Journal Vol – 14 No -1, February 2019

Abstract:

This study is targeted for reducing the power losses for a branch of Peshawar Electric Supply Company (PESCO), a small electric power grid in Pakistan, starting from Shahibagh and ending at Hayatabad substation. This study evaluates the current configuration of the transmission network, and then by using Particle Swarm Optimization, the best possible configuration that will ensure maximum throughput and minimum transmission and distribution losses is determined. The study is verified using Newton Raphson Method. Newton Raphson method is used to find the state of the mentioned network and then after the new configuration is proposed, the state estimation is done again to evaluate various parameters of the network and confirm its feasibility. The reconfiguration resulted from the PSO and NR methods have shown electric power losses minimization of the selected grid with 15.021%, amounting to a total of 0.3MW power loss minimization.

Keywords:

Power systems, Power system measurements, Power grids,Power system planning,Power transmission,

Refference:

I.Cui-Ru Wang et al., “A modified particle swarm optimization algorithm and its application in optimal power flow problem,” in 2005 International Conference on Machine Learning and Cybernetics, 2005, vol. 5, no. August, p. 2885–2889 Vol. 5.

II.F. R. Zaro and M. A. Abido, “Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems,” 2011 11th International Conference on Intelligent Systems Design and Applications. IEEE, 2011.

III.H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” IEEE Trans. Power Syst., vol.15, no. 4, pp. 1232–1239, 2000.

IV.I. M. Malik and D. Srinivasan, “Optimum power flow using flexible genetic algorithm model in practical power systems,” 2010 Conference Proceedings IPEC. IEEE, 2010.

V.J. A. J. A. Momoh, S. X. X. Guo, E. C. C. Ogbuobiri, andR. Adapa, “the Quadratic Interior Point Method Solving Power System Optimization Problems,” IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1327–1336, 1994.

VI.L. L. Lai et al., “Particle Swarm Optimization for Economic Dispatch of Units with Non-Smooth Input-Output Characteristic Functions,” Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems. IEEE.

VII.M. A. Abido, “Multiobjective Particle Swarm Optimization for OptimalPower Flow Problem,” in 12th International Middle-East Power System Conference, 2008. MEPCON 2008., 2008, pp. 392–396.

VIII.Muhammad Aamir Aman, 2Muhammad Zulqarnain Abbasi, 3Akhtar Khan, 4Waleed Jan, 5Mehr-e-Munir.Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan. Power Generator Automation, Monitoring and Protection System. J.Mech.Cont.& Math. Sci., Vol. -13, No. -4, September-October (2018) Pages 122 –133.

IX.Muhammad Aamir Aman, 2Muhammad Zulqarnain Abbasi, 3Hamza Umar Afridi, 4KhushalMuhammad, 5Mehr-e-Munir.. Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan. Prevailing Pakistan’s Energy Crises. J.Mech.Cont.& Math. Sci., Vol. -13, No. -4, September-October (2018) Pages 147-154.

X.N.P. Padhy, M. A. Abdel-Moamen, and B. J. Praveen Kumar, “Optimal location and initial parameter settings of multiple TCSCs for reactive power planning using genetic algorithms,” IEEE Power Eng. Soc. Gen. Meet. 2004., vol. 2, pp. 1110–1114, 2004.

XI.Weibing Liu, Min Li, and Xianjia Wang, “An improved particle swarm optimization algorithm for optimal power flow,” 2009 IEEE 6th International Power Electronics and Motion Control Conference. IEEE, pp. 2448–2450, 2009.

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Abstract:

3D-printing technology is otherwise called added substance assembling or fast prototyping, is an advanced manufacturing technique which builds 3D parts directly in layer by layer from the computer aided plan model in raster way with minimal wastage of material. Rather than in conventional manufacturing process where material is removed by the hard tool to bring the 3D component in desired model, 3D printing is completely contrast to it where material is added in sequence parts are built in layer by layer, it doesn’t require any post processing as in conventional process. 3D printed parts are more performing under different loading conditions and easy to build and repair parts any stage of design cycle. Due its flexibility of manufacturing, it shows its applications in auto ancillaries, aerospace and medical filed. 3D printing technology showing it influencing in making medical implants. Manufacturing of medical implants in conventional process is very expensive. As these implants vary patient to patient, and it is difficult to make tailor made implants in conventional manufacturing processes. Hence 3D printing technology can overcome this issue with minimal cost for making tailor made implants for individual patients

Keywords:

Additive manufacturing,bio-materials,medical implants,

Refference:

I.C. Nastase-Dan, P. Doru Dumitru, G. Gheorghe Ion, and P. Sanda, “Innovative technology through selective laser sintering in mechatronics, biomedical engineering and industry,” Incas Bull., vol. 3, no. 1, pp. 31–37, 2011.

II.D. T. R. S. G. Pham, “A Comparsion of RP Technologies.pdf.

III.D. V Mahindru, P. Mahendru, V. Mahindru, and P. Mahendru, “Review of Rapid Prototyping-Technology for the Future,” Glob. J. Comput. Sci. Technol. Graph. {&} Vis., vol. 13, no. 4, pp. 27–38, 2013.

IV.F. P. W. Melchels, J. Feijen, and D. W. Grijpma, “A review on stereolithography and its applications in biomedical engineering,” Biomaterials, vol. 31, no. 24, pp. 6121–6130, 2010.

V.G. Suresh and K. L. Narayana, “3D Printing: Breakthroughs in Research and Practice,” in 3D Printing, IGI Global, 2016, pp. 1–21.

VI.G. Suresh and K. L. Narayana, “A Review on Fabricating Procedures in Rapid Prototyping,” Int. J. Manuf. Mater. Mech. Eng., vol. 6, no. 2, 2016.

VII.G. Suresh, K. L. Narayana, and M. K. Mallik, “A Review on Development of Medical Implants by Rapid PrototypingTechnology,” Int. J. Pure Appl. Math., vol. 117, no. 21, pp. 257–276, 2017.

VIII.Ganzi Suresh, K L Narayana and M. Kedar Mallik., “Bio-Compatible Processing of LENSTM Deposited Co-Cr-W alloy for Medical Applications”. International Journal of Engineering and Technology (UAE). 7 (2.20) (2018) 362-366. DOI:10.14419/ijet.v7i2.20.16734.

IX.Ganzi Suresh, K L Narayana, M. Kedar Mallik, V. Srinivas and G. Jagan Reddy., “Processing & Characterization of LENSTM Deposited Co-Cr-W Alloy for Bio-Medical Applications”. International Journal of Pharmaceutical Research (IJPR) Volume 10, Issue-1, 2018, 276-285.

