bluuebunny/crossref_metadata_2025_split huggingface dataset:
Review of Software Fault-Tolerance Methods for Reliability Enhancement of Real-Time Software Systems
Authors: Verma Anjushi , Ghartaan Ankur , Gayen Tirthankar 
Published: October 2019

Real time systems are those systems which must guarantee to response correctly within strict time constraint or within deadline. Failures can arise from both functional errors as well as timing bugs. Hence, it is necessary to provide temporal correctness of programs used in real time applications in addition to providing functional correctness. Although, there are several researches concerned with achieving fault tolerance in the presence of various functional and operational errors but many of them did not address the problem concerned with the timing bugs which is an important issue in real time systems. As for real time systems, many times it becomes a necessity for a given service to be delivered within the specified time deadline. Therefore, this paper reviews the existing approaches from the perspective of real time systems to analyse the shortcomings of these approaches to present a versatile and cost effective approach in the presence of timing bugs for providing fault tolerance to enhance the reliability of the real time software applications.
Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System
Authors: Jaya K. , Gupta Deepa 
Published: October 2019

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model. The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language. This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used. With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.
A Numerical Model of Joule Heating in Piezoresistive Pressure Sensors
Authors: Beddiaf Abdelaziz , Kerrour Fouad , Kemouche Salah 
Published: October 2019

Thermal drift caused by Joule heating in piezoresistive pressure sensors affects greatly the results in the shift of the offset voltage of the such sensors. The study of the thermal behavior of these sensors is essential to define the parameters that cause the output characteristic drift. The impact of Joule heating in a pressure sensor has been studied. The study involves the solution of heat transfer equation considering the conduction in Cartesian coordinates for the transient regime using Finite Difference Method. We determine how the temperature affects the sensor during the applying a supply voltage. For this, the temperature rise generated by Joule heating in piezoresistors has been calculated for different geometrical parameters of the sensor as well as for different operating time. It is observed that Joule heating leads to important rise temperature in the piezoresistor and, hence, causes drift in the output voltage variations in a sensor during its operated in a prolonged time. This paper put emphasis on the geometric influence parameters on these characteristics to optimize the sensor performance. The optimization of geometric parameters of sensor allows us to reducing the internal heating effect. Results showed also that low bias voltage should be applied for reducing Joule heating.
Comparative Study on the AC Brekadown Voltage of Palm Fatty Acid Ester Insulation Oils Mixed With Iron Oxide Nanoparticles
Authors: Mohamad Mohd Safwan , Zainuddin Hidayat , Ab Ghani Sharin , Chairul Imran Sutan 
Published: October 2019

Mineral oils are are derived from petroleum which is a non-renewable and non-sustainable source, and therefore there is a critical need to develop alternative insulation oils for use in transformers. Ester oils offer a number of benefits over mineral oils such as good biodegradability, high cooling stability, good oxidation stability and excellent insulation performance. Nowadays, nanotechnology has become one of the most important research fields in both the academia and industry and it has been shown in previous studies that nanoscale materials are beneficial for transformers. In this regard, the objective of this study is to compare the AC breakdown voltage of palm fatty acid ester (PFAE) oils mixed with iron oxide (Fe<sub>3</sub>O<sub>4</sub>) nanoparticles. The PFAE-based nanofluids are prepared using two methods: (1) Method I (weight-based method whereby the concentration of the Fe<sub>3</sub>O<sub>4 </sub>nanoparticles is 0.01 g/l) and (2) Method II (volume-fraction method whereby the concentration of the Fe<sub>3</sub>O<sub>4 </sub>nanoparticles is 0.01, 0.02 and 0.03%). The AC breakdown voltage test is conducted on the PFAE-based nanofluids in accordance with the ASTM D1816 standard test method. Weibull statistical analysis is carried out to analyse the AC breakdown voltage of fresh PFAE oil and PFAE-based nanofluids. It is found that there is enhancement of the AC breakdown voltage for all PFAE-based nanofluids with the exception of with the exception of one sample prepared using Method II (0.01% Fe<sub>3</sub>O<sub>4 </sub>nanoparticles).
Performance Analysis of Preemptive Based Uniprocessor Scheduling
Authors: Shanmugasundaram M , Kumar R , M Kittur Harish 
Published: October 2019

All the real-time systems are bound with response time constraints, or else, there is a risk of severe consequences, which includes failure. The System will fail when not able to meet the requirements according to the specifications. The problem of real-time scheduling is very vast, ranging from uni-processor to complicated-multiprocessor. In this paper, we have compared the performance of real-time tasks that should be scheduled properly, to get optimum performance. Analysis methodology and the concept of optimization leads to the design of appropriate scheduling. We have done the analysis among RM and EDF algorithm that are important for scheduling in uni-processor.
MOCCCDTA-based Current Mode Tunable Universal Biquad Filter for Bluetooth Applications
Authors: Sharma Jyoti , Chakraborty Shantanu 
Published: October 2019

<p>In the last decade, there has been much effort to reduce the supply voltage of electronic circuits due to the demand for portable and battery-powered equipment. Since a low-voltage operating circuit becomes necessary, the current-mode technique is ideally suited for this purpose more than the voltage-mode one. In this paper, performance of multi output current controlled current differencing transconductance amplifier (MOCCCDTA) is evaluated using 180nm, 90nm and 45nm CMOS technology. It is found that the 45nm CMOS-based<br />MOCCCDTA provides highest frequency i.e. 33GHz. Further a Universal biquad filter has been designed using a single MOCCCDTA as an active element and two capacitors. Filter offers high frequency in GHz. Tunability of all the filter outputs with respect to a bias current has been analyzed. The tunability of the filter circuit for Bluetooth applications is also shown in this work. The performances of MOCCCDTA circuit and Universal biquad filter are illustrated by HSPICE. The simulation results are found to be in agreement with the theoretical predictions.</p>
Design and Fabrication of Rectifying Antenna Circuit for Wireless Power Transmission System Operating At ISM Band
Authors: Doan Chuc Huu , Bach Duong Gia 
Published: October 2019

This paper introduces an overview of a rectifying antenna (rectenna) circuit topology for microwave power transmission system. Specially, a rectenna based on a microstrip patch antenna and a microwave double voltage rectifier at 2.45GHz were designed and fabricated. The antenna’s return loss is achieved of -15 dB at 2.45GHz. The microwave to DC conversion efficiency of the rectenna was measured as 71.5% with 22 dBm input power and 810 Ohm load. The design and simulated results were carried out by the microwave engineering professional design software, known as ADS2009 package. All design and simulation results will be reported.
Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP
Authors: Konda Srinivas , Balmuri Kavitha Rani , Basireddy Ramasubba Reddy , Mogili Ravindar 
Published: October 2019

<strong><span>: </span></strong><span>In data mining classification techniques are used to predict group membership for data instances. These techniques are capable of processing a wider variety of data and the output can be easily interpreted. The aim of any classification algorithm is the design and conception of a standard model with reference to the given input. The model thus generated may be deployed to classify new examples or enable a better comprehension of available data. Medical data classification is the process of transforming descriptions of medical diagnoses and procedures used to find hidden information. Two experiments are performed to identify the prediction accuracy of Cardiovascular Disease (CVD).A hybrid approach for classification is proposed in this paper by combining the results of the associate classifier and artificial neural networks (MLP). The first experiment is performed using associative classifier to identify the key attributes which contribute more towards the decision by taking the 13 independent attributes as input. Subsequently classification using Multi Layer Perceptrons (MLP) also performed to generate the accuracy of prediction using all attributes. In the second experiment, identified key attributes using associative classifier are used as inputs for the feed forward neural networks for predicting the presence or absence of CVD.</span>
Segmentation of Retail Mobile Market Using HMS Algorithm
Authors: Anusha Koyi , C Yashaswini , S Manishankar 
Published: October 2019

<p class="Abstract">In the modern world of marketing, analyzing the trends in market is a key point towards to scope of improvement of any company. Considering the analysis of a retail market where market trends change very frequently based on customer needs and interest is highly challenging. Market segmentation is one of the approaches included in analysis of market trends which gives a diverse view of the market. The research here concentrates, especially on a case study based on fast moving consumable goods market and identifying market change patterns by applying a novel data mining approach. Data mining includes a wide variety of techniques and algorithm which can be effectively used in the process of market analysis. The research work carried out coins a new algorithm which combines various association rules and techniques, the HMS (Hybrid market segmentation) algorithm with some specialized criteria is used to support the market segmentation. The primary data needed for the analysis and operation are collected through a questionnaire based survey conducted on people from various demographic regions as well as various age groups. Used a quota based sampling approach for the research, The data mining approach here helps to study the large dataset collected and also to extract the useful information required to model the system. The system here is a learning system which improves the market segmentation functionality as data set improves, The paper implements a hybrid data mining approach which effectively segments the retail mobile market in to various customer and product groups and also provides a prediction and suggestion system for company as well as customer. </p>
Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer
Authors: Jena Tamanna , Mohanty J.R. 
Published: October 2019

