Electrostatic-discharge (ESD) is a process of high voltage, strange E-field, transient high current pulse, which would disturb the electronic equipment by conduction and radiation. So, the effects of ESD on electronic equipment have become more and more serious and the question of ESD protection is paid more attention to. In this paper, we research how the coupled field changes with the change of contact discharge voltage on the external wall of the Metal Shield of the electronic system by using Matlab and finite-difference time-domain (FDTD) method.

Engineering Systems performance usually degrades over time; when shocks exist, the degradation could be faster. This paper analyzes the stochastic performance of the deteriorating systems when degradation and shocks are involved. Deterioration is caused by aging subject to gamma process, sudden damages modeled by Poisson process and additional damages modeled by Poisson square wave process. The effect of two shocks and their arrival times are statistically independent. The failure time of the system is obtained using Monte Carlo Simulation when the cumulative damage exceeds the breaking threshold. The most commonly used graphical presentation of the empirical reliability function is obtained on the basis of the simulated dataset. A numerical example with sensitivity analyses is illustrated for the proposed model and simulation approach.

In photovoltaic systems, electricity is generated through solar cells. A single cell generates very minimal power. Several such cells are suitably connected to obtain a feasible value of power. Such an arrangement is called a module. Several such arrangements are again suitably connected to get the desired power. In this study, analysis of a photovoltaic (PV) system is carried out to obtain maximum power output under fixed radiation and variable temperature conditions. The modelled system consists of three different parts. The first one is the generation part that is formed by a solar PV array. The second part is a power electronic interface. For the present study, the system is taken to be a dc system and hence, the interface is a DC-DC converter. Three such DC-DC converters are analyzed in the present study. The third part is the load, which is taken to be a pure resistance in the study as the system is a DC system. It is evident that the output of a PV system is depended on solar irradiation falling on the PV arrangement as well as the ambient temperature. Therefore, the system will be more effective if it is operated on the maximum power point. In this regard, the power electronic interface helps the system obtain maximum power point tracking (MPPT). In the present study, a controller based on Perturb and Observe (P&O) method is used to control the duty ratio of the power electronic interface so that the system runs on the maximum power point position.

Storing and transmission of images over the Internet have become a great concern nowadays keeping limited resources in hand. To conquer these issues, one of the solutions that have proved to be correct is image compression. Image compression techniques try to reduce the redundant information from an image with minimum loss of quality. In this paper, Ant lion optimization (ALO) and Particle swarm optimization (PSO) are hybridized as a proposed algorithm to compress an image of RGB subject. Both image encoding and decoding processes are simulated in our experiments. Results show that the compression ratio and PSNR of various images using the proposed algorithm are much higher than the ones using individual algorithms.

Nowadays, wireless power transfer (WPT) has received a lot of attention due to its inherent advantages, such as convenience, safety, low maintenance, weather proof, etc. However, the parameter tuning is critical in conventional techniques of L2CL - LCL compensation WPT systems as they have difficulty in system designing and poor capability of higher order harmonic suppression. In order to overcome these deficiencies, a novel machine learning NSGA-II (Non-dominated Sorting Genetic Algorithm) based optimization topology has been proposed for wireless power transfer. This is a derivative free open-source circuit optimizer which designs circuits simpler than ever before. It optimizes the drawing current and system efficiency as specified by the user. Implementation was carried out using the Python Language under the simulator of "Python Power Electronics". To verify the efficiency and feasibility of the proposed NSGA-II optimization method, it is compared with the conventional tuning of the L2CL-LCL Compensation topology. The overall efficiency of the proposed system has been increased from 95.2% to 98.5% compared to the conventional method.

Medical image processing encompasses the utilization of three dimensionally reconstructed images from CT or MRI scans to diagnose pathologies, surgical planning, and radiation dose calculation. The 3D reconstructed images help the radiologists and surgeons to better understand the complicated internal anatomy. This paper proposes a novel technique of using the curve fitting process through optimization for the three dimensional reconstruction of a brain tumor. The curve fitting process achieves accurate reconstruction by estimating the boundaries as well as the corner points of the model. The process of optimization in the curve fitting process is carried out using the new Levy Walk Trajectory based Seagull Optimization Algorithm which combines the merits of Levy walk with the metaheuristic Seagull Optimization Algorithm to obtain better accuracy. The boundary fitting is precisely done by considering the minimization of root mean square error among original and fitted boundaries. Finally, performance of the adopted method is validated over other existing schemes with respect to curve fit analysis and convergence analysis.

Recommendation Systems have been well established to reduce the problem of information overload and have become one of the most valuable tools applicable to different domains like computer science, mathematics, psychology etc. Despite its popularity and successful deployment in different commercial environments, this area is still exploratory due to the rapid development of social media which has accelerated the development of social recommendation systems. This paper addresses the key motivation for social media sites to apply recommendation techniques, unique properties of social recommendation systems, classification of social recommendation systems on the basis of basic models, comparison with existing traditional recommender systems, key findings from positive and negative experiences in applying social recommendation systems. Consequently, the aim of this paper is to provide research directions to improve the capability of social recommendation systems including the heterogeneous nature of social networks, understanding the role of negative relations, cold-start problems, integrating the cross-domain data and its applicability to a broader range of applications. This study will help the researchers and academicians in planning future social recommendation studies for designing a unified and coherent social recommendation system.