X.Ganzi Suresh, K L Narayana, M. Kedar Mallik, V. Srinivas, G. Jagan Reddy and I.Gurappa.,“Electro Chemical Corrosion Behavior of LENSTM Deposited Co-Cr-W Alloy for Bio-Medical Applications”. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) Special Issue, Jun 2018, 41-5.

XI.Hangobo Lan, “Web-based rapid prototyping and manufacturing systems: A review,” vol. 60, pp. 643–656, 2009.

XII.I.Palčič, M. Balažic, M. Milfelner, and B. Buchmeister, “Potential of laser engineered net shaping (LENS) technology,” Mater. Manuf. Process., vol. 24, no. 7–8, pp. 750–753, 2009.

XIII.Kumar, G. N. S. and A. Srinath. 2018. “An Ergonomical conditions of Pedestrians on Accelerating Moving Walkway: A People Mover System.” International Journal of Mechanical and Production Engineering Research and Development 8 (Special Issue 7): 1376-1381. www.scopus.com.

XIV.Kumar, Gurram Narendra Santosh, and A. Srinath. “Exploration of Accelerating Moving Walkway for Futuristic Transport System in Congested and Traffical Areas.” (2018): 616-624.

XV.L. Villalpando, H.Eiliat, and R. J. Urbanic, “An optimization approach for components built by fused deposition modeling with parametric internal structures,” Procedia CIRP, vol. 17, pp. 800–805, 2014.

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XXI.P. Chennakesava and Y. S. Narayan, “Fused Deposition Modeling -Insights,” Int. Conf. Adv. Des. Manuf., pp. 1345–1350, 2014.

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XXIII.Q. Wei et al., “Selective laser melting of stainless-steel/nano-hydroxyapatite composites for medical applications: Microstructure, element distribution, crack and mechanical properties,” J. Mater. Process. Technol., vol. 222, pp. 444–453, 2015.

XXIV.Rama ChandraManohar, K et al. Modeling and Analysis of Kaplan Turbine Blade Using CFD.International Journal of Engineering & Technology, [S.l.], v. 7, n. 3.12, p. 1086-1089, july 2018. ISSN 2227-524X. Available at: https://www.sciencepubco.com/index.php/ijet/article/view/17766>. Date accessed: 05 jan. 2019. doi:http://dx.doi.org/10.14419/ijet.v7i3.12.17766.

XXV.Sk.Hasane Ahammad,V.Rajesh, “Image Processing based segmentation for spinal cord in MRI”,Indian Journal of Public Health Research and Development 9(6), pp.317-323XVI.M. Domingo-espin, I. Engineering, and U. Ramon, “A methodology to choose the best building direction for Fused Deposition Modeling end-use parts.

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Abstract:

Limitations of Wireless nodes are the battery power and storage capacity, while plotting a MANET, these are to be considered. By improvising battery life, the energy used by nodes shall be increased such that network is operational. To move data packets efficiently the network, MANET uses smallest Hop Count routing protocol. Most power is used by data transmission process. Key challenges in Ad Hoc networks are the recurring changes in network topology. Network topology changes happen due to motility and finite battery power of the mobile devices. Mostly links are not available in the network as depletion of power source may cause early unavailability of nodes. This paper discusses about the protocol that incorporates link failure prediction at network layer and Power Control Protocol at MAC layer to improve network performance. Performance enhancement in regards to total power transmission, energy regulation and consumption per node along with throughput of our proposed cross layer routing protocol is shown by simulation results when compared to AODV.

Keywords:

MANET,MAC Protocol,Cross layer,AODV,RDSR, LBP-AOMSV,LP-PCP,

Refference:

I.Abdule.S.M etHassan.S, “Divert Failure Route Protocol Based on AODV”, In Network Applications Protocols and Services (NETAPPS), 2010 Second International Conference on. IEEE, 2010.

II.Aman Kumar and Rahul Hans, ”Performance Analysis of DSDV, I-DSDV, OLSR, ZRP Proactive Routing Protocol in Mobile Ad Hoc Networks in IPv6”, International Journal of Advanced Science and Technology Vol.77,pp.25-36, 2015. III.Chakrabarti. S and Mishra. A, “Quality of service challenges for wireless mobile Ad hoc networks”, Wiley J. Wireless Communication and Mobile Computing, vol. 4, n°12, p. 29-153, 2004.

IV.Chang.R and Leu.J, “Long-lived path routing with received signal strength for ad hoc networks”, In Wireless Pervasive Computing, 1stInternational Symposium on. IEEE, 2006.

V.Crawley .E, Nair R., Rajagopalan. B and Sandick. H, “A Framework for QoS-based Routing in the Internet IETF RFC2386”, 2002.

VI.Frank Aune, “Cross-Layer Tutorial”,NTNU 2014.

VII.F. Sophia Pearlin and G. Rekha,“ Performance Comparison of AODV, DSDV and DSR Protocols in Mobile Networks using NS-2”, Indian Journal of Science and Technology, Vol 9(8), February 2016.

VIII.Hwang.YetVarshney.P, “An adaptive QoS routing protocol with dispersity for ad-hoc networks”, chez System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on.IEEE,2003.

IX.John Novatnack , Lloyd Greenwald and Harpreet Arora, “Evaluating ad hoc routing protocols with respect to quality of service”, Wireless And Mobile Computing, Networking And Communications, Volume 3, pp. 205-212,Aug.2005.

X.L. Qin and T. Kunz, “Proactive Route Maintenance in DSR”, SIGMOBILE Mob. Comput. Commun. Rev., Vol. 6, No. 3, pp. 79–89, 2002.

XI.M. Al-Shurman, S.-M.Yoo, and S. Park, “A Performance Simulation for Route Maintenance in Wireless Ad Hoc Networks”, in ACM-SE 42:Proceedings of the 42nd annual Southeast regional conference, New York, USA: ACM, pp. 25–30, 2004.

XII.Mamoun Hussein Mamoun, “Location Aided Hybrid Routing Algorithm for MANET,” Int. Journal of Engineering& Technology IJET/IJENS, Vol. 11, No. 01, pp. 51-57, Feb. 2011.

XIII.Mamoun Hussein Mamoun,”A Proposed Route Selection Technique in DSR Routing Protocol for MANET”, International Journal of Engineering & Technology IJET-IJENS, Vol. 11, No. 02, April 2011.

XIV.MerlindaDrini and Tarek Saadawi, ”Modeling Wireless Channel for Ad-Hoc Network Routing Protocol”, ISCC MarakechMarocco, pp. 549-555, July 2008.

XV.M. F. Sjaugi, M. Othman, and M. F. A. Rasid,“A New Distance Based Route Maintenance Strategy for Dynamic Source Routing Protocol”, Journal of Computer Science, Vol. 4, No. 3, pp. 172–180, 2008.

XVI.M. Tsai, N. Wisitpongphan, and O.K. Tonguz, “Link-Quality Aware AODV Protocol”, in Proc. IEEE International Symposium on Wireless Pervasive Computing (ISWPC) 2006, Phuket, Thailand, January 2006.

XVII.Perkins.C, Belding.E ,Royer and Das.S, “Ad hoc on-demand distance vector routing”, RFC 3561, IETF, 2003.

XVIII.P. Srinivasan and K. Kamalkkannan, “Signal Strength And Energy Aware Reliable Route Discovery in Manet”, International Journal of Communication Network Security, Vol. 1, Issue 4, 2012.

XIX.QoS Forum, July 1999. [On line]. Available: http://www.qosforum.com.

XX.RjabHajlaoui, Sami Touil and Wissemachour,” O-DSR: Optimized DSR Routing Protocol For Mobile Ad Hoc Network”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 7, No. 4, August 2015.

XXI.RAJESHKUMAR, P.SIVAKUMAR, ”Comparative Study of AODV, DSDV and DSR Routing Protocols in MANET Using NetworkSimulator-2”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 12, December 2013.

XXII.Rajeshwar Singh, Dharmendra K Singh and Lalan Kumar,” Performance Evaluation of DSR and DSDV Routing Protocols for Wireless Ad Hoc Networks”, Int. J. Advanced Networking and Applications 732 Volume: 02, Issue: 04, pp. 732-737 2011.

XXIII.Ravneetkaur, Dr.Neeraj Sharma, “Dynamic node recovery in MANET for high recovery probability”, International Journal of Computer Networks and Applications (IJCNA), Vol 2, Issue 4, July -August 2015.

XXIV.Rohan Gupta, Harbhajan Singh and Gurpreet Singh,” Performance Evaluation of Routing Protocols for Mobile AdhocNetworks ”, Indian Journal of Science and Technology, Vol 10, No. 31, August 2017.

XXV.Rupinder Kaur, Paramdeep Singh et al, ” Performance Enhancement of AODV with Distributed-DSR Routing Protocol in Manet”, Indian Journal of Science and Technology, Vol. 8, No. 28, October 2015.

XXVI.Sarma.N and Nandi.S, “Route stability based QoS routing in mobile AdHoc networks”, Wireless Personal Communications , vol. 54, n° 11,pp. 203-224, 2010.

XXVII.S. Wu, S. Ni, Y. Tseng, and J. Sheu, “Route Maintenance in a Wireless Mobile Ad Hoc Network”, 33rd Hawaii International Conference onSystem Sciences, Maui, 2000.

XXVIII.VivekSoi,and Dr. B.S. Dhaliwal,“ Performance comparison of DSR and AODV Routing Protocol in Mobile Ad hoc Networks”, International Journal of Computational Intelligence Research Volume 13, No. 7, pp. 1605-1616, 2017.

XXIX.Y. Ramesh, Usha Ch. andJagadishGurrala,” CBR based Performance Evaluation on FSR, DSR,STAR-LORA, DYMO Routing Protocols in MANET”, International Journal of Engineering Research and Development, Vol. 2, Issue 9, PP. 17-27, (August 2012).

XXX.ZekiBilgin, Bilal Khan, “A Dynamic Route Optimization Mechanism for AODV in MANETs”, Journal of Computer Science, 2014.

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Abstract:

The venturi scrubber has been used as air pollution controlling device. These scrubbers are promising device for cleaning the contaminated gases. It is found in the literature that the performance of venturi scrubber (i.e. collection efficiency), is significantly influenced by droplet distribution, pressure drop, disintegration of liquid, droplet sizes and injection methods. Effect of submergence height, multi-stage injection, position of the orifice, diameter of orifice, throat length and angle of convergence and divergence of venturi scrubber is found scarce and these parameters are affecting collection efficiency drastically. Therefore, it is necessary to study their effect to improve the performance of self-priming venturi scrubber. This article is the review of numerical and experimental study of the performance in venturi scrubber.

Keywords:

Venturi Scrubber,Self-Priming,CFD Modelling,Collection efficiency,

Refference:

I.A.Rahimi,J.Fathikalajahi,andM.Taheri,“ANewMethodofEddyDiffusivityCalculationforDropletsofaVenturiScrubber,”vol.84,no.February,pp.310–315,2006.

II.A.Moharana,P.Goel,andA.K.Nayak,“N12:Performanceestimationofaventuriscrubberanditsapplicationtoself-primingoperationindecontaminatingaerosolparticulates,”Nucl.Eng.Des.,vol.320,pp.165–182,2017.

III.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinlargescaleventuriscrubber.PartII.Dropletsize,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.424–431,2009.

IV.A.SharifiandA.Mohebbi,“AcombinedCFDmodelingwithpopulationbalanceequationtopredictpressuredropinventuriscrubbers,”2013.

V.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinalarge-scaleventuriscrubber.PartI:Pressuredrop,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.59–67,2009.

VI.A.Majid,Y.Changqi,S.Zhongning,W.Jianjun,andG.Haifeng,“CFDsimulationofdustparticleremovalefficiencyofaventuriscrubberinCFX,”Nucl.Eng.Des.,vol.256,pp.169–177,2013.

VII.A.Majid,C.Yan,S.Zhongning,J.Wang,andA.Rasool,“N6:CFDSimulationofThroatPressureinVenturiScrubberMajidAli,”Appl.Mech.Mater.,vol.173,pp.3630–3634,2012.

VIII.A.Rahimi,A.Niksiar,andM.Mobasheri,“Consideringrolesofheatandmasstransferforincreasingtheabilityofpressuredropmodelsinventuriscrubbers,”Chem.Eng.Process.ProcessIntensif.,vol.50,no.1,pp.104–112,2011.

IX.C.Goniva,Z.Tukovic,C.Feilmayr,T.Bürgler,andS.Pirker,“SimulationofoffgasscrubbingbyacombinedEulerian-Lagrangianmodel,”SeventhInt.Conf.CFDMiner.ProcessInd.,no.December,pp.1–7,2009.

X.D.B.RobertsandJ.C.Hill,“Atomizationinaventuriscrubber,”Chem.Eng.Commun.,vol.12,no.1–3,pp.33–68,1981.

XI.D.FernándezAlonso,J.A.S.Gonçalves,B.J.Azzopardi,andJ.R.Coury,“DropsizemeasurementsinVenturiscrubbers,”Chem.Eng.Sci.,vol.56,no.16,pp.4901–4911,2001.

XII.F.AhmadvandandM.R.Talaie,“CFDmodelingofdropletdispersioninaVenturiscrubber,”Chem.Eng.J.,vol.160,no.2,pp.423–431,2010.

XIII.H.E.Hesketh,“FineParticleCollectionEfficiencyRelatedtoPressureDrop,ScrubbantandParticleProperties,andContactMechanism,”J.AirPollut.ControlAssoc.,vol.24,no.10,pp.939–942,1974.

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XVI.J.R.Coury,G.Guerra,R.Be,andJ.A.S.Gonc,“PressureDropandLiquidDistributioninaVenturiScrubber:ExperimentalDataandCFDSimulationVad,”2012.

XVII.J.Fathikalajahi,M.Taheri,andM.R.Talaie,“Theoreticalstudyofnonuniformdropletsconcentrationdistributiononventuriscrubberperformance,”Part.Sci.Technol.,vol.14,no.2,pp.153–164,1996.

XVIII.J.F.andM.R.Talaie,“THEEFFECTOFDROPLETSIZEDISTRIBUTIONONLIQUIDDISPERSIONINAVENTURISCRUBBER,”J.AerosolSci.Vol.,vol.28,no.1,pp.291–292,1997.

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XX.J.A.S.Gonçalves,D.F.Alonso,M.A.M.Costa,B.J.Azzopardi,andJ.R.Coury,“Evaluationofthemodelsavailableforthepredictionofpressuredropinventuriscrubbers,”J.Hazard.Mater.,vol.81,no.1–2,pp.123–140,2001.

XXI.K.C.GoalandK.G.T.Hollands,“AGeneralMethodforPredictingParticulateCollectionEfficiencyofVenturiScrubbers,”Ind.Eng.Chem.Fundam.,vol.16,no.2,pp.186–193,1977.

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XXVI.M.Ali,C.Q.Yan,Z.N.Sun,J.J.Wang,andK.Mehboob,“N5:CFDSimulationofPredictionofPressureDropinVenturiScrubber,”Appl.Mech.Mater.,vol.166–169,pp.3008–3011,2012.

XXVII.M.Costa,A.Riberio,E.Tognetti,M.Aguiar,J.Gonclaves,andJ.Coury,“Performanceofaventuriscrubberintheremovaloffinepowderfromaconfinedgasstream,”Mater.Res.,vol.18,no.2,pp.177–179,2005.

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XXXII.N.Horiguchi,H.Yoshida,andY.Abe,“N9:Numericalsimulationoftwo-phaseflowbehaviorinVenturiscrubberbyinterfacetrackingmethod,”Nucl.Eng.Des.,vol.310,pp.580–586,2016.

XXXIII.N.Horiguchi,H.Yoshida,S.Uesawa,A.Kaneko,andY.Abe,“Icone21-16287FilterVenting:PreliminaryAnalysisandObservationofHydraulic,”pp.1–6,2013.

XXXIV.P.Goel,A.Moharana,andA.K.Nayak,“Experimentalstudyofpressuredropinself-primingandsubmergedventuriscrubber,”pp.14–17.

XXXV.P.Goel,A.Moharana,andA.K.Nayak,“Measurementofscrubbingbehaviourofsimulatedradionuclideinasubmergedventuriscrubber,”Nucl.Eng.Des.,vol.327,no.December2017,pp.92–99,2018.

XXXVI.R.H.Boll,“ParticleCollectionandPressureDropinVenturiScrubbers,”Ind.Eng.Chem.Fundam.,vol.12,no.1,pp.40–50,1973.

XXXVII.R.W.K.AllenandA.VanSanten,“DesigningforpressuredropinVenturiscrubbers:Theimportanceofdrypressuredrop,”Chem.Eng.J.Biochem.Eng.J.,vol.61,no.3,pp.203–211,1996.

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XLIV.S.Nasseh,A.Mohebbi,A.Sarrafi,andM.Taheri,“N4:Estimationofpressuredropinventuriscrubbersbasedonannulartwo-phaseflowmodel,artificialneuralnetworksandgeneticalgorithm,”Chem.Eng.J.,vol.150,no.1,pp.131–138,2009.

XLV.S.IlKim,J.B.Lee,J.H.Jung,K.S.Ha,H.Y.Kim,andJ.H.Song,“IntroductionoffilteredcontainmentventingsystemexperimentalfacilityinKAERIandresultsofaerosoltest,”Nucl.Eng.Des.,vol.326,no.November2017,pp.344–353,2018.

XLVI.T.J.OvercampandS.R.Bowen,“EffectofThroatLengthandDiffuserAngleonPressureLossAcrossaVenturiScrubber,”J.AirPollut.ControlAssoc.,vol.33,no.6,pp.600–604,1983.

XLVII.V.Sekar,A.W.Gnyp,andC.C.S.Pierre,“ExaminationofGas-LiquidFlowinaVenturiScrubber,”Ind.Eng.Chem.Fundam.,vol.23,no.3,pp.303–308,1984.

XLVIII.V.G.Guerra,M.A.F.Daher,M.V.Rodrigues,J.A.S.Gonçalves,andJ.R.Coury,“DropletInteractionintheLiquidInjectionbyMultipleOrificesinthePerformanceofaVenturiScrubber,”Mater.Sci.Forum,vol.591–593,pp.896–901,2008.

XLIX.V.G.Guerra,J.A.S.Gon??alves,andJ.R.Coury,“ExperimentalinvestigationontheeffectofliquidinjectionbymultipleorificesintheformationofdropletsinaVenturiscrubber,”J.Hazard.Mater.,vol.161,no.1,pp.351–359,2009.

L.V.G.Guerra,J.A.S.Gonçalves,andJ.R.Coury,“ExperimentalverificationoftheeffectofliquiddepositionondropletsizemeasuredinarectangularVenturiscrubber,”Chem.Eng.Process.ProcessIntensif.,vol.50,no.11–12,pp.1137–1142,2011.

LI.X.Gamisans,M.Sarrà,F.J.Lafuente,andB.J.Azzopardi,“Thehydrodynamicsofejector-Venturiscrubbersandtheirmodellingbyanannularflow/boundarylayermodel,”Chem.Eng.Sci.,vol.57,no.14,pp.2707–2718,2002.

