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Abstract: Positive output elementary Luo converters perform the conversion from positive DC input voltage to positive DC output voltage.  Since Luo converters are non-linear and time-variant systems, the design of high performance controllers for such converters is a challenging issue. The controllers should ensure system stability in any operating condition and good static and dynamic performances in terms of rejection of supply disturbances and load changes.  To ensure that the controllers work well in large signal conditions and to enhance their dynamic responses, soft computing techniques such as Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) are suggested.  Fuzzy logic is expressed by means of the human language.  A fuzzy controller converts a linguistic control strategy into an automatic control strategy and fuzzy rules are constructed by expert experience or knowledge database.  Genetic Algorithm is a powerful optimizing tool that is based on the mechanism of natural selection and natural genetics.  Since fuzzy parameters are obtained by trial and error method, Genetic Algorithm can be used to optimize the fuzzy rules, membership functions and scaling gains thereby improving the performance of the Luo converter.  In order to test the robustness of the designed GA-Fuzzy and Fuzzy  based Luo converter, the controllers and converter have been modeled using Matlab – Simulink software.  From the simulation results, it is seen that GA- FLC gives fast response, good transient performance and robustness to variations in line and load disturbances.  Performance comparison show improvement of transient responses in terms of settling time, peak overshoot and ISE in the GA-Fuzzy than FLC for Luo converter.

Index Terms: Fuzzy Logic controller, Genetic Algorithm, Luo Converter, Membership function.

REFERENCES

1. F. L. and Hong Ye. , Advanced DC/DC Converters, First edition, CRC Press, LLC, London, 2014.
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6. Luo,F.L. (1997), Luo Converters: New DC-DC step-up Converters, Proceedings ofIEEE international Symposium on Integrated Circuit-97, Singapore, Vol.2, Jun., pp. 227-230.
7. Sheroz Khan, Salami Femi Abdulazeez, LawalWahabAdetunji, AHM ZahirulAlam, Momoh Jimoh E. Salami, Shihab Ahmed Hameed, Aisha HasanAbdalla and MohdRafiqul Islam, (2008), Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm Journal of Computer Science, Vol. 4(10), pp. 799-806.

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Keywords: computer networks, computer network management, network architecture.

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Abstract: Production and loss prediction play an important role in the effective planning and operation of solar photovoltaic systems. The performance of a solar PV system depends on the geographical location, system design, horizon and orientation of the solar panels in a given system. Professionals can use a variety of forecasting tools to effectively plan and predict grid connections and standalone solar PV systems. In this paper, we have developed an equivalent mathematical model for 1MWp grid-connected solar PV systems that have been developed and installed for Jaipur. With the help of the PV Syst design tool, performance parameters, yield and loss have been predicted for the equivalent model. Monthly earnings and losses as well as annual gains and losses have been synthesized. For the performance evaluation of the forecast data, we compared the techno economic simulation for different manufacturing technologies. Thin film, Ploy Crystalline and Mono Crystalline based systems have been developed. Predicted data can be used as an important tool for analyzing location and seasonal specific losses. Conducted research will be helpful in calculating area specific impact on selection of manufacturing technology of panels as well as its impact on active area of installed system.

Keywords: Yield Forecasting, Loss Forecasting, PVsyst, Performance Ratio, Grid Connected Solar PV

REFERENCES

1. S. Morshed, S. M. Ankon, M. T. H. Chowdhury and M. A. Rahman, "Designing of a 2kW stand-alone PV system in Bangladesh using PVsyst, Homer and SolarMAT,"Green Energy and Technology (ICGET), 2015 3rd International Conference on, Dhaka, 2015, pp. 1-6.
2. C. Ozerdem, S. Tackie and S. Biricik, "Performance evaluation of Serhatkoy (1.2 MW) PV power plant," 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, 2015, pp. 398-402.
3. Kaundinya, D. P., Balachandra, P. & Ravindranath, N. H. (2009). Grid-connectedversus stand-alone energy systems for decentralized power—A review ofliterature. Renewable and Sustainable Energy Reviews, 13 (8): 2041-2050.
4. Dong, J. Huang, M. Ding, H. Li and S. Zhang, "Performance test and evaluation of photovoltaic system," International Conference on Renewable Power Generation (RPG 2015), Beijing, 2015, pp. 1-4.
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6. Kumar, P. Yadav and S. S. Chandel, "Comparative analysis of four different solar photovoltaic technologies,"Energy Economics and Environment (ICEEE),2015 International Conference on, Noida, 2015
7. Yadav, N. Kumar and S. S. Chandel, "Simulation and performance analysis of a 1kWp photovoltaic system using PVsyst," Computation of Power, Energy Information and Commuincation (ICCPEIC), 2015 pp. 0358-0363.
8. Irwanto, Y. M. Irwan, I. Safwati, W. Z. Leow and N. Gomesh, "Analysis simulation of the photovoltaic output performance," Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, Langkawi, 2014, pp. 477-481.
9. H. Fatehi and K. J. Sauer, "Modeling the incidence angle dependence of photovoltaic modules in PVsyst," 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), Denver, CO, 2014, pp. 1335-1338.

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Abstract: Volatility Forecasting is an interesting and challenging problem in current financial instruments. There are many financial risks and rewards directly associated with volatility. Hence forecasting volatility becomes the most discussed topic in finance. In this research we apply various univariate conditional heteroskedasticity models for forecasting volatility. The various extensions of the standard Generalized ARCH models such as SGarch, CSGarch, Egarch, IGarch and GJRGarch are used for forecasting. Volatility values are forecasted for 10 days in advance and values are compared with the actual values. Mean square error is computed between Garch forecast and actual values for the 10 days. The model with lowest MSE values over 10 forecasted periods is selected as the best performing model. The forecasted results for 10 days show that GJRGarch ranks top in the accuracy of forecasting and IGarch at the bottom. GJRGarch outperforms all other univariate ARCH models.

Index Terms: SGARCH, CSGARCH, EGARCH, IGARCH

REFERENCES

1. Marius Matei, “Assessing Volatility Forecasting Models: Why Garch Models Take the Lead”, Romanian Journal of Economic Forecastin, pp. 42-69, 2009.
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15. Christian Schittenkopf, Georg Dorffner and Engelbert J. Dockner, “Forecasting Time-dependent Conditional Densities: A Semi-nonparametric Neural Network Approach, Journal of Forecasting, Vol 8, pp. 244-263, 1996.

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Abstract: Mobile and smart computing devices have become ubiquitous and the preferred computing device for their daily tasks and business activities. Despite the phenomenal increase in the computational power of these devices, they still are unable to cope with the exceptionally high demand for resources by current applications. It has been suggested that, resource demanding applications and intensive computations from mobile devices should be offloaded to other systems with higher resource capacities in the cloud. Mobile cloud computing is a form of cloud computing that seeks to enhance the capacity and capabilities of mobile and smart computing devices by enabling mobile devices to offload some computational tasks to the cloud for processing. The study also presented an energy model for computation offloading.

Keywords: Cloud Computing, Mobile Cloud Computing, Computation Offloading.

REFERENCES

1. Arockiam L., Monikandan S., Parthasarathy G., (2011) “Cloud Computing: A Survey” International Journal of Internet Computing (IJIC), ISSN No: 2231 – 6965, Vol1, Issue-2.
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3. Cuervo, E., et. al, (2010), "MAUI: Making Smartphones last longer with code offload", Proceedings of 8th International Conference on MobiSystems, pp. 49-62
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7. Deng, S., et. al, (2015), "Computation offloading for service workflow in mobile cloud computing", IEEE Transactions Manuscript
8. Dudeja, M. & Soni, K., (2014), "Offloading Schemes in Mobile Cloud", International Journal of Computer Applications, Vol 96, No. 8.
9. Fangming, L., et. al, (2013), "Gearing Resource-Poor Mobile Devices with Powerful Clouds: Architectures, Challenges and Applications", IEEE, Wireless Communications.
10. Giurgiu, I., et. al, (2009), "Calling the Cloud: Enabling mobile phones as interfaces to cloud applications", Proceedings of 10th ACM/USENIX, International conference, pp. 1-20
11. Hoang, T. D., et. al, (2013), "A survey of mobile cloud computing: Architectures, applications and approaches", Wireless Communications and Mobile Computing, Vol 13, Issue 18, pages 1587-1611
12. Juntunen, A., et. al, (2012), "Mobile Computation Offloading - Factors Affecting Technology Evolution", Proceedings of 11th ICMB.
13. Karthik, K. et. al, (2012), "A survey of computation offloading for mobile systems", Mobile Network Application, DOI 10.1007/s11036-012-0368-0
14. Kemp, R. et. al, (2010), "Cuckoo: A computation offloading framework for smartphones" Proc. 2nd International Conference of Mobile Computing, Application, Services, CA, USA
15. Kovachev, D. & Klamma, R. (2012), "Framework for Computation Offloading in Mobile Cloud Computing", Department of Information Systems and Databases, RWTH Aachen University.
16. Khan, R. A., et. al, (2014), "A survey of mobile cloud computing application models", IEEE Communications Survey.
17. Niroshinie, F., et. al, (2013), "Mobile Cloud Computing: A Survey", Journal of Future Generation Computer Systems
18. Orsini, G., et. al, (2015), "Context-Aware computation offloading for mobile cloud computing: Requirement Analysis, Survey and Design Guideline", 12th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2015.
19. Prasad, R. P., et. al, (2012), "Mobile Cloud Computing: Implications and Challenges", Journal of Information Engineering and Applications, Vol. 2, No. 7
20. Pragaladan, R. & Leelavathi, M., (2014), "A Study of Mobile Cloud Computing and Challenges", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 7
21. Srinivasa, R. V., Nageswara, R. N. K., and Ekusuma K. (2009) “Cloud Computing: An Overview” Journal of Theoretical and Applied Information Technology.
22. Suradkar, J. A. & Bharati, R.D., (2016), "Computation offloading: Overview, Frameworks and Challenges", International Journal of Computer Applications, Vol 134, No 6
23. Satyanarayan, M., et. al, (2009), "VM-based Cloudlets in mobile computing", IEEE Pervasive Computing Proceedings
24. Shravanthi, C. & Guruprasad, H.S., (2014), "Mobile Cloud Computing as Future for Mobile Applications", International Journal of Research in Engineering and Technology.
25. Wolski, S., et. al, (2008), "Using Bandwidth data to make computation offloading decisions", Proceedings of IEEE, IPDPS, pp. 1-8.
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