• ÖZ

Lightning continues to possess threats to the reliable supply of electrical energy by causing damages to electrical equipment. Lightning transient causes outages either by directly hitting electrical equipment or by the electromagnetic interference induced by it upon hitting the ground near to the equipment. An electrical equipment which is witnessing a rise in application over the years are the insulated power cables. Use of these cables are not only confined to transferring electrical energy over distances but they are also used as transformer winding in a type of transformer pioneered by ABB called powerformers. Using XLPE insulated power cables as a case in point, the present study seeks to unravel the trajectory of research undertaken so far in this area of transient behaviour of insulated power cables against lightning transients. In particular, attention has been focused on the response of insulated power cables against non-standard lightning transient voltage waveforms. This review brings to the light that while transients response against standard 1.2/50 µs lightning waveform is relatively well studied but more work needs to be further undertaken to fully appreciate the behaviour of insulted power cables against the non-standard lightning transient waveforms which are more prevalent in actual field.

• ÖZ

This study presents a nonlinear observer that estimates structured and unstructured uncertainties based on a control methodology that is an adaptive backstepping sliding mode controller to aim wheel slip tracking of a car-like robot. In the vehicle system, the scaling factor for the lengthwise force between tire and road contact is considered as unknown, then adaptation law with Lyapunov-based analysis is derived for the unknown parameter. The lump uncertainties estimated values that are estimated by the nonlinear disturbance observer and the scaling factor estimated values are directly applied in the control input. The closed-loop system stability under the proposed controller is proven using Lyapunov’s stability analysis. Then, the adaptive backstepping sliding mode controller's performance with the nonlinear disturbance observer is verified through simulations by comparing to the neural network (radial basis function) observer, which is estimated as the lump uncertainties on quarter-vehicle dynamics

• ÖZ

This paper aims to investigate a 3D chaotic system for applications on the secure communication system and random number generation. There exists a sinusoidal nonlinearity in the system making it uncommon of its type. Infinitely many chaotic attractors indicate multi-stability of the system, which is desired; for instance, the same system can be implemented for a multiple channel secure communication and switching between the channels can be achieved just by changing initial conditions. A brief mathematical analysis of the system is performed, and the circuit of the system is designed using active circuit elements. A synchronized system for secure communication is mathematically analyzed on MATLAB and simulated on PSPICE OrCAD. Synchronization of the system with the proposed circuit structure shows that this dynamic system can be used for chaotic communication. In addition, as an application of cryptography, a NIST* statistical test is performed on 10 bitstreams generated by the system. The bitstream produced has successfully passed all tests giving results in the length of the generated bit

• ÖZ

Malaria is known as an acute febrile disease caused by the bite of female Anopheles mosquitoes, and it manifests itself with symptoms such as headache, fever, chills, vomiting, and fatigue. The diagnosis of malaria is still based on manual identification of Plasmodium parasitized cells in microscopic examinations of blood cells known as parasite-based microscopy diagnostic testing. The accuracy of this manual diagnosis method is clearly affected by the level of microscopist’s experience, which makes this diagnosis method susceptible to manual error and time consuming. Diagnoses of diseases made using deep-learning methods have had great repercussions in the medical world, especially in recent years; and this indicates that the diagnosis of malaria can also be achieved by deep-learning methods. On the basis of this fact, this paper presents a novel deep-learning-based malaria disease detection technique. A convolutional neural network (CNN) architecture, which has 20 weighted layers is designed and proposed to identify parasitized microscopic images from uninfected microscopic images. A total of 27,558 thin blood cell images were used to train and test the CNN model, and 95.28% overall accuracy was obtained. The experimental results on large clinical dataset show the effectiveness of the proposed deep-learning method for malaria disease detection.

• ÖZ

Implanted hybrid three quasi Z source inverter has been designed to achieve high voltage gain for a single-stage power conversion system. The net input voltage applied to the converter is the sum of the voltages of four independent sources to which the converter is configured. It is a fact that the power from renewable energy sources will not be constant and varies. So there is uncertainty in the uniform availability of energy from renewable energy sources and the resulting voltage variations in any of the four sources affects net input voltage of the converter. A dual-loop control method has been developed to control the dc-link voltage across the converter to attain a constant ac output voltage across load terminals and meet load requirements. The controller maintains the stable operation of the converter in the absence of any of the four voltage sources. Simulation results using MATLAB software are presented for the validation of the proposed work under the conditions of input voltage variations as well as a sudden change of loads