(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Reinforcement learning (RL) has now evolved as a major technique for adaptive optimal control of nonlinear systems. However, majority of the RL algorithms proposed so far impose a strong constraint on the structure of environment dynamics by assuming that it operates as a Markov decision process (MDP). An MDP framework envisages a single agent operating in a stationary environment thereby limiting the scope of application of RL to control problems. Recently, a new direction of research has focused on proposing Markov games as an alternative system model to enhance the generality and robustness of the RL based approaches. This paper aims to present this new direction that seeks to synergize broad areas of RL and Game theory, as an interesting and challenging avenue for designing intelligent and reliable controllers. First, we briefly review some representative RL algorithms for the sake of completeness and then describe the recent direction that seeks to integrate RL and game theory. Finally, open issues are identified and future research directions outlined. © 2009 Elsevier B.V. All rights reserved.

World University root node

Electrical Engineering

Applied Soft Computing Journal

Synergizing reinforcement learning and game theory-A new direction for control

Dynamic axial displacement of high voltage (HV) winding disk of a power transformer has been calculated by Green's function approach, when time varying axial electromagnetic forces corresponding to worst case of short circuit condition, are applied. An elaborate analysis of the axial electromagnetic forces and corresponding axial displacement of a top-end disk of HV winding is carried out. Under the action of these time varying forces, the winding moves in the axial direction as a function of time between radially placed insulation spacers. Between the spacer supports the winding sections can be considered as clamped-clamped (C-C) beams. Static Green's function of such a beam structure is determined first, and convolving it with axial electromagnetic force determines the axial displacement (bending) at any point on the beam for the purpose of verification of the methodology. Dynamic Green's function of the same winding sector is subsequently derived, which is used to obtain displacement of the winding section by convolving it with an applied time varying axial electromagnetic force. The results are verified with commercial FEM software. © 2010 - IOS Press and the authors.

International Journal of Applied Electromagnetics and Mechanics

Analysis of winding disk in transformer under action of axial electromagnetic forces by Green's function approach

Synthesis of 14-membered macrocyclic complexes of cobalt(II) and nickel(II) of the type [M(mac1-3)] are reported. The complexes have been characterized on the basis of analytical, metal conductivity, magnetic susceptibilities, infrared, electronic, spectral data and electrochemical studies. Infrared spectral data show that the ligands behave in a diabasic tetradentate manner in all the complexes. Electronic spectral data and magnetic moment value are indicative of square planar geometry. The nickel complexes were red or orange to yellow in colour, whereas cobalt(II) complexes were chocolate brown in colour. High dilute conditions were maintained to dispense with polymerization reaction. The thermodynamic parameters E1/2 are found within the range -1.34 to -1.50 V.

Chemical Engineering

Asian Journal of Chemistry

Synthesis, spectroscopic and electrochemical studies of Cobalt(II) and Nickel(II) macrocyclic complexes derived from Oxalodihydrazide and Aldehydes/Propanone

The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented. © 2010 IEEE.

Proceedings - International Conference on Pattern Recognition

Document image retrieval using feature combination in Kernel space

This manuscript presents a new multiscale framework for the analysis of failure of thin heterogeneous structures. The new framework is based on the asymptotic homogenization method with multiple spatial scales, which provides a rigorous mathematical basis for bridging the microscopic scales associated with the periodic microstructure and thickness and the macroscopic scale associated with the in-plane dimensions of the macrostructure. The proposed approach generalizes the Caillerie-Kohn-Vogelius elastostatic heterogeneous plate theory for failure analysis when subjected to static and dynamic loads. Inelastic fields are represented using the eigendeformation concept. A computationally efficient n-partition computational homogenization model is developed for simulation of large scale structural systems without significantly compromising on the solution accuracy. The proposed model is verified against direct 3D finite element simulations and experimental observations under static and dynamic loads. {\textcopyright} 2010 The Author(s).

Civil Engineering

Fulltext

A multiscale failure model for analysis of thin heterogeneous plates

For sequential design processes, the min-max strategy minimizes the worst-case performance cost. This is a game against nature, where the agent attempts to minimize a specified cost criterion, while nature attempts to maximize it. In this paper, we formulate the problem of decision making under uncertainty as a game in which the opponent (nature) is " disinterested" and plays at random, while the agent forces to pick a strategy that maximizes the probability of wining. The potency of proposed worst-case design strategy for games against nature has been established through simulation experiments on inverted-pendulum swing-up. Simulation results show the accelerated learning, and better relative stability of the system. ©2010 IEEE.

Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

Markov game based control: Worst case design strategies for games against nature

Non-Destructive Testing Conference 2010, NDT 2010

Matched energy comparison of the pulse, lock-in and frequency modulated thermography NDE techniques

Cu-doped Zn1-xCdxS nanoparticles were synthesized by coprecipitation technique in ice bath at 280 K. The band gap energy of Zn 1-xCdxS:Cu nanoparticles can be tuned to a lower energy by increasing the Cd content, indicating the formation of the alloyed nanoparticles. The alloy structure is further supported by the systematic shifting of characteristic x-ray diffraction peaks to lower angles with increase in Cd content. Systematic copper doping induces a red shift in the energy band gap of Zn0.9Cd0.1S:Cu nanoparticles with increase in copper concentration. Cu-doped Zn0.9Cd0.1S nanoparticles were found to have ferromagnetic nature at 5 K whereas undoped particles were found to be diamagnetic. Green luminescence further proves proper doping of Cu into the ZnCdS matrix. It is believed that the green luminescence originates from the recombination between the shallow donor level (sulfur vacancy) and the t2 level of Cu2+. This method provides an inexpensive and simple procedure to produce ternary ZnCdS:Cu nanoparticles with tunable optical properties via changing Cd and/or Cu concentrations. © 2010 American Institute of Physics.

Journal of Applied Physics

Study of composition dependent structural, optical, and magnetic properties of Cu-doped Zn1-xCdxS nanoparticles

The paper presents three novel features for handwritten data based identity recognition. A novel framework for combining the features for identification is presented. The framework combines the features in kernel space in MKL based framework. The application of features individually and in combination is presented for writer recognition and signature verification. The writer recognition results have been presented for Devanagari script input and signature verification results have been presented for open dataset [1]. The experiments have shown encouraging results. © 2010 IEEE.

Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010

Identity determination with offline handwritten input using multi kernel feature combination

The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application ofMKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. TheMKLbased classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results. © Copyright 2010 ACM.