LII.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Structuredesignonimprovinginjectionperformanceforventuriscrubberworkinginself-primingmode,”Prog.Nucl.Energy,vol.80,pp.7–16,2015.

LIII.Yung,“venturiscrubberperformancemodel.PDF,”vol.7,no.9,pp.10–13,1978.

LIV.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Experimentalresearchonaerosolscollectionperformanceofself-primingventuriscrubberinFCVS,”Prog.Nucl.Energy,vol.85,pp.771–777,2015.

LV.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Performanceofiodidevapourabsorptionintheventuriscrubberworkinginself-primingmode,”Ann.Nucl.Energy,vol.87,pp.426–434,2016.

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Abstract:

The most challenging task is searching Telugu script from the database because of difficulty in differentiating the Characteristics of the Telugu word or scripts. In this, we introduced robust approach for Telugu script retrieval using transformation-based methodology. Non-subsampled contourlet transform (NSCT) is utilized for texture classification which will function based on Non-subsampled pyramid filter bank (NSPFB) and Non-subsampled directional filter bank (NSDFB). Spatial dependence matrix is utilized to extract the texture features. In addition, image statistics is computed to enhance the retrieval performance further. Finally, hamming similarity metric is calculated which calculates the distance between trained and test word templates, which an effective distance metric over conventional Euclidean distance. In order to test, missing segment, noisy, corrupted and occlusion effected words are used as an input and taken into consideration multi conjunct vowel consonant clustered word images for showing the robustness of presented algorithm. In the substantial simulation analysis gives the presented technique finds most similar word images from database although if it is under testing conditions. Our presented scheme has superior performance compared to the traditional approaches described in the literature with respect to mean Average Precision (mAP) and mean Average Recall (mAR).

Keywords:

Telugu script,texture features,statistical properties,non-subsampled contourlet transform,statistical parameters,feature vector and hammingdistance metric,

Refference:

I.Arthur L. da Cunha, Jianping Zhou,and Minh N. Do, “The Non-subsampled Contourlet Transform: Theory, Design and Applications”, IEEE Transaction on Image Processing, Vol. 15, No. 10, pp. 3089-3100, 2006.

II.B. Verma, M. Blumenstein, S. Kulkarni, “Recent achievements in off-line handwriting recognition systems”, School of Information Technology, Griffith University, Gold Coast Campus.

III.C. V. Jawahar and A. Kumar, “Content-level Annotation of Large Collection of Printed Document Images”, In: Proc. of International Conf. on Document Analysis and Recognition, Parana, Brazil, 2007.

IV.C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, “A Bilingual OCR for Hindi-Telugu Documents and its Applications”, Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad.

V.D. G. Lowe, “Distinctive Image Features from Scale-Invariant Key points,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.

VI.Danish Nadeem and Saleha Rizvi, “Character recognition using template matching”, Department of Computer Science, JamiaMilliaIslamia, New Delhi, 2015.

VII.E Candes and D. Donoho, “Curvelets –a surprisingly effective nonadaptive representation for objects with edges.” In: A. Cohen, C. Rabut and L. Schumaker, Editors,Curves and Surface Fitting: Saint-Malo 1999, Vanderbilt University Press, Nashville, pp. 105–120, 2000.

VIII.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.

IX.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.

X.I. Z. Yalniz and R. Manmatha, “An Efficient Framework for Searching Text in Noisy Document Images”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 48-52, 2012.

XI.J. van Gemert, C. J. Veenman, A. W. M. Smeulders, and J.-M. Geusebroek, “Visual Word Ambiguity”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 7, pp.1271-1283, 2010.

XII.J. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel Codebooks for Scene Categorization”, In: Proc. Of European Conf. on Computer Vision, Berlin, Heidelberg, pp. 696-709, 2008.

XIII.Jangala. Sasi Kiran, N. Vijaya Kumar, N. SashiPrabha and M. Kavya, “A Literature Survey on Digital Image Processing technique in character recognition of Indian languages”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 3, pp. 2065-2069, 2015.

XIV.Jatin M Patil and Ashok P. Mane, “Multi Font and Size Optical Character Recognition Using Template Matching”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 1, pp. 504-506, 2013.

XV.K Mohana Lakshmi and T RangaBabu, “Searching for Telugu Script in Noisy Images using SURF Descriptors”, IEEE 6th International Conference on Advance Computing, pp: 480-483, 2016.

XVI.K. Takeda, K. Kise, and M. Iwamura, “Real-time document image retrieval for a 10 Million pages database with a memory efficient and stability improved LLAH”, International Conf. on Document Analysis and Recognition, Beijing, China, pp. 1054-1058, 2011.

XVII.K.Mohana Lakshmi, Dr.T.Ranga Babu, “A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming , ICCCPE(Springer LNS), 978-981-13-0211-4, 2018.

XVIII.KesanaMohana Lakshmi and TummalaRangaBabu, “A New Hybrid Algorithm for Telugu Word Retrieval and Recognition”, International Journal of Intelligent Engineering and Systems, Vol. 11, No. 4, pp.117-127, 2018.

XIX.M N Do and M Vetterli, “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106, 2005.

XX.M. J. Shensa, “The discrete wavelet transform: Wedding the àtrous and Mallat algorithms,” IEEE Trans. Signal Process., Vol. 40, No. 10, pp. 2464–2482, 1992.

XXI.M. Wenying and D. Zuchun, “A Digital Character Recognition Algorithm Based on the Template Weighted Match Degree”, International Journal of Smart Home, Vol.7, No. 3, pp. 53-60, 2013.

XXII.Md. Mahbubar Rahman, M. A. H. Akhand, Shahidul Islam, Pintu Chandra Shill and M. M. Hafizur Rahman, “Bangla Handwritten Character Recognition using Convolutional Neural Network”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 8, pp. 42-49, 2015.

XXIII.N. Sharma, S. Chanda, U. Pal and M. Blumenstein, “Word-wise Script Identification from Video Frames”, In: Proc. of International Conf.on Document Analysis and Recognition, Washington, DC, USA, pp.867-871, 2013.

XXIV.N. Shobha Rani Vasudev T and Pradeep C.H. “A Performance Efficient Technique for Recognition of Telugu Script Using Template Matching”, International Journal of Image, Graphics and Signal Processing, Vol. 8, No. 3, pp.15-23, 2016. XXV.N. Shobha Rani, T. Vasudev, “A Generic Line Elimination Methodology using Circular Masks for Printed and Handwritten Document Images”, Emerging research in computing, information, communication and applications ELSEVIER science and technology, Vol. 3, No. 1, pp. 589-594, 2014.

