National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Liquid−Liquid Equilibrium of Poly(ethylene glycol) 6000 + Triammonium Citrate + Water Systems at Different Temperatures

Abstract: Liquid−liquid equilibrium for an aqueous two-phase system containing poly(ethylene glycol) 6000 + triammonium citrate + water was studied at five different temperatures, (25, 30, 35, 40, and 45) °C. The binodal curve was fitted to an empirical equation relating the concentrations of PEG 6000 and triammonium citrate, and the coefficients were estimated for the respective temperatures. The effective excluded volume values were obtained from the binodal data. Tie line compositions were estimated and correlated using Othmer−Tobias and Bancroft equations, and the parameters are reported.

Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach

Abstract: In this paper, a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is proposed. This technique overcomes the incapability of the existing series expansion methods to approximate multimodal probability distributions. A mix of Gaussian, non-Gaussian, and discrete type probability distributions for input bus powers is considered. Probability distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion method. At the same time, multiple input correlations are considered. Performance of the proposed method is demonstrated in IEEE 14 and 57 bus test systems. Results are compared with cumulant and Gram Charlier expansion, cumulant and Cornish Fisher expansion, dependent discrete convolution, and Monte Carlo simulation. Effects of different correlation cases on distribution of bus voltages and line power flows are also studied.

Stone Columns with Vertical Circumferential Nails: Laboratory Model Study

Abstract: This paper presents results from a series of laboratory plate load tests carried out in unit cell tanks to investigate the improvement in stiffness, load carrying capacity and resistance to bulging of stone columns installed in soft soils. A new method of reinforcing the stone columns with vertical nails installed along the circumference of the stone column is suggested for improving the performance of these columns. Tests were carried out with two types of loading (1) the entire area in the unit cell tank loaded, to estimate the stiffness of improved ground and (2) only the stone column loaded, to estimate the limiting axial capacity. It is found that stone columns reinforced with vertical nails along the circumference have much higher load carrying capacity and undergo lesser compression and lesser lateral bulging as compared to conventional stone columns. The benefit of vertical circumferential nails increases with increase in the diameter, number and depth of embedment of the nails. The improvement in the performance of stone column was found to be more significant, even with lower area ratio. It is found that reinforcing stone column with vertical circumferential nails at the top portion to a depth equal to three times the diameter of stone columns, will be adequate to prevent the column from excessive bulging and to improve its load carrying capacity substantially.

Students’ affective content analysis in smart classroom environment using deep learning techniques

Abstract: In the era of the smart classroom environment, students’ affective content analysis plays a vital role as it helps to foster the affective states that are beneficial to learning. Some techniques target to improve the learning rate using the students’ affective content analysis in the classroom. In this paper, a novel max margin face detection based method for students’ affective content analysis using their facial expressions is proposed. The affective content analysis includes analyzing four different moods of students’, namely: High Positive Affect, Low Positive Affect, High Negative Affect, and Low Negative Affect. Engagement scores have been calculated based upon the four moods of students as predicted by the proposed method. Further, the classroom engagement analysis is performed by considering the entire classroom as one group and the corresponding group engagement score. Expert feedback and analyzed affect content videos are used as feedback to the faculty member to improve the teaching strategy and hence improving the students’ learning rate. The proposed smart classroom system was tested for more than 100 students of four different Information Technology courses and the corresponding faculty members at National Institute of Technology Karnataka Surathkal, Mangalore, India. The experimental results demonstrate the train and test accuracy of 90.67% and 87.65%, respectively for mood classification. Furthermore, an analysis was performed over incidence, distribution and temporal dynamics of students’ affective states and promising results were obtained.