Archive

Abstract:

In the subject matter of mathematical statistics, let the domain of mathematical activity that draws its inspiration from and nurtures the lead provided by the seminal paper of the American Economist and Nobel Prize (1970) winner P.A. Samuelson entitled, “How Deviant can you be?” and published in the Journal of the American Statistical Association in 1968, on the maximum and the minimum deviations, from the mean (denoted presently by m and 'm respectively) in a set of n observations with given mean μ and standard deviation σ, be henceforth defined as the Samuelson Domain. The present communication is in the Samuelson Domain. A circle theorem in the −σmplane is rigorously established and exhibited step by step for the sheer delight of its simplicity and elegance. A crude first approximation yields a result that is inferior to Samuelson’s but a more precise investigation of the consequences of the circle theorem shows that Samuelson’s famous work on the existence of bounds, for a set of n real numbers, in terms of σμ, and n can be improved upon provided n exceeds a critical value.

Keywords:

Samuelson Domain ,mean ,standard deviation ,maximum and the minimum deviations,critical value,

Refference:

I. Samuelson,P.A., How deviant can you be?, J. American Statistical Association, 63,1522-1525,1968.

II. Banerjee, M.B. and Shandil, R.G., A theorem on mean and standard deviation of a statistical variate, Ganita,46, 21-23,1995.

Natural Convective Heat Transfer Transitory Flow in Presence of Induced Magnetic Field

Authors:

M. M. Haque, M. A. Islam, M. S. Islam, R. Mahjabin

DOI NO:

https://doi.org/10.26782/jmcms.2014.01.00005

Abstract:

The effects of induced magnetic field on a free convective heat transfer transient flow of fluid past an infinite vertical plate through a porous medium have been investigated numerically. A mathematical model of the problem is developed from the basis of studying magneto-fluid dynamics(MFD) and the equations are solved by the finite difference method. The numerical values of non-dimensional velocity, induced magnetic field and temperature are computed for the different values of associated parameters in different times. In order to discuss the results, the obtained numerical values of flow variables are plotted in graphs. Finally the important findings of this work are concluded here.

Keywords:

magnetic field ,free convective heat transfer ,magneto-fluid dynamics ,non-dimensional velocity,

Refference:

I.Finston M. “Free convection past a vertical plate” J. Appl. Math. Phy. Vol. 7, pp 527 – 529 (1956).

II.Sparrow E. M. and Gregg J. L. “Similar solutions for free convection from a non-isothermal vertical plate” ASME J. Heat Trans. Vol. 80, pp 379 – 386 (1958).

III.Soundalgekar V. M. and Ganesan P. “Finite difference analysis of transient free convection on an isothermal flat plate” Reg. J. Energy Heat Mass Trans. Vol. 3, pp 219 – 224 (1981).

IV.Camargo R. Luna E. and Treviňo C. “Numerical study of the natural convective cooling of a vertical plate” Heat Mass Trans. Vol. 32, pp 89 – 95 (1996).

V.Yamamoto K. and Iwamura N. “Flow with convective acceleration through a porous medium” J. Engng. Math. Vol. 10, pp 41 – 54 (1976).

VI.Raptis A. Tzivanidis G. and Kafousias N. “Free convection and mass transfer flow through a porous medium bounded by an infinite vertical limiting surface with constant suction” L. Heat Mass Trans. Vol. 8, pp 417 – 424 (1981).

VII.Ahmed N. and Sarma D. “Three dimensional free convection flow and heat transfer through a porous medium” Indian J. Pure Appl. Math.Vol. 26, pp 1345 – 1353 (1997).

VIII.Magyari E. Pop I. and Keller B. “Analytic solutions for unsteady free convection in porous media” J. Eng. Math. Vol. 48, pp 93 – 104 (2004).

Weather Prediction by the use of Fuzzy Logic

Authors:

Sudipta Ghosh, Arpan Dutta, Suman Roy Chowdhury , Gopal Paul

DOI NO:

https://doi.org/10.26782/jmcms.2014.01.00006

Abstract:

In this paper, a Fuzzy Knowledge – Rule base technique is used to predict the ambient atmospheric temperature. The present study utilizes historical temperature as well as database of various meteorological parameters to develop a prediction process in fuzzy rule domain to estimate temperature. Daily observations of Rain, Atmospheric Pressure, and Relative Humidity are analyzed to predict the Temperature. The topic of Fuzzy Logic as a decision-making technique is introduced. It is recommended that applications of this technique could be effectively applied in the area of operational meteorology. An example of such an application, the forecast of the probability of temperature, is discussed and examples of the method are presented. Other possible meteorological applications are suggested. Additionally, a software package which aids in the development of such applications is briefly described.

Keywords:

Fuzzy Logic ,atmospheric temperature ,Atmospheric Pressure ,Relative Humidity ,probability of temperature,

Refference:

I.Radhika, Y. and M. Shashi, 2009. AtmosphericTemperature Prediction using Support VectorMachines. International Journal of Computer Theoryand Engineering, 1: 55-58.

II. Pal, N.R., S. Pal, J. Das and K. Majumdar, 2003. A Hybrid Neural Network for Atmospheric Temperature Prediction. IEEE Transaction on Geoscience and Remote Sensing, 41: 2783-2791. doi: 10.1109/TGRS.2003.817225.

III. De, S. and A. Debnath, 2009. Artfitial Neural Network Based Prediction of maximum and Minimum Temperature in the summer Monsoon Months over India. Applied Physics Research, 1: 37-44.

IV. Long, J., J. Jian and Y. Cai, 2005. A short – term Climate Prediction model Based on a modular Fuzzy Neural Network. Advances in Atmospheric Sciences, 22: 428-435.

V. Kaonga, C.C., H.W.T. Mapoma, I.B.M. Kosamu and C. Tenthani, 2012. Temperature as an Indicator of Climate Variation at a Local Weather Station. World Applied Sciences Journal, 16(5): 699-706.

VI. Zadeh, L.A., 1965. Fuzzy Sets Information and Control, pp: 338-353.

VII. Kosko, B., 1992. Neural networks and Fuzzy systems. Prentice Hall. Englewood Cliffs, N.J.

Non-Similar Solution of Unsteady Thermal Boundary Layer Equation

Authors:

Md. Saidul Islam, Md. Hasanuzzaman, M. A. K. Sazad , M. A. Hakim

DOI NO:

https://doi.org/10.26782/jmcms.2014.01.00007

Abstract:

To obtain this present study we studied basic equations. We studied the equation of continuity and derived the Navier-Stockes (N-S) equations of motion for viscous compressible and incompressible fluid flow. Boundary layer and thermal boundary layer equations are also derived. Then we studied similar solution of boundary layer and thermal boundary layer equations. We also performed unsteady solutions of thermal boundary layer equations. We used some non-dimensional variable to non-dimensionalised thermal boundary layer equations. The non-dimensional boundary layer equations are non-linear partial differential equations. To find out the non-similar solutions of unsteady thermal boundary layer equation we used finite difference method. The effect on the velocity and temperature profiles for various parameters entering into the problems are separately discussed and shown graphically.

Keywords:

the Navier-Stockes equations ,viscous compressible and incompressible fluid ,thermal boundary layer,finite difference method,

Refference:

I. Falkner, V. M. and Skan, S. W., 1931. Soam approximate solutions of the boundary equations, Phill. Mag.12, pp. 865-896.

II. Callahan and Marner, 1976. Solving a transisent free convection flow with mass transfer past a semiinfinite plate.

III. G. Revathi, P. Saikrishnan, A. Chamkha, (2013) “Non-similar solution for unsteady water boundary layer flows over a sphere with non-uniform mass transfer”, International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 23 Iss: 6, pp.1104 – 1116

IV. T. Javed, M. Sajid, Z. Abbas, N. Ali, (2011) “Non-similar solution for rotating flow over an exponentially stretching surface”, International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 21 Iss: 7, pp.903 – 908

V. Prandtl, L., 1935. The mechanics of viscous fluids, Division G., Vol. III, Aerodynamic theory, edited by W.F. Durand, Julius Springer, Berlin.

VI. Raisinghania, M. D., 1982. Fluid Dynamics (with Hydrodynamics).

Facial feature extraction techniques for face detection: A review

Authors:

Moumita Roy , Madhura Datta

DOI NO:

https://doi.org/10.26782/jmcms.2014.01.00008

Abstract:

The researches in the area of face detection have made significant progress in the past few decades. The main challenge in this stage of face detection is to find a suitable effective method for finding facial features. Sub-areas under feature extraction methods are skin color and texture based segmentation, deformable template matching, snake models, feature searching and constellation analysis. In this paper we represented a review on some important contribution in the field of feature extraction for face detection.

Keywords:

face detection, skin color and texture,snake models, constellation analysis,

Refference:

I.Jeng, S. H., Liao, H. Y. M, Hua, C. C. et al.: Facial Feature Detection Using Geometrical Face Model: An Efficient Approach. Pattern Recognition. Vol. 31, 1998, No. 3, pp. 273–282.

II.Jianguo, W., Tieniu, T.: A New Face Detection Method Based on Shape Information. Pattern Recognition Letters, Vol. 21, 2000, No. 3, pp. 463–471.

III.Shinn-Ying, H., Hui-Ling, H.: Facial Modeling from an Uncalibrated Face Image Using a Coarse-to-Fine Genetic Algorithm. Pattern Recognition, Vol. 34, 2001, No. 9, pp. 1015–1031.

IV. Terrillon J.C., Akamatsu S.: Comparative performance of different chrominance spaces for color segmentation and detection of human faces in complex scenes. Proceedings of Vision Interface 99, May 1999, pp. 180–187.

V. Fan, L., Sung, K. K.: A Combined Feature-Texture Similarity Measure for Face Alignment under Varying Pose. Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2000.

VI. Cascia, M. L., Sclaoff, S.: Fast, Reliable Head Tracking under Varying Illumination. Proceedings of the International Conference on Computer Vision and PatternRecognition, 1999.

VII. Dass, S. C., Jain, A. K.: Markov Face Models. Proceedings of the International Conference on Computer Vision, 2001.

VIII. Bobick, A. F., Davis, J. W.: The Recognition of Human Movement Using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, 2001, No. 3, pp. 257–267.

IX. Ying, Z., Schwartz, S.: Discriminant Analysis and Adaptive Wavelet Feature Selection for Statistical Object Detection.ICPR 4,pp. 86-89, 2002.

X. Rowley, H. A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 20, 1998, No. 1, pp. 23–30.

XI. Yilmaz, A., Gokmen, M.: Eigenhill vs. Eigenface and Eigenedge. Pattern Recognition, Vol. 34, 2001, No. 1, pp. 181–184.

XII. Lai, J. H., Yuen, P. C., Feng, G. C.: Face Recognition Using Holistic Fourier Invariant Features. Pattern Recognition, Vol. 34, 2001, No. 1, pp. 95–109.

XIII. Park G.T., Bien, Z.: Neural Network-Based Fuzzy Observer with Application to Facial Analysis. Pattern Recognition Letters, Vol. 21, 2000, No. 1, pp. 93–105.

XIV.Georghiades, A. S., Belhumeur, P. N., Kriegman, D. J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23,2001, No. 6, pp. 643–660.

XV. Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. Proceedings of International Conference on Computer Vision and Pattern Recogntion, 1991, pp. 586–591.

XVI. Yongzhong Lu, Jingli Zhou, Shengsheng Yu: A survey of face detection, extraction and recognition, computing and informatics, Vol. 22, 2003.

XVII. Adini, Y., Moses, Y., and Ullman, S.: Face Recognition: The Problems of Compensating for Changes in Illumination Direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, pp. 721-732.

XVIII. Brunelli R., Poggio T.: Face Recognition through geometrical features. Proceedings European Conf. Computer Vision, pp. 792-800, May 1992.

XIX. Yuille, A.L., Hallinan, P.W., Cohen, D.S.: Feature Extraction from Faces Using Deformable Templates”. International Journal of Computer Vision, Vo1. 8, No. 2, 1992, pp. 99-111.

XX. Beymer, D. J.: Face Recognition under Varying Pose. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp. 756-761.

XXI. Rao, Rajesh P.N.: Dynamic Appearance-Based Recognition. CVPR 97, IEEE Computer Society, 1997, pp. 540-546.

XXII. Hu, Y., Wang, Z.:A Low-dimensional Illumination Space Representation of Human Faces for Arbitrary Lighting Conditions. ICPR, pp. 1147-1150, Volume 3, 2006.

XXIII. Epstein, R., Hallinan, P.W., Yuille A.L.: 5±2 Eigenimages Suffice: An Empirical Investigation of Low-dimensional Lighting models. Proceedings IEEE Workshop on Physics-Based Vision, 1995, pp. 108-116.

XXIV. Sirovich, L., Kirby, M.: Low-Dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America, Vol. 4, No. 3, March 1987, pp. 519-524.

XXV. Fang J., Qiu Guoping: A Color Histogram-Based Approach to Human Face Recognition. Institute of Electrical Engineers, Michael Faraday House Publications, 2003, pp. 133-136.

XXVI. Cristinacce, D., Cootes, T.F.: A Comparison of Shape Constrained Facial Feature Detectors. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR), 2004.

XXVII.Sung Kah-Kay, Poggio Tomaso: Example-based Learning for View-Based Human Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, January 1998.

XVIII. Li, Stan Z., Lu, Juwei: Face Recognition Using the Nearest Feature Line Method. IEEE Transactions on Neural Networks, Vol. 10, No. 2, March 1999, pp. 439-443.

XXIX. Aggarwal, J., Nandhakumar, N.: On the Computation of Motion of Sequences of Images, A Review. Proceedings IEEE, Vol. 69, No. 5, pp. 917-934, 1988.

Fault Detection technique of electronic gadgets using Fuzzy Petri net abduction method

Authors:

Sudipta Ghosh , Arpan Dutta

DOI NO:

https://doi.org/10.26782/jmcms.2014.07.00001

Abstract:

Fuzzy technique using Petri net is a formal tool for describing a Discrete event system model of an actual system. The advantage of this technique is that concurrent evolutions with various processes evolving simultaneously and partially independently can be easily represented and analyzed. In local control applications conditions /events are used to describe the control sequences of elementary devices. Petri nets are made up of places, transitions and tokens. A state is represented by distribution of tokens in places. Various approaches can be used to combine Petri nets and Fuzzy sets. In this paper the authors speak about the fault finding technique of electronic networks with different illustrations.

Keywords:

Fuzzy sets,Petri nets,control sequences,technique of electronic networks ,

Refference:

I. Bugarin, A. J. and Barro, S., “Fuzzy reasoning supported by Petri nets”, IEEE Trans. on Fuzzy Systems, vol. 2, no.2, pp 135-150,1994.

II. Buchanan, B. G., and Shortliffe E. H., Rule Based Expert Systems: The MYCIN Experiment of the Stanford University, Addison-Wesley, Reading, MA, 1984.

III. Cao, T. and Sanderson, A. C., “A fuzzy Petri net approach to reasoning about uncertainty in robotic systems,” in Proc. IEEE Int. Conf. Robotics and Automation, Atlanta, GA, pp. 317-322, May 1993.

IV. Cao, T., “Variable reasoning and analysis about uncertainty with fuzzy Petri nets,” Lecture Notes in Computer Science, vol. 691, Marson, M. A., Ed., Springer-Verlag, New York, pp. 126-145, 1993.

V. Cao, T. and Sanderson, A. C., “Task sequence planing using fuzzy Petri nets,” IEEE Trans. on Systems, Man and Cybernetics, vol. 25, no.5, pp. 755-769, May 1995.

