Archive

Abstract:

The small perturbation method is widely used in attempting to model non-linear systems. Many systems nowadays in different domains exist as adaptive-parameter type models, where the control effort is not applied as in input to the system (as is usually the case) but as a change to the parameter within the system itself. This paper attempts to analyze a non-linear adaptive-parameter type system, using the small perturbation method for linearization. The Ward-Leonard DC Motor with thyristor field control is used as a “test bench” here as it is suited for being an adaptive parameter system. The results and inferences from this study can easily be generalized to a wide variety of systems in applied mathematics, general control systems, power systems, robotics etc.

Keywords:

Non-linear adaptive-parameter,Small perturbation approach,DC drive,Thyristorized W-L method,silicon-controlled rectifier,

Refference:

I. A. Bara, S. Dale, C. Rusu and H. Silaghi, “DC electrical drive control with fuzzy systems,” 2015 13th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, 2015, pp. 1-4. doi: 10.1109/EMES.2015.7158437.
II. A. Choudhary, S. A. Singh, M. F. Malik, A. Kumar, M. K. Pathak and V. Kumar, “Virtual lab: Remote access and speed control of DC motor using Ward-Leonard system,” 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), Kerala, 2012, pp. 1-7. doi: 10.1109/ICTEE.2012.6208666.
III. A.S.Avila Balula, M.S. “Nonlinear Control of an inverted pendulum”Thesis in engineering physics,and Technology, School of engineering University of Lisbon, Portugal September,2016.
IV. F. P. A. Vaccaro, M. Janusz and K. Kuhn, “Digital control of a Ward Leonard drive system,” 3D Africon Conference. Africon ’92 Proceedings (Cat. No.92CH3215), Ezulwini Valley, Swaziland, 1992, pp. 123-127. doi: 10.1109/AFRCON.1992.624433.
IV. G. A. Biacs and M. S. Adzic, “Modeling of the thyristor controlled rectifiers for control of Ward – Leonard system,” 2009 7th International Symposium on Intelligent Systems and Informatics, Subotica, 2009, pp. 193-196.doi: 10.1109/SISY.2009.5291167.
V. Meng Cno, X. jin & R.E. White, “Nonlinear state-variable method for solving physical based Li-Ion cell model with High frequency inputs” journal of Electrochimical Society, Feb 2017.
VI. Q. Zhong, “Speed-sensorless AC Ward Leonard drive systems,” SPEEDAM 2010, Pisa, 2010, pp. 1512-1517. doi: 10.1109/SPEEDAM.2010.5544901.

Modelling and Forecasting of GDP in Bangladesh: An ARIMA Approach

Authors:

M. M. Miah, Mimma Tabassum, M. Shohel Rana

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00012

Abstract:

This paper aims to model and forecasting on GDP data of Bangladesh for the period of 1960 to 2017. To test the stationarity of the series graphical method, correlogram and unit root test were used. The time series plot of GDP shows a non-stationary pattern and overall this is like exponential curvature shape. Hence the data have been differenced twice to convert the data from non-stationary to stationary. From the autocorrelation function (ACF) and partial autocorrelation function (PACF) we obtain the order of the time series model. The chosen model was autoregressive integrated moving average ARIMA (1, 2, 1). The model has been fitted on data to estimate the parameters of autoregressive and moving average components of ARIMA (1, 2, 1) model. For residual diagnostics, correlogram, Q-statistic, histogram, and normality test were used. Also, Chow test was used for stability testing. Using model selection criterion and checking model adequacy, wesee that the model is suitable in shape. It is found that the forecast values of GDP in Bangladesh are steadily improving over the next thirteen years.

Keywords:

GDP,ARIMA Modeling,Forecasting,Bangladesh,

Refference:

I.Box GEP, Gwilym MJ, Gregory CR. Time Series Analysis: Time Series Analysis Forecasting & Control. New Jersey: Prentice Hall, Englewood Cliffs; 1994.

II.Dickey DA, Fuller WA. Distributions of the Estimators for Autoregressive Time Series with a Unit Root. J Am Stat Assoc. 1979; 74(366),pp:427–481.

III.Dr. ChaidoDritsaki (2015). Forecasting Real GDP rate through Econometric Models: An Empirical Study from Greece. J of Internal Business and Economics, 3(1), pp: 13-19.

IV.Gujarati DN, Porter DC, Gunasekar S. Econometric Modeling: Specification and Diagnostic Testing. Basic Econometrics. 4th Edn. McGraw Hill International; 2003.

V.Hanke JE, Wichern DW. Business Forecasting. 8th Edn. Int J Forecast. 2005; 22(4), pp: 823–824.

VI.Imon AHMR. Box-Jenkins ARIMA Models: Introduction to Regression TimeSeries and Forecasting. NanitaProkash; 2017.VII.Jovanovic, B. &Petrovska M. (2010). Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting. Working Paper, National Bank of the Republic of Macedonia.

VIII.Ljung, G. M., & Box G. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika, 75(2), pp: 335-346.

IX.Maity, B., &ChatterjeeB. (2012). Forecasting GDP growth rates of India: An empirical study. IntJof Economics and Management Sciences, 1(9), pp: 52-58.

X.Ning, W., Kuan-jiang, B. and Zhi-fa, Y. (2010), Analysis and forecast of Shaanxi GDP based on the ARIMA model, Asian Agricultural Research, Vol.2 No. 1, pp. 34-41.

XI.Shahini, L. &Haderi S. (2013). Short term Albanian GDP forecast: One quarter to one year ahead. European Scientific Journal, 9(34),pp: 198-208.

XII.Wei Ning, BianKuan-Jiang. &Yuan Zhi-fa (2010).Analysis and forecast of Shaanxi GDP based on the ARIMA model. Asian Agricultural Research, 2(1), pp: 34-41.

XIII.Zakai, M. (2014). A time series modeling on GDP of Pakistan. J of Contemporary Issