Special Issue No. – 5, January, 2020

Abstract:

This paper discusses power quality improvement in a DFIG based WECS connected to a distribution system. A WECS is usually affected by issues such as high frequency oscillations, harmonics, transients, voltage sags, swells, voltage unbalance etc., These result in malfunction or damage to the electrical equipment in the system and lead to financial losses. So in order to mitigate the power quality events a UPQC device is employed which integrates series and shunt active filters that provide satisfactory compensation for power quality problems. The performance of UPQC is compared by using Fractional-Order PID controller and Adaptive Neuro Fuzzy Logic Controller. Modeling of DFIG and WECS systems is discussed with relevant equations. Design of UPQC and controllers is also discussed in detail. The proposed DFIG based WECS employing UPQC is simulated on MATLAB/ Simulink platform. The compensation capabilities of UPQC are assessed for both controllers for the proposed system and simulation results are presented.

Keywords:

DFIG (Double Fed Induction Generator),Grid,Power Quality,Voltage sag,Voltage Swell,reactive power compensation,Wind Turbine,UPQC (Unified Power Quality Conditioner),

Refference:

I. A. Albakkar, O. P. Malik, “Adaptive Neuro-Fuzzy Controller Based on
Simplified ANFIS Network”, IEEE 978-1-4673-2729-9/12/$31.00, 2012.
II. A. Tepljakov, E. Petlenkov, I. Petras, “Design of a MATLAB-based
Teaching Tool in Introductory Fractional-Order Systems and Controls”, IEEE
978-1-5090-5920-1/17/$31.00, 2017.
III. B. Lin, P. Han, D.Wang, Q. Guo, “Control of Boiler-Turbine Unit Based on
Adaptive Neuro-Fuzzy Inference System”, IEEE 0-7803-7952-7/03/$17.00,
pp: 2821 – 2826, 2003.
IV. G. P. R. Reddy, M. V. Kumar, “ANFIS Based SVPWM Technique for HBridge
Multilevel Matrix Converter in Wind Energy Conversion System
Employing DFIG”, International Journal of Control Theory and Applications,
Vol.: 10, Issue: 16, pp:105 – 113, 2017.
V. H. Afghoul, F. Krim, A. Beddar, M. Houabes, “Switched fractional order
controller for grid connected wind energy conversion system”, The 5th
International Conference on Electrical Engineering – Boumerdes (ICEE-B)
October 29-31, Boumerdes, Algeria, 2017.
VI. H. Arpacıa, Ö F. Özgüven, “ANFIS & FOPID controller design and
comparison for overhead cranes”, Indian Journal of Engineering & Materials
Sciences Vol. 18, pp. 191-203, 2011.
VII. J. G. Sreeram, V. Gowtham, “Implementation of Fractional Order PID
Controller for An AVR System Using GAOptimization Technique”,
International Journal for Research in Applied Science & Engineering
Technology (IJRASET), Vol.: 5, Issue: V, pp: 1588 -1594, 2017.
VIII. L. Wang, D. N. Truong, “Stability Enhancement of a Power System With a
PMSG-Based and a DFIG-Based Offshore Wind Farm Using a SVC With an
Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on
Industrial Electronics, Vol. 60, Issue: 7, 2013.

IX. L. Wang, M. S. N. Thi, “Stability Enhancement of a PMSG-Based Offshore
Wind Farm Fed to a Multi-Machine System through an LCC-HVDC Link”,
IEEE Transactions on Power Systems, Vol. 28, Issue: 3, 2013.
X. M. Al-Dhaifallah , N. Kanagaraj, K. S. Nisar, “Fuzzy Fractional-Order PID
Controller for Fractional Model of Pneumatic Pressure System”, Hindawi
Mathematical Problems in Engineering Volume, Article ID 5478781, 2018.
XI. M. El-Sayed, M. Essa, M. A. S. Aboelela, M. A. M. Hassan, “A Comparative
Study between Ordinary and Fractional Order PID Controllers for
Modelingand Control of an Industrial system Based on Genetic Algorithm”,
6th International Conference on Modern Circuits and Systems Technologies
(MOCAST), IEEE 978-1-5090-4386-6/17/$31.00, 2017.
XII. R. Bhavani, N. R. Prabha, C. Kanmani, “Fuzzy Controlled UPQC for Power
Quality Enhancement in a DFIG based Grid Connected Wind Power
System”, International Conference on Circuit, Power and Computing
Technologies. IEEE 978-1-4799-7075-9/15/$31.00, 2015.
XIII. R. Melício, J. P. S. Catalão, V. M. F. Mendes, “Fractional-Order Control and
Simulation of Wind Turbines with Full-Power Converters”, IEEE 978-1-
4244-5794-6/10/$26.00, 2010.
XIV. S. S. Sathri, M. Manohara, “Power quality improvement in grid connected
wind energy conversion systems by using custom power device”,
International Research Journal of Engineering and Technology (IRJET), Vol.:
04, Issue: 06, pp: 3254 – 3259, 2017.

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Abstract:

This The direct torque control (DTC)operation merely depends on the selection of inverter switching sequences with look-up table thereby stator flux of the motor is not exactly equal to desired reference which causing high flux and torque ripple. This paper considers the effect of switching sequences of space vector modulation (SVM) and advanced space vector modulation (ASVM) on the performance of DTC. The SVM comprises unique pattern of switching sequences whereas ASVM is having two different patterns of switching sequences in the every sector which influences on the d-q axes flux distortion it shows significant effect on the ripple. The interpretation of ASVM versus SVM switching sequences required for the analysis of flux and torque ripple is shown with the case study of DTC. The flux ripple analysis shows that q-axis flux distortion merely effects on the torque ripple and d-axis flux distortion effects on flux ripple. The performance investigation of induction motor with DTC using SVM and proposed ASVM strategies are verified with simulations and experimental results. The experimental verification is conducted with Opal-RT real time digital simulator

Keywords:

Direct Torque Control (DTC),Space Vector Modulation (SVM),Advanced Space Vector Modulation (ASVM),Flux Ripple,Torque Ripple,

Refference:

I. A. Sikorski, M. Korzeniewski, A. Ruszczyk, M. P. Kazmierkowski, P.
Antoniewicz, W. Kolomyjski, M. Jasinski, “A Comparison of Properties of
Direct Torque and Flux Control Methods (DTC-SVM, DTC-δ, DTC-2×2, DTFC-
3A)”, International Conference on “Computer as a Tool” – EUROCON, pp.
1733-1739, 2007.
II. A. Tripathi, A. M. Khambadkone, S. K. Panda, “Torque ripple analysis and
dynamic performance of a space vector modulation based control method for
AC-drives,” IEEE Trans. Power Electron., Vol.: 20, Issue: 2, pp. 485–492, 2005.
III. B. Naresh, K. V. Kumar, Y. Narri, “DTC of induction motor with advanced
SVM strategies” IEEE Region 10 Conference (TENCON-2017), pp. 1737-1742,
2017.
IV. C. Lascu, I. Boldea, F. Blaabjerg, “A Modified Direct Torque Control for
Induction Motor Sensorless Drive” IEEE Trans. on Industry Applications, Vol.
36, Issue: 1, pp. 122-130, 2000.
V. D. Casadei, F. Pmfumo, G. Serra, A. Tani, “FOC and DTC: two viable schemes
for iuduction motors torque control,” IEEE Trans. Power Elect., Vol.: 17, Issue:
5, pp. 779-787, 2002.
VI. D. Soumitra, C. B. Arathil, G. Narayanan, “Analytical evaluation of harmonic
distortion factor corresponding to generalized bus-clamping pulse widt
modulation” IET Trans. Power Electron., Vol.: 7, Issue: 12, pp.3072-3082,
2014.
VII. D. Stando, M. P. Kazmierkowski,”Novel speed sensorless DTC-SVM scheme
for induction motor drives,”International Conference on Compatibility and
Power Electronics (CPE), pp.225-230, 2013.
VIII. D. Zhao, R. Ayyanar, “Space Vector PWM with DC link Voltage Control and
using Sequences with Active State Division,” Ind. Electron., in Proc. IEEE Int.
Symp., Vol. 2, pp. 1223–1228, 2006.
IX. E. Ozkop, H. I. Okumus, “Direct Torque Control of Induction Motor using space
vector modulation (SVM-DTC)”, Power System Conj., MEPCON, pp. 368-372,
2008.
X. F. Mak, R. Sundaram, V. Santhaseelan, S. Tandle, “Laboratory setup for Real-
Tme study of Electyric Drives with integrated interfaces for Test and
Measurement”, 38th ASEE/IEEE Frontier in Eductaion Conference, NY,
pp.T3H-1 to T3H-6, 2008.
XI. G. Adamidis, Z. Koustsogiannis, P. Vagdatis, “Investigation of the Performance
of a variable-speed Drive using Direct Torque control with Space Vector
Modulation” Tylor & Francis Trans. on Electric Power Components and
Systems, Vol.: 39, pp. 1227-1243, 2011.
XII. G. Narayanan, D. Zhao, H. K. Krishnamurthy, R. Ayyanar, V. T. Ranganathan,
“Space vector based hybrid PWM techniques for reduced current ripple,” IEEE
Trans. Ind. Electron., Vol.: 55, Issue: 4, pp. 1614–1627, 2008.

XIII. G. Narayanan, H. K. Krishnamurthy, Di Zhao, R. Ayyanar “Advance busclamping
PWM techniques based on space vector approach,” IEEE Trans. Power
Electron., Vol.: 21, Issue: 4, pp. 974–984, 2006.
XIV. G. S. Buja, M. P. Kazmierkowski, “Direct torque control of PWM inverter-fed
AC motors – a survey,” IEEE Trans. Ind. Appl., Vol.51, pp.744-757, 2004.
XV. I. Takahashi, T. Noguchi, “A new quick-response and high- efficiency control
strategy of an induction motor,” IEEE Trans. Ind. Appl., Vol. 22, Issue: 5, pp.
820-827,1986.
XVI. J. K. Kang, S. K. Sul, “New direct torque control of induction motor for
minimum torque ripple and constant switching frequency”, IEEE Trans. Ind.
Appl., Vol.35, Issue: 5, pp.1076–1082, 1999.
XVII. K. K. Shyu, J. K. Lin, V.T. Pham, M. J. Yang, T. W. Wang, “Global minimum
torque ripple design for direct torque control of induction motor drives,” IEEE
Trans. Ind. Electron., Vol. 57, Issue: 9, pp. 3148–3156, 2010.
XVIII. K. P. M. Sheif, J. Peter, R. Ramachand, “Spaced Vector based hybrid PWM for
VSI fed varaible speed induction motor drives” IEEE Annual India Conference
(INDICON), pp. 1-6, 2016.
XIX. M. Wang, J. Yang, C. Zhu, “Hybrid SVPWM technique for reduced torque
ripple in permanent magnet synchronous motor,” International Power Electronics
and Application Conference and Exposition, pp. 1297-1302, 2014
XX. N. Rumzi, N. Idris, C. L. Toh, M. E. Elbuluk, “A new torque and flux controller
for direct torque control of induction motor”, IEEE Trans. on Industry
Applications, Vol. 42, Issue: 6, pp. 1358-1366, 2006.
XXI. T. G. Habetler, F. Profumo, M. Pastorelli, L. M. Tolbert, “Direct Torque Control
of Induction Machines Using Space Vector Modulation,” IEEE Trans. on
Industry Applications, Vol. 28, Issue: 5, pp. 1045-1053, 1992.

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Abstract:

Nearest Neighbor Classification technique (NNC) is an elegant classifier in machine learning and its related fields like Artificial Intelligence, Machine Learning and Data Mining etc. It is simple and easy to understand classifier. However it has some issues. This paper presents overview of the problems which researchers face with the NNC and the reference are given tosolve issues.

Keywords:

Nearest Neighbor Classification,Pattern Recognition,Classifier,Data Mining,Issues,

Refference:

I. C. W. Swonger, “Sample set condensation for a condensed nearest neighbor
decision rule for pattern recognition”, Front.Pattern Recognition, pp. 511–
519, 1972
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proximity graphs”, Pattern Recognition Lett., vol. 18(6), pp. 507–513, 1995.
III. G. Gates, “The reduced nearest neighbor rule”, IEEE Trans. Information
Theory, vol. vol IT-14, no. 3, pp. 431–33, 1972.
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vol.SMC-6 no 11, pp. 769–772, 1972.
V. Keogh, Eamonn, A. Mueen “Curse of dimensionality”, Encyclopedia of
Machine Learning. Springer US, 257-258, 2011.
VI. K. R. Raja, P. Viswanath, C. S. Bindu,“A Cascaded Method to Reduce
theComputational Burden of Nearest Neighbor Classifier”, In Proceedings of
the First InternationalConference on Computational Intelligence and
Informatics, Springer, pp. 275-288, 2016.
VII. K. R. Raja, P. Viswanath, C. S. Bindu, “An Approach to Reduce the
Computational Burden of Nearest Neighbor Classifier”,Procedia Computer
Science Vol.:85, pp. 588-597, 2016.
VIII. K. R. Raja, P. Viswanath, C. S. Bindu, “A New Prototype Selection Method
for Nearest Neighbor Classificatio””, IEEE Transactions on Very Large
Scale Integration (VLSI) Systems, Vol.: 15, Issue: 3, pp: 338 – 345, 2007.
IX. K. R. Raja, P. Viswanath, C. S. Bindu, “Nearest Neighbor Classifiers:
Reducing the Computational Demands”, In Advanced Computing (IACC),
2016 IEEE 6th International Conference, pp. 45-50, 2016.
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XI. P. Murphy, D. W. Aha, UCI repository of machine learning databases–a
machine readable repository, 1995.
XII. P. Viswanath, N. Murty, S. Bhatnagar, “Overlap pattern synthesis with an
efficientnearestneighbor classifier”, Pattern recognition, Vol. 38, Issue: 8,
pp. 1187–1195, 2005.
XIII. P. Viswanath, T. H. Sarma, “An improvement to k-nearest neighbor
classifier”,Recent Advances in Intelligent Computational Systems (RAICS),
IEEE, pp. 227–231, 2011.
XIV. R. O. Duda, P. E. Hart, D. G. Stork, Pattern classification. 2nd. Edition. New
York, 2002.
XV. V. S. Babu, P. Viswanath, “Weighted k-nearest leader classifier for large
data sets”, Pattern Recognition and Machine Intelligence, pp. 17– 24, 2007.
XVI. V. S. Devi, M. N. Murty, “An incremental prototype set building technique”,
Pattern Recognition, Vol.: 35, Issue: 2, 505-513, 2002.

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Abstract:

In this paper load compensation using shunt active power filter (SAPF) is investigated balanced and unbalanced source voltages. PQ and d-q control algorithms are used for compensating the load current. The reference current given to the hysteresis controller is attained from PQ and d-q control methods. The exhibition of the proposed methods is assessed regarding reactive power, source voltage, and source currents, compensating currents and harmonics compensation as per IEEE-519 standard. To find out the suitability of the proposed control method, the paper is assessed under various source voltage conditions under balanced sinusoidal source voltage condition, all control techniques accumulated similar results. Under unbalanced sinusoidal source voltage condition, dq and PQ theories have demonstrated similar result, but in transient response PQ control has better performance. Simulation results are presented to validate the control methods.

Keywords:

SAPF,PQ,SRF,Unbalanced source voltage,Power quality,

Refference:

I. A. Ketabi, M. Farshadnia, M. Malekpour, R. Feuillet, “A New Control Strategy
for Active Power Line Conditioner (APLC) using adaptive notch filter”,
Electrical Power and Energy Systems, Vol.47, pp.31-40, 2013.
II. B. Singh, A. Chandra, K. Al-Haddad, Power Quality-Problems and Mitigation
Techniques, Chapter 9, pp.405,New York, USA: Wiley, 2015.
III. C. K. Duffey, R. P. Stratford, “Update of harmonic standard IEEE-519: IEEE
recommended practices, requirements for harmonic control in electric power
systems,” IEEE Trans. on Indus. App., Vol. 25, pp.1025–1034, 1989.
IV. K. Al-Haddad, A. Hamadi, S. Rahmani, N. Mendalek, “A new control technique
for three-phase shunt hybrid power filter”, IEEE Trans Ind Electron.
V. K. S. Rani, K. Porkumaran, “Performance evaluation of PI and fuzzy controller
based Shunt active power filter”, Eur J Sci Res, Vol.: 61, Issue: 3, ISSN 1450–
216X, 2011.
VI. L. Moran, D. Rivas, J. R. Espinoza, J. W. Dixon, “Improving passive filter
compensation performance with active techniques. IEEE Trans Ind Electron,
Vol.: 50, Issue: 1, pp.161–170, 2003
VII. M. Badoni, A. Singh, B. Singh, “Comparitive Performance of Wiener Filter and
Adaptive Least Mean Square Based control for power Quality improvement”,
IEEE transaction on Industrial Electronics, Vol.: 63, Issue: 5, pp:3028-3037,
2016.

VIII. M. Qasim, P. Kanjiya, V. Khadkikar, “Optimal Current Harmonic Extractor
Based on Unified ADALINEs for ShuntActive Power Filters”, IEEE Trans. on
Power Elec., Vol.29, Issue: 12, pp. 6383-6393, 2014.
IX. P. Chittora, A. Singh, M. Singh, “Gauss– Newton-based fast and simple
recursive algorithm for compensation using shunt active power filter”, IET
Generation, Transmission & Distribution , Vol.: 11, Issue: 6, pp-1521-1530,
May 2017
X. P. Garanayak, G. Panda, P. K. Ray, “Harmonic estimation using RLS algorithm
and elimination with improved current control technique based SAPF in a
distribution network”, Electrical Power and Energy Systems, Vol.73, pp.209-
217, 2015.
XI. Q. N. Trinh, H. H. Lee, “An advanced current control strategy for three-phase
shunt active power filters”, IEEE Trans Ind Electron, Vol.: 60, Issue: 12, pp:
5400–5410, 2013.
XII. S. Mikkili, A. K. Panda, “Instantaneous active and reactive power and current
strategies for current harmonics cancellation in 3-ph 4-Wire SHAF with both PI
and fuzzy controllers”, Energy Power Eng, Vol.: 3, pp: 285–298, 2011.
XIII. S. Shukla, S. Mishra, B. Singh, S. Kumar ,“Implementation of empirical mode
Decomposition Based Algorithm for Shunt Active Filter”, IEEE Transactions on
Industry Applications, Vol.: 53 Issue: 3, pp:2392-2400, 2017.
XIV. W. C. Lee, “Cost-effective APF/UPS system with seamless mode transfer”, J.
Electr. Eng. Technol., Vol.: 10, Issue: 1, pp. 195–204, 2015.
XV. Y. G. Jung, “Graphical representation of the instantaneous compensation power
flow for single-phase active power filters”, J Electr Eng Technol, Vol.: 8 Issue:
6, pp. 1380– 1388, 2013.

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Abstract:

Now a days Job satisfaction represents one of the most complex areas facing today’s managers when it comes to managing their employees. Many studies have demonstrated an unusually large impact on the job satisfaction on the motivation of workers, while the level of motivation has an impact on productivity, and hence also on performance of business organizations. Unfortunately, in our regionJob satisfaction has not still received the proper attention from neither scholars nor managers of various business organizations. The Goals of SBI Rayalaseema Division in Andhra Pradesh it is very difficult to improve the employee satisfaction levels and to increase the business also because of more competitors are available in society. Generally the employees perceptions are very different to one to others in Rayal aseema division, because of this area as very difficult to other state peoples more than ninety percent of the people are very illiterates. The present employees job satisfaction measured it is very difficult in the sense of every human being behavior, attitude, Economical status, environment changes, Relationships, societal status, culture, income levels, family impact, and thinking and decision are changes. This study mainly focused on employees level of satisfaction in the present job and employees spouse based satisfaction to the job satisfaction in the present job.

Keywords:

Job satisfaction,SBI Banks,Happy workers,Performance,Organization,

Refference:

I. Asha, “Job Satisfaction among woman in relation to their family
environment”, Journal of community Guidance and Research, Vol. II, Issue:
1, pp.43 -50, 1994.
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on district hamirpur (H. P) published in International Journal of science,
Environment. Vol-3, No.4, pp. 1582-1591, 2014.
III. B. A. Jenaibi, “Job satisfaction – It’s the little things that count”,
Management Science and Engineering, Vol. 4, Issue: 3, pp. 60-79, 2010.
IV. C. B. Gupta, “Human Resource Management- published by Sultan Chand &
Sons.
V. Chakraborty, Parul, “Job satisfaction”, Industrial relations, Vol. 17, Issue: 3,
1965.
VI. C. K. Basu, “Incentives and Job Satisfaction”, Indian Journal of Industrial
Relations, Vol. 1, Issue: 3, 1966.
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Organizational Behavior and Human Performance”, Vol.: 5, pp.23 – 27,
1970.
VIII. F. W. Taylor, “Organizational Behavior”, Mumbai: Sultan and Chand
Publications, p 114.
IX. J. M. Shah, M. Rehman, A. Gulnaz, H. Zafar, “Job Satisfaction and
Motivation of Teachers of Public Educational Institutions”, International
Journal Business and Social Sciences, Vol.3 Issue. 8, 2012.
X. J. Uli, B. Parasuraman, M. M. Abdullah, “Job satisfaction among secondary
school teachers”, Journal Kemanusiaan bil.13, 2009.
XI. K. Aswathappa, Human Resource and Personnel Management.
XII. L. W. Porter, E. E. Lawler. “The Effect of Performance on Job Satisfaction”,
Indian Journal of Industrial Relations, Vol. 7, pp. 20-28, 1989.

XIII. M. E. Malik, Job Satisfaction and Organizational Commitment of
University, International Journal of Business and Management Vol. 5, Issue:
6, pp. 17, 2010.
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Pharmaceutical Sector”, Australian Journal of Business and Management
Research, Vol. 1, Issue: 9, pp. 113-123, 2011.
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Nationalised Bank Officers Summary of a Study”, Indian Journal of Training
and Development, Vol.: XXVII, Issue: 2, pp.97, 1997.
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rewards and employees Motivation in the non-profit organizations of
Pakistan”, Business Intelligence Journal, Vol.4, Issue: 2, pp. 327-334, 2011.
XVII. N. Muhammad, M. Akhter, “Supervision, Salary and Opportunities for
Promotion as Related to Job Satisfaction.
XVIII. N. N. Vrinda, A. Nisha, N. N. Jacob, “The Impact on job satisfaction on job
performance”, published in International Journal in commerce, IT & social
sciences, Vol.: 2, Issue: 2, pp. 27-37, 2015.
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conduit towards achieving employee retention and Job Satisfaction in
Banking Sector of Pakistan”, International Journal of Human Resource
Studies, Vol. 1, Issue: 1, pp. 2162-3058, 2011.
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approach”, International Journal of Organizational Behavior, Vol. 8, Issue: 3,
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practices”, Boston: McGraw – Hill Irwin. 2003
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and Private Banks in Rayalaseema Region”, A.P. International Journal of
Research in Management, Vol.2 Issue. 6, 2016.
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Publication, 1990.
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Chand Publications, p 114, 2003.

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Abstract:

In this paper a natural habitat inspired metaheuristic Bird Swarm Optimization algorithm is implemented with improvisations made for the development of solution for the optimal allocations and optimal sizeprediction problem of Dispersed generation/ Distributed Generator in a radial power system distribution system in consideration of the drawbacks in the previous algorithms both in the context of convergence time and the optimal sizing with respect to the cost analysis for operation of the system with different number of DG’s installed in such a way that the optimal locations and sizes of DG’s installed is finalised with highest priority to the economical operation along with the immediate priority given to the network losses along with voltage deviations. To avoid the draw backs in previous optimisation algorithm regarding accuracy and run time. Along with the Cognitive component Weighted factor Bird Swarm optimisation (CWFBSO) algorithm a new concept is introduced called DG Size tuner Such that cost effective economical installation is possible as by the size tuner it is possible to compare the losses and voltage profile within the mean difference of the optimal sizes of final allocation determined by the main algorithm i.e., CWFBSO. Obtained results using CWFBSO in determining optimal locations and sizes of DG’s is capable showing good performance with less run time and convergence time and by using size tuner the optimal size selected economically with respect to less voltage deviation and minimal losses.

Keywords:

Load flow,forward-backward sweep method,loss factors analysis,Voltage sensitivity factors,Cognitive component,Weighted factor,Bird Swarm optimisation,Distributed generation,Optimal location,Optimal size,Real Power losses,Size Tuner,

Refference:

I. A. Alsaadi, B. Gholami, “An effecctive approach for distribution system
power flow solution”, Intern. Journ. of Electr. and Electron. Eng., 2009 .
II. A. M. Imran, M. Kowsalya, “Optimal size and siting of multiple distribution
generators in distribution system using bacterial foraging optimization”,
Swarm and Evolut. Comput., Vol.: 15, pp. 58-65, 2014.
III. C. V. Suresh, M. S. Giridhar, “Analysing the effect of distributed generators
on economical and technical aspects of distributed systems”.
IV. J. Kennedy, R. C. Eberhart, “Particle swarm optimization”, Proceedings of
the IEEE International Conference on Neural Networks IV, Piscataway, NJ:
IEEE Service Center, pp. 1942-1948, 1995.
V. J. Federico, V. Gonzalez, C. Lyra, “Learning classifiers shape reactive power
to decrease losses in power distribution networks,” Proc. IEEE Power Eng.
Soc. General Meet., Vol.: 1, pp. 557–562, 2005.
VI. L. d. S. Coelho, V. C. Mariani, “Particle Swarm Optimization with Quasi-
Newton Local Search for Solving Economic Dispatch Problem”, IEEEInternational Conference on Systems, Man, and Cybernetics, Taipei, Taiwan,
2006.
VII. M. N. Moradi, M. Abedini, “A combination of genetic algorithm and particle
swarm optimization for optimal DG location and sizing in distribution
systems”, Elsevier, Science Direct, Electr. Power Energy Syst., pp. 66-74,
2012.
VIII. M. Z. A. C. Wanik, A. Mohamed, “Intelligent management of distributed
generators for loss minimization and voltage control ”,15th Intenational
(MELCON), pp. 685-690, 2010.
IX. N. Acharya, P. Mahat, N. Mithulananthan, “An analytical approach for
distributed generation allocation in distribution network ”, Electr. Power
and Energy Systems, pp. 669-678, 2006.
X. P. S. Georgilakis, , N. D. Hatziargyriou, “Optimal distributed generation
placement on power distribution networks: models, methods, and future
research’ , IEEE Trans. Power Syst, Vol.: 28, Issue: 3, pp. 3420-3428.,2013
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distributed generation”, IET Gener, transm. Distrib., 2009, Vol.: 3, Issue: 8,
pp. 713-723
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based on nodal pricing for profit, loss reduction, and voltage improvement
including voltage rise issue”, Elsevier, Science Direct, Electr. Power Energy
Syst., Vol.: 32, pp. 637–644, 2010.
XIV. S. K. Injeti, N. P. Kumar, “A Novel Approach to Identify Optimal Access
Point and Capacity of Multiple DGs in a Small, Medium and Large Scale
Radial Distribution Systems”, Elsevier, Science Direct, Electr. Power Energy
Syst., pp. 142-151, 2013.
XV. S. K. Injeti, V. K. Thunuguntla, M. Shareef, “Optimal allocation of capacitor
banks in radial distribution systems for minimization of real power loss and
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7, (3), pp.,265-275, 2013.

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Abstract:

This paper proposes an algorithmic approach to find QRS complex in an ECG signal. These QRS complexes help to identify the functioning of heart and to detect the symptoms of cardiac arrest. Tele-health applications are increasing its range day by day. Normal algorithms cannot analyses the Telehealth ECG signal. So proposed algorithm used to analyses Tele ECG signals. Normal algorithms can detect QRS complex which are recorded in pure clinical ECG where the noise level will be low. The proposed algorithm is able to detect QRS

Keywords:

ECG,QRS complex,Tele-Health,Detection,Bio Medical Signal Processing,

Refference:

I. A. Ruha, S. Sallinen, S. Nissila, “A real-time microprocessor QRS detector
system with a 1ms timing accuracy for measurement of ambulatory HRV”,
IEEE Trans.Biomed.Eng., pp,159-167,1997.
II. C. C. Lin, C. M. Yang, “Heartbeat classification using normalized intervals
and morphological features”, Mathematical Problems in Engineering, 2014.
III. C. Ye, B. Kumar, M. Coimbra, “An automatic subject-adaptable heartbeat
classifier based on multi-view learning,” IEEE J. Biomed. Health
Informatics, Vol. 20, Issue. 6, 2016.
IV. C. Ye, B.V.K. Vijaya Kumar, M.T. Coimbra, “Heartbeat classifi-cation
using morphological and dynamic features of ECG signals,” IEEE Trans.
Biomed. Eng., Vol. 59, Issue. 10, pp.2930-2941, 2012.
V. E. Alickovic, A. Subasi, “Medical decision support system for diagnosis of
heart arrhythmia using DWT and random forests classifier”, J. Medical
Systems, Vol. 40, Issue. 4, pp. 1-12, 2016.
VI. F. Melgani, Y. Bazi, “Classification of electrocardiogram signals with
support vector machines and particle swarm optimization,” IEEE Trans.
Inform. Technol. Biomedicine, Vol. 12, Issue. 5, pp. 667-677, 2008.
VII. G. B. Moody, “A noise stress test for arrhythmia detectors”,
Comput.Cardiol., Vol. 11, pp. 381–384, 1984.
VIII. G. B. Moody, R. G. Mark, “The impact of the MIT-BIH arrhyth-mia
database, IEEE Eng. Med. Biol. Mag., Vol.: 20, Issue: 3, pp. 45–50, 2001
IX. J. Kim, S. D. Min, M. Lee, “An arrhythmia classification algorithm using a
dedicated wavelet adapted to different subjects,” Biomed. Eng. Online, Vol.
10, Issue. 1, 2011.
X. K. V. Surez, J. C. Silva, Y. Berthoumieu, P. Gomis, M. Najim, “ECG beat
detection using a geometrical matching approach,” IEEE Trans. Biomed.
Eng., Vol. 54, Issue. 4, pp. 641-650, 2007.
XI. M. Elgendi, “Fast QRS detection with an optimized knowledgebased
method: evaluation on 11 standard ECG databases,” PLoS One, Vol.: 8,
article e73557, 2013.
XII. M. K. Das, S. Ari, “Patient-specific ECG beat classification tech-nique,”
Healthcare Technol. Lett., Vol.: 1, Issue: 3, 2014.
XIII. M. Milanesi, N. Martini, N. Vanello, L. Landini, “Independent component
analysis applied to the removal of motionartefacts from electrocardiographic
signals,” Med. Biol. Eng. Comput., Vol. 46, pp. 251–261, 2008.
XIV. P. d. Chazal, M. O. Dwyer, R. B. Reilly, “Automatic classification of
heartbeats using ECG morphology and heartbeat interval features,” IEEE
Trans. Biomed. Eng., Vol.: 51, Issue: 7, pp. 1196-1206, 2004.

XV. P. d. Chazal, R. B. Reilly, “A patient-adapting heartbeat classifier using
ECG morphology and heartbeat interval feature,” IEEE Trans. Biomed.
Eng., Vol.: 53, pp. 2535-2543, 2006.
XVI. P. Ghorbanian, A. Ghaffari, A. Jalali, C. Nataraj, “Heart arrhythmia
detection using continuous wavelet transform and principal component
analysis with neural network classifier,” Computing Cardiol, pp. 669-672,
2010.
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tachycardia classification using a machine learning approach,” IEEE Trans.
Biomed. Eng., Vol.: 61, Issue: 6, pp. 1607-1613, 2014.
XVIII. S. Banerjee, M. Mitra, “Application of cross wavelet transform for ecg
pattern analysis and classification,” IEEE Trans. Instrum. Meas., Vol. 63,
Issue. 2, pp. 326-333, 2014.
XIX. S. Kiranyaz, T. Ince, M. Gabbouj, “Real-Time patient-specific ECG
classification by 1-D convolutional neural networks,” IEEE Trans. Biomed.
Eng., Vol. 63, Issue. 3, pp. 664-675, 2016.
XX. S. Mitra, M. Mitra, B. B. Chaudhuri, “A rough-set-based inference engine
for ECG S. Graja, J. M. Boucher, “Hidden Markov tree model applied to
ECG delineation,” IEEE Trans. Instrum. Meas., Vol. 54, Issue. 6, pp. 2163-
2168, 2005.
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Hermite polynomials and neuro-fuzzy network,” IEEE Trans. Instrum.
Meas., Vol. 52, Issue. 4, pp. 1224-1231, 2003.
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automated patient-specific classification of ECG signals,” IEEE Trans.
Biomed. Eng., Vol. 56, pp. 1415-1426, 2009.
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ECG classification by means of feature selection,” IEEE Trans. Biomed.
Eng., Vol.: 58, Issue: 8, pp. 2168-2177, 2011
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Electrocardiogram Signal ” IJAERD, Vol.: 4, Issue 8, pp. 2348-6406, 2017
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signal classification,” IEEE Trans. Neural Net., Vol. 18, Issue. 6, pp. 1750-
1761, Nov. 2007.

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Abstract:

Due to extra effective control on emissions of carbon gas and economic benefits, Fuel cell EV is evolving into more favourite in the vehicle industry. Now- adays pollution is one of the important factors to reduce for better life. This paper introduces a neural network system based MPP tracking controller to track the maximum power from the01.260-kW proton exchange membrane fuel cell (PEMFC). The proposed neural network MPPT controller utilizes an adaptive-network based fuzzy inference system (ANFIS) algorithm to track the PEMFC's maximum power point. Switching frequency, voltage-gain required high for the propulsion of fuel cell EV. A 3-phase high voltage gain with interleaved technique based boost converter is intended for the fuel cell EV system to get the maximum voltage gain. The interleaving type technique minimizes ripple in input fuel cell current and the voltage stress on the used semiconductor devices. The analysis of the performance of the Fuel cell EV system with ANFIS controller used is compared with the radial basis FN controller in Simulink/MATLAB.

Keywords:

High voltage IBC,Fuel cell EV,HVG,ANFIS,

Refference:

I. A. Giustiniani, G. Petrone, G. Spagnuolo, M. Vitelli, “Low
current oscillations and maximum power point tracking in grid
fuel-cell-based systems”, IEEE Trans Indus Electron., Vol.: 57, Issue: 6, pp.
2042-2053, 2010.
II. B. Geng, J. K. Mills, D
optimization for a fuel cell/battery plug
multi-objective particle swarm optimization”, Inter J. Automot Techn.,
Vol.:15, Issue: 4, pp.645
III. F. Sobrino-Manzanares, A. Garri
multi-phase and multi
applications”, Int J of Hydrogen Enery., Vol.: 40, Issue: 36, pp. 12447
2015.
No.-5, January (2020) pp
107
three-phase andmaximum voltage gain IBC is used for Fuel
controller is designed for
D. Sun, “Combined power management/design
plug-in hybrid electric vehicle using
645-654, 2014.
Garrigós, “An interleaved, FPGA
multi-switch synchronous boost converter for fuel cell
95-108
ler performance
Low-frequency
grid-connected
. gós, FPGA-controlled,
12447-56,