VI.Cardoso, J., Valette, R., and Dubois, D., “Petri nets with uncertain markings”, in Advances in Petri nets, Lecture Notes in Computer Science, Rozenberg, G., Ed., vol.483, Springer-Verlag, New York, pp. 65-78, 1990.

VII. Chen, S. M., Ke, J. S. and Chang, J. F., “Knowledge representation using fuzzy Petri nets,” IEEE Trans. on Knowledge and Data Engineering, vol. 2 , no. 3, pp. 311-319, Sept. 1990.

VIII. Chen, S. M., “A new approach to inexact reasoning for rule-based systems,” Cybernetic Systems, vol. 23, pp. 561-582, 1992.

IX. Daltrini, A., “Modeling and knowledge processing based on the extended fuzzy Petri nets,” M. Sc. degree thesis, UNICAMP-FEE0DCA, May 1993.

X. Doyle, J., “Truth maintenance systems,” Artificial Intelligence, vol. 12, 1979.

XI. Garg, M. L., Ashon, S. I., and Gupta, P. V., “A fuzzy Petri net for knowledge representation and reasoning”, Information Processing Letters, vol. 39, pp.165-171,1991.

XII. Graham, I. and Jones, P. L., Expert Systems: Knowledge, Uncertainty and Decision, Chapman and Hall, London, 1988.

13) Hirota, K. and Pedrycz, W., ” OR-AND neuron in modeling fuzzy set connectives,” IEEE Trans. on Fuzzy systems, vol. 2 , no. 2 , May 1994.

XIV. Hutchinson, S. A. and Kak, A. C., “Planning sensing strategies in a robot workcell with multisensor capabilities,” IEEE Trans. Robotics and Automation, vol. 5, no. 6, pp.765-783, 1989.

XV. Jackson, P., Introduction to Expert Systems, Addison-Wesley, Reading, MA, 1988.

XVI. Konar, A. and Mandal, A. K., “Uncertainty management in expert systems using fuzzy Petri nets ,” IEEE Trans. on Knowledge and Data Engineering, vol. 8, no. 1, pp. 96-105, February 1996.

XVII. Konar, A. and Mandal, A. K., “Stability analysis of a non-monotonic Petrinet for diagnostic systems using fuzzy logic,” Proc. of 33rd Midwest Symp. on Circuits, and Systems, Canada, 1991.

XVIII.Konar, A. and Mandal, A. K., “Non-monotonic reasoning in expert systems using fuzzy Petri nets,” Advances in Modeling & Analysis, B, AMSE Press, vol. 23, no. 1, pp. 51-63, 1992.

XIX. Konar, S., Konar, A. and Mandal, A. K., “Analysis of fuzzy Petri net models for reasoning with inexact data and knowledge-base,” Proc. of Int. Conf. on Control, Automation, Robotics and Computer Vision, Singapore, 1994.

XX. Konar, A., “Uncertainty Management in Expert System using Fuzzy Petri Nets,” Ph. D. dissertation , Jadavpur University, India, 1994.

XXI. Konar, A. and Pal, S., Modeling cognition with fuzzy neural nets, in Fuzzy Theory Systems: Techniques and Applications, Leondes, C. T., Ed., Academic Press, New York, 1999.

XXII. Kosko, B., Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliffs, NJ, 1994.

XXIII. Lipp, H. P. and Gunther, G., “A fuzzy Petri net concept for complex decision making process in production control,” in Proc. First European congress on fuzzy and intelligent technology (EUFIT ’93), Aachen, Germany, vol. I, pp. 290 – 294, 1993.

XXIV. Looney, C. G., “Fuzzy Petri nets for rule-based decision making,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 18, no.1, pp.178-183, 1988.

XXV. McDermott, V. and Doyle, J., “Non-monotonic logic I,” Artificial Intelligence, vol. 13 (1-2), pp. 41-72, 1980.

XXVI. Murata, T., “Petri nets: properties, analysis and applications”, Proceedings of the IEEE, vol. 77, no.4, pp. 541-580,1989.

