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International Conference on Advances in computer engineering and Applications (ICACEA)

作者： S. Chakraverty B. Juneja U. Pandey A. Arora Department of Computer Engineering Netaji Subhas Institute of Technology Dwarka New Delhi INDIA Department of Computer Sciences & Engineering IMS Engineering College Ghaziabad UP INDIA

Text Classification enhances the accessibility and systematic organization of the vast reserves of data populatingthe world-wide-web. Despite great strides in the field, the domain of context driven text classification provides fresh opportunities to develop more efficient context oriented techniques with refined metrics. In this paper, we propose a novel approach to categorize text documents using a dual lexical chaining technique. The algorithm first prepares a cohesive category-keyword matrix by feeding category names into the WordNet and Wikipedia ontology, extracting lexically and semantically related keywords from them and then adding to the keywords by employing a keyword enrichment process. Next, the WordNet is referred again to find the degree of lexical cohesiveness between the tokens of a document. Terms that are strongly related are woven together into two separate lexical chains; one for their noun senses and another for their verb senses, that represent the feature set for the document. This segregation enables a better expression of word cohesiveness as concept terms and action terms are treated distinctively. We propose a new metric to calculate the strength of a lexical chain. It includes a statistical part given by Term Frequency-Inverse Document Frequency-Relative Category Frequency (TF-IDF-RCF) which itself is an improvement upon the conventional TF-IDF measure. The chain's contextual strength is determined by the degree of its lexical matching with the category-keyword matrix as well as by the relative positions of its constituent terms. Results indicate the efficacy of our approach. We obtained an average accuracy of 90% on 6 categories derived from the 20 News Group and the Reuters corpora. Lexical chaining has been applied successfully to text summarization. Our results indicate a positive direction towards its usefulness for text classification.

关键词： Training Encyclopedias computers Electronic publishing Internet Semantics

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Rational approximation of fractional order systems using Carlson method

Rational approximation of fractional order systems using Car...

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International Conference on Soft Computing Techniques and Implementations (ICSCTI)

作者： Nitisha Shrivastava Pragya Varshney Instrumentation & Control Engineering Department Netaji Subhas Institute of Technology New Delhi India

Fractional order systems can be modeled more adequately using fractional calculus theory rather than integer calculus. Since fractional order system is an infinite dimensional filter, proper approximation of the infin... 详细信息

ISBN: (纸本)9781467367936

关键词： Instruments Transfer functions Fitting

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Comparison of sigmoidal FFANN training algorithms for function approximation problems

Comparison of sigmoidal FFANN training algorithms for functi...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： MPS Bhatia Veenu Pravin Chandra Division of Computer Engineering Netaji Subhas Institute of Technology (NSIT) Dwarka New Delhi INDIA Netaji Subhas Institute of Technology New Delhi Delhi IN Guru Gobind Singh Indraprastha University New Delhi Delhi IN

The estimation of unknown function from a number of data inputs has number of various applications like in engineering, Artificial intelligence, Statistics, Artificial Neural Networks, Genetic algorithms etc. Many papers have described the individual methods. But very less is known about the comparative performance of various methods. In this paper we give the comparative performance of the neural network using ten different approximation functions and twelve various training algorithms. Our study uses MATLAB 2013a 8.1 Neural Network toolbox for experimentation. The performance of the method on the neural network depends on the approximation function type and the various properties of training data. We found that Bayesian Regulation Backpropagation method proved to be best in performance using function 6 given in the paper out of twelve different algorithms used.

关键词： Backpropagation Training Approximation methods Artificial neural networks Approximation algorithms computer architecture

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Application of Probabilistic Neural Network in Fault Diagnosis of Wind Turbine Using FAST, TurbSim and Simulink

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Procedia computer Science 2015年 58卷 186-193页

作者： Hasmat Malik Sukumar Mishra Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi-110016 India ICE Division Netaji Subhas Institute of Technology New Delhi-110078 India

This paper presents an intelligent diagnosis technique for wind turbine imbalance fault identification based on generator current signals. For this aim, Probabilistic Neural Network (PNN), which is powerful algorithm for classification problems that needs small training time in solving nonlinear problems and applicable to high dimension applications, is employed. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind-turbine (WTG) model are imitated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e., aerodynamic asymmetry, rotor furl imbalance, tail furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG stator current are decomposed into the Intrinsic Mode Frequency (IMF) using EMD method, which are utilized as input variable in PNN. The analyzed results proclaim the effectiveness of the proposed approach to identify the healthy condition from imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG.

关键词： TurbSim FAST Simulink EMD ANN Wind Turbine Fault Diagnosis Condition Monitoring Imbalance Faults Current Signals

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Meeting Our Obligations and Making a Difference [Editorial]

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IEEE Systems, Man, and Cybernetics Magazine 2016年第4期2卷 3-3页

作者： M.E. El-Hawary Azad Hind FaujMarg Sector- 3 Netaji Subhas Institute of Technology Dwarka New Delhi India UESTC School of Computer Science & Engineering Chengdu China

Presents the introductory editorial for this issue of the publication.

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Extreme learning machine based fault diagnosis of power transformer using IEC TC10 and its related data

Extreme learning machine based fault diagnosis of power tran...

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IEEE India Conference (INDICON)

作者： Hasmat Malik Sukumar Mishra Netaji Subhas Institute of Technology New Delhi India Department of Electrical Engineering Indian Institute of Technology New Delhi India

The dissolved gas-in-oil analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. However various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI) based methodology is varied with variation of input variable. Thus, selection of input variable to an AI model is major research area. In this paper, principle component analysis using RapidMiner software is applied to IEC TC10 and related datasets to identify most relevant input variables for incipient fault classification. Thereafter, extreme learning machine (ELM) is implemented to classify the incipient faults of power transformer and its performance is compared with fuzzy-logic and artificial neural network. The compared results shows that ELM provides better diagnosis results with proposed input variables.