IV. H. Hemi, J. Ghouili, A. Cheriti, “A real time fuzzy logic power management
strategy for a fuel cell vehicle”, Energy Convers. Manag., Vol.: 80, pp. 63-
70, 2014.
V. H. J. Chiu, L.W. Lin, “A bidirectional DC–DC converter for fuel cell electric
vehicle driving system”, IEEE Trans Power Electron., Vol.21(4), pp.950-
958, 2006.
VI. J. P. Ram, N. Rajasekar, M. Miyatake, “Design and overview of maximum
power point tracking techniques in wind and solar photovoltaic systems”,
Renew Sustain Energy Rev., Vol. 73, pp. 1138-1159, 2017.
VII. K. J. Reddy, N. Sudhakar, “High voltage gain interleaved boost converter
with neural network based MPPT controller for fuel cell based electric
vehicle applications”, IEEE Access, Vol.: 6, 2018:3899e908.
VIII. K. Kumar, N. R. Babu, K. R. Prabhu, “Design and Analysis of RBFN-Based
Single MPPT Controller for Hybrid Solar and Wind Energy System”, IEEE
Access., Vol. 5, pp.15308-15317, 2017.
IX. M. Buragohain, C. Mahanta, “ANFIS Modeling of Nonlinear Systems based
on Subtractive Clustering and V-fold Technique”, Proceedings of IEEE
Annual India Conference, New Delhi, 2006.
X. M. Buragohain, C. Mahanta, “ Full Factorial Design based ANFIS Model for
Complex Systems”, Proceedings of IEEE Annual India Conference, New
Delhi, 2006.
XI. N. Mebarki, T. Rekioua, Z. Mokrani, D. Rekioua, S.Bacha, “PEM fuel
cell/battery storage system supplying electric vehicle”, Int. J. of Hydrogen
Enery., Vol.: 41, Issue: 45, pp.20993-21005, 2016.
XII. S. Abdi, K. Afshar, N. Bigdeli, S. Ahmadi, “A novel approach for robust
maximum power point tracking of PEM fuel cell generator using sliding
mode control approach”, Int. Jou. Elec. Sci., pp. 4192-4209, 2012.
XIII. S. Saravanan, N. R. Babu, “Maximum power point tracking algorithms for
photovoltaic system–A review”, Renew Sustain Energy Rev., Vol.: 57, pp.
192-204, 2016.
XIV. T. Esram, P. L. Chapman, “Comparison of photovoltaic array maximum
power point tracking techniques”, IEEE Trans Energy Conver., Vol.: 22,
Issue: 2, pp.439-49, 2007.
XV. V. Bist, B. Singh, “Reduced sensor configuration of brushless DC motor
drive using a power factor correction-based modified-zeta converter”, IET
Power Electron., Vol.: 7, Issue: 9, pp. 2322-2335, 2014.

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Abstract:

In this paper, we introduce, “an ensemble of Support Vector Machines (SVM) using Fuzzy-PAM” for network-based intrusion detection. First, the given set of features in a data set is partitioned into blocks or clusters based on correlation coefficient values between pairs of features or attributes. Then the data set is projected onto these feature set to obtain various data sets. SVM is applied on each data set. The given query pattern is also projected onto the feature set and the decision of each SVM is obtained. Weightage is given to each cluster, which is combined with decision of each SVM to obtain a final decision for classifying the given query. We shown the results of applying an ensemble of Support Vector Machines to 1999 KDD Cup data set.

Keywords:

Classification,svm,ensemble techniques,intrusion detection,correlation coefficient,

Refference:

I. A. K. Jain, R. C. Dubes, “Algorithms for Clustering Data”, Prentice Hall,
Engle-wood Clis NJ, U.S.A., 1988.
II. A. W. H. Debar, M. Dacier, “Towards a taxonomy of intrusion-detection
systems”, Computer Networks, Vol.: 31, pp.805–822, 1999.
III. C.C. Chang, C.J. L. Libsvm, “A library for support vector machines”,
Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm, 2001.
IV. C. Cortes, V. Vapnik, “Support-vector network”, Machine Learning, Vol.: 20,
pp. 273–297, 1995.
V. C. J. C. Burges, “A tutorial on support vector machines for pattern
recognition”, Data Mining and Knowledge Discovery, Vol.: 2, Issue: 1, pp.
47, 1998.
VI. G. B. E. Boser, V. Vapnik, “A training algorithm for optimal margin classifiers”,
Proceedings of the Fifth Annual Workshop on Computational
Learning Theory, ACM Press, pp. 144–152, 1992.

VII. I. Graf, R. P. Lippman, D. J. Fried, M. A. Zissman, “Evaluating intrusion
detection systems: The 1998 darpa online intrusion detection evaluation”, In
Proceedings of DARPA Information Survivability Conf. and Exosition
(DISCEX’00), pp. 12–26, 2000.
VIII. J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Aca-demic
Press, 2001.
IX. Lincoln Laboratory MIT. DARPA Intrusion Detection Data Sets.
http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/index.h
tml.
X. L. Kaufman, P. Rousseeuw, “Finding groups in data: An introduction to
cluster analysis”, John Wiley & Sons, New York, 1990.
XI. P. Cunningham, “Ensemble techniques”, Technical Report UCD-CSI-2007-5,
2007.
XII. R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification”, A Wileyinterscience
Publication, John Wiley & Sons, 2nd edition, 2000.
XIII. S. Bhatnagar, P. Viswanath, M. Murty, “Partition based pattern synthesis
technique with ecient algorithms for nearest neighbor classification”, Pattern
Recognition Letters, Vol.: 27, pp.1714–1724, 2006.
XIV. S. J. Stolfo, W. Lee, “A data mining framework for building intrusion
detection mod-els”, Proceedings of the IEEE Symposium on Security and
Privacy, 1999.
XV. V. N. Vapnik, “An overview of statistical learning theory”, IEEE Transactions
on Neural Networks, Vol.: 10, Issue: 5, 1999.
XVI. W. L. M. Tavallaee, E. Bagheri, A. A. Ghorbani, “A deatiled analysis of the
kdd cup 99 data set”, In Proceedings of IEEE Symposium on Computer
Intelligence in Security and Defense Applications(CISDA), 2009.
XVII. Y. Freund, R. E. Schapire, “Experiments with a new boosting algorithm”, In
Proceedings 13th International Conference on Machine Learning, San
Francisco, pp. 148–146, 1996.

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Abstract:

The amount of text generated a day dramatically increases. Computers cannot easily process and perceive this enormous amount of mostly unstructured text. Therefore, to discover useful patterns, efficient and effective techniques and algorithms are required. Text mining is the process of extracting meaningful information from the text, which has received considerable attention in recent years. In this paper, we discuss several of the most basic tasks and techniques of text mining, including pre-processing, classification, and clustering. We also explain briefly text mining in the fields of biomedicine and health care.

Keywords:

classification,clustering,Text mining,information retrieval,information extraction,

Refference:

I. A. Kao, S. R. Poteet. “Natural language processing and text mining”,
Springer, 2007.
II. A. K. Uysal, S. Gunal, “The impact of preprocessing ontext classification”,
Information Processing & Management, Vol.: 50, Issue: 1, 104–112, 2014.
III. A. M. Callum, K. Nigam, “A comparison of event modelsfor naive bayes
text classification”, In AAAI-98 workshop on learning for text
categorization, Vol.: 752, pp. 41–48, 1998.
IV. C. C. Aggarwal, C. X. Zhai, “Mining text data”, Springer, 2012.
V. D. M. Bikel, S. Miller, R. Schwartz, R. Weischedel, “Nymble: a highperformance
learning name-finder”, In Proceedings of the fifth conference
on Applied natural language processing. Association for Computational
Linguistics, pp. 194–201, 1997.
VI. G. R. Kumar, G. A. Ramachandra, K. Nagamani, “An Efficient Prediction of
Breast Cancer Data using Data Mining Techniques”, International Journal of
innovations in Engineering and Technology, Vol.: 2, Issue: 4, pp: 139-144,
2013.
VII. G. R. Kumar, K. Nagamani, “A Framework of Dimensionality Reduction
utilizing PCA for Neural Network Prediction”, Proceedings of the
International Conference on Data Science and Management(ICDSM-2019),
Published in the book series Lecture Notes on Data Engineering and
Communications Technologies of Springer Publishing House, 2019.

