Comparative Study of Fault Identification and Classification on EHV Lines Using Discrete Wavelet Transform and Fourier Transform Based ANN

An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.

Resource Discovery in Web-Services Based Grids

A Web-services based grid infrastructure is evolving to be readily available in the near future. In this approach, the Web services are inherited (encapsulated or functioned) into the same existing Grid services class. In practice there is not much difference between the existing Web and grid infrastructure. Grid services emerged as stateful web services. In this paper, we present the key components of web-services based grid and also how the resource discovery is performed on web-services based grid considering resource discovery, as a critical service, to be provided by any type of grid.

Analysis of Sequence Moves in Successful Chess Openings Using Data Mining with Association Rules

Chess is one of the indoor games, which improves the level of human confidence, concentration, planning skills and knowledge. The main objective of this paper is to help the chess players to improve their chess openings using data mining techniques. Budding Chess Players usually do practices by analyzing various existing openings. When they analyze and correlate thousands of openings it becomes tedious and complex for them. The work done in this paper is to analyze the best lines of Blackmar- Diemer Gambit(BDG) which opens with White D4... using data mining analysis. It is carried out on the collection of winning games by applying association rules. The first step of this analysis is assigning variables to each different sequence moves. In the second step, the sequence association rules were generated to calculate support and confidence factor which help us to find the best subsequence chess moves that may lead to winning position.

Feasibility Investigation of Near Infrared Spectrometry for Particle Size Estimation of Nano Structures

Determination of nano particle size is substantial since the nano particle size exerts a significant effect on various properties of nano materials. Accordingly, proposing non-destructive, accurate and rapid techniques for this aim is of high interest. There are some conventional techniques to investigate the morphology and grain size of nano particles such as scanning electron microscopy (SEM), atomic force microscopy (AFM) and X-ray diffractometry (XRD). Vibrational spectroscopy is utilized to characterize different compounds and applied for evaluation of the average particle size based on relationship between particle size and near infrared spectra [1,4] , but it has never been applied in quantitative morphological analysis of nano materials. So far, the potential application of nearinfrared (NIR) spectroscopy with its ability in rapid analysis of powdered materials with minimal sample preparation, has been suggested for particle size determination of powdered pharmaceuticals. The relationship between particle size and diffuse reflectance (DR) spectra in near infrared region has been applied to introduce a method for estimation of particle size. Back propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near infrared diffuse reflectance spectra. Thirty five different nano TiO2 samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP- ANN.

Emotion Classification by Incremental Association Language Features

The Major Depressive Disorder has been a burden of medical expense in Taiwan as well as the situation around the world. Major Depressive Disorder can be defined into different categories by previous human activities. According to machine learning, we can classify emotion in correct textual language in advance. It can help medical diagnosis to recognize the variance in Major Depressive Disorder automatically. Association language incremental is the characteristic and relationship that can discovery words in sentence. There is an overlapping-category problem for classification. In this paper, we would like to improve the performance in classification in principle of no overlapping-category problems. We present an approach that to discovery words in sentence and it can find in high frequency in the same time and can-t overlap in each category, called Association Language Features by its Category (ALFC). Experimental results show that ALFC distinguish well in Major Depressive Disorder and have better performance. We also compare the approach with baseline and mutual information that use single words alone or correlation measure.

A study on a Generic Development Process for the BPM+SOA Design and Implementation

In order to optimize annual IT spending and to reduce the complexity of an entire system architecture, SOA trials have been started. It is common knowledge that to design an SOA system we have to adopt the top-down approach, but in reality silo systems are being made, so these companies cannot reuse newly designed services, and cannot enjoy SOA-s economic benefits. To prevent this situation, we designed a generic SOA development process referred to as the architecture of “mass customization." To define the generic detail development processes, we did a case study on an imaginary company. Through the case study, we could define the practical development processes and found this could vastly reduce updating development costs.

Attitude Change after Taking a Virtual Global Understanding Course

A virtual collaborative classroom was created at East Carolina University, using videoconference technology via regular internet to bring students from 18 different countries, 2 at a time, to the ECU classroom in real time to learn about each other-s culture. Students from two countries are partnered one on one, they meet for 4-5 weeks, and submit a joint paper. Then the same process is repeated for two other countries. Lectures and student discussions are managed with pre-determined topics and questions. Classes are conducted in English and reading assignments are placed on the website. Administratively all partners are independent, students pay fees and get credits at their home institution. Familiarity with technology, knowledge in cultural understanding and attitude change were assessed, only attitude changes are reported in this paper. After taking this course, all students stated their comfort level in working with, and their desire to interact with, culturally different others grew stronger and their xenophobia and isolationist attitudes decreased.

Effective Online Staff Training: Is This Possible?

