arXiv subjects
Publications and source records attributed to Himanshu Jain.
In this paper we present a bottom up procedure for segmentation of text lines written or printed in the Latin script. The proposed method uses a combination of image morphology, feature extraction and Gaussian mixture model to perform this task. The experimental results show the validity of the procedure.
The primary objective of this paper is to highlight the need for and benefits of studying the steady state and dynamic response of power systems using three phase integrated transmission and distribution (T&D) system models (hereafter referred to as hybrid models). Modeling and simulation requirements for building and analyzing hybrid models are also discussed. Finally, results from steady state and dynamic simulations of a large hybrid model are presented to demonstrate insights that can be obtained from hybrid models which cannot be obtained from the study of transmission and distribution systems separately.
This paper presents a new method for studying electromechanical transients in power systems using three phase, combined transmission and distribution models (hybrid models). The methodology models individual phases of an electric network and associated unbalance in load and generation. Therefore, the impacts of load unbalance, single phase distributed generation and line impedance unbalance on electromechanical transients can be studied without using electromagnetic transient simulation (EMTP) programs. The implementation of this methodology in software is called the Three Phase Dynamics Analyzer (TPDA). Case studies included in the paper demonstrate the accuracy of TPDA and its ability to simulate electromechanical transients in hybrid models. TPDA has the potential for providing electric utilities and power system planners with more accurate assessment of system stability than traditional dynamic simulation software that assume balanced network topology.
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace. Still, leading embedding approaches have been unable to deliver high prediction accuracies or scale to large problems as the low rank assumption is violated in most real world applications. This paper develops the X-One classifier to address both limitations. The main technical contribution in X-One is a formulation for learning a small ensemble of local distance preserving embeddings which can accurately predict infrequently occurring (tail) labels. This allows X-One to break free of the traditional low-rank assumption and boost classification accuracy by learning embeddings which preserve pairwise distances between only the nearest label vectors. We conducted extensive experiments on several real-world as well as benchmark data sets and compared our method against state-of-the-art methods for extreme multi-label classification. Experiments reveal that X-One can make significantly more accurate predictions then the state-of-the-art methods including both embeddings (by as much as 35%) as well as trees (by as much as 6%). X-One can also scale efficiently to data sets with a million labels which are beyond the pale of leading embedding methods.
We show that strong non--linear electron transport in the ferromagnetic insulating (FMI) state of manganites, responsible for phenomena such as colossal electroresistance and current induced resistance switching, can occur due to a hot electron effect. In the FMI state, which we show is an insulator with a Coulomb gap, the temperature of the electron and lattice baths can decouple at high input power levels, leading to heating of the electron bath. Parameters of the hot electron effect model were independently determined via time dependence experiments and are in good agreement with the experimental values.
Colossal electroresistance (CER) has been observed in the ferromagnetic insulating (FMI) state of a manganite. Notably, the CER in the FMI state occurs in the absence of magnetoresistance (MR). Measurements of electroresistance (ER) and current induced resistivity switching have been performed in the ferromagnetic insulating state of a single crystal manganite of composition Nd$_0.7$Pb$_0.3$MnO$_3$ (NPMO30). The sample has a paramagnetic to ferromagnetic (Curie) transition temperature, Tc = 150 K and the ferromagnetic insulating state is realized for temperatures, T <~ 130 K. The colossal electroresistance, arising from a strongly nonlinear dependence of resistivity ($ρ$) on current density (j), attains a large value ($\approx 100%$) in the ferromagnetic insulating state. The severity of this nonlinear behavior of resistivity at high current densities is progressively enhanced with decreasing temperature, resulting ultimately, in a regime of negative differential resistivity (NDR, d$ρ$/dj < 0) for temperatures <~ 25 K. Concomitant with the build-up of the ER however, is a collapse of the MR to a small value (< 20%) even in magnetic field, H = 7 T. This demonstrates that the mechanisms that give rise to ER and MR are effectively decoupled in the ferromagnetic insulating phase of manganites. We establish that, the behavior of ferromagnetic insulating phase is distinct from the ferromagnetic metallic (FMM) phase as well as the charge ordered insulating (COI) phase, which are the two commonly realized ground state phases of manganites.