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IJIRST Journal: Volume 3, Issue 3

International Journal for Innovative Research in Science and Technology (IJIRST) is a one of the popular international multidisciplinary, open access, peer-reviewed, fully refereed journal. It is an international journal that aims to contribute to the constant innovative research and training, so as to promote research in the field of science and technology.

Goal:

IJIRST is a monthly international journal publishing the finest peer-reviewed research and review articles in all fields of Science and technology. IJIRSTfollows stringent guidelines to select the manuscripts on the basis of its originality, importance, timeliness, accessibility, grace and astonishing conclusions. IJIRST is also popular for rapid publication of accepted manuscripts. 

                      IJIRST also aims to reach a large number of audiences worldwide with original and current research work completed on the vital issues of the above important disciplines.

International Journal for Innovative Research in Science and Technology (IJIRST)

Volume 3, Issue 3

Submit your Article: www.ijirst.org/index.php?p=SubmitArticle

International Journal for Innovative Research in Science and Technology (IJIRST)

Innovations in Micro-electronics, Signal Processing and Communication Technologies "National Conference(V-IMPACT-2016)" at VIVEKANANDA INSTITUTE OF TECHNOLOGY,Jaipur,Rajasthan,India

"National Conference(V-IMPACT-2016)"  

on

Innovations in Micro-electronics, Signal Processing and Communication Technologies

In association with

IJIRST – International Journal for Innovative Research in Science and Technology

VIT Campus is organizing a conference on 'Innovations in Micro-Electronics, Signal Processing and Communication Technologies'. This conference is fifth in succession to conferences held in years 2012, 2013, 2014 and 2015. The aim of the conference is to review the recent advancement in understanding the science and technology, facilitate exchange of new ideas and explore emerging directions both in basic sciences and technological applications of Electronics, Signal Processing Communication. Recently, scientific activities are on surge on the MEMS, VLSI, DSP and Communication. So the increase in the research activities and the consequent enthusiasm is on rise day by day. 

The new fields such as CAD, VLSI and MATLAB are at the horizon highlighting many important issues involved in the preparation and applications of these useful Systems & Fields. These topics which constitute the frontiers of devices and technology are expected to lead to the development of new systems and new technologies. 

The Conference is intended to bring theorists, experimentalists and experts on a common platform and foster inter disciplinary research. The thrust of the conference will be to facilitate emergence of collaborations between the participants. The informal atmosphere that will prevail is expected to facilitate interactions between young researchers and experts which will be particularly useful for graduate/ research students. We invite you all to participate, deliver talks, present your work and make this event a great success.

VIVEKANANDA INSTITUTE OF TECHNOLOGY

Sisyawas, NRI Road, Jagatpura, Jaipur-303012

Website : www.vitej.ac.in

Publication Partner:

International Journal for Innovative Research in Science and Technology 

Website:- www.ijirst.org

#IJIRST Journal: Dynamic Clustering in Wireless Sensor Networks Based on the Data Traffic Flow and the Node Residual Battery Life Computation

Paper Title:- Dynamic Clustering in Wireless Sensor Networks Basedon the Data Traffic Flow and the Node Residual Battery Life Computation

Author Name:- Sachin Sharma, Professor (Dr.) K C Roy, Professor (Dr.) Dinesh Goyal

Department of Computer Science and Engineering 

Suresh Gyan Vihar University, Jagatpura

Abstract:- Wireless Sensor Networks forms the core of the infrastructural facilities and amenities that constitutes a major part of modern living. Wireless Sensor Networks founds tremendous applications in domains such as theft alarms, wildlife monitoring, radiation/pressure/light/heat sensor networks and the list is endless. It constitutes the core part of the modern Internet of Things (IoT) that will revolutionize the modern living. The Iot specifies a scenario in which the devices can communicate with each other using the internet over a flexible framework and can be programmed to perform specific actions based on the programming customization made by the users. For example, a refrigerator is runs out of milk or bread can email the requirement to the dairy that can entertain the mail and ship a delivery of the same to the location of the refrigerator. As sensor nodes are battery powered, there is a critical aspect to same battery power. This is possible only by avoiding the in-network communication as much as possible. A fraction of communication overhead can be reduced through clustering. In this paper, an approach for dynamic clustering is proposed based on the varying traffic loads to various PAN coordinators so as to maximize the battery life and therefore the network lifetime.

Keywords:- Wireless sensor network, Clustering Protocols, Battery Life etc.

I.    Clustering in Wireless Sensor Networks

Clustering forms, the backbone towards the persistence of sensor nodes towards sensing data in such a way that a single lithium ion battery can work even for one and a half year continuously. This is because of the reduction in in-network communication to the central node through the creation of clusters in such a way that all the node in the cluster transmit the data to the cluster head and the cluster head is responsible to transmit the data to the central node. The senario is expressed in the following figures.