The purpose of this paper is to consider the introduction of online courses to replace the current classroom-based staff training. The current training is practical, and must be completed before access to the financial computer system is authorized. The long term objective is to measure the efficacy, effectiveness and efficiency of the training, and to establish whether a transfer of knowledge back to the workplace has occurred. This paper begins with an overview explaining the importance of staff training in an evolving, competitive business environment and defines the problem facing this particular organization. A summary of the literature review is followed by a brief discussion of the research methodology and objective. The implementation of the alpha version of the online course is then described. This paper may be of interest to those seeking insights into, or new theory regarding, practical interventions of online learning in the real world.

A Dual Model for Efficiency Evaluation Considering Time Lag Effect

A DEA model can generally evaluate the performance using multiple inputs and outputs for the same period. However, it is hard to avoid the production lead time phenomenon some times, such as long-term project or marketing activity. A couple of models have been suggested to capture this time lag issue in the context of DEA. This paper develops a dual-MPO model to deal with time lag effect in evaluating efficiency. A numerical example is also given to show that the proposed model can be used to get efficiency and reference set of inefficient DMUs and to obtain projected target value of input attributes for inefficient DMUs to be efficient.

Research on Transformer Condition-based Maintenance System using the Method of Fuzzy Comprehensive Evaluation

This study adopted previous fault patterns, results of detection analysis, historical records and data, and experts- experiences to establish fuzzy principles and estimate the failure probability index of components of a power transformer. Considering that actual parameters and limiting conditions of parameters may differ, this study used the standard data of IEC, IEEE, and CIGRE as condition parameters. According to the characteristics of each condition parameter, relative degradation was introduced to reflect the degree of influence of the factors on the transformer condition. The method of fuzzy mathematics was adopted to determine the subordinate function of the transformer condition. The calculation used the Matlab Fuzzy Tool Box to select the condition parameters of coil winding, iron core, bushing, OLTC, insulating oil and other auxiliary components and factors (e.g., load records, performance history, and maintenance records) of the transformer to establish the fuzzy principles. Examples were presented to support the rationality and effectiveness of the evaluation method of power transformer performance conditions, as based on fuzzy comprehensive evaluation.

Dynamical Network Transmission of H1N1 Virus at the Local Level Transmission Model

A new strain of Type A influenza virus can cause the transmission of H1N1 virus. This virus can spread between the people by coughing and sneezing. Because the people are always movement, so this virus can be easily spread. In this study, we construct the dynamical network model of H1N1 virus by separating the human into five groups; susceptible, exposed, infectious, quarantine and recovered groups. The movement of people between houses (local level) is considered. The behaviors of solutions to our dynamical model are shown for the different parameters.

Piezoelectric Polarization Effect on Debye Frequency and Temperature in Nitride Wurtzites

We have investigated the effect of piezoelectric (PZ) polarization property in binary as well as in ternary wurtzite nitrides. It is found that with the presence of PZ polarization property, the phonon group velocity is modified. The change in phonon group velocity due to PZ polarization effect directly depends on piezoelectric tensor value. Using different piezoelectric tensor values recommended by different workers in the literature, percent change in group velocities of phonons has been estimated. The Debye temperatures and frequencies of binary nitrides GaN, AlN and InN are also calculated using the modified group velocities. For ternary nitrides AlxGa(1-x)N, InxGa(1-x)N and InxAl(1-x)N, the phonon group velocities have been calculated as a functions of composition. A small positive bowing is observed in phonon group velocities of ternary alloys. Percent variations in phonon group velocities are also calculated for a straightforward comparison among ternary nitrides. The results are expected to show a change in phonon relaxation rates and thermal conductivity of III-nitrides when piezoelectric polarization property is taken into consideration.

Effect of Speed and Torque on Statistical Parameters in Tapered Bearing Fault Detection

The effect of the rotational speed and axial torque on the diagnostics of tapered rolling element bearing defects was investigated. The accelerometer was mounted on the bearing housing and connected to Sound and Vibration Analyzer (SVAN 958) and was used to measure the accelerations from the bearing housing. The data obtained from the bearing was processed to detect damage of the bearing using statistical tools and the results were subsequently analyzed to see if bearing damage had been captured. From this study it can be seen that damage is more evident when the bearing is loaded. Also, at the incipient stage of damage the crest factor and kurtosis values are high but as time progresses the crest factors and kurtosis values decrease whereas the peak and RMS values are low at the incipient stage but increase with damage.

Alternative Approach in Ground Vehicle Wake Analysis

In this paper an alternative visualisation approach of the wake behind different vehicle body shapes with simplified and fully-detailed underbody has been proposed and analysed. This allows for a more clear distinction among the different wake regions. This visualisation is based on a transformation of the cartesian coordinates of a chosen wake plane to polar coordinates, using as filter velocities lower than the freestream. This transformation produces a polar wake plot that enables the division and quantification of the wake in a number of sections. In this paper, local drag has been used to visualise the drag contribution of the flow by the different sections. Visually, a balanced wake can be observed by the concentric behaviour of the polar plots. Alternatively, integration of the local drag of each degree section as a ratio of the total local drag yields a quantifiable approach of the wake uniformity, where different sections contribute equally to the local drag, with the exception of the wheels.

