National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

Optimized Vector Control Strategy for Dual-Rotor Axial Flux Permanent Magnet Synchronous Motor for in-Wheel Electric Drive Applications

Abstract: This paper analyzes the performance of dual-rotor axial flux permanent magnet synchronous motor for its application in electric motor drive. Dual-rotor single-stator topology is employed for analysis due to its superior features compared to other topologies of the motor for electric drive application. Constant torque angle control strategy with hysteresis current controller for inverter switching is implemented with the axial flux motor and is analyzed for validation. To enhance the robustness of the control strategy, the coefficients of proportional-integral controller are optimized with particle swarm optimization algorithm using Matlab/Simulink software to minimize the torque and speed ripples obtained from the conventional setting of proportional-integral controller. The performance analysis of the motor drive with optimized controller coefficients is carried out using Ansys co-simulation with Maxwell and Simplorer softwares. The simulation analysis of the motor with optimized constant torque angle strategy shows good motor performance and robustness which is inferred from results.

The Effect of Lightning on Insulator Flashover Characteristics

Abstract: Lightning transient has detrimental effect on electrical system. This paper discusses the effect of lightning on insulator flashover characteristics in railway traction mast. The dependence of footing resistance and soil ionization on flashover behavior is explored. The CI GRE model of lightning stroke is considered for analysis. The modeling and simulation is carried out using Electro-Magnetic Transient Program (EMTP) software. It is observed that footing resistance strongly affects the insulator flashover characteristics. With increase in footing resistance, insulator is most likely to flashover. Leader development model is used to understand leader propagation along insulator surface causing flashover. The non-linear behaviour of footing resistance due to soil ionisation is seen to increase the flashover time and occurs at the tail end of lightning stroke. Further, the effect of double exponential lightning stroke model is studied. It is observed that flashover occurs early with CIGRE model compared to double exponential model.

Melanoma Classification Through Transfer Learning by the Analysis of Skin Lesion Images

Abstract: Malignant melanoma is a form of skin cancer that develops from melanocytic cells in the human body and can prove to be dangerous if not treated early. With the advent of artificial intelligence, deep learning approaches have been applied for the diagnosis of the disease which in turn shall help medical professionals in the line of treatment. In this paper, transfer learning is used by implementing the pre-trained VGG19 Net model. The model is pre-trained with ImageNet dataset. Certain layers of the pre-trained model are used for the analysis of the dataset of our interest by freezing rest of the convolutional layers. Here, skin lesions images from the ISBI2016 challenge (Gutman et al. in skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging, (Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC), [1]) dataset are considered for the classification purpose. Proposed classification method records a commendable validation accuracy of 81.33% along with a testing accuracy of 86.67%, precision of 95.08%, recall of 82.25%, IoU of 78.26% and Dice score of 85.29%.

Highly Linearized GaN HEMT Based Class E/F3 Power Amplifier

Abstract: The study of a GaN based mixed mode Class E/F3 power amplifier is presented in this article. For large signal simulations, we used the Cree transistor's nonlinear device model CG2H40010F and simulation is done at Keysight’s Advanced Design System (ADS) platform. The harmonic balance (HB) simulator is run to select the most appropriate impedances for efficiency and linearity of PA. The simulation results are used to describe and analyze a Class E/F3 GaN-HEMT PA with lumped components operating at 3 GHz. The output power Pout at 1 dB compression is 40.23 dBm with a gain greater than 10 dB, drain efficiency (DE) ranges from 70% to 86% according to simulation results.