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.

Ground leakage current elimination in a transformerless unipolar modulation based single phase grid-connected photovoltaic system

Abstract: Unipolar sinusoidal pulse width modulation (SPWM) full-bridge inverter poses advantages such as higher dc voltage utilization, smaller current ripple in the filter inductor, and higher processing efficiency. However, unipolar SPWM full-bridge inverter causes time varying common-mode voltage. The variable common-mode voltage excites leakage current through the parasitic capacitance between the PV array and the ground, which restricts its application in transformerless grid-connected photovoltaic (PV) inverter. In order to solve this problem, an optimized full-bridge topology is proposed in this paper. The topology uses an additional branch to be clamped the potential of the freewheeling path to the half of the input voltage in the freewheeling period, which guarantees no variable common-mode voltage in the unipolar SPWM full-bridge inverter. The proposed topology poses advantages such as higher efficiency, simple switching, and topological simplicity as compared to existing topologies. The proposed topology has been verified in MATLAB/Simulink environment with satisfactory results.

An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks

Abstract: Intrusion detection is a critical issue in the wireless sensor networks (WSNs), specifically for security applications In literature, many classification algorithms have been applied to address the intrusion detection problems However, their efficiency and scalability still need to be improved This paper proposes an improved convolutional deep belief network-based intrusion detection model (ICDBN_IDM), which consists of a redundancy detection algorithm based on the convolutional deep belief network and a performance evaluation strategy The redundancy detection can remove non-effective nodes and data, and save the energy consumption of the whole network The improved algorithm extracts features from normal and abnormal behaviour samples by using unsupervised learning and overcomes the problem of unknown or less prior samples Compared with the commonly used machine learning mechanisms, the proposed ICDBN_IDM achieves high intrusion detection accuracy, reduces the ratio of the false alarm while saving the energy consumption of sensor nodes

A Study of Drilling Behavior of Unidirectional Bamboo Fiber-Reinforced Green Composites

Abstract: In this study, the biodegradable composite (unidirectional bamboo fiber/polylactic acid) was developed by means of film stacking technique in a hot compression molding setup. The drilling characteristic of the biodegradable composites was experimentally studied by varying different factors. The effect of three different input factors such as feed (8, 16, and 22.4 mm/min), speed (710, 1400, and 2000 rpm), and drill geometry (8-facet, dagger, and slot drill) on the drilling forces (thrust force and torque) was studied. The signals of drilling-induced forces were found to be different for the different drill geometries studied. The slot drill induces minimum forces (thrust force and torque) while making a hole in the composites among all drill geometries. The experimental results reveal that drilling-induced forces reduce at high spindle speed and low feed.