Memory Estimation of Internet Server Using Queuing Theory: Comparative Study between M/G/1, G/M/1 and G/G/1 Queuing Model

How to effectively allocate system resource to process the Client request by Gateway servers is a challenging problem. In this paper, we propose an improved scheme for autonomous performance of Gateway servers under highly dynamic traffic loads. We devise a methodology to calculate Queue Length and Waiting Time utilizing Gateway Server information to reduce response time variance in presence of bursty traffic. The most widespread contemplation is performance, because Gateway Servers must offer cost-effective and high-availability services in the elongated period, thus they have to be scaled to meet the expected load. Performance measurements can be the base for performance modeling and prediction. With the help of performance models, the performance metrics (like buffer estimation, waiting time) can be determined at the development process. This paper describes the possible queue models those can be applied in the estimation of queue length to estimate the final value of the memory size. Both simulation and experimental studies using synthesized workloads and analysis of real-world Gateway Servers demonstrate the effectiveness of the proposed system.

A Review of Methanol Production from Methane Oxidation via Non-Thermal Plasma Reactor

Direct conversion of methane to methanol by partial oxidation in a thermal reactor has a poor yield of about 2% which is less than the expected economical yield of about 10%. Conventional thermal catalytic reactors have been proposed to be superseded by plasma reactors as a promising approach, due to strength of the electrical energy which can break C-H bonds of methane. Among the plasma techniques, non-thermal dielectric barrier discharge (DBD) plasma chemical process is one of the most future promising technologies in synthesizing methanol. The purpose of this paper is presenting a brief review of CH4 oxidation with O2 in DBD plasma reactors based on the recent investigations. For this reason, the effect of various parameters of reactor configuration, feed ratio, applied voltage, residence time (gas flow rate), type of applied catalyst, pressure and reactor wall temperature on methane conversion and methanol selectivity are discussed.

Analytic and Finite Element Solutions for Temperature Profiles in Welding using Varied Heat Source Models

Solutions for the temperature profile around a moving heat source are obtained using both analytic and finite element (FEM) methods. Analytic and FEM solutions are applied to study the temperature profile in welding. A moving heat source is represented using both point heat source and uniform distributed disc heat source models. Analytic solutions are obtained by solving the partial differential equation for energy conservation in a solid, and FEM results are provided by simulating welding using the ANSYS software. Comparison is made for quasi steady state conditions. The results provided by the analytic solutions are in good agreement with results obtained by FEM.

Performance Analysis of Learning Automata-Based Routing Algorithms in Sparse Graphs

A number of routing algorithms based on learning automata technique have been proposed for communication networks. How ever, there has been little work on the effects of variation of graph scarcity on the performance of these algorithms. In this paper, a comprehensive study is launched to investigate the performance of LASPA, the first learning automata based solution to the dynamic shortest path routing, across different graph structures with varying scarcities. The sensitivity of three main performance parameters of the algorithm, being average number of processed nodes, scanned edges and average time per update, to variation in graph scarcity is reported. Simulation results indicate that the LASPA algorithm can adapt well to the scarcity variation in graph structure and gives much better outputs than the existing dynamic and fixed algorithms in terms of performance criteria.

Effects of Opening Shape and Location on the Structural Strength of R.C. Deep Beams with Openings

This research investigates the effects of the opening shape and location on the structural behavior of reinforced concrete deep beam with openings, while keeping the opening size unchanged. The software ANSYS 12.1 is used to handle the nonlinear finite element analysis. The ultimate strength of reinforced concrete deep beam with opening obtained by ANSYS 12.1 shows fair agreement with the experimental results, with a difference of no more than 20%. The present work concludes that the opening location has much more effect on the structural strength than the opening shape. It was concluded that placing the openings near the upper corners of the deep beam may double the strength, and the use of a rectangular narrow opening, with the long sides in the horizontal direction, can save up to 40% of structural strength of the deep beam.

Study of γ Irradiation and Storage Time on Microbial Load and Chemical Quality of Persian Saffron

Irradiation is considered one of the most efficient technological processes for the reduction of microorganisms in food. It can be used to improve the safety of food products, and to extend their shelf lives. The aim of this study was to evaluate the effects of gamma irradiation for improvement of saffron shelf life. Samples were treated with 0 (none irradiated), 1.0, 2.0, 3.0 and 4.0 kGy of gamma irradiation and held for 2 months. The control and irradiated samples were underwent microbial analysis, chemical characteristics and sensory evaluation at 30 days intervals. Microbial analysis indicated that irradiation had a significant effect (P < 0.05) on the reduction of microbial loads. There was no significant difference in sensory quality and chemical characteristics during storage in saffron.