Critical Review of Reliability Centred Maintenance (RCM) for Asset Management in Electric Power Distribution System

The purpose of maintenance is to extend equipment lifetime or at least the mean time to the next failure. Maintenance too incurs expenditures that result in very costly consequences when not performed or performed too little, and it may not even be economical to perform it too frequently. Therefore the two costs must be balanced. In the past, this balance had been estimated by extrapolating the experience obtained from existing systems and using the rule - of – thumb methods. Nowadays, the tempo of advanced and softiscated research in that direction has rendered such rule – of – thumb methods obsolete. The literature works describing the reliability centred maintenance methods for managing distribution assets have grown until the papers can now be numbered in thousands. This paper presents critical review of the various existing methods that have been developed by different reseachers and proposes a probabilistic model that will provide a quantitative connection between reliability and maintenance, a link missing in all the heuristic approaches

Vehicle Classification Algorithm using Size and Shape

Automatic classification of vehicles into different classes based on their sizes and shapes is very useful for traffic control and toll collection process. Effective intelligent transportation system that incorporates vehicle classification technique is needed in many cities to prevent road accident and traffic congestion caused by illegal movement of vehicles. This work presents method of getting structural information from detected vehicle images and then uses it to classify vehicles into different classes. The technique involves extraction of contour features from vehicle images side view using morphological operations. The extracted features were combined and used to generate feature vector that serve as input data to vehicle classification algorithm based on Euclidean distance measure. Impressive result was achieved from the proposed vehicle classification method

Estimating An Optimal Backpropagation Algorithm for Training An ANN with the EGFR Exon 19 Nucleotide Sequence: An Electronic Diagnostic Basis for Non–Small Cell Lung Cancer(NSCLC)

One of the most common forms of medical malpractices globally is an error in diagnosis. An improper diagnosis occurs when a doctor fails to identify a disease or report a disease when the patient is actually healthy. A disease that is commonly misdiagnosed is lung cancer. This cancer type is a major health problem internationally because it is responsible for 15% of all cancer diagnosis and 29% of all cancer deaths. The two major sub-types of lung cancer are; small cell lung cancer (about 13%) and non-small cell lung cancer (%SCLC- about 87%). The chance of surviving lung cancer depends on its correct diagnosis and/or the stage at the time it is diagnosed. However, recent studies have identified somatic mutations in the epidermal growth factor receptor (EGFR) gene in a subset of non-small cell lung cancer (%SCLC) tumors. These mutations occur in the tyrosine kinase domain of the gene. The most predominant of the mutations in all %SCLC patients examined is deletion mutation in exon 19 and it accounts for approximately 90% of the EGFR-activating mutations. This makes EGFR genomic sequence a good candidate for implementing an electronic diagnostic system for %SCLC. In this study aimed at estimating an optimum backpropagation training algorithm for a genomic based A%% system for %SCLC diagnosis, the nucleotide sequences of EGFR’s exon 19 of a noncancerous cell were used to train an artificial neural network (A%%). Several A%% back propagation training algorithms were tested in MATLAB R2008a to obtain an optimal algorithm for training the network. Of the nine different algorithms tested, we achieved the best performance (i.e. the least mean square error) with the minimum epoch (training iterations) and training time using the Levenberg-Marquardt algorithm

Person Identification System using Static-dyamic Signatures Fusion

Off-line signature verification systems rely on static image of signature for person identification. Imposter can easily imitate the static image of signature of the genuine user due to lack of dynamic features. This paper proposes person identity verification system using fused static-dynamic signature features. Computational efficient technique is developed to extract and fuse static and dynamic features extracted from offline and online signatures of the same person. The training stage used the fused features to generate couple reference data and classification stage compared the couple test signatures with the reference data based on the set threshold values. The system performance is encouraging against imposter attacker in comparison with previous single sensor offline signature identification systems

Novel Feature Extraction Technique For Off-Line Signature Verification System

Feature extraction stage is the most vital and difficult stage of any off-line signature verification system. The accuracy of the system depends mainly on the effectiveness of the signature features use in the system. Inability to extract robust features from a static image of signature has been contributing to higher verification error-rates particularly for skilled forgeries. In this paper, we propose an off-line signature verification system that incorporates a novel feature extraction technique. Three new features are extracted from a static image of signatures using this technique. From the experimental results, the new features proved to be more robust than other related features used in the earlier systems. The proposed system has 1% error in rejecting skilled forgeries and 0.5% error in accepting genuine signatures. These results are better in comparison with the results obtained from previous systems

Efficient on-line signature verification system

In this paper, a robust automatic on-line signature verification system is proposed. The effectiveness of any on-line signature verification system depends mainly on the robustness of the dynamic features use in the system. Inability to extract highly discriminative dynamic features from signature has been contributing to higher verification error-rates. On-line signature verification experiments are conducted on seven dynamic signature features extracted from signature trajectories. Three features are found to be highly discriminative in comparison with others. The proposed system incorporates these three features for signature verification. Verification is based on the average of all the distances obtain from the cross-alignment of the features. The proposed system is tested with quality signature samples and it has 0.5% error in rejecting skilled forgeries while rejecting only 0.25% of genuine signatures. These results are better in comparison with the results obtained from previous systems

TEXT CONTENT DEPENDENT WRITER IDENTIFICATION

Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new feature extraction method is engaged to produce active text features for developing an effective personal identification system. The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine (SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the proposed system, it was able to identify writers of query handwritten texts

Fish Classification Algorithm using Single Value Decomposition

Automatic fish classification system plays a very useful role in the process of separating fishes into species for human consumption,ornamentation and other usages. Manual classificationof fishes into different types is difficult and boring. This work proposes a fast and accurate system capable of classifying fish images into distinct classes based on their physical form. The system comprises image-processing, feature extraction and classification method. Fishfeature vector is obtained from Single Value Decomposition (SVD) product extracted from fish block images. Training and testing of the proposed fish classification system are done using Artificial Neural Network (ANN). Experimental test was carried out to determine the species of query fish images. Thirty-six fish images were tested, 94% correct classification result is recorded

Algorithm for Fingerprint Verification System

Extraction of minutiae based features from good quality fingerprint images is more effective for fingerprint recognition in comparison with features from low quality fingerprint. In this paper, a new technique for fingerprint feature extraction based on ridge pattern is proposed. Robust features are extracted from fingerprint image notwithstanding the quality of the image. The variation within different person fingerprint is established using centre of gravity of the fingerprint image as the reference point for effective classification. Similarity measure in term of Euclidean distance is compute for test fingerprint image

Hand Vein Based Personal Identification System

Personal Identification is necessary and useful to prevent crime in our society. Biometric traits extracted from hand image are more secure to use for identification compared to Personal Identification Number (PIN) because these features cannot be stolen. Developing an effective hand vein based personal identification system using robust feature with appropriate classifier is a serious task. Therefore this work incorporates new hand vein feature and two different classifiers to develop a personal identification system. Experiments were carried-out to affirm appropriateness of Support Vector Machine (SVM) and Euclidean Distance Measure (EDM) for the proposed system. The results obtained show that the proposed system give better result using SVM compared to EDM