National Institute of Technology Kurukshetra
Thanesar, India

National Institute of Technology, Kurukshetra , is a public engineering university located in Kurukshetra. In December 2008, it was accredited with the status of Institute of National Importance . It is one of the thirty National Institutes of Technology established and administered by Government of India. It runs undergraduate and postgraduate programs in Engineering and Doctor of Philosophy programme in Engineering, science and Humanities.The institute is situated in the city of Kurukshetra, which is 168 km north of New Delhi on National Highway 1. Wikipedia.

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Gupta P.,University of Delhi | Bhatia R.S.,National Institute of Technology Kurukshetra | Jain D.K.,Sudan University of Science and Technology
IEEE Transactions on Smart Grid | Year: 2015

An average absolute frequency deviation value based active islanding detection technique is proposed in this paper. The inverter's classical q-axis current controller is modeled with a continuous periodic reference current of a small value. During the loss of mains, the island's frequency deviates with respect to the variation in the reference current; this is detected by making the use of an average absolute frequency deviation value. In case of a stable island formation, there is a small periodic frequency deviation owing to the small value of the periodic reference current, and the frequency deviation is so small that it falls inside the nondetection zone (NDZ) of the frequency relay. The main advantage of the proposed algorithm is that it detects the stable island formation but without forcing the island to lose its stable operation. In case of nonislanding switching events, which may transiently impose a significant deviation in the frequency, the possibility of false detection is eliminated by reconfirming the occurrence of islanding once it is suspected. The reference current is kept to a small value to limit the degradation of the power quality and the power factor. Computer simulation is done with MATLAB. © 2014 IEEE.

Rana C.,University Institute of Engineering and Technology | Jain S.K.,National Institute of Technology Kurukshetra
Swarm and Evolutionary Computation | Year: 2014

The use of internet and Web services is changing the way we use resources and communicate since the last decade. Although, this usage has made life easier in many respects still the problem of finding relevant information persists. A naïve user faces the problem of information overload and continuous flow of new information makes the problem more complex. Furthermore, user′s interests also keeps on changing with time. Several techniques deal with this problem and data mining is widely used among them. Recommender Systems (RSs) assist users in finding relevant information on the web and are mostly based on data mining algorithms. This paper addresses the problem of user requirements changing over a period of time in seeking information on web and how RSs deal with them. We propose a Dynamic Recommender system (DRS) based on evolutionary clustering algorithm. This clustering algorithm makes clusters of similar users and evolves them depicting accurate and relevant user preferences over time. The proposed approach performs an optimization of conflicting parameters instead of using the traditional evolutionary algorithms like genetic algorithm. The algorithm has been empirically tested and compared with standard recommendation algorithms and it shows considerable improvement in terms of quality of recommendations and computation time. © 2013 Elsevier B.V.

Pal M.,National Institute of Technology Kurukshetra
International Journal of Applied Earth Observation and Geoinformation | Year: 2012

This paper evaluates the performance of three feature selection methods based on multinomial logistic regression, and compares the performance of the best multinomial logistic regression-based feature selection approach with the support vector machine based recurring feature elimination approach. Two hyperspectral datasets, one consisting of 65 features (DAIS data) and other with 185 features (AVIRIS data) were used. Result suggests that a total of between 15 and 10 features selected by using the multinomial logistic regression-based feature selection approach as proposed by Cawley and Talbot achieve a significant improvement in classification accuracy in comparison to the use of all the features of the DAIS and AVIRIS datasets. In addition to the improved performance, the Cawley and Talbot approach does not require any user-defined parameter, thus avoiding the requirement of a model selection stage. In comparison, the other two multinomial logistic regression-based feature selection approaches require one user-defined parameter and do not perform as well as the Cawley and Talbot approach in terms of (i) the number of features required to achieve classification accuracy comparable to that achieved using the full dataset, and (ii) the classification accuracy achieved by the selected features. The Cawley and Talbot approach was also found to be computationally more efficient than the SVM-RFE technique, though both use the same number of selected features to achieve an equal or even higher level of accuracy than that achieved with full hyperspectral datasets. © 2011 Elsevier B.V.

Chauhan R.P.,National Institute of Technology Kurukshetra
Indian Journal of Pure and Applied Physics | Year: 2010

The environmental monitoring of radon, thoron and their progeny in different dwellings of Northern Haryana has been carried out. The radon-thoron twin dosimeter cups are used for the study. The annual dose received due to radon-thoron and their progeny by the inhabitants in the dwellings under study have also been calculated. The health risk assessment in the dwellings under consideration has been done.

Pal M.,National Institute of Technology Kurukshetra | Foody G.M.,University of Nottingham
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

The accuracy of a conventional supervised classification is in part a function of the training set used, notably impacted by the quantity and quality of the training cases. Since it can be costly to acquire a large number of high quality training cases, recent research has focused on methods that allow accurate classification from small training sets. Previous work has shown the potential of support vector machine (SVM) based classifiers. Here, the potential of the relevance vector machine (RVM) and sparse multinominal logistic regression (SMLR) approaches is evaluated relative to SVM classification. With both airborne and spaceborne multispectral data sets, the RVM and SMLR were able to derive classifications of similar accuracy to the SVM but required considerably fewer training cases. For example, from a training set comprising 600 cases acquired with a conventional stratified random sampling design from an airborne thematic mapper (ATM) data set, the RVM produced the most accurate classification, 93.75%, and needed only 7.33% of the available training cases. In comparison, the SVM yielded a classification that had an accuracy of 92.50% and needed 4.5 times more useful training cases. Similarly, with a Landsat ETM+ (Littleport, Cambridgeshire, UK) data set, the SVM required 4.0 times more useful training cases than the RVM. For each data set, however, the classifications derived by each classifier were of similar magnitude, differing by no more than 1.25%. Finally, for both the ATM and ETM+ (Littleport) data sets, the useful training cases by SVM and RVM had distinct and potentially predictable characteristics. Support vectors were generally atypical but lay in the boundary region between classes in feature space while the relevance vectors were atypical but anti-boundary in nature. The SMLR also tended to mostly, but not always, use extreme cases that lay away from class boundary. The results, therefore, suggest a potential to design classifier-specific intelligent training data acquisition activities for accurate classification from small training sets, especially with the SVM and RVM. © 2008-2012 IEEE.