VIII. G. R. Kumar, K. Nagamani, “Banknote Authentication System utilizing
Deep Neural Network with PCA and LDA Machine Learning Techniques”,
International Journal of Recent Scientific Research, Vol.: 9,Issue:12(D),
2018.
IX. J. Lafferty, A. McCallum, F. C. N. Pereira, “Conditionalrandom fields:
Probabilistic models for segmenting and labeling sequence data”, 2001.
X. K. Alsabti, S. Ranka, V. Singh, “An efficient k-means clustering algorithm”,
1997.
XI. K. Nigam, A. McCallum, S. Thrun, T. Mitchell, “Learning to classify text
from labeled and unlabeled documents”, AAAI/IAAI 792, 1998.
XII. L. Breiman, J. Friedman, C. J. Stone, R. A. Olshen, “Classification and
regression trees”, CRC press, 1984.
XIII. M. Allahyari, K. Kochut, “Automatic topic labeling using ontology based
topic models”, In Machine Learning and Applications (ICMLA), IEEE 14th
International Conference on. IEEE, pp. 259–264, 2015.
XIV. M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E. D. Trippe, J. B. Gutierrez,
K.Kochut, “Text Summarization Techniques: A Brief Survey”, ArXiv eprints,
arXiv:1707.02268, 2017.
XV. M. V. Lakshmaiah, G. R. Kumar, G. Pakardin, “Frame work for Finding
Association Rules in Bid Data by using Hadoop Map/Reduce Tool”,
International Journal of Advance and Innovative Research, Vol.: 2, Issue:
1(I), pp:6-9, 2015.
XVI. S. R. Basha, J. K. Rani, “A Comparative Approach of Dimensionality
Reduction Techniques in Text Classification”, Engineering, Technology &
Applied Science Research, Vol.: 9, Issue: 6, pp. 4974-4979, 2019.
XVII. S. R. Basha, J. K. Rani, J. J. C. P. Yadav, “A Novel Summarization-based
Approach for Feature Reduction Enhancing Text Classification Accuracy”,
Engineering, Technology & Applied Science Research, Vol.: 9, Issue: 6, pp.
5001-5005, 2019.
XVIII. S. R. Basha, J. K. Rani, J. J. C. P. Yadav, G. R. Kumar, “Impact of
featureselection techniques in Text Classification:An Experimental study”,
J. Mech. Cont.& Math. Sci., Issue: 3, pp 39-51, 2019.
XIX. T. Kalt, W. B. Croft, “A new probabilistic model of text classification and
retrieval”, Technical Report, Citeseer, 1996.
XX. U. M. Fayyad, G. P. Shapiro, P. Smyth, “Knowledge Discovery and Data
Mining: Towards a Unifying Framework”, In KDD, Vol.: 96, pp. 82–88,
1996.

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Abstract:

Internet of Things (IoT) is distinct as a paradigm wherein things armed with sensors, actuators, and embedded processors connect with everyone to help significant determinations. Secure communication in IoT is a demanding issue since IoT suffers from different vulnerabilities. Low memory capabilities, limited power supply, constrained resources are unique characteristics but at the same time these are making IoT prone to various attacks. These attacks turn into more effective when introduced with collaboratively. Various protocols and secure algorithms are developed to make the communication problem free from intruders. However, the existing protocols are not providing absolute solutions to the security issues for evergrowing IoT applications. This paper tries to figure out the performance issues by evaluating routing protocols AODV, LOADng, RPL, and CORPL under collaborative attacks.

Keywords:

Internet of Things,Security,Collaborative attack,

Refference:

I. A. David, J. Gutierrez, S. K. Ray, “Securing RPL routing protocol from
blackhole attacks using a trust-based mechanism”, Telecommunication
Networks and Applications Conference (ITNAC), 26th International. IEEE,
2016.
II. Aijaz, A. Aghvami, “Cognitive machine-to-machine communications for
internet-of-things: A protocol stack perspective,” IEEE Internet of Things
Journal, Vol.: 2, Issue: 2, pp. 103-112, 2015.
III. A. Shoukat, “Detection and prevention of Black Hole Attacks in IOT &
WSN”, Fog and Mobile Edge Computing (FMEC), Third International
Conference on. IEEE, 2018.
IV. C. Christian, “Detection of sinkhole attacks for supporting secure routing on
6LoWPAN for Internet of Things”, IM, 2015.
V. C. Perkins, E. B. Royer, S. Das, “Ad hoc On-Demand Distance Vector
(AODV) Routing,” IETF, RFC 3561, 2003.
VI. G. Ghada, A. Rachedi, A. Meddeb, “A secure routing protocol based on RPL
for Internet of Things”, Global Communications Conference
(GLOBECOM), IEEE, 2016.
VII. G. Jorge, E. Monteiro, J. S. Silva, “Security for the internet of things: a
survey of existing protocols and open research issues”, IEEE
Communications Surveys & Tutorials, Vol.: 17, Issue: 3, pp. 1294-1312,
2015.
VIII. J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, “Internet of things (IOT):
A vision, architectural elements, and future directions,” Future Generation
Computer Systems, 2013.

IX. K. Iuchi, “Secure parent node selection scheme in route construction to
exclude attacking nodes from RPL network”, IEICE Communications
Express, Vol.: 4, Issue: 11, pp. 340-345, 2015.
X. N. Kushalnagar, G. Montenegro, C. Schumacher, “IPv6 over low-power
wireless personal Area networks (6LoWPANs): Overview, assumptions,
problem statement, and goals,” RFC 4919, 2007.
XI. T. Clausen, A. C. D. Verdiere, J. Yi, A. Niktash, Y. Igarashi, U. Herberg,
“The Lightweight On-demand Ad hoc Distance-vector Routing Protocol –
Next Generation (LOADng),” IETF, Draft, 2012.
XII. T. Winter, P. Thubert, A. Brandt, “RPL: IPv6 routing protocol for lowpower
and lossynetworks”, RFC 6550, 2012.
XIII. W. Kevin, K. Pister, “Evaluating sinkhole defense techniques in RPL
networks”, Network Protocols (ICNP), 20th IEEE International Conference
on. IEEE, 2012.
XIV. W. Linus, S. Raza, T. Voigt, “Routing Attacks and Countermeasures in the
RPL-based Internet of Things”, International Journal of Distributed Sensor
Networks, Vol.: 9, Issue: 8, 2013.

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Abstract:

Reactive Powder Concrete is a budding composite material which has created a platform for the industry to optimize materials for economic profit and building structures that are strong and durable in nature . In early 1990’s , Bouygues’ lab in France introduced RPC to the world . RPC represents a new class of ultra high performance concrete with compressive strengths in range of 180MPa . RPC does not include any coarse aggregates and but has steel fibersto enhance the strength.RPC includes Portland cement, silica fume, fine sand, Superplasticizer, water and steel fibers. In this study, RPC is developed using metakaolin as replacement for cement to achieve target compressive strength more than 120MPa.

