Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN) model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM) algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.

Agent-based technology has generated a lot of attention in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. Some problems in real world cannot be handled by a single agent. Multiple agents work together to accomplish some task. Although multi-agent systems (MASs) provide many potential advantages, they also present many difficult challenges. This paper illustrates the importance of communication for planning in a multi-agent setting by considering a grid world domain that consists of obstacles at different locations. This paper provides a theoretical framework that is validated by the experimental results. Performance analysis with respect to plan size and execution time is also reported.

The scaling of simple gate transistors requires the scaling and transistor elements like source/drain junction became difficult to scale further after a limit due to adverse effect of electrostatic and short-channel performance. The solution of the problem is tri-gate where we can increase the performance without increasing the width and without scaling. In this paper we have described the parameter of tri-gate and taking the high dielectric as substrate.

This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer’s perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers’ preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.

This paper highlights the significance of Time-Delay ANN models for predicting shelf life of processed cheese stored at 7-8oC. Bayesian regularization algorithm was selected as training function. Number of neurons in single and multiple hidden layers varied from 1 to 20. The network was trained with up to 100 epochs. Mean square error, root mean square error, coefficient of determination and nash - Sutcliffe coefficient were used for calculating the prediction capability of the developed models. Time-Delay ANN models with multilayer are quite efficient in predicting the shelf life of processed cheese stored at 7-8^oC.

Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.

In the present paper, we have been analyzed the high temperature variations testing in order to be used to determine light emitting diode lifetime, even though laser diode failure mechanisms are more sensitive to increases in current density. As a measured parameter of degradation, the current density is of great significance when searching for failure modes in a laser diode. Raising the current density however, is not really indicative of lifetime since it is more likely a situation to be avoided than one that simulates normal lifetime degradation. The reliability of semiconductor sources is very dependent on the degradation modes. This paper has investigated some of the degradation modes and capabilities of typical LEDs currently used in many communication and sensing systems over wide range of the affecting parameters. LED’s are typically used in multimode transmission systems where data rates no larger than 50 Mbit/sec are required. They have larger spectral widths and can add to the problem of dispersion in communications systems. Laser diodes are used in systems that require coherent and often single mode light such as high data rate communications and sensing applications.

With the increasing use of computing devices in day to day life, the need of user friendly interfaces has lead towards the evolution of different types of interfaces for human computer interaction. Real time vision based hand gesture recognition affords users the ability to interact with computers in more natural and intuitive ways. Direct use of hands as an input device is an attractive method which can communicate much more information by itself in comparison to mice, joysticks etc allowing a greater number of recognition system that can be used in a variety of human computer interaction applications. The gesture recognition system consist of three main modules like hand segmentation, hand tracking and gesture recognition from hand features. The designed system further integrated with different applications like image browser, virtual game etc. possibilities for human computer interaction. Computer Vision based systems has the potential to provide more natural, non-contact solutions. The present research work focuses on to design and develops a practical framework for real time hand gesture.