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Volume-5 Issue-3: Published on July 30, 2016
05
Volume-5 Issue-3: Published on July 30, 2016

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S. No

Volume-5 Issue-3, July 2016, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

S. Hitesh Kumar, R. Kavya Reddy, Preetha K.S

Paper Title:

Fall Detection System for Monitoring Elderly People

Abstract: Different fall-recognition arrangements have been already proposed to make a dependable observation framework for elderly individuals with high necessities on exactness, affectability and specificity. In this paper, an improved fall recognition framework is proposed for elderly individual observing that depends on keen sensors worn on the body and working through purchaser home systems. With treble limits, inadvertent falls can be distinguished in the home social insurance environment. By using data assembled from an accelerometer, cardio tachometer and shrewd sensors, the effects of falls can be logged and recognized from ordinary every day exercises. The proposed framework has been conveyed in a model framework as itemized in this paper.

Keywords:
ARM, pulse sensor, GSM, GPS, MMA7660FC MEMS accelerometer, LPC2148 microcontroller

References:

1.       Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Journal of Computer Networks, vol. 38, no. 4, pp. 393-422, March 2002.
2.       J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Journal of Computer Networks, vol. 52, no. 12, pp. 2292-2330, Aug. 2008.
3.       K. Kinsella and D. R. Phillips, “Global aging: the challenge of success,” Population Bulletin, vol. 60, 2005..
4.       Q. Zhang, L. Ren, and W. Shi, “HONEY a multimodality fall detection and telecare system,” Telemedicine and e-Health, vol. 19, no. 5, pp. 415-429, Apr. 2013.
5.       F. Bagalà, C. Becker, A. Cappello, L. Chiari, and K. Aminian, “Evaluation of accelerometer-based fall detection algorithm in realworld falls,” PLoS ONE, vol. 7, no. 5, pp. 1-8, May 2012.
6.       S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and A.Vecchio, “A smartphone-based fall detection system,” Pervasive and Mobile Computing, vol. 8, no. 6, pp. 883-899, Dec. 2012.
7.       S. Abbate, M. Avvenuti, G. Cola, P. Corsini, J.V. Light, and A.Vecchio, “Recognition of false alarms in fall detection systems,” in Proc. 2011 IEEE Consumer Communications and Networking Conference, Las Vegas, USA, pp. 23-28, Jan. 2011.
8.       Y.W Bai, S.C. Wu, and C.L. Tsai, “Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone,” IEEE Trans. Consumer Electron., vol. 58, no. 4, pp. 1269-1275, Nov.

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2.

Authors:

Fatah Bouteldjaoui, Ahmed Kettab, Mohamed Bessenasse

Paper Title:

Evaluation of Spatiotemporal Variability of Groundwater Level Fluctuations in Zahrez basin, Algeria: Geostatistical Approach

Abstract: Groundwater level fluctuation in aquifer system plays the main role in quantitative water management especially in semi arid and arid areas. In practice, due to aspects of time and cost, data monitoring of water levels is conducted at a limited number of sites, and interpolation technique such as kriging is widely used for estimation of this variable in unsampled sites. The main objective of this study is to get a better understanding of the temporal and spatial variability of the groundwater level fluctuation in regular monitoring wells constructed by the ministry of water resources in zahrez basin, Algeria. In this study, seasonal groundwater level data for 57 wells were collected from 1994 to 2011 and analyzed using the geostatistical approach. The Kolomogorov–Semirnov test revealed that all data followed normal or log-normal distribution. Experimental variograms are fitted by variogram models and computed to be used in the kriging estimations. The results of variographic analysis indicate that experimental variograms are best fitted by the gaussian model and the exponential model. Nugget-sill ratio (<25%) revealed that the groundwater levels have strong spatial dependence in the area. Kriging interpolation techniques have indicated the groundwater flow directions remained almost constant over the years

Keywords:
  Geostatistics, Groundwater levels, Semivariogram, Universal kriging, Zahrez basin


References:

1.     Kettab, “Water resources in Algeria : strategies, investments, and vision”, Desalination, vol.136, no.1-3, pp.25-33, 2001. 
2.     Kettab, R. Mitiche, N. Bennaçar, “Water for a sustainable development: challenges and strategies”, Revue des Sciences de l'Eau 21 (2): 247-256, 2008.

