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Optimal location and parameters of GUPFC for transmission loss minimization using PSO algorithm
Transmission losses are one of the major losses faced by our power system. Reduction of transmission losses will benefit us by saving a large amount of power. The transmission losses can be reduced by placing FACTS devices in the power system. Among all the FACTS devices Unified Power Flow Controller (UPFC) and Generalized Unified Power Flow Controller (GUPFC) are the best. Incorporating the GUPFC in to the power system and placing it to the optimal location and setting its output to optimal values can reduce the transmission losses. This paper explains the way to locate the optimal location of GUPFC and finding the optimal setting using PSO algorithm to reduce the total transmission losses. Voltage variation is taken as the criteria for finding the location and PSO is used for finding the settings of GUPFC. This study is conducted on an IEEE 14-bus system using MATLAB software. 2017 IEEE. -
The secured data provenance: Background and application oriented analysis
It is with the advancement of overwhelming wireless internet access in mobile environments, users and usage data has become huge and voluminous on regular basis. For instance, the financial transactions performed via online by users are unsecure and unauthenticated in many contexts. Methods and algorithms exist for secure data transmission over different channels, perhaps lacks to achieve high performance with respect to the basic goals of security; confidentiality, integrity, availability at a considerable level. The origin of the data i.e., by whom the original transaction thread have been started, is the critical question to be answered while finalizing with the financial transaction. This concept of 'history of data' have attained good attention by the researchers from many decades at different application domains and is named as Data Provenance. However, provenance with security has got a little progress with research in the recent times especially in cyber security. This study focuses on the security aspects of data provenance with a unique approach in cryptography. The blend of these two technologies could provide an indigenous solution for securing the provenance of the related data. 2016 IEEE. -
A framework for smart transportation using Big Data
In the current era of information technology, data driven decision is widely recognized. It leads to involvement of the term 'Big Data'. The use of IOT and ICT deployment is a key player of the smart city project in India. It leads to smart transportation systems with huge amounts of real time data that needs to be communicated, aggregated, interpreted, analyzed and maintained. These technologies enhance the effective usage of smart transportation systems, which is economical and has a high social impact. Social applications like transportation can be benefited by using IOT, ICT and big data analytics to give better prediction. In this paper, we present how big data analytics can be used to build a smart transportation system. Increasing traffic and frequent jams in today's scenario are becoming a routine, citizens are facing various health issues due to the bad transportation systems such as high blood pressure, stress, asthma due to air and noise pollution. In smart transportation mobility can be easily implemented as most of the citizens use smartphones. It can be easily linked to smart traffic signals to achieve the objective of smart transportation. Smart transportation is a key component to attract companies as it leads to better services, business planning, support beneficial environment and social behavior. 2016 IEEE. -
Hybrid short term load forecasting using ARIMA-SVM
In order to perform a stable and reliable operation of the power system network, short term load forecasting is vital. High forecasting accuracy and speed are the two most important requirements of short-term load forecasting. It is important to analyze the load characteristics and to identify the main factors affecting the load. ARIMA method is most commonly used, as it predict the load purely based on the historical loads and no other assumptions are considered. Therefore there is a need for Outlier detection and correction method as the prediction is based on historical data, the historical data may contain some abnormal or missing values called outliers. Also the load demand is influenced by several other external factors such as temperature, day of the week etc., the Artificial Intelligence techniques will incorporate these external factors which improves the accuracy further. In this paper a hybrid model ARIMA-SVM is used to predict the hourly demand. ARIMA is used to predict the demand after correcting the outliers using Percentage Error (PE) method and its deviation is corrected using SVM. Main objective of this method is to reduce the Mean Absolute percentage Error (MAPE) by introducing a hybrid method employing with outlier detection technique. The historical load data of 2014-2015 from a utility system of southern region is taken for the study. It is observed that the MAPE error got reduced and its convergence speed increased. 2017 IEEE. -
A Survey on Enhancing System Performance of Wireless Sensor Network by Secure Assemblage Based Data Delivery
To provide secure data transmission in Cluster Wireless Sensor Networks (CWSNs), the challenging task is to provide an efficient key management technique. To enhance the performance of sensor networks, clustering approach is used. Wireless Sensor Network (WSN) comprises of large collection of sensors having different hardware configurations and functionalities. Due to limited storage space and battery life, complex security algorithms cannot be used in sensor networks. To solve the orphan node problem and to enhance the performance of the WSN, authors introduced many secure protocols such as LEACH, Sec-LEACH, GS-LEACH and R-LEACH, which were not secure for data transmission. The energy consumption in existing approach is more due to overhead incurred in computation and communication in order to achieve security. This paper studies about different schemes used for secure data transmission. We are proposing new methodology called IBDS and EIBDS that will increase the performance of WSN by reducing computational overhead and also increases resilience against the adversaries. 2017 IEEE. -
Parametrical variation and its effects on characteristics of microstrip rectangular patch antenna
This paper represents a brief description about design of rectangular microstrip patch antenna and its parameter effects in size, efficiency and compactness and parametric analysis in terms of return loss, bandwidth, directivity and gain by using same and different dielectric substrate materials with same and different thickness of rectangular microstrip patch antenna. The important parameters of patch such as L, W, r and h has its own impact in antenna characteristics. This parametrical impact is studied and verified. As thickness of dielectric substrate increases, the gain & directivity of rectangular microstrip patch antenna decreases and bandwidth increases. As r increases, the size of the antenna decreases but when height of dielectric substrate increase antenna size also increases. There will be always a compromise between miniaturization and other antenna characteristics. This antenna is designed for microstrip feed line technique and with center frequency (f0) at 4GHz. The parametric analysis is obtained by comparing the simulated results of rectangular microstrip patch antenna for different cases. The proposed antenna is simulated using HFSS tool at resonance frequency of 4 GHz. 2017 IEEE. -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved. -
Cluster analysis for european neonatal jaundice
The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means. 2018, Springer International Publishing AG. -
Enhancement of coal nanostructure and investigation of its novel properties
Coal is a mineral and is extensively used as a solid fuel in developing nations and has a sizeable share in the global fossil fuel reserve. Utilization of this resource generates excess spoil and large volume of low grade waste to the environment. In recent years there have been serious research on enhancing its value and exploring the utility of this carbonaceous material to novel carbon materials. The Minerals, Metals & Materials Society 2018. -
A classified study on semantic analysis of video summarization
In today's world data represented in the form of a video are prolific and has increased the requisite of storage devices unconditionally. These video sets takes up a huge space for amassing data and takes a long time to ascertain the content that requires a higher cognitive process for content search and retrieval. The efficient method for storing video data is to remove high-degree redundancies and for creating an index of important events, objects and a preview video based on vital key-frames. These requirements imbibes the need to build algorithms that can concise the necessity of space and time for video and adequate approaches are to be developed to solve the needs of summarization. The three effective attributes for a semantic summarized video system are Un-supervision, efficient and dynamically scalable system that can help in reducing time and space complexities. Dimensionality reduction based on sub space analysis helps in plummeting the multidimensional data into a low-dimensional data to enable faster feature extraction and summarization. In this paper we have made a study and description related to several summarization methodologies for video's that are available. 2017 IEEE. -
DNA based cryptography to improve usability of authenticated access of electronic health records
The quality of health care has been drastically improved with the evolution of Internet. Electronic health records play a major role in interoperability and accessibility of patients data which helps in effective and timely treatment irrespective of the demographic area. The proposed model is to ensure and monitor maternal health during pregnancy and to create awareness alerts (options include messages, voice alerts or flash the system) based on the individual health record. The system aims to prevent maternal death due to medical negligence and helps to make recommendations to prevent future mortality based on medical history and take appropriate action. Authentication is a critical aspect considering the trade-off between usability and security whereas data breach and related cybercrime are major concerns in health care. The proposed model uses DNA based authentication techniques to ensure usability and confidentiality of electronic data, Aadhaar to prevent unauthorized access to patients data in case of emergency without affecting availability. 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
PCRS: Personalized Course Recommender System Based on Hybrid Approach
The traditional system of selecting courses to carry out research work is time consuming, risky and a tedious task, that not only badly affect the performance but the learning experience of a researcher as well. Therefore, choosing appropriate courses in seminal years could help to do research in a better way. This Study presents a recommender system that will suggest and guide a learner in selecting the courses as per their requirement. The Hybrid methodology has been used along with ontology to retrieve useful information and make accurate recommendations. Such an approach may be helpful to learners to increase their performance and improve their satisfaction level as well. The proposed recommender systems would perform better by mitigating the weakness of basic individual recommender systems. 2018 The Authors. Published by Elsevier B.V. -
Design considerations of an inductive sensor for segmented mirror telescopes
The Segmented mirror technology has become natural choice for any optical telescope larger than 8 meter in size, where small mirror segments are aligned and positioned with respect to each other to an accuracy of few tens of nanometer. Primary mirror control system with the help of edge sensor and soft linear actuator maintains that alignment which changes due to gravity and wind loading. For any segmented mirror telescope edge-sensor plays very critical role. It should have very high spatial resolution (few nanometer), large range, multidimensional sensing, high temporal stability as well as immunity towards relative change in temperature and humidity. Though capacitive sensors are widely used for this purpose, however, their inherent sensitivity towards humidity and dust make them unsuitable for telescopes operating at humid low altitude regions. Whereas, inductance based sensors, working on the principal of mutual inductance variation between two planar inductor coils, produce promising results in such a situation. Looking at stringent requirements, design and development of a planar inductive sensor is a challenge. As a first step toward sensor development, we have explored the design aspects of it. The inductive coils are first simulated and analyzed using electromagnetic FEA software for different coil parameters. The design considerations include optimization of coil parameters such as geometry of coils, trace densities, number of turns, etc. and operational requirements such as number of degree of freedoms to be sensed, range of travel, spatial resolution, as well as required sensitivity. The simulation results are also verified through experimentation. In this first paper we report the design and analysis results obtained from FEA simulations. 2018 SPIE. -
Monitoring nyiragongo volcano using a federated cloud-based wireless sensor network
Current Nyiragongo Volcano observatory systems yield poor monitoring quality due to unpredictable dynamics of volcanic activities and limited sensing capability of existing sensors (seismometers, acoustic microphones, GPS, tilt-meter, optical thermal, and gas flux). The sensor node has limited processing capacity and memory. So if some tasks from the sensor nodes can be uploaded to the server of cloud computing then the battery life of the sensor nodes can be extended. The cloud computing can be used both for processing of aggregate query and storage of data. The two principal merits of this paper are the clear demonstration that the Cloud Computing model is a good fit with the dynamic computational requirements of Nyiragongo volcano monitoring and the novel optimization algorithm for seismic data routing. The proposed new model has been evaluated using Arduino-Atmega328 as hardware platform, Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. 2017 IEEE. -
An improvised grid resource allocation and classfication through regression
The resource allocation is one of the important mechanisms of grid computing, which helps to assign the available resources very efficiently. The one of the issue of grid computing is fixing the target nodes during the grid job execution. In existing method, resource monitored data are collected from grid then jobs are allocated to the resources based on available data, through regression algorithm. In this method total execution time of an application and run time of jobs should be high. The proposed method mitigate running time by classify the resources in the data collected from grid based on dwell time using novel classification algorithm. It reduces the jobs run time and fit the best available resources to the jobs in the computational grid. 2017 IEEE. -
Computational approach of artificial neural network
