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Jha T.K.,BITS Pilani K K Birla Goa Campus | Panda K.C.,Sambalpur University
Pramana - Journal of Physics | Year: 2014

Recent observations of high mass pulsar PSRJ1614-2230 has raised serious debate over the possible role of exotics in the dense core of neutron stars. The precise measurement of mass of the pulsar may play a very important role in limiting equation of state (EoS) of dense matter and its composition. Indirectly, it may also shape our understanding of the nucleon-hyperon or hyperon-hyperon interactions which is not well known. Within the framework of an effective chiral model, we compute models of neutron stars and analyse the hyperon composition in them. Further related implications are also discussed. © Indian Academy of Sciences.


Gepperth A.R.T.,ENSTA ParisTech | Hecht T.,ENSTA ParisTech | Gogate M.,BITS Pilani K K Birla Goa Campus
Cognitive Computation | Year: 2016

We present a system for performing multi-sensor fusion that learns from experience, i.e., from training data and propose that learning methods are the most appropriate approaches to real-world fusion problems, since they are largely model-free and therefore suited for a variety of tasks, even where the underlying processes are not known with sufficient precision, or are too complex to treat analytically. In order to back our claim, we apply the system to simulated fusion tasks which are representative of real-world problems and which exhibit a variety of underlying probabilistic models and noise distributions. To perform a fair comparison, we study two additional ways of performing optimal fusion for these problems: empirical estimation of joint probability distributions and direct analytical calculation using Bayesian inference. We demonstrate that near-optimal fusion can indeed be learned and that learning is by far the most generic and resource-efficient alternative. In addition, we show that the generative learning approach we use is capable of improving its performance far beyond the Bayesian optimum by detecting and rejecting outliers and that it is capable to detect systematic changes in the input statistics. © 2016 Springer Science+Business Media New York


Sheth C.,BITS Pilani K K Birla Goa Campus | Venkatesh Babu R.,Indian Institute of Science
ACM International Conference Proceeding Series | Year: 2014

Super pixels, which are a result of over-segmentation provide a reasonable compromise between working at pixel level versus with few optimally segmented regions. One fundamental challenge is that of defining the search space for merging. A naive approach of performing iterative clustering on the local neighborhood would be prone to under segmentation. In this paper, we develop a framework for generating non-compact super pixels by performing clustering on compact super pixels. We define the optimal search space by generating both over-segmented and under-segmented clustering of compact super pixels. Using this spatial information of the under-segmented scale, we look to improve the over-segmented scale. Our work is based on performing Kernel Density Estimation in 1D and further refining it using angular quantization. In all we propose three angular quantization formulations to generate the three scales of segmentation. Our results and comparison with the state-of-the-art super pixel algorithms show that merging a large number of super pixels with our algorithm is able to provide better results than using the underlying super pixel algorithm to obtain a smaller number of super pixels. Copyright 2014 ACM.


Mohanty D.K.,BITS Pilani K K Birla Goa Campus | Singru P.M.,BITS Pilani K K Birla Goa Campus
International Journal of Heat and Mass Transfer | Year: 2014

A local linear wavelet neural network based model has been developed to predict the temperature differences on both the tube and shell side and the heat exchanger efficiency. This network replaces the straightforward weight by a local linear model. The working process of the proposed network can be viewed as to decompose the complex, nonlinear system into a set of locally active submodels and then smoothly integrate those submodels by their associated wavelet basis functions. For a given approximation or prediction problem with sufficient accuracy, the local linear models provide more power than a constant weight model as the dilation and translation parameters of LLWNN are randomly generated and optimized without predetermination. The closeness of the predicted results with the actual experimental results and higher accuracy with maximum error of 1.25% indicates that LLWNN can be used as a suitable tool for simulation of heat exchangers subjected to fouling. © 2014 Elsevier Ltd. All rights reserved.


Mohanty D.K.,BITS Pilani K K Birla Goa Campus | Singru P.M.,BITS Pilani K K Birla Goa Campus
Korean Journal of Chemical Engineering | Year: 2012

Through proper monitoring, problems can be identified and isolated well before the economics of the process are threatened. In contrast to most conventional methods, fouling can be detected when the heat exchanger operates in transient states. Statistical analysis is used to develop a fouling growth model of a heat exchanger subjected to fouling. The statistical analysis was considered for four different types of distributions out of which the lognormal distribution was found to be most suitable. Experiments were conducted with a single pass shell and tube heat exchanger with water both as the hot and cold fluids. The results show that the proposed tool is very effective in detecting critical fouling in a heat exchanger, which can be utilized for predicting the optimal maintenance schedule. Hence, the results of this work can find application in predicting the reduction in heat transfer efficiency due to fouling in heat exchangers that are in operation and assist the exchanger operators to plan cleaning schedules. © 2012 Korean Institute of Chemical Engineers, Seoul, Korea.

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