3.     M. Aflatooni, M. Mardaneh, “Time series analysis of groundwater table fluctuations due to temperature and rainfall change in Shiraz plain”, International Journal of Water Resources and Environmental Engineering 3 (9): 176– 188, 2011.

4.     RW. Healy, PG. Cook, “Using groundwater levels to estimate recharge”,  Hydrogeology Journal 10: 91–109, 2002.

5.     S. Ahmadi, A. Sedghamiz, “Geostatistical analysis of spatial and temporal variations of groundwater level”, Environmental Monitoring Assessement 129(1–3):277–294, 2007.

6.     D. Kumar, Sh. Ahmed, “Seasonal behaviour of spatial variability of groundwater level in a granitic aquifer in monsoon climate”, Current Science 84: 188–196, 2003.

7.     N.Theodossiou, P. Latinopoulos, “Evaluation and optimization of groundwater observation networks using the kriging methodology.” Environmental Modelling and Software: 21: 991– 1000, 2006.

8.     V. Kumar, Remadevi, “Kriging of groundwater levels—a case study.” Journal of Spatial Hydrology 6 (1): 8 1–92, 2006.

9.    ANRH, “Synthèse hydrogéologique du synclinal de Djelfa.” Agency for Water Ressources, 1997, unpublished.

10.  M. F. Sidi Moussa,  “ Ressources Hydrauliques de la zone du projet GTZ-HCDS.” Coopération Algero-Allmande, 176 P, 2000,  unpublished.

11.  M. F. Sidi Moussa , M.Deramchi, “Synthèse des études et exploitation des données existantes sur le Synclinal de Djelfa”, Agence Nationale des Ressources Hydrauliques (ANRH), Rapport Technique, 40P, 1993, unpublished.

12.  B.Chibane, A. Boutaleb, “Geochemistry study and Isotopic Approach in Semi-Arid Region: Case of the Djelfa Syncline (Algeria).” European Journal of Scientific Research 45(2):270-290, 2010.

13.  Y. Pannatier, “Variowin Software for Spatial Data Analysis in 2D”, edited by Springer, New York,1996.

14.  D.G. Krige, “A statistical approach to some basic mine valuation problems on the Witwatersrand”, Journal of the Chemical, Metallurgy and Mining. Society. Southern. Africa 52 (6): 119–139, 1951.

15.  G. Matheron, “Principles of geostatistics.” Economic. Geology 58: 1246–1266, 1963.

16.  A.G. Journel,  C.J. Huijbregts, “Mining Geostatistics”, edited by Academic Press Inc, London, UK, 1978,  600pp.

17.  EH. Isaaks, RM. Srivastava, “An Introduction to Applied Geostatistics”, edited by Oxford University Press: New York,1989, 322 pp.

18.  M. David, “Geostatistical ore reserve estimation.”, edited by Elsevier, Amsterdam, 1977.

19.  P. Goovaerts, “Geostatistics for natural resources evaluation”, edited by Oxford University Press, New York,1997.


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3.

Authors:

Suresha Gowda M. V., Ranganatha S., Vidyasagar H. N.

Paper Title:

Role of Ball and Coating Materials under Un-lubricated Condition

Abstract:  The performance, reliability and load transferring capabilities of bearing elements are very important in industrial applications. The newer design of high speed machines demands better bearing system. The reliability is of primary importance in case of bearing elements used in aerospace industries. Exhaustive studies have been carried out by different researchers under two extreme conditions. One is using a fluid as lubricants which do not bear shear loads. The other extreme were using hard coatings which bears enormous amount of shear loads. In the present investigation an attempt has been made to understand the kinematics of deformation of balls and coatings which are not as hard as conventional coatings without lubricants. Different ball materials like case hardened carbon steel, high carbon high chromium steel and stainless steel, case hardened carbon steel and high carbon high chromium steel balls were coated with tin and case hardened carbon steel and stainless steel balls were coated with zinc by electroplating coating technique. The thickness of the coating was maintained at 25 µm. Four ball test rig was used to simulate the field condition. The experiments were conducted without lubricants. The normal loads were 100N, 300N and 500N respectively for case hardened carbon steel and high carbon high chromium steel, run for a period of 5 minutes. The normal loads were 50N, 75N and 100N respectively for stainless steel and run for a period of 5 minutes. The frictional load and normal load were monitored and co-efficient of friction was estimated. The wear scar was studied under scanning electron microscope. The co-efficient of friction was found to be dependent on normal load and type of coating material. The co-efficient of friction was found to be minimum of value 0.27 for a maximum normal load of 500N for tin coatings and 0.41 for a maximum load of 500N for zinc coatings. The morphology of wear scar studied in scanning electron microscope explains the dependency of co-efficient of friction on normal load and different coating materials.

Keywords:
   Rolling contact fatigue, four ball tester, Coatings.


References:

1.             R. Ahmed and M. Hadfield, Wear 203/204     (1997) 98–106.
2.             Makela, P. Vouristo, M. Lahdensuo, K. Niemi and T. Mantyla, Proceedings of the 7th International Thermal Spray Conference, Boston,

3.             Massachuesetts, 20–24 June 1994, pp. 759–763.

4.             M. Faraday, Philos. Trans. R. Soc. 147 (1857) 145.

5.             K. Kirner, Schweissen Schnieden 41 (1989) 583–586.

6.             M.E. Vinayo, F. Kassabji, J. Guyonnet and P. Fauchais, J. Vac. Sci. Technol. A3 (1985) 2483–2489.

7.             J. Nerz, B.A. Kushner and A.J. Rotolico, Proceedings of the Thermal Spraying Conference, Essen, Germany, 29–31 August 1990, pp. 47–51.

9.             M.P. Subrahmanyam, M.P. Srivastava and R. Sivakumar, Mater. Sci. Eng. 84 (1984)209–214.

10.          R. Nieminen, P. Vouristo, K. Niemi, T. Mantyla and G. Barbezat, Wear 212 (1997) 66–77.

11.          R. Ahmed and M. Hadfield, Tribol. Int. 30 (1997) 129–137.

12.          R. Ahmed and M. Hadfield, Surf. coatings Technol. 82 (1996) 176–186.
13.          R. Ahmed and M. Hadfield, Wear 230 (1999) 39–55.
14.          S. Tobe, S. Kodama and H. Misawa, Proceedings of the National Thermal Spray Conference, Tokoyo, Japan, 1990, pp. 171–178.

15.          R. Ahmed and M. Hadfield, Proceedings of the International Thermal Spray Conference, Singapore, ISBN 0871707373, 2001, pp. 1009– 

16.          1015.

17.          M. Yoshida, K. Tani, A. Nakahira, A. Nakajima and T. Mawatari, Proceedings of ITSC, Kobe, May 1995, 1992, pp. 663–668.

18.          R. Ahmed and M. Hadfield, Wear 209 (1997) 84–95.19.          Nakajima, T. Mawatari, M. Yoshida, K. Tani and A. Nakahira, Wear 241 (2000) 166–173.

20.          S. Kuroda, T. Fukishima and S. Kitahara, Proceedings of the International Thermal Spray Conference, Orlando,OH, 1992, pp. 903–909.

21.          T. Morishita, E. Kuramochi, R.W. Whitfield and S. Tanabe, Proceedings of the International Thermal Spray Conference, Orlando, OH,   1992, pp. 1001–1004.

22.          O.C. Brandt, J. Therm. Spraying 4 (1995) 147–152.

23.          G.R. Millar, L.M. Keer and H.S. Cheng, Proc. R. Soc. London, Ser. A 397 (1985) 197–209.

24.          T.A. Harris, Rolling Bearing Analysis, 3rd ed., Wiley, New York, 1991, p. 23.

25.          H.S. Cheng, T.P. Chang and W.D. Sproul, Proceedings of the 16th Leeds–Lyon Symposium, Elsevier, 1990, pp. 81–88.

26.          R. Thom, L. Moore, W.D. Sproul and T.P. Chang, Surf. Coatings Technol. 62 (1993) 423–427.

27.          O. Knolek, B. Bosserhoff, A. Schrey, T. Leyendecker, O. Lemmer and S. Esser, Surf. Coating Technol. 54/55 (1992) 102–107.

28.          L. Rosado, V.K. Jain and H.K. Trivedi, Wear 212(1997) 1–6.

29.          I.A. Polonsky, T.P. Chang, L.M. Keer and W.D. Sproul, Wear 208 (1997) 204–219.

30.          Suresha Gowda M. V, Vidyasagar H. N and Ranganatha S, IJRTE, ISSN: 2277-3878,Vol 4 Jan 2016, 1-8.

31.          Suresha Gowda M. V, Ranganatha S and Vidyasagar H. N, IJITEE, ISSN: 2278-3075, Vol 5, Feb 2016, 24-31.


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4.

Authors:

Abhinav V. Deshpande

Paper Title:

Analysis of Current Steering Digital to Analog Converter

Abstract:   The current steering Digital to Analog Converter (DAC) with a Digital random return to zero technique in order to improve the namic performance is presented in this research paper. In order to demonstrate the proposed technique, an 8 bit CMOS DAC is designed and the layout is prepared in 90 nm technology. The chip layouts run on low power and have small area overhead.

Keywords:
 DAC, Current Steering, Converter


References:

1.        R. Bugeja, B.S. Song, P. L. Rakers and S. F. Gillig, “A 14-b 100-MS/sec CMOS DAC Designed for Spectral Performance”, IEEE Journal of Solid State Circuits, Volume 34, No. 12, pp. 1719-1732, December 1999.
2.        R. Bugeja and B. S. Song, “A Self-Trimming 14-b 100-MS/sec CMOS DAC”, IEEE Journal of Solid State Circuits, Volume 35, No. 12, pp. 1841-1852, December 2000.

3.        Q. Huang, P.A. Francese, C. Martelli and J. Nielsen, “A 200 MS/sec 14 b 97 mW DAC in 0.18 mµm CMOS”, in Proceedings of IEEE International Solid State Circuits Conference Dig. Tech. Papers, February 2004, pp. 364-532.

4.        S. Park, G. Kim, S. C. Park and W. Kim, “A Digital-to-analog Converter Based on Differential Quad Switching”, IEEE Journal of Solid State Circuits, Volume 37, No. 10, pp. 1335-1338, October 2002.

5.        Wei-Hsin Tseng, Jieh-Tsorng Wu and Yung-Cheng Chu, “A CMOS 8 bit 1.6 GS/sec DAC with Digital Random Return-to-Zero”, IEEE Transactions on Circuits and Systems II: Express Briefs, Volume 58, No. 1, pp. 01-05, January 2011.


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5.

Authors:

Niharika Soni, Rajesh Bhatt, Girish Parmar

Paper Title:

Comparative study of PSO/PI and PSO/PID Approaches for AGC of Two Area Interconnected Thermal Power System

Abstract: The solution of one or more control optimization problems regarding automatic generation control (AGC) through the efficient techniques are required in interconnected areas of power system.  Heuristic optimization methods have replaced the analytical methods to tune the parameters of controllers used in power system since the analytical methods suffer from slow convergence and the curse of dimensionality. In the same direction, the Particle Swarm Optimization (PSO) technique among the swarm intelligence family has been applied in order to improve the performance of the two area interconnected thermal power plants. Proportional Integral Derivative (PID) controllers and Proportional Integral (PI) controllers  have been employed in the system under study and the PSO optimization technique has been used to optimize the parameters of PI and PID controllers by considering the Integral Time Multiplied by Absolute Error (ITAE) as the objective function. Both the approaches PSO/PI and PSO/PID have been compared for the same system under investigation to show the superiority of the PSO/PID approach. The results have been simulated with the MATLAB /SIMULINK environment and it has been shown that PSO optimized PID controllers for the LFC provides the better dynamic responses than PSO optimized PI controllers for same system.

Keywords:
automatic generation control, two areas interconnected power systems, PID-controllers, particle swarm optimization.


References:

1.       Kundur, P. Power System Stability and Control. New Delhi: Tata McGraw Hill, 2009.
2.       Elgerd, OI. Electric Energy Systems Theory – An Introduction, New Delhi: Tata McGraw Hill, 2000.

3.       H. Shayeghi, H.A. Shayanfar, A. Jalili, Load frequency control strategies: A state-of-the art survey for the researcher, Int J of Energy Conversion and Management, Vol. 50, No. 2, pp. 344–353, 2009.

4.       L.C. Saikia, J. Nanda, S. Mishra, Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Elect Power &
Energy Systs 2011; 33: 394-401.

5.       E.S. Ali, S.M. Abd-Elazim, Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Elect Power & Energy Systs 2011; 33: 633–638.

6.       H. Gozde, M.C. Taplamacioglu, Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Elect Power & Energy Systs 2011; 33: 8–16.

7.       H. Shabani, B. Vahidi, M. Ebrahimpour, A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Transactions 2012:52:88–95.

8.       U.K. Rout, R.K. Sahu, S. Panda,  Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Engineering Journal 2013; 4(3):409-421.

9.       B. Mohanty, S. Panda, P.K. Hota, Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system, Int. J. Elect. Power & Energy Syst 2014; 54:77–85.

10.    A.K. Barisal, Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-sources power systems, Int. J. Electr. Power Energy Syst. 66 (2015) 67–77.

11.    R. Farhangi, M. Boroushaki, S.H. Hosseini,  Load frequency control of inter- connected power system using emotional learning based intelligent controller, Int. J. Electr. Power Energy Syst. 36 (1) (2012) 76–83.

12.    K. Naidu, H. Mokhlis,  A.H.A. Bakar, V. Terzija,  H.A. Illias, Application of firefly algorithm with online wavelet filter in automatic generation control of an interconnected reheat thermal power system, Int. J. Electr. Power Energy Syst. 63 (2014) 401–413.

13.    H. Gozde, M.C. Taplamacioglu, I. Kocaarslan,  Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system, Int. J. Elect. Power Energy Systs, 42 (2012) 167–178.

14.    R.K. Sahu, S. Panda, S. Padhan, Optimal gravitational search algorithm for interconnected power systems, Ain Shams Eng. J.5 (3) (2014) 721–733.

15.    Y. L. Abdel-Magid and M. A. Abido, “AGC tuning of interconnected reheat thermal systems with particle swarm optimization,” Proc.10th IEEE International Conference on Electronics, Circuits and Systems, ICECS, vol. 1, 2003, pp. 376- 379.

16.    S. P. Ghoshal, “Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system,” Electric Power Systems Research, vol. 70, no. 2, pp. 115-127, July 2004.

17.    G. A. Chown, R. C. Hartman, “Design & experience of fuzzy logic controller for automatic generation control (AGC),” IEEE Trans. on Power Systems, vol. 13, no. 3, pp. 965-970, Aug. 1998.

18.    Earl Cox, Fuzzy Fundamentals. IEEE Spectrum, Oct 1992, pp. 58-61.

19.    P. Saraswat, G. Parmar, A comparative study of Differential Evolution and Simulated Anealing  for order Reduction of Large Scale Systems, International Conference on Communication, Control and Intelligent Systems (ICCIS), Nov. 7-8, 2015

20.    Soni, g. Parmar, m. Kumar and s. Panda, hybrid grey wolf optimization-pattern search (hgwo-ps) optimized 2dof-pid controllers for load frequency control (lfc) in interconnected thermal power plants, ictact journal on soft computing, volume: 06, Issue: 03, April 2016.


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6.

Authors:

Rahul Hodage, Ravindra K. Lad

Paper Title:

Multi Criteria Decision Making Approach for Success Potential of Real Estate Project

Abstract:  The main purpose of this study is to develop a comprehensive success model for real estate projects considering both external and internal factors. In this respect, a wide range of success criteria are identified based on an extensive literature survey, and these criteria are classified into their respective sub criteria. In this context, a questionnaire was developed to facilitate systematic data collection in this study. In this survey, experts’ opinion from professionals and academicians of civil engineering field is taken through questionnaire. Top level managers, Senior civil engineer and Professors were involved for data gathering and finalizing the project Success Criteria and their respective sub Criteria. Further attempt has been made to formulate a fuzzy set numbers and employing Fuzzy Multiple Criteria Decision Making technique with a view to determine success potential of real estate project. The two main objectives achieved from this study, first, to provide a success potential index for completed projects in order to compare them with each other and to establish for improvement in future projects. Real estate companies may benefit from the findings of the proposed model in assessing the performance of their projects and may take the necessary actions to achieve better success in their projects to make a reputation in market.

Keywords:
 Fuzzy logic, Multi Criteria, Project success, Real Estate, Success potential index.


References:

1.       Chien-Chang Chou, “Integrated Short- Term and Long-Term MCDM Model for solving location selection problem” Vol. 135, No. 11, November 1, 2009, ©ASCE
2.       Chua, D. K. H., Kog, Y. C., and Loh, P. K. (1999). “Critical success factors for different project objectives.” Journal Of Construction Engineering And Management , Vol. 125, No. 3, May/June, 1999. ©ASCE, pp 142-150.               

3.       C.S. Lim, M.Z. Mohamed. Criteria of project success: an exploratory re-examination. International Journal of Project Management. 1999, 17(4), pp 243-48.

4.       Didem Erdem and Beliz Ozorhon. (2014), “Assessing Real Estate Project Success Using an Analytic Network Process.” Journal Of Construction Engineering And Management , Vol. 31, No. 4, July, 2015. ©ASCE, pp 142-150. 

5.       D. Singh and Robert L. K. Tiong, “A Fuzzy Decision Framework for Contractor Selection” Journal of construction Engineering and management @  Vol. 1, No. 62, January, 2005, ©ASCE, pp 62-70.               

6.       Eric, C.E., (2003) Facility Design and Management Hand book, McGraw-Hill, New York.

7.       Khosravi Shahrzad  and Afshari Hamidreza, (2011) “A Success Measurement Model for Construction Projects.” 2011 International Conference on Financial Management and Economics IPEDR vol.11 (2011) © (2011) IACSIT Press, Singapore

8.       M. Medineckiene, E.K. Zavadskas and Z. Turskis, “Dwelling selection by applying fuzzy game theory” Vol. XI, No. 3, @ Archives of civil and Mechanical engineering

9.       Prabhakar, G. P. (2008), “What is project success: A literature review”. International Journal of Business and Management, ISSN 1833-3850 Vol 3 (9). pp 3-10.

10.    Stoy Christian and  Hans-Rudolf Schalcher “Residential Building Projects: Building Cost Indicators and Drivers.” Journal Of Construction Engineering And Management, Vol. 133, No. 2, February 1, 2007. ©ASCE, pp 139-145

11.    Syed Zafar Shahid Tabish and Kumar Neeraj Jha (2012). “Success Traits for a Construction Project.” Journal of Construction Engineering And Management, Vol. 138, No. 10, October 1, 2012. ©ASCE, pp 1131-1138


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