This paper makes an attempt to predict the movement of the stock price for the following day using Artificial Neural Network (ANN). For the purpose of this research, two companies from each industry have been chosen that is, TATA Motors and Honda Motors from the Automobile industry and Cadila Pharmaceuticals Ltd. and Glenmark Pharmaceuticals from the Pharmaceutical industry. The historical prices of these companies were collected and by using Artificial Neural Network (ANN), the movement of the stock price for the next day is predicted. 2017 IEEE. -
Real time conversion of sign language to speech and prediction of gestures using artificial neural network
Sign language is generally used by the people who are unable to speak, for communication. Most people will not be able to understand the Universal Sign Language (unless they have learnt it) and due to this lack of knowledge about the language, it is very difficult for them to communicate with mute people. A device that helps to bridge a gap between mute persons and other people forms the crux of this paper. This device makes use of an Arduino Uno board, a few flex sensors and an Android application to enable effective communication amongst the users. Using the flex sensors, gestures made by the wearer is detected and then according to various pre-defined conditions for the numerous values generated by the flex sensors, corresponding messages are sent using a Global System for Mobile(GSM) module to the wearer?s android device, which houses the application that has been designed to convert text messages into speech. The GSM module is also used to send the sensor inputs to a cloud server and these values are taken as input parameters into the neural network for a time series based prediction of gestures. The system is designed to be a continually learning device and improve reliability by monitoring every individual?s behaviour at all times. 2018 The Authors. Published by Elsevier B.V. -
Sensitivity and tolerance analysis of 2D Profilometer for TMT primary mirror segments
The primary mirror (M1) of Thirty Meter Telescope (TMT) consists of 492 segments of which, 86 are ground and polished by India-TMT. These segments are off-Axis and aspheric in nature and one of the effective methods to polish such segments is through Stressed Mirror Polishing (SMP). During SMP, consistent in-situ metrology of the surface is needed to achieve the required profile. A 2D Profilometer (2DP) will be used by India-TMT for the low frequency profile metrology. The 2DP is a contact-Approach metrology, consisting of probes positioned in a spiral pattern, measuring the sag of segment surface. Initial section of this paper deals with the sensitivity and tolerance analysis of the 2DP. This is followed by the study on position and rotational errors of the 2DP as a whole. Simulation of these analysis is carried out initially on a sphere and then on different segments of the TMT, in order to study the induced measurement errors. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
Synthesis and characterization of Chitosan-CuO-MgO polymer nanocomposites
In the present work, we have synthesized Chitosan-CuO-MgO nanocomposites by incorporating CuO and MgO nanoparticles in chitosan matrix. Copper oxide and magnesium oxide nanoparticles synthesized by precipitation method were characterized by X-ray diffraction and the diffraction patterns confirmed the monoclinic and cubic crystalline structures of CuO and MgO nanoparticles respectively. Chitosan-CuO-MgO composite films were prepared using solution- cast method with different concentrations of CuO and MgO nanoparticles (15 - 50 wt % with respect to chitosan) and characterized by XRD, FTIR and UV-Vis spectroscopy. The X-ray diffraction pattern shows that the crystallinity of the chitosan composite increases with increase in nanoparticle concentration. FTIR spectra confirm the chemical interaction between chitosan and metal oxide nanoparticles (CuO and MgO). UV absorbance of chitosan nanocomposites were up to 17% better than pure chitosan, thus confirming its UV shielding properties. The mechanical and electrical properties of the prepared composites are in progress. 2018 Author(s). -
Spectroscopic analysis of lead borate systems
Oxide glass systems are interesting because of their bonding like bridging and non-bridging oxygens. Depending on the modifier, the B2O3 glass system can have various Boron-Oxygen network. It is found that, PbO modifies the borate network and increases the formation of penta and diborate groups. In this work, we investigated optical properties of Lead Borate glass systems (x PbO: (1-x) B2O3) with x varying from 30-85 mol % using UV-VIS Spectra and the corresponding band gap was estimated using Tauc relation and these systems behave like direct allowed band gap systems. These results show that, Eg decreases with the addition of lead content. Further the refractive index measurements also have been carried out at various wavelengths. Many correlation is found between the band gap and refractive index for different compositions. Using different theoretical models a best fit has been tried and Ravindra's relation is found to match with our experimental results. 2018 Author(s).