Earticle

As the size of web keep on growing, the job of a web crawler has become more cumbersome in covering maximum web. There are enormous numbers of web pages available over the web but out of them a smaller number is available to user. A sitemap is an XML file in which a list of URLs of that particular site is mentioned. The sitemap protocol may play a very important role in covering maximum web. In this paper, the information provided by sitemap protocol is used for the purpose of crawling the quality web pages. With the help of sitemap, web crawlers will maintain their repository up-to-date. It also tries to make user to access maximum pages over the web by covering as maximum as possible while crawling. It also helps crawler to visit the pages based on their change frequency and downloads updated pages only, thereby reduces unnecessary network traffic.

The reliability of shell and tube heat exchanger is predicted using a 2-stage physics-based simulation. The first stage uses stochastic porous models of the bundle to estimate operational and environmental uncertainties in flow conditions, such as flow velocity and inlet temperature. The results are used for the second stage, which is the CFD simulation of the sub-model. The most critical part of shell and tube heat exchangers is the tube bundle. The technique integrates stochastic fluid-structure interaction with fatigue and Monte Carlo simulations. Heating loads are transferred between models by mapping of actual temperature distributions. A sensitivity analysis is conducted to determine the significant factors that may lead to failure, as it is impractical to consider all input variables. In the sub-model, which is a tube bank, air flow and heat transfer inter-effects between the three tubes and the fins are measured. Thermal stresses are highest at the bonding regions between the water tubes and the fins, and that is because of the high temperature gradient combined with different material properties. Thermal stress in the model is relatively low, however, it has a high effect on fatigue life, and consequently on reliability result. The presented model can be used as a useful tool for reliability-based design of shell and tube heat exchangers.

Mining of data is the process of discovering patterns from a large data set and uses this knowledge for matching purpose. In this paper a neural network based approach of data mining is used to verify dynamic signature patterns. In an authentication process, everyone may have a signature that is used to legally prove the document and to bind the individual with the inclination contained in the document. Signature verification is the verification process in which a given input is examined and is either rejected as forgery or accepted as genuine. The proposed algorithm is applied to a set of 500 signature samples collected from 20 individuals. Performance of the system is depicted by using three parameters that are accuracy, false acceptance rate (FAR) and false rejection rate (FRR). Experiments are performed by training the system with more and more number of samples. The results show that the system with neural network has better performance as compared to support vector machines.

This paper describes the tracking system of gas image is to open path to detect the leakage of hazardous gases. The proposed system is able to identify whether or not there are hazardous gases by analyzing the spectrum and absorption’s bandwidth of the target gas, and selecting a corresponding band-pass filter. For this work, the gas detectable system which is to use the bandwidth from middle wavelength to long wavelength is designed to apply for the environment using hazardous gases such as methane, ethane, hydrogen sulfide and benzene. IR detector technology is presented to provide a basis for the application of gas-imaged technique. In this paper, our proposed system had been executed the experiments about using gas such as benzene.

Because of its lower density and plenty of availability, Magnesium alloy is a good choice material in automobile and aerospace industry. There are more and more materials available in the market to serve the common sake. Material selection plays an important role in the process of designing any physical product. A better methodology is required to help the organizations for selecting the best material. Multi Criterion Decision Making (MCDM) methods provide a ranking of the available alternatives thereby, decision of critical thinking become easier. A branch of MCDM methods named Vlse Kriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR) is used in the present work. The work presents the selection of a Magnesium alloy material, where eight materials and ten properties are considered to identify the best material. The influence of weightage factors by three different methods was also discussed.

Experiments is conducted on a die-sinking electric discharge machine under different conditions of process parameters. The experiment is carried out by using En-353 grade stainless steel as a work material and a copper as a tool electrode in EDM. This study highlights the application of technique for order preference by similarity to an ideal solution. In this (TOPSIS), simple additive weighting (SAW) based MCDM methods is used and conducts a study through computational experiments. Comprehensive discussions have been made on the influence of weight constraints and score functions. Where the score function represents an aggregated effect of positive and negative evaluations in performance ratings closeness to the ideal solution. The model developed in this study can be used as a systematic framework for process parameter optimization in conscious manufacturing process.