XXVI.N.sharma, U.Pal, and M. Blumenstein, “A Study on Word Level Multi-script Identification from Video Frames”, In: Proc. of International Joint Conf. on Neural Networks, Beijing, China, pp.1827-1833, 2014.

XXVII.Nagasudha D and Y MadhaviLatha, “Keyword Spotting using HMM in Printed Telugu Documents”, In: Proc. of International Conf. on Signal Processing, Communication, Power and Embedded Systems, Paralakhemundi, India, pp: 1997-2000, 2016.

XXVIII.Nikhil Rajiv Pai and Vijaykumar S. Kolkure, “Design and implementation of optical character recognition using template matching for multi fonts size”, International Journal of Research in Engineering and Technology, Vol. 4, No. 2, pp. 398-400, 2015.

XXIX.P. Shivakumara, N. Sharma, U. Pal, M. Blumenstein, and C. L. Tan, “Gradient-Angular-Features for Word-wise Video Script Identification”, In: Proc. of International Conf. on Pattern Recognition, Stockholm, Sweden, pp.3098-3103, 2014.

XXX.R. Shekhar and C. V. Jawahar, “Word Image Retrieval Using Bag of Visual Words”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 297-301, 2012.

XXXI.Ravi Shekhar and C V Jawahar, “Word Image Retrieval Using Bag-of-Visual Words”, In: Proc. of IAPR International Workshop on Document Analysis Systems, Gold Cost, QLD,

XXXII.Rinki Singh, Manideep Kaur, “OCR for Telugu Script Using Back-Propagation Based Classifier”, International Journal of Information Technology and Knowledge Management, Vol. 2, No. 2, pp. 639-643, 2010.

XXXIII.S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories”, In: Proc. of IEEE Computer Society Conf. onComputer Vision and Pattern Recognition, New York, USA, 2006.

XXXIV.Soumendu Das and Sreeparna Banerjee, “An Algorithm for Japanese Character Recognition”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 1, pp. 9-15, 2014.

XXXV.Suman V Patgar, Vasudev T, Murali S, “A system for detection of fabrication in photocopy document”, Journal of Computer Science & Information Technology, Vol. 5, No. 14, pp. 29–35, 2015.

XXXVI.T. M. Rath and R. Manmatha, “Word spotting for historical documents”, International Journal of Document Analysis and Research, Vol. 9, No. 2-4,pp.139-152, 2007.

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Abstract:

Transportation model provides a powerful framework to meet the Business challenges. In highly competitive market the pressure is increasing rapidly to the organizations to determine the better ways to deliver goods to the customers with minimum transportation cost. In this paper we proposed a new algorithm based on Least Cost Method(LCM)for finding Initial Basic Feasible Solution(IBFS) to minimize transportation cost .Our proposed algorithm provides a IBFS which is either optimal or near to the optimal value with minimum steps comparatively better than those obtain by traditional algorithm or method .For the validity of this algorithm we considered a numerical transportation problem and comparative study has been made minimum cost with graphically.

Keywords:

Transportation Cost, Least Cost Method, Supply,Demand, Initial Basic feasible Solution,Optimum solution,

Refference:

I.Ahuja, R.K.(1986). Algorithms for minimax transportation problem. Naval Research Logistics Quarterly.33 (4), 725-739. II.A.Gupta, S.Khanna and M. Puri, (1992), Paradoxical situations in transportation problems, Cahiers du Centre d’Etudes de RechercheOperationnell, 37–49.

III.Charnes, A. and Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming, 1, John Wiley & Sons, New York.

IV.Erlander S.B (2010) Cost-Minimizing Choice Behavior in Transportation Planning: A Theoretical. Page 8-10.

V.Goyal, S.K.(1984). Improving VAM for unbalanced transportation problem. Journal of Operational Research Society. 35(12), 1113-1114.

VI.Hadley, G., (1972). Linear Programming, Addition-Wesley Publishing Company, Massachusetts.

VII.Hemaida, R. & Kwak, N. K. (1994). A linear goal programming model for transshipment problems with flexible supply and demand constraints. Journalof Operational Research Society, 45(2), 1994, 215-224.

VIII.Hitchcock, F.L.(1941). The distribution of a product from several sources to numerous localities. Journal of Mathematics & Physics. 20, 224-230.

IX.Kvanli, A. (1980). Financial planning using goal programming. Omega, 8, 207-218.

X.Kwak, N.K. & Schniederjans, M.J.(1979) “A goal programming model for improved transportation problem solutions,” Omega, 12, 367-370.

XI.Lee, S.M., (1972). Goal Programming for Decision Analysis, Auerbach, Philadelphia.

XII.M.A .Hakim, M. A. Hossen, M. Sarif Uddin (2016),A credit policy approach of an inventory model for deteriorating item with price and time dependent demandaccepted for publication inJournal of Mechanics of Continua and Mathematical Sciences, ISSN 0973-8975,Volume -10 No. -2 .

XIII.Tolstoi, A.N. (1939). Methody ustraneniya neratsional’nykh perevozok pri planirovanii [Russian; Methods of removing irrational transportation in planning], Sotsialisticheskii Transport 9, 28-51 [also published a ‘pamphlet’: Methods of Removing Irrational Transportation in the construction of Operations Plans], Transzheldorizdat, Moscow, 1941.

XIV.V K Kapoor ,Operation Research (Problem and solution) ,sultan chand &sons, educational publishers ,new delhi.

XV.Veena Adlakha and Krzysztof Kowalski (2001), A heuristic method for more –for-less in distribution related problems, International Journal of Mathematical Education in Science and Technology, 32 61-71.

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Abstract:

This letter presents a single feed novel miniaturized patch antenna for WiMax applications and small wireless technologies. Antenna is fabricated on FR4 substrate with 1.6mm thickness and copper sheet of 0.035mm. The miniaturization of 82% is achieved by etching a Fork shape slot in ground plane as response is observed at 3.4GHz. Simulated and measured results shows acceptable gain of 3.4 to 3.6dB and efficiency ranging to 82% with 260MHz bandwidth. The proposed antenna is simulated in Computer Simulation Technology 2015. The measurement results demonstrate that the proposed antenna provides acceptable radiation performances with directional radiation patterns at desired frequency.

Keywords:

Miniaturization,Microstrip Patch Antenna (MPA),directivity,gain,bandwidth,Slots,Computer Simulation Technology (CST),

Refference:

I.Aguilar, Suzette M., Mudar A. Al-Joumayly, Matthew J. Burfeindt, Nader Behdad, and Susan C. Hagness. ”Multiband miniaturized patch antennas for a compact, shielded microwave breast imaging array.” IEEE transactions on antennas and propagation 62, no. 3 (2014): 1221-1231.

II.Ali, M. S. M., Rahim, S. K. A., Sabran, M. I., Abedian, M., Eteng, A., Islam, M. T. (2016). Dual band miniaturized microstrip slot antenna for WLAN applications. Microwave and Optical Technology Letters, 58(6), 1358-1362.

III.Amit K. Singh*, Mahesh P.Abegaonkar, and Shiban K. Koul, “Miniaturized Multiband Microstrip Patch Antenna Using Metamaterial Loading for Wireless Application” Progress In Electromagnetics Research C, Vol. 83, 71–82, 2018.

IV.Boukarkar, Abdelheq, Xian Qi Lin, Yuan Jiang, and Yi QiangYu. “Miniaturized single-feed multiband patch antennas.” IEEE Transactions on Antennas and Propagation 65, no. 2 (2017): 850-854.

V.Chen, Richard H., and Yi-Cheng Lin. “Miniaturized design of microstrip-fed slot antennas loaded with C-shaped rings.” IEEE Antennas and Wireless Propagation Letters 10 (2011): 203-206.

VI.Fritz-Andrade, E., Tirado-Mendez, J. A., Jardon-Aguilar, H., & Flores-Leal, R. (2017). Application of complementary split ring resonators for size reduction in patch antenna arrays. Journal of Electromagnetic Waves and Applications, 31(16), 1755-1768.

VII.Gupta, Ashish. “Miniaturized dual‐band metamaterial inspired antenna with modified SRR loading.” International Journal of RF and Microwave Computer‐Aided Engineering (2018): e21283.

VIII.Li, Ziyang, Leilei Liu, Pinyan Li, and Jian Wang. “Miniaturized design of CPW-Fed slot antennas using slits.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

IX.M. M. Bait-Suwailam and H. M. Al-Rizzo, “Size reduction of microstrip patch antennas using slotted Complementary Split-Ring Resonators,” in Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, 2013, pp. 528-531.

X.Motevasselian, Alireza, and William G. Whittow. “Miniaturization of a Circular Patch Microstrip Antenna Using an Arc Projection.” IEEE Antennas and Wireless Propagation Letters 16 (2017): 517-520.

XI.Saad Hassan Kiani, Khalid Mahmood, Mehre Munir and Alex James Cole, “A Novel Design of Patch Antenna using U-Slotand Defected Ground Structure” International Journal of Advanced Computer Science and Applications(ijacsa),8(3),2017. http://dx.doi.org/10.14569/IJACSA.2017.080303E.

XII.Tirado‐Mendez, J. A., Jardon‐Aguilar, H., Flores‐Leal, R., & Rangel‐Merino, A. (2018). Multiband reduced‐size patch antenna by employing a modified DMS‐spur‐line combo technique. International Journal of RF and Microwave Computer‐Aided Engineering, 28(4), e21232.

XIII.Wang, Qian, Ning Mu, Linli Wang, Jingping Liu, and Ying Wang. “Miniaturization microstrip antenna design based on artificial electromagnetic structure.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

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Abstract:

This study is a very good approach to find the solution of secure authentication for IOT based smart home environment and its appliances. The study aims to compare the different authentication methods with respect to smart home environment and trying to identify its limitation. After analyzing the existing authentication methods its limitation and core issues then targeted the message authentication for SHE. Presently SHE authentication is based on Exchange of six message authentication techniques in Enhance authentication and key establishment scheme 6LOWPAN (EAKES6Lo) which is advance version of secure authentication and key establishment scheme (SAKES). This authentication method cause much high end to end delay, energy consumption, overall throughput of the system, complexity and poor security approach. By simulation of EAKES6Lo and SAKES scheme found some results, in contrast to these results, there may be another solution to access any SHE lights, fans, refrigerators, air condition, geezer, door lock, microwave oven, television and water pump, HVAC control and security alarms etc remotely with better security, better complexity, minimum energy consumption, better key length, better throughput and minor end to end delay named two step authentication (TSA). The proposed model also helps to monitor accessing system by comparing security codes and its complexity.

Keywords:

Internet of Things(IOT),Smart Home Environment (SHE),Version 6 Low Power Wireless Personal Area Network (6LoWPAN),Enhanced Authentication and Key Establishment Scheme for 6LoWPAN (EAKES6Lo),Secure Authentication and Key Establishment Scheme(SAKES),Two Step Authentication(TSA),

Refference:

I.Atzori, L., Iera, Antonio,Morabito, Giacomo, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.

II.Commission, E., The alliance for internet of things innovation (AIOTI). 2016.

III.Costin Badic ̆ a ̆, M.B., Amelia Badic ̆, a ̆, An Overview of Smart Home Environments: Architectures, Technologies and Applications. 2017: p. 8.

IV.Ding, F.S., A.; Tong, E.;Li,J., A smart gateway architecture for improving effeciency of home network application. 2016.

V.Geoff Mulligan , M.y., Patrick Wetterwal, ColinPatrickO’Flyn, MakingsensornetworksIPv6ready. 2008.

VI.Huichen Lin, N.W.B., IoT Privacy and Security Challenges for Smart Home Environments. 2016(4 July 2016).

VII.Internet, ADVANCE AUTHENTICATION TECHNIQUES.

VIII.Kenji, I.M., T.; Toyoda, K.; Sasase, I, Secure parent node selection scheme in route construction to exclude attacking nodes from rpl network. 2015. 4: p.5.

IX.Komninos, N., Phillppou, E. & Pitsillides, A. , Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures 2014.

X.Madakam, S., R. Ramaswamy, and S. Tripathi, Internet of Things (IoT): A Literature Review. IT Applications Group, 2015 3: p. 164-173.

XI.Mangal Sain, Y.J.K., Hoon Jae Lee, Survey on Security in Internet of things: state of the art and challenges 2014.

XII.Md. Alam Hossain, M.B.H., Md. Shafin Uddin, Shariar Md. Imtiaz MD6 Message Digest Algoritham. Reasearch Gate, 2016.16.

XIII.Rescorla, E.M., N., Datagram Transport Layer Security. Internet Engineering task force, 2012.

XIV.Sandeep Kumar Rao, D.M., Dr. Danish Ali Khan, A Survey on Advanced Encryption Standard 2017.

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Abstract:

With the passage of time, the demand of electricity is increasing day by day. The conventional electricity resources are getting depleted because of limited reserves of coal, natural gas and oil. Also most of the electricity resources are not environmental friendly. There was a need to design a mechanism that can be used as an alternative resource for the production of electricity that can be environmental friendly as well as a cheap source of generation. In the last couple of years, it is indicated that energy obtained from the sun can be the best alternate resource for energy. In this research work, the system design approach based on the Maximum Power Point Tracking (MPPT) Controller has been designed. This approach is utilized for extracting maximum available power from PV module through simulation in protius software. This system is quite efficient, effective and has high performances. Buck and boost converter have been utilized for better efficiency.

Keywords:

Electricity,Renewable Energy,Solar Charge Controller, Maximum Power Point Tracking,

Refference:

I.A. Ali, Y. Wang, W. Li and X. He, “Implementation of simple moving voltage average technique with direct control incremental conductance method to optimize the efficiency of DC microgrid,” in Emerging Technologies (ICET), 2015 International Conference on, 2015.

II.A. Argentiero, C. A. Bollino, S. Micheli and C. Zopounidis, “Renewable energy sources policies in a Bayesian DSGE model,” Renewable Energy, vol. 120, pp. 60-68, 2018.

III.A. Naserbegi, M. Aghaie, A. Minuchehr and G. Alahyarizadeh, “A novel exergy optimizationof Bushehr nuclear power plant by Gravitational Search Algorithm (GSA),” Energy, 2018.

IV.A. Soetedjo, A. Lomi and B. J. Puspita, “A Hardware Testbed of Grid-Connected Wind-Solar Power System,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 1, pp. 52-56, 2018.

V.A. M. Atallah, A. Y. Abdelaziz and R. S. Jumaah, “Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters,” Emerging Trends in Electrical, Electronics & Instrumentation Engineering: An international Journal (EEIEJ), vol. 1, pp. 31-44, 2014.

VI.B. Gjorgiev and G. Sansavini, “Electrical power generation under policy constrained water-energy nexus,” Applied Energy, vol. 210, pp. 568-579, 2018.

VII.F. Zhou, Y.-F. Chang, B. Fowler, K. Byun and J. C. Lee, “Stabilization of multiple resistance levels by current-sweep in SiOx-based resistive switching memory,” Applied Physics Letters, vol. 106, p. 063508, 2015.

VIII.J. Ahmed and Z. Salam, “An improved perturb and observe (P&O)maximum power point tracking (MPPT) algorithm for higher efficiency,” Applied Energy, vol. 150, pp. 97-108, 2015.

IX.K. Khanafer and K. Vafai, “A review on the applications of nanofluids in solar energy field,” Renewable Energy, 2018.

X.K. Ishaque, Z. Salamand G. Lauss, “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions,” Applied Energy, vol. 119, pp. 228-236, 2014.

XI.K. S. Tey and S. Mekhilef, “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level,” Solar Energy, vol. 101, pp. 333-342, 2014.

XII.L.-L. Li, G.-Q. Lin, M.-L. Tseng, K. Tan and M. K. Lim, “A Maximum Power Point Tracking Method for PV System with Improved Gravitational Search Algorithm,” Applied Soft Computing, 2018.

XIII.M. Peng, Y. Li, Z. Zhao and C. Wang, “System architecture and key technologies for 5G heterogeneous cloud radio access networks,” IEEE network, vol. 29, pp. 6-14, 2015.

XIV.P. Sivakumar, A. A. Kader, Y. Kaliavaradhan and M. Arutchelvi, “Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions,” Renewable Energy, vol. 81, pp. 543-550, 2015.

XV.P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 309-313, 2015.

XVI.R. Kardooni, S. B. Yusoff, F. B. Kari and L. Moeenizadeh, “Public opinion on renewable energy technologies and climate change in Peninsular Malaysia,” Renewable Energy, vol. 116, pp. 659-668, 2018.

XVII.R. M. Linus and P. Damodharan, “Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems,” IET Renewable Power Generation, vol. 9, pp. 682-689, 2015.

XVIII.R. Cheng and Y. Jin, “A social learning particle swarmoptimization algorithm for scalable optimization,” Information Sciences, vol. 291, pp. 43-60, 2015.

XIX.S. Dincer and I. Dincer, “Comparative Evaluation of Possible Desalination Options With Various Nuclear Power Plants,” in Exergetic, Energetic and Environmental Dimensions, Elsevier, 2018, pp. 569-582.

XX.S. Krauter, “Simple and effective methods to match photovoltaic power generation to the grid load profile for a PV based energy system,” Solar Energy, vol. 159, pp. 768-776, 2018.

XXI.S. Carley, “State renewable energy electricity policies: An empirical evaluation of effectiveness,” Energy policy, vol. 37, pp. 3071-3081, 2009.

XXII.S. P. Ayeng’o, T. Schirmer, K.-P. Kairies, H. Axelsen and D. U. Sauer, “Comparison of off-grid power supply systems using lead-acid and lithium-ion batteries,” Solar Energy, vol. 162, pp. 140-152, 2018.

XXIII.S. Kiranyaz, T. Ince and M. Gabbouj, “Particle swarm optimization,” in Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Springer, 2014, pp. 45-82.

XXIV.T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on energy conversion, vol. 22, pp. 439-449, 2007.

XXV.V. Salas, E. Olias, A. Barrado and A. Lazaro, “Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems,” Solar energy materials and solar cells, vol. 90, pp. 1555-1578, 2006.

XXVI.Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001.

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Abstract:

Social media data made real world like a web of data which is highly categorical in nature. Data having categorical attributes are omnipresent in existing real world. Clustering is an effective approach to deal with categorical data. However, partitional clustering algorithms are prone to fall into local optima for categorical data. A novel approach of ABC K-modes has been proposed to address this issue but acceleration issue of this algorithm was still a challenge for it. In this paper, we address this challenge to reduce the acceleration factor of algorithm and proposing a novel modified ABC K-modes approach which we refer as N-ABC K-modes approach. In our approach, unlike existing ABC K-modes we introduces different attribute matrix for each data sets. In further step, we apply XOR operation to combine the matrix of similar attributes. In last phase, dissimilar data would form a cluster and we apply clustering follow by searching on this cluster. The performance of New ABC K-modes evaluated by a series of tests and experiments over real time streaming social media data like twitter and facebook in comparison with that of other popular algorithms for categorical data.

Keywords:

Big data,Twitter,Clustering,Big data Analysis,Artificial Bee Colony(ABC), Data classification,

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