XXVII. Pal, S. and Konar, A., “Cognitive reasoning using fuzzy neural nets,” IEEE Trans. on Systems , Man and Cybernetics, August, 1996.

XXVIII. Peral, J., “Distributed revision of composite beliefs,” Artificial Intelligence, vol. 33, 1987.

XXIX. Pedrycz, W. and Gomide, F., “A generalized fuzzy Petri net model,” IEEE Trans. on Fuzzy systems, vol . 2, no.4, pp 295-301, Nov 1994.

XXX. Pedrycz, W, Fuzzy Sets Engineering, CRC Press, Boca Raton, FL, 1995.

XXXI. Pedrycz, W., “Fuzzy relational equations with generalized connectives and their applications,” Fuzzy Sets and Systems, vol. 10, pp. 185-201, 1983.

XXXII. Pedrycz, W. and Gomide, F., An Introduction to Fuzzy Sets: Analysis and Design, MIT Press, Cambridge, MA, pp. 85-126, 1998.

Pressure-Driven Flow Instability with Convective Heat Transfer through a Rotating Curved Channel with Rectangular Cross-section: The Case of Negative Rotation

Authors:

Md. Zohurul Islam, Md. Sirajul Islam, Muhammad Minarul Islam

DOI NO:

https://doi.org/10.26782/jmcms.2014.07.00002

Abstract:

Due to engineering applications and its intricacy, the flow in a rotating curved duct has become one of the most challenging research fields of fluid mechanics. A comprehensive numerical study is presented for the fully developed two-dimensional thermal flow of viscous incompressible fluid through a rotating curved rectangular duct of constant curvature1.0=δ. Numerical calculations are carried out by using a spectral method and covering a wide range of the Taylor number 02000<≤−Trand the Dean number 1000100≤≤Dn for the constant Grashof number100=Gr. A temperature difference is applied, that is the outer wall of the duct is heated while the inner wall is cooled. The rotation of the duct about the center of curvature is imposed, and the effects of rotation (Coriolis force) on the unsteady flow characteristics are investigated. Flow characteristics are investigated for the case of negative duct rotation. We investigate the unsteady flow characteristics for the Taylor number02000<≤−Tr and it is found that the unsteady flow undergoes in the scenario ‘steady-state→ periodic→ multi-periodic → steady-state’, if Tr is increased in the negative direction. Contours of secondary flow patterns and temperature profiles are also obtained at several values of Tr, and it is found that there exist two- and multi-vortex solutions if the duct rotation is involved in the negative direction.

Keywords:

thermal flow,viscous incompressible fluid ,duct rotation,Taylor number,Grashof number,

Refference:

I. Nandakumar, K. and Masliyah, J. H. (1986). Swirling Flow and Heat Transfer in Coiled and Twisted Pipes, Adv. Transport Process., Vol. 4, pp. 49-112.

II. Ito, H (1987). Flow in curved pipes. JSME International Journal, 30, pp. 543–552.

III. Yanase, S., Kaga, Y. and Daikai, R. (2002). Laminar flow through a curved rectangular duct over a wide range of the aspect ratio, Fluid Dynamics Research, Vol. 31, pp. 151-183.

IV. Selmi, M. and Namdakumar, K. and Finlay W. H., 1994. A bifurcation study of viscous flow through a rotating curved duct, J. Fluid Mech. Vol. 262, pp. 353-375.

V. Wang, L. Q. and Cheng, K.C., 1996. Flow Transitions and combined Free and Forced Convective Heat Transfer in Rotating Curved Channels: the Case of Positive Rotation Physics of Fluids, Vol. 8, pp.1553-1573.

VI. Selmi, M. and Namdakumar, K. (1999). Bifurcation Study of the Flow through Rotating Curved Ducts, Physics of Fluids, Vol. 11, pp. 2030-2043.

VII. Yamamoto, K., Yanase, S. and Alam, M. M. (1999). Flow through a Rotating Curved Duct with Square Cross-section, J. Phys. Soc. Japan, Vol. 68, pp. 1173-1184.

VIII. Mondal, R. N., Alam M. M. and Yanase, S. (2007). Numerical prediction of non- isothermal flows through a rotating curved duct with square cross section, Thommasat Int. J. Sci and Tech., Vol. 12, No. 3, pp. 24-43.

IX. Mondal, R. N., Datta, A. K. and Uddin, M. K. (2012). A Bifurcation Study of Laminar Thermal Flow through a Rotating Curved Duct with Square Cross-section, Int. J. Appl. Mech. and Engg. Vol. 17 (2). (In Press).

X. Mondal, R. N., Islam, M. S., Uddin, M. K. and Hossain, M. A. (2013). “Effects of Aspect Ratio on Unsteady Solutions through a Curved Duct Flow”, Appl. Math. & Mech. (Springer), Vol. 34(9), pp. 1-16

XI. Mondal, R. N., Islam, Md. Zohurul., and Md. saidul Islam, Editors. Transient Heat and Fluid Flow through a Rotating Curved Rectangular Duct: The Case of Positive and Negative Rotation. Proceedings of the 5th BSME International Conference on Thermal Engineering, (2012),December 21-23; IUT, Dhaka.

State Space Analysis of a Solar Power Array Taking a Higher Degree Of Non-Linearity into Account

Authors:

Adhir Baran Chattopadhyay, Sunil Thomas, Aliakbar Eski, Ruchira Chatterjee

DOI NO:

https://doi.org/10.26782/jmcms.2014.07.00003

Abstract:

This paper develops a mathematical technique for the solution of a non linear state variable model of a solar array power system powering a non linear load. The significance of the technique lies in the fact that experimental complexities can be avoided to reach a desired conclusion regarding the design of the controller associated with a solar power array system. An iterative method has been used in which the initiating assumption has been made to consider the system to depend entirely upon its initial values at the instant t = 0 and taking the forcing function to be zero at that instant. In the next step we use the solution at t = 0 and plug it into the equation iteratively while having a non zero value of the forcing equation during the second iteration. The non linearity lies in the fact that the forcing function is a function of the state variable itself. We have applied the Maclaurin series to find the laplace transform of certain mathematical functions containing a singularity at the zero time instant. The time response is obtained and then it is plotted by using MATLAB and various graphs have been obtained.

Keywords:

solar array power system ,non linear state variable model, forcing function,laplace transform,time response,

Refference:

I. Bae, H. S., J.H. Lee, S.H. Park and B.H. Cho, 2008. “Large-Signal Stability Analysis of Solar Array Power System”. IEEE Transactions on Aerospace and Electronic Systems, 44 Issue-2: 538-547. DOI: 10.1109/TAES.2008.4560205.

II. A. B. Chattopadhyay, A Choudhury, A. Nargund, 2011, “State Variable Model of a Solar Power System”, Trends in Applied Sciences Research, 563-579, DOI : 10.3923/tasr.2011.563.579

III. Bondar, D., D. Budimir and B. Shelkovnikov, 2008. “A new approach for non-linear analysis of power amplifiers”. In: 18th International Crimean Conference Microwave & Telecommunication Technology, 2008. Sevastopol, Crimea, 8-12 September 2008. IEEE, pp: 125 – 128.

IV. Bouchafaa, F., D. Beriber and M.S. Boucherit, 2010. “Modeling and control of a gird connected PV generation system”. In: 18th Mediterranean Conference on Control & Automation (MED), 2010. Marrakech. 23-25 June 2010. IEEE, pp: 315 – 320.

V. Chattopadhyay, A.B., S.S. Dubei and A. Bhattacharjee, 2005. “Modelling of DC-DC boost converter analysis of capacitor voltage dynamics”. A.M.S.E Journal, France, 78 no.6: 15-24.

VI. Chattopadhyay, A.B., S.S. Dubei, A. Bhattacharjee and K. Raman, 2009. “Modelling of DC-DC Boost converter state variable modeling and error analysis”. A.M.S.E Journal, France, Modelling Measurement & Control, 82 Issue-4: 1-16.

