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.

Effect of Fiber Orientation on the Tensile and Wear Properties of Flax Fiber-Reinforced Composites

Abstract: The demand for the uses of natural fiber-reinforced composites is increasing in various fields of engineering due to the global environmental concern. Flax fiber is one of the potential fiber used as reinforcing material in polymer composites. The present work studies the influence of fiber orientation angle (45°, 60°, and 90°) on the tensile and wear characteristics of the developed flax fiber-reinforced epoxy composites. The wear tests were performed by varying the applied normal loads of 10–30 N at a perpetual sliding gap of 3000 m and speed of 2 m/s under dry atmospheric condition. The results showed that the maximum wear occurs for the composites having fiber orientation angle of 45° and tensile strength is higher for the composites having fiber orientation angle of 90° in comparison to the other composite specimens.

A Comparative Analysis of Supervised Classifiers Employing NCA for Feature Selection to Secure Generation Control

Abstract: The advancement of modern grids and its excessive dependency on information and communication networks have paved the way for cyber-attacks in the power grid. Automatic Generation Control (AGC) system is one such field in the power grid which is extensively vulnerable to cyber-attacks. Attackers may disrupt the AGC functionality and degrade system stability by falsifying the tie-power and frequency deviation data sent by remotely installed sensors. There may exist several ways of data falsification, such as by injecting step, ramp, random, pulse, replay and stealthy signals to the original healthy data. To address the issue, this paper studies and compares the performance of nine different supervised classifiers in detecting such FDI attacks in the AGC loop. As per the experimental results, Gaussian SVM, KNN, AdaBoost and Random Forest showed similar and satisfactory performance, followed by Decision Tree. However, classifiers such as Linear SVM, Polynomial SVM, Logistic Regression and Naive Bayes' failed to effectively classify normal and compromised instances. Further, the performance comparison is conducted by exposing the test data to various levels of channel noise. It is found that, when the SNR is below 30 dB, the overall performance of the classifiers are significantly affected; however, the Precision of Positive class is less impacted by the presence of noise.

A Better Understanding of the Unimolecular Dissociation Dynamics of Weakly Bound Aromatic Compounds at High Temperature: A Study on C6H6–C6F6 and Comparison with C6H6 Dimer

Abstract: Chemical dynamics simulations are performed to study the unimolecular dissociation of the benzene (Bz)–hexafluorobenzene (HFB) complex at five different temperatures ranging from 1000 to 2000 K, and the results are compared with that of the Bz dimer at common simulation temperatures. Bz–HFB, in comparison with Bz dimer, possesses a much attractive intermolecular interaction, a very different equilibrium geometry, and a lower average quantum vibrational excitation energy at a given temperature. Six low-frequency modes of Bz–HFB are formed by Bz + HFB association which are weakly coupled with the vibrational modes of Bz and HFB. However, this coupling is found much stronger in Bz–HFB compared to the same in the Bz dimer. The simulations are done with very good potential energy parameters taken from the literature. Considering the canonical (TST) model, the unimolecular dissociation rate constant at each temperature is calculated and fitted to the Arrhenius equation. An activation energy of 5.0 kcal/mol and ...