Singh R.S.,National Institute of Technology Kurukshetra
AIP Advances | Year: 2015

Influence of oxygen impurity on electronic properties of carbon and boron nitride nanotubes (CNTs and BNNTs) is systematically studied using first principle calculations based on density functional theory. Energy band structures and density of states of optimized zigzag (5, 0), armchair (3, 3), and chiral (4, 2) structures of CNT and BNNT are calculated. Oxygen doping in zigzag CNT exhibits a reduction in metallicity with opening of band gap in near-infrared region while metallicity is enhanced in armchair and chiral CNTs. Unlike oxygen-doped CNTs, energy bands are drastically modulated in oxygen-doped zigzag and armchair BNNTs, showing the nanotubes to have metallic behaviour. Furthermore, oxygen impurity in chiral BNNT induces narrowing of band gap, indicating a gradual modification of electronic band structure. This study underscores the understanding of different electronic properties induced in CNTs and BNNTs under oxygen doping, and has potential in fabrication of various nanoelectronic devices. © 2015 Author(s).

Murthy V.V.S.N.,National Institute of Technology Kurukshetra | Kumar A.,National Institute of Technology Kurukshetra
International Journal of Electrical Power and Energy Systems | Year: 2013

Integration of renewable energy based distributed generation (DG) units provides potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in energy losses, and reliability improvement. Therefore, the location of DG units should be carefully determined with the consideration of different planning incentives. This paper presents a comparison of novel, combined loss sensitivity, index vector, and voltage sensitivity index methods for optimal location and sizing of distributed generation (DG) in a distribution network. The main contribution of the paper is: (i) location of DGs based on existing sensitivity methods, (ii) proposing combined power loss sensitivity based method for DG location, (iii) modified Novel method for DG location, (iv) comparison of sensitivity methods for DG location and their size calculations, and (v) cost of losses and determining cost of power obtained from DGs and the comparison of methods at unity and lagging power factors. The results show the importance of installing the suitable size of DG at the suitable location. The results are obtained with all sensitivity based methods on the IEEE 33-bus and 69-bus systems. © 2013 Elsevier Ltd. All rights reserved.

Kumar J.,National Institute of Technology Kurukshetra
Machining Science and Technology | Year: 2013

Ultrasonic machining (USM) is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion, and an abrasive slurry. The fundamental principles of stationary ultrasonic machining, the material removal mechanisms involved, proposed models for estimation of machining rate, the effect of operating parameters on material removal rate, tool wear rate, and workpiece surface finish, research work reported on rotary mode USM, hybrid USM, process capabilities of USM have been extensively reviewed in this article. The limitations of USM, gaps observed from the literature review, and the directions for future research have also been presented. Overall, this article presents a comprehensive review of USM process for advancement of the process through fundamental insights into the process. © 2013 Taylor and Francis Group, LLC.

Singh S.,National Institute of Technology Kurukshetra | Bandyopadhyay M.,National Institute of Technology Kurukshetra
IEEE Electrical Insulation Magazine | Year: 2010

Transformers are one of the most important and complex components of electricity transmission and distribution. To have a reliable electricity supply, it is necessary to give considerable attention to the maintenance of transformers. To maximize the lifetime and efficacy of transformers, it is important to be aware of possible faults that may occur and to know how to prevent them. These faults can all lead to the thermal degradation of the oil and paper insulation in the transformer. The composition and quantity of the gases generated depend on the types and severity of the faults, and regular monitoring and maintenance can make it possible to detect incipient flaws before damage occurs. The four main types of transformer faults are © 2010 IEEE.

Pal M.,National Institute of Technology Kurukshetra | Foody G.M.,University of Nottingham
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

Support vector machines (SVM) are attractive for the classification of remotely sensed data with some claims that the method is insensitive to the dimensionality of the data and, therefore, does not require a dimensionality-reduction analysis in preprocessing. Here, a series of classification analyses with two hyperspectral sensor data sets reveals that the accuracy of a classification by an SVM does vary as a function of the number of features used. Critically, it is shown that the accuracy of a classification may decline significantly (at 0.05 level of statistical significance) with the addition of features, particularly if a small training sample is used. This highlights a dependence of the accuracy of classification by an SVM on the dimensionality of the data and, therefore, the potential value of undertaking a feature-selection analysis prior to classification. Additionally, it is demonstrated that, even when a large training sample is available, feature selection may still be useful. For example, the accuracy derived from the use of a small number of features may be noninferior (at 0.05 level of significance) to that derived from the use of a larger feature set providing potential advantages in relation to issues such as data storage and computational processing costs. Feature selection may, therefore, be a valuable analysis to include in preprocessing operations for classification by an SVM. © 2010 IEEE.

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