Fig. 1: Wireless Sensor Network without clustering

Fig. 2: Wireless Sensor Network with clustering and Data Aggregation

       The individual collections shown in figure 1.2 are known as clusters and the nodes that belongs to a particular cluster sends the data only to the cluster head. Thus, reducing the data transmission over long distance from the individual nodes to the central computer. In the clustered approach, the nodes transmit the data to the cluster head over a relatively very short distance, thus, conserving the battery life and enhancing the network lifetime.

II.    Dynamic Clustering over the WirelessSensor Network

Consider a network of N nodes and a static number set initially k as the total number of cluster over the network. Thus, on an average, there are N/k in each cluster. Also, consider a rectangular plane of dimension aXa over which the sensor nodes are (approximately evenly) speeded.

      As state previously, there are k clusters each having (N/k)-1 nodes as ordinary sensing nodes and a Cluster head that hold the responsibility of aggregating data from each of the (N/k)-1 nodes. Also assume that each packet senses the medium and sends the data packet to the cluster head in specified TDMA frame.

      Considering the first order radio energy dissipation model, let the energy consumption per bit in the transmission circuitry be E_t and the energy consumption per bit in the processing circuitry be E_p. Let there be B bits in a TDMA packet. Considering the initial energy level in the battery be E, one can approximate the residual battery life after N rounds.

    Let Me be the number of rounds after which the leader election takes place and a message is broadcasted to all the other nodes in the cluster regarding the node which is elected as the leader so that all the nodes may transmit the data to the specific node. The specified node then aggregates the data from all the nodes in its cluster and transmit the data to the central computer.

      It is important to note that the leader election process is an overhead and is incurred only to manage the network traffic. Rapidly electing new heads and consequently broadcasting the message to all other nodes in the network induce an overhead which is to be avoided. On the other hand, it is also important to note that the node which is elected as the cluster head depletes its energy very frequently as it has to perform all the data aggregation processing all be itself for all the nodes in the network. Thus, frequent leader election leads to an evenly consumption of battery power in all the nodes of the cluster. If no election of leader takes place, then the node which handles the task of leader will soon run out of the battery.

     In addition to the depltion of the battery in the normal rounds during the data gathering, the leader will deplete the energy

E = E_broad*n*[(N/k)-1]

in view of broadcasting the message, where n is the number of bits in the broadcasted message, and all the nodes depletes an amount of energy equals to

E = n*E_p

in view of the reception of the message regarding the leader of the cluster.

      Let p be the average number of packets that are transmitted by any node and let the length of each packet be l. For implementation, the case study of Zigbee radio sensors is considered in which the underlying operating system is tiny OS having packet size of l=114 bytes.

The important points to analyze in the scenario is:

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Retrofitting of Reinforced Concrete Beam with Externally Bonded CFRP

Title:- Retrofitting of Reinforced Concrete Beam with Externally Bonded CFRP

Author Name:- J. Gopi Krishna

Abstract:- In our country many of the existing reinforced concrete structures are in need of repair or reconstruction, rehabilitation, because of deterioration due to various factors like corrosion, lack of detailing, failure of bonding between beam-column joints, increase in service loads, improper design and unexpected external lateral loads such as wind or seismic forces acting on a structure, environment and accident events etc., leading to cracking, spalling, loss of strength, deflection, etc. Strengthening of existing reinforced concrete structures is necessary to obtain an expected life span and achieve specific requirements. The need for efficient rehabilitation and strengthening techniques of existing concrete structures has resulted in research and development of composite strengthening systems. Recent experimental and analytical research have demonstrated that the use of composite materials for retrofitting existing structural components is more cost-effective and requires less effort and time than the traditional means. Fiber Reinforced Polymer (FRP) composite has been accepted in the construction industry as a capable substitute for repairing and strengthening of RCC structures. The superior properties of (FRP) polymer composite materials like high corrosion resistance, high strength, high stiffness, excellent fatigue performance and good resistance to chemical attack etc., has motivated the researchers and practicing engineers to use the polymer composites in the field of rehabilitation of structures. During past two decades, much research has been carried out on shear and flexural strengthening of reinforced concrete beams using different types of fiber reinforced polymers and adhesives. A detailed Literature review based on the previous experimental and analytical research on retrofitting of reinforced concrete beams is presented. Proposed method of strengthening the RC beam is decided based on the previous experimental and analytical research. Behaviors of retrofitted reinforced concrete beams with externally bonded CFRP with various types of resins (Epoxy, Orthophthalic Resin (GP), ISO resin) after initial load (60 % control beam) is investigated. Static load responses of all the beams under two point load method had evaluated in terms of flexural strength, crack observation, compositeness between CFRP fabric and concrete, and the associated failure modes.  

Keywords: Fiber Reinforced Polymer (FRP), CFRP fabric, reinforced concrete structures

I.   Introduction

Concrete is the most widely used man-made construction material in world. It is obtained by mixing cementing materials, water and aggregates, and sometimes admixtures is required proportions. Concrete has high compressive strength, low cost and abundant raw material, but its tensile strength is very low. Reinforced concrete, which is concrete with steel bars embedded in it. Concrete is an affordable material, which is extensively used throughout in the infrastructure of nation’s construction, industry, transportation, defense, utility, and residential sector. The flexibility and mould ability of this material, its high compressive strength, and the discovery of the reinforcing and prestressing techniques which helped to make up for its low tensile strength have contributed largely to its widespread use.