Keywords:

Reactive powder concrete,Steel fibers,Compressive strength,Metakaolin,Mechanical Properties,

Refference:

I. A. A. Al-Azzawi, A. S. Ali, H. K. Risan, “Behavior Of Ultra High
Performance Concrete Structures”, ARPN Journal of Engineering and
Applied Sciences, Vol.: 6, Issue: 6, 2011
II. IS: 383, Specifications for Coarse and Fine Aggregates from Natural Sources
for Concrete. Bureau of Indian Standards, New Delhi. 1970
III. IS: 2386, Indian standard “code of practice for methods of test for aggregates
for concrete” Bureau of Indian standards, New Delhi. 1963
IV. K. S. Kumar, G. Acharya, S. R. K. Mhamai, “Reactive Powder Concrete
with mineral admixtures”, JETIR, Vol : 2, Issue : 6, 2015
V. M. Panjehpour, A. A. A. Ali, R. Demirboga, “A Review For
Characterization Of Silica Fume And Its Effects On Concrete Properties”,
International Journal of Sustainable Construction Engineering &
Technology, Vol 2, Issue 2, 2011.
VI. N. Roux, C. Andrade, M. A. Sanjuan, “Experimental Study of Durability of
Reactive powder Concretes”, ASCE Journal of Materials in Civil
Engineering, Vol: 8, Issue :1, 1996.
VII. P. Richard, M. Cheyrezy, “Composition of reactive powder concrete”,
Cement and Concrete Research, Vol: 25, Issue: 7, 1995.
VIII. S. Ali, “Mechanical Properties and Durability of Polymer Modified RPC
Exposed to Oil Products”, Ph.D. Thesis, University of Technology,
Baghdad, 2012.
IX. W. I. Khalil, “Some Properties of Modified Reactive Powder Concrete”,
Journal of Engineering and Development, Vol. 16, Issue: 4, 2012.
X. W. Kushartomo, I. Bali, B. Sulaiman, “Mechanical behavior of reactive
powder concrete with glass powder substitute”, 5th International Conference
of Euro Asia Civil Engineering Forum (EACEF-5), 2015.
XI. Y. W. Chan, S. H. Chu, “Effect of silica fume on steel fiber bond
characteristics in reactive powder concrete,” Cement and Concrete Research,
No- 34, 2004

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Abstract:

The marketing is a process of exchanging products with suitable information. The information about the exchangeable products carried through advertising and this information passes to the potential customers. In the competitive world, effective advertising is important to capture and retain the consumers. Grabbing the attention of existing or potential customers is essential, to positively influence the sales. After all, attention is the first principle of the AIDA theory in advertising. The theory calls for advertisements to catch the attention of the audience, create interest in the offerings, generate desire for the product / service and trigger action for buying the products. Advertising is a means of communicating information of comparing and selecting the available products to the consumers. It stimulates the availability of goods and services is higher, Stabilization in price of products which leads to upsurge in the production system and generates employment.

Keywords:

Advertisements,Social Media,

Refference:

I. A. Bleakleya, A. B. Jordana, M. Hennessya, K. Glanzb, A. Strasserc, S. Vaalaa,
“Do Emotional Appeals in Public Service Advertisements Influence
Adolescents’ Intention to Reduce Consumption of Sugar-Sweetened
Beverages?”, Journal of Health Communication: International Perspectives,
Vol.: 20, Issue: 8, 2015.
II. A. Burmaster, “Global faces and networked places”,
https://nielsen.com/nielsenwire/wpcontent/uploads/2009/03/nielsen_globalfaces
_mar09.pdf, 2009
III. A. M. Kaplan, M. Haenlein. “Users of the world, unite! The challenges and
opportunities of social media‖”, Business Horizons, Vol.: 53, pp. 59–68, 2010
IV. C. Riegner, “Word of mouth on the web: the impact of web 2.0 on consumer
purchase decisions‖”, Journal of Advertising Research, Vol.: 47, Issue: 4, pp.
437–447, 2007.
V. D. Barefoot, J. Szabo. “Friends with benefits: A social media-marketing
handbook”, San Francisco: No Starch Press, 2010.
VI. N. Hafele, “Social Media Marketing: Interaction”, Trends & Analytics‖, ICT
511 Fall, Vol.: 51, Issue: 3, pp. 1-6, 2011.
VII. P. R. Hill, N. Moran, “Social marketing meets interactive media: lesson for
advertising company”, International Journal of Advertising, Vol.: 30, Issue: 5,
pp. 815–838, 2011.
VIII. S. Hill, F. Provost, C. Volinsky, “Network-Based Marketing: Identify likely
adopters via consumer networks‖”, Journal of Statistical Science, Vol.: 21,
Issue: 2, pp .256-276, 2006.
IX. S. Paul, “What Exactly is Public Service Advertising”,
http://advertising.about.com/od/advertisingglossary/g/Public-Service-
Advertising-A-Complete-Definition-Of-Public-Service-Advertising.html, 2016.
X. V. K. Gadda, “Imapact of Social Advertising towards Health &Hygeine”,
International Journal Recent Technology and Engineering, Vol. 08, Issue: 3,
pp.7056-7061, 2019.

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Abstract:

Fundamental analysis is a method of analyzing the financial data (fundamental data) for determining the stock value of a company which it considers variables such as company’s earnings, dividends, and sales. Fundamental analysis does not look at the behavior variables and does not considering the overall state of the market. Exclusively fundamental analysis focuses on the business companies for determining whether the stock should be bought or sold. In Indian stock market as well economy the position banking industry is very strong. The objective of the study is to analyze the profitability position of the public and private sector banks in India. The data collected from the financial statements of both public sector(State Bank of India-SBI, Bank of Baroda-BOB, Punjab National Bank-PNB) and private sector banks (Hosing Development Finance Corporation-HDFC & Industrial Credit Investment Corporation of India-ICICI) from the period of 2014-2015 to 2018-2019. This analysis helps to the shareholders for taking decision making statements in terms of profitability. The variables used in this study are Operating Profit Margin (OPM), Net Profit Margin (NPM),Return on Capital Employed(ROCE), Earnings per Share (EPS) and Price/Earnings (P/E) ratio

Keywords:

Profitability Analysis,Banks,ANOVA,

Refference:

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01.pdf

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Abstract:

Better torque control can be obtained by the use of Direct torque control (DTC) instead of Field oriented control (FOC) in steady state and transient state operating conditions because of its simple control structure. Robustness and fast torque response are the advantages of Direct torque control (DTC). Stator flux estimation is difficulty under low speed operation due to existence of open loop integrator and improper working of an open loop voltage model, hence an adaptive fuzzy system is adopted which improves the machine performance by eliminating open loop integration, minimizing stator current distortions, constant switching frequency, fast response of rotor speed and stator flux electro-magnetic torque without ripples. In this paper an adaptive fuzzy controller is adapted which improves system performance and subdues high torque ripples. For the proposed system simulation results are carried out.

Keywords:

Direct torque control,Adaptive fuzzy system (AFS),Modelling of induction motor drive,

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