Browsing by Author "Verma, N.K."

Now showing 1 - 9 of 9

An optimized fault diagnosis method for reciprocating air compressors based on SVM
(2011) Verma, N.K.; Roy, A.; Salour, A.
Fault diagnosis in reciprocating air compressors is essential for continuous monitoring of their performance and thereby ensuring quality output. Support Vector Machines (SVMs) are machine learning tools based on structural risk minimization principle and have the advantageous characteristic of good generalization. For this reason, four well-known and widely used SVM based methods, one-against-one (OAO), oneagainst-all (OAA), fuzzy decision function (FDF), and DDAG have been used here and an optimized SVM based technique is proposed for classification based fault diagnosis in reciprocating air compressors. The results obtained through implementation of all five techniques are thus compared as per their accuracy rate in percentages and the performance of the proposed method with 98.03 percent accuracy rate was found to be better than all other classification methods. With the compressor datasets being complex natured, proposed method is found to be of vital importance for classification based fault diagnosis pertaining to reciprocating air compressors. Â© 2011 IEEE.
Energy harvesting by foot-propelled battery charger using shoe-model
(2012) Verma, N.K.; Singla, P.; Roy, A.
This paper proposes an effective and convenient mechanism to transform and utilize biomechanical energy to electrical energy by presenting a self-powered shoe-model in order to tap the energy obtained for charging mobile phone battery. While walking in general, negative work is done by every human being in every single step taken. This negative work can be converted into electrical energy using a dc machine. The resulting energy could serve as ancillary source of energy for charging the batteries. The proposed self-powered shoe-model contains a permanent magnet DC machine, rack and pinion section and a signal conditioning circuit for charging mobile phone battery. The designed shoe-model has been successfully tested on Li-ion battery of a mobile phone from a reputed brand. � (2012) Trans Tech Publications.
Energy harvesting by foot-propelled battery charger using shoe-model
(2012) Verma, N.K.; Singla, P.; Roy, A.
This paper proposes an effective and convenient mechanism to transform and utilize biomechanical energy to electrical energy by presenting a self-powered shoe-model in order to tap the energy obtained for charging mobile phone battery. While walking in general, negative work is done by every human being in every single step taken. This negative work can be converted into electrical energy using a dc machine. The resulting energy could serve as ancillary source of energy for charging the batteries. The proposed self-powered shoe-model contains a permanent magnet DC machine, rack and pinion section and a signal conditioning circuit for charging mobile phone battery. The designed shoe-model has been successfully tested on Li-ion battery of a mobile phone from a reputed brand. Â© (2012) Trans Tech Publications.
Medical image segmentation using improved mountain clustering technique version-2
(2010) Verma, N.K.; Roy, A.; Vasikarla, S.
This paper proposes Improved Mountain Clustering version-2 (IMC-2) based medical image segmentation. The proposed technique is a more powerful approach for medical image based diagnosing diseases like brain tumor, tooth decay, lung cancer, tuberculosis etc. The IMC-2 based medical image segmentation approach has been applied on various categories of images including MRI images, dental X-rays, chest X-rays and compared with some widely used segmentation techniques such as K-means, FCM and EM as well as with IMC-1. The performance of all these segmentation approaches is compared on widely accepted validation measure, Global Silhouette Index. Also, the segments obtained from the above mentioned segmentation approaches have been visually evaluated. � 2010 IEEE.
Medical image segmentation using improved mountain clustering technique version-2
(2010) Verma, N.K.; Roy, A.; Vasikarla, S.
This paper proposes Improved Mountain Clustering version-2 (IMC-2) based medical image segmentation. The proposed technique is a more powerful approach for medical image based diagnosing diseases like brain tumor, tooth decay, lung cancer, tuberculosis etc. The IMC-2 based medical image segmentation approach has been applied on various categories of images including MRI images, dental X-rays, chest X-rays and compared with some widely used segmentation techniques such as K-means, FCM and EM as well as with IMC-1. The performance of all these segmentation approaches is compared on widely accepted validation measure, Global Silhouette Index. Also, the segments obtained from the above mentioned segmentation approaches have been visually evaluated. Â© 2010 IEEE.
An optimized fault diagnosis method for reciprocating air compressors based on SVM
(2011) Verma, N.K.; Roy, A.; Salour, A.
Fault diagnosis in reciprocating air compressors is essential for continuous monitoring of their performance and thereby ensuring quality output. Support Vector Machines (SVMs) are machine learning tools based on structural risk minimization principle and have the advantageous characteristic of good generalization. For this reason, four well-known and widely used SVM based methods, one-against-one (OAO), oneagainst-all (OAA), fuzzy decision function (FDF), and DDAG have been used here and an optimized SVM based technique is proposed for classification based fault diagnosis in reciprocating air compressors. The results obtained through implementation of all five techniques are thus compared as per their accuracy rate in percentages and the performance of the proposed method with 98.03 percent accuracy rate was found to be better than all other classification methods. With the compressor datasets being complex natured, proposed method is found to be of vital importance for classification based fault diagnosis pertaining to reciprocating air compressors. � 2011 IEEE.
Self-optimal clustering technique using optimized threshold function
(Institute of Electrical and Electronics Engineers Inc., 2014) Verma, N.K.; Roy, A.
This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index. © 2007-2012 IEEE.
SVM based methods for arrhythmia classification in ECG
(2010) Kohli, N.; Verma, N.K.; Roy, A.
In this study, Support Vector Machine (SVM) based methods have been used to classify the electrocardiogram (ECG) arrhythmias. Among various existing SVM methods, three well-known and widely used algorithms one-against-one, one-against-all, and fuzzy decision function are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the Cardiac Arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM, include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. The results obtained through implementation of all three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using one-against-all (OAA) method was found to be better than other techniques. ECG arrhythmia data sets are of generally complex nature and SVM based one-against-all method is found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats. �2010 IEEE.
SVM based methods for arrhythmia classification in ECG
(2010) Kohli, N.; Verma, N.K.; Roy, A.
In this study, Support Vector Machine (SVM) based methods have been used to classify the electrocardiogram (ECG) arrhythmias. Among various existing SVM methods, three well-known and widely used algorithms one-against-one, one-against-all, and fuzzy decision function are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the Cardiac Arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM, include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. The results obtained through implementation of all three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using one-against-all (OAA) method was found to be better than other techniques. ECG arrhythmia data sets are of generally complex nature and SVM based one-against-all method is found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats. Â©2010 IEEE.