     Reinforced concrete structures often have to face modification and improvement of their performance during their service life. In such circumstances there are two possible solutions. The first is replacement and the other is retrofitting. Full structure replacement might have determinate disadvantages such as high costs for material and labour, a stronger environmental impact and inconvenience due to interruption of the function of the structure e.g. traffic problems. So if possible, it is often better to repair or upgrade the structure by retrofitting. Retrofitting methods is shown in figure 2.1.1. In recent years repair and retrofit of existing structures such as buildings, bridges, etc., have been quite prevalent among the most important challenges in Civil Engineering. 

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Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Author Name:- Mohammad Aslam Ansari 

Department of Electrical Engineering 

Abstract:- Power transformer is one of the most important and expensive equipment in electrical network. The transformer oil is a very important component of power transformers. It has twin functions of cooling as well as insulation. The oil properties like viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant suffer a change with respect to time. Hence it is necessary that the oil condition be monitored regularly to predict, if possible, the remaining lifetime of the transformer oil, from time to time. Six properties such as moisture content, resistivity, tan delta, interfacial tension and flash point have been considered. The data for the six properties with respect to age, in days, has been taken from literature, whereby samples of ten working power transformers of 16 to 20 MVA installed at different substations in Punjab, India have been considered. This paper aims at developing ANN and ANFIS models for predicting the age of in-service transformer oil samples. Both the the models use the six properties as inputs and age as target. ANN (Artificial Neural Network) model uses a multi-layer feedforward network employing back propagation algorithm, and ANFIS (Adaptive Neuro Fuzzy Inference System) model is based on Sugeno model. The two models have been simulated for estimating the age of unknown transformer oil samples taken from generator transformers of Anpara Thermal Power Project in state of U.P. India. A comparative analysis of the two models has been made whereby ANFIS model has been found to yield better results than ANN model.     

Keywords: ANN, ANFIS, Power Transformer, Regression, Performance, Backpropagation Algorithm   

I.         Introduction

Power transformer is one of the most important constituent of electrical power system. The transformer oil, a very important ingredient of power transformers, acts as a heat transfer fluid and also serves the purpose of electrical insulation. Its insulating property is subjected to the degradation because of the ageing, high temperature, electrical stress and other chemical reactions. Hence it is necessary that the oil condition be monitored regularly. This will help to predict, if possible, the in-service period or remaining lifetime of the transformer oil, from time to time.

       There are several characteristics which can be measured to assess the present condition of the oil. The main oil characteristics are broadly classified as physical, chemical and electrical characteristics; some of these are viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant. There exists a co-relation among some of the oil properties and suffer a change in their values with respect to time [2]. This variation of oil properties with respect to time has been utilised to develop the two models as said earlier

      The training data for the proposed work have been obtained from literature, whereby ten working transforms of 16 to 20 MVA, 66/11 KV installed at different substations in the state of Punjab, India have been considered. The six properties of transformer oil such as breakdown voltage (BDV), moisture, resistivity, tan delta, interfacial tension and flash point have been considered as inputs and age as target. Test data have been taken from generator transformers of 250 MVA, 15.75kV/400kV from Anpara Thermal Power Project in state of U. P., India.

II.     “Ann” and “Anfis” methods

It is known that classical models need linear data for their processing, therefore models like ANN and ANFIS that are based on soft computing techniques, play an important role for solving these kinds of non-linear problems.

        Neural networks exhibit characteristics such as mapping capabilities or pattern association, generalization, robustness, fault tolerance, parallel and high speed processing. Neural networks can be trained with known examples of a problem to acquire knowledge about it. Once trained successfully, the network can be put to effective use in solving unknown or untrained instances of the problem. ANN model which uses multilayer feed forward network is based on back propagation (BP) learning algorithm of neural network. Backpropagation gives very good answers when presented with inputs never seen before. This property of generalization makes it possible to train a network on giving set of input-target pairs and get good output.

          ANFIS stands for Adaptive Neural Fuzzy Inference System. Using a given input/output data set, the toolbox function ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone, or in combination with a least squares type of method. This allows the fuzzy systems to learn from the data they are modelling. These techniques provide a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. This learning method works similarly to that of neural networks.

III.       Development of ann model

The proposed ANN model uses “Levenburg-Marquardt (trainlm) algorithm which is independent of learning rate, hence by simply changing the number of neurons in hidden layer, training and testing error could be reduced. A total of 700 data sets obtained from literature [2] were arranged in tabular form and used for training the neural network. The model uses a simple two layer network, one hidden layer and one output layer. Input layer comprises of six neurons, one for the each input, while the output layer has a single neuron for a single output, the age of oil sample.

          It has been found that network architecture that uses 20 neurons in hidden layer gave the best performance with a regression of 0.999 and mean square error (MSE) of 83.0 ( data is non –normalized, so error looks large ) . The training continued for 184 iterations with training functions logsig in hidden layer and purelin in output layer respectively.

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