关键词： Input variables Testing Training Fault diagnosis Power transformers Databases Artificial neural networks

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Note on performance analysis of reduced order models using pade approximation method

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ICIC Express Letters 2015年第8期9卷 2071-2074页

作者： Hote, Yogesh V. Jha, Amar Nath Department of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Sector-3 Dwarka New Delhi India

In literature, it is reported that the Routh stability method and stability equation method may approximate the non-dominant poles of the system whereas Pade approximation method does not show this drawback. Here, it is shown that this conclusion is not completely true. The proposed analysis is carried out by determining frequency domain plots as well as performance indices. This analysis will be helpful to faculty and the research scholars. © 2015 ICIC International.

关键词： Frequency domain analysis

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Bilateral teleoperation control using recursive least squares filter with forgetting factor

Bilateral teleoperation control using recursive least square...

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IEEE India Conference (INDICON)

作者： Akshay Bhardwaj Vijyant Agarwal Harish Parthasarathy Department of Manufacturing Processes and Automation Engineering Department of Electronics and Communication Engineering Netaji Subhas Institute of Technology Delhi India

In this research, we have designed and implemented recursive least squares (RLS) algorithm in master slave tracking on Geomagic® Touch™ (Phantom Omni) haptic device. RLS algorithm enables us to achieve optimal tracking in a teleoperation system in which the system parameters vary with time and the noise is weakly non-stationary. In our previous work on teleoperation, we employed Widrow's least mean square algorithm instead of RLS algorithm and achieved satisfactorily high tracking accuracy. There, we employed instantaneous errors to update filter coefficients and hence slave positions. This study initiated with the idea that if we account all or some of the previous errors in updating filter coefficients and thus reducing current error, we might be probably able to achieve even higher tracking accuracy than that achieved with WLMS. Therefore, in order to understand this influence of older errors on tracking accuracy, we have applied RLS algorithm with forgetting factor. The use of forgetting factor in the least squares algorithm enables us to base our tracking on different weights of past errors that further helps us in understanding this influence at a broader level.

关键词： Robots Mathematical model Heuristic algorithms Force Dynamics Algorithm design and analysis Phantoms

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AVAILABILITY MODEL FOR SELF TEST AND REPAIR IN FAULT TOLERANT FPGA-BASED SYSTEMS

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Journal of Electronics(China) 2014年第4期31卷 271-283页

作者： Shampa Chakraverty Anubhav Agarwal Broteen Kundu Anil Kumar Division of Computer Engineering Netaji Subhas Institute of Technology

Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or in the configuration memory. The aim of our research is to characterize self-test and repair processes in Fault Tolerant(FT) dr-FPGA systems in the presence of environmental faults and explore their interrelationships. We develop a Continuous Time Markov Chain(CTMC) model that captures the high level fail-repair processes on a dr-FPGA with periodic online Built-In Self-Test(BIST) and scrubbing to detect and repair faults with minimum latency. Simulation results reveal that given an average fault interval of 36 s, an optimum self-test interval of 48.3 s drives the system to spend 13% of its time in self-tests, remain in safe working states for 76% of its time and face risky fault-prone states for only 7% of its time. Further, we demonstrate that a well-tuned repair strategy boosts overall system availability, minimizes the occurrence of unsafe states, and accommodates a larger range of fault rates within which the system availability remains stable within 10% of its maximum level.

关键词： Dynamically reconfigurable Field Programmable Gate Array （dr-FPGA） Built-In Self-Test （BIST） Fault Tolerance （FT） Single Event Effects （SEEs） Continuous Time Markov Chain （CTMC） ScrubbingCLC number：TN47

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Secure data communication in a cloud environment using Row Column Diagonal (RCD)

Secure data communication in a cloud environment using Row C...

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International Conference on Soft Computing Techniques and Implementations (ICSCTI)

作者： S. K. Agrawal S. Joshi B. M. Purohit Division of EC Engineering Netaji Subhas Institute of Technology (NSIT) Dwarka New Delhi India Computer Science Department Graphic Era Institute of Technology India Computer Software Engineering Department Govind Ballabh Pant Engineering College India

This Nowadays system hear that CLOUD computing is ubiquitous. Most of tech giants have converted their owned in-house data centers to using CLOUD technology and outsourcing the part of their services to public cloud and to opt for hybrid cloud architecture. There is no doubt that CLOUDs have the potential for being the next generation model computational services, but with that potential comes the risk of theft and concerns related to security and privacy. A lot of development and progress has already been made in CLOUD technologies, there still remains a wide range of concerns this article system identify and classify the main security concerns and solutions in cloud computing, and propose a method of security in cloud environment. In this paper Row Column Diagonal (RCD) based engine has been proposed. Along with the cloud sub-cloud. Data Security using RCD engine in Cloud Environment is also explained. Further, RCD engine system has been proposed in "Central Cloud" and "RCD Supervisor" modes in Cloud environment along with the situation of uploading and downloading. Validation and analysis have been performed for RCD Engine and indicates RCD based engine is much powerful and secure. As RCD Algorithm is much more robust and easy to implement with considering all the aspects of main and sub cloud using RCD supervisor as the central body.

关键词： Cloud computing Engines Encryption Buffer storage computer architecture

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