used in the literature. The results show that the proposed approach is promising.

Among the agricultural products, mushroom is one of the best candidates for robotic harvesting methods. One of the main problems of mushroom growers is a critical need for labor within the specified time for harvesting mushrooms, so mushroom growing is labor intensive. In this research, we attempted to develop and apply a harvesting robot for this crop to reduce the problems that growers face in terms of harvesting labor. For ease of end-effector movement between shelves, the robot was developed by Cartesian Mechanism. This robot used image processing and a computer expert system to detect the position of mushrooms on the substrate on the shelf. The end-effector acts by using a suction cup and a non-shear mechanism to harvest mushrooms from the substrate. By testing this robot on the substrate, we could harvest perfect whole mushrooms on average of 81.5% of mushrooms on the prepared substrates. Harvesting time per mushroom was obtained 12.45s, which is the amount of respective time of 8.45s more than harvesting time by labor hands, but in robotic harvesting, robots unlike humans can work 24 hours a day, continuously during growing time. An expert system also could be a valuable asset to change the grower’s strategy in terms of harvested mushrooms size and quality based on customer needs in the market.

Assistive Technology (AT) is designed to aid elderly individuals and those with disabilities in overcoming tasks that may pose challenges or be inaccessible without support. Despite substantial research and innovation dedicated to the advancement of AT, opportunities for further improvement persist in this domain. This study aims to enhance existing technology by proposing an innovative, efficient, and environmentally friendly outdoor laundry garment hanging and retrieval system. The proposed system employs the OMRON CPM1A PLC system as the central controller for the motorized mechanism, offering a straightforward yet intelligent approach. The prototype harnesses solar power to operate the automated clothesline system, contributing to energy and carbon emissions reduction, and promoting energy efficiency and cost-effectiveness, hence improving sustainability. The prototype allows both manual and automatic modes for controlling the DC motor's actions in extending or retracting the scissor-like cloth hanger. In manual mode, a push-button switch governs the cloth hanger's movement, while automatic mode relies on input signals from rain and temperature sensors to dictate its behavior. The DC motor will operate to extend the hanger (for drying) whenever the rain sensor detects no water droplets, or, the light sensor detects more than 150 lux, or, the temperature is greater than 24.5℃. Otherwise, the motor will move to retract the hanger back into its original position when these criteria are the opposites. This proposed solution not only reduces physical strain for elderly and disabled users during laundry drying but also contributes to their enhanced well-being, accessibility, and improved quality of life.

This paper focuses on the development of a Graphical User Interface (GUI) and Artificial Neural Network (ANN) for the prediction of photovoltaic (PV) power output. PV power is generated based on the time, location, and surrounding climate conditions. Therefore, solar power generation predictions using computational methods are needed since the changing weather, which will impact the output power will not generate according to its rating. The objectives of this research are to predict photovoltaic power output at Universiti Tun Hussein Onn Malaysia (UTHM), develop an ANN configuration that can perform the prediction of solar power generation, and design GUI system that can both perform the calculations of power generation and ANN. In order to test the efficiency and reliability, MATLAB software has been used to develop the GUI and ANN, and the output is compared with the proposed mathematical equations. The real data as input data was obtained from the PV solar panel located at GSEnergy Focus Group fertigation site. The GUI with user-friendly features and ANN have been successfully designed and developed which can perform daily prediction of solar power output. On top of that, the results have shown that the ANN predictions are more precise to the real data than the GUI.

The functioning of a thermoelectric system in a stationary mode is often inefficient because it does not allow flexible control of the temperature regime. The design of thermoelectric devices and systems in transient modes requires identifying their dynamic models, first of all, the control object – the Peltier thermoelectric module. As a rule, well-known identification techniques involve calculating the parameters of a fractional-rational transfer function (or, equivalently, an autoregressive model) of the object of control. At the same time, the increase in the accuracy of identification requirements is associated with significant computational costs. The proposed identification technique requires the determination of the time dependencies of the control current and temperature deviation, the calculation of their spectra based on piecewise linear approximation, and the calculation of the transfer coefficient as the ratio of the spectra of the output and input signals. The calculated relations of piecewise linear approximating functions, spectral densities, and time forms of input and output parameters of the model under study are presented. The amplitude and phase frequency response of the Peltier module are calculated. The low RMS error in the identification of the amplitude-frequency characteristic and phase-frequency characteristic showed the effectiveness of the proposed identification technique.