Paradigm need to shifts from cloud computing to intercloud for disaster recoveries, which can outbreak anytime and anywhere. Natural disaster treatment includes radically high voluminous impatient job request demanding immediate attention. Under the disequilibrium circumstance, intercloud is more practical and functional option. There are need of protocols like quality of services, service level agreement and disaster recovery pacts to be discussed and clarified during the initial setup to fast track the distress scenario. Orchestration of resources in large scale distributed system having muli-objective optimization of resources, minimum energy consumption, maximum throughput, load balancing, minimum carbon footprint altogether is quite challenging. Intercloud where resources of different clouds are in align, plays crucial role in resource mapping. The objective of this paper is to improvise and fast track the mapping procedures in cloud platform and addressing impatient job requests in balanced and efficient manner. Genetic algorithm based resource allocation is proposed using pareto optimal mapping of resources to keep high utilization rate of processors, high througput and low carbon footprint. Decision variables include utilization of processors, throughput, locality cost and real time deadline. Simulation results of load balancer using first in first out and genetic algorithm are compared under similar circumstances.
Neural Networks for Flow Bottom Hole Pressure Prediction
Authors: Awadalla Medhat , Yousef Hassan 
Published: October 2019

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.
Power-Flow Development Based on the Modified Backward-Forward for Voltage Profile Improvement of Distribution System
Authors: Suyanto Suyanto , Rahmadhani Citra , Penangsang Ontoseno , Soeprijanto Adi 
Published: October 2019

<p>Unbalanced three-phase radial distribution system has a complex problem in power system. It has many branches and it is sometimes voltage profile’s not stable at every end branches. For improvement of voltage profile, it can be performed by penetrating of a distributed generation models. Information of voltage profile can be gained by study of power flow. The Modified Backward-Forward is one of the most widely used methods of development of power flow and has been extensively used for voltage profile analysis. In this paper, a study of power flow based on the Modified Backward-Forward method was used to capture the complexities of unbalanced three phase radial distribution system in the 20 kV distribution network in North Surabaya city, East Java, Indonesia within considering distributed generation models. In summary, for the informants in this study, the Modified Backward-Forward method has had quickly convergence and it’s just needed 3 to 5 iteration of power flow simulation which’s compared to other power flow development methods. Distributed Generation models in the modified the modified 34 BUS IEEE system and 20 kV distribution network has gained voltage profile value on limited range. One of the more significant findings to emerge from this development is that the Modified Backward-Forward method has average of error voltage about 0.0017 % to 0.1749%.</p>
Optimal Placement of TCSC Based on Sensitivity Analysis for Congestion Management
Authors: Kumari.CH Naga Raja , Sekhar K. Chendra 
Published: October 2019

In a deregulated electricity market whenever congestion management problem occurs, the network collapse because of voltage instability. In this paper total real and reactive power loss deviation based sensitivity indexes (PLDS and QLDS) with rank co-relation concept, has been proposed for the optimal location and operating range of TCSC device. With this placement the power flow in over loaded overhead lines has been reduced and that results in an increased loadability of the power system and also improves the voltage stability and security and also solves the congestion management problem. So ultimately, a more energy efficient transmission system is possible. The case studies were conducted on IEEE 14 bus test system. The ensue corroborate the intended approach for social welfare maximization inreal time.
Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem
Authors: Krishnamurthy Senthil , Tzoneva Raynitchka 
Published: October 2019

<p>Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. High-Performance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange’s function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models.</p>
Experimental and Simulation Studies of Thermal Distribution on Modified Connector of Li-Ion Battery for Electric Vehicles Application
Authors: Risdiyanto Agus , Khayam Umar , A. Rachman Noviadi , Arifin Maulana 
Published: October 2019

<p>One of the several failure cases in electric vehicle could be occured at the Lithium-ion (Li-ion) battery connectors when loaded by high current. This failure caused by bad contact of connectors so that the contact resistance increase and lead to high power losses, overheating, and it can even cause a fire hazard. This paper presents a thermal distributions of Li-ion battery connectors on different coating material in relation to the value of contact resistance. There were two test samples of modeled: copper connection without coating and copper connection with silver coating. Each sample was loaded by the DC current of 350A, and temperature at the connection was measured until steady state condition reached and simulated by Solidwork software. The results show that the temperature at the inside contact area was higher than the outside contact area of connection that appears caused by higher of the contact resistance. Both measurement and simulation results have same tendency that copper connection with silver coating having lower contact resistance, lower maximum temperature, and lower losses about 32 % than copper connection without coating. Silver coating can be considered as other alternative to prevent overheating, high losses, and failure in Li-ion battery connector.</p>
Speech Recognition Using Combined Fuzzy and Ant Colony algorithm
Authors: Jalili Fooad , Jafari Barani Milad 
Published: October 2019

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm. First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals. Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>
Single Perceptron Model for Smart Beam forming in Array Antennas
Authors: Senthilkumar K.S. , Pirapaharan K. , Hoole P.R.P , Hoole R.R.H 
Published: October 2019

<p>In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.</p>