VII. Cho, B.H., J.R. Lee and F.C.Y. Lee, 1990. “Large-Signal Stability Analysis of Spacecraft Power Processing System”. IEEE Transactions on Power Electronics, 5 Issue-1: 110 – 116. DOI: 10.1109/63.46005.

VIII. Cho, Y.J. and B.H. Cho, 2001. “Analysis and design of the inductor-current-sensing peak-power-tracking solar array regulator”. AIAA Journal of Propulsion and Power, 17: 467—471.

IX. Hua, C. and C. Shen, 1998. “Comparative Study of Peak Power Tracking Techniques for Solar Storage System”. In: Thirteenth Annual Applied Power Electronics Conference and Exposition 1998. Anaheim, CA, USA. 15-19 February 1998. IEEE, pp: 679 – 685.

X. Huynh, P. and B. H. Cho, 1996. “Design and Analysis of a Microprocessor-Controlled Peak-Power-Tracking System”. IEEE Transactions on Aerospace and Electronic Systems, 32 Issue-1: 182-190. DOI: 10.1109/7.481260.

XI. Jensen, Michael, Russell Louie, Mehdi Etezadi-Amoli and M. Sami Fadali, 2010. “Model and Simulation of a 75kW PV Solar Array”. In: IEEE PES Transmission and Distribution Conference and Exposition 2010. New Orleans, LA, USA. 19-22 April 2010. IEEE, pp: 1 – 5.

XII. Mourra, O., A. Fernandez and F. Tonicello, 2010. “Buck Boost Regulator (B2R) for Spacecraft Solar Array Power conversion”. In: Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), 2010. Palm Springs, CA, USA. 21-25 February 2010. IEEE, pp: 1313 – 1319.

XIII. Paulkovich, John, 1967. “Solar Array Regulators of Explorer Satellites XII, XIV, XV, XVIII, XXI, XXVI, XXVIII and Ariel I”. NASA Technical Note: 1 – 15.

XVI. Ramaprabha, R., B.L. Mathur and M. Sharanya, 2009. “Solar Array Modeling and Simulation of MPPT using Neural Network”. In: International Conference on Control, Automation, Communication and Energy Conservation, 2009. Perundurai, Tamil Nadu, India. 4-6 June 2009. IEEE, pp: 1 – 5.

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Some Results on Normal meet Semilattices

Authors:

Momtaz Begum, A.S.A.Noor

DOI NO:

https://doi.org/10.26782/jmcms.2014.07.00004

Abstract:

In this paper we introduce the concept of normal semilattices in presence of 0-distributivity and include a nice characterization of normal semilattices. We also study the p-ideals in pseudo complemented meet semilattices. Then we give the notion of S-semilattices and prove that every S-semilattice is comaximal, although its converse in not true. Finally, we prove that every S-semilattice is normal, but the converse need not be true.

Keywords:

normal semilattices,0-distributivity, ideals,meet semilattices,

Refference:

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V.Momtaz Begum and A.S.A. Noor, Semi prime ideals in meet semilattices, Annals of Pure & appl.Math.Vol.1, No.2, 2012, 149-157. Nag , C. Begum S.N. and Taluhder, M.R. Some characterizations of subclasses of P-algebras. Manuscript.

VI. A.S.A. Noor and Momtaz Begum, Some Properties of 0-distributive Meet Semilattices, Annals of Pure & appl. Math.Vol.2, No.1, 2012, 60-66.

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CERTAIN FEATURES OF PARTIALLY α – COMPACT FUZZY

Authors:

M. A. M. Talukder , D. M. Ali

DOI NO:

https://doi.org/10.26782/jmcms.2014.07.00005

Abstract:

In this paper , we introduce the concept of partially α– shading ( resp. partially *α– shading ), in ahort, αp– shading ( resp. *αp– shading ) and partially α– compact ( resp. partially *α– compact ), in short, αp– compact ( resp. *αp– compact ) fuzzy sets and study their several features in fuzzy topological spaces.

Keywords:

fuzzy sets,compact fuzzy sets,fuzzy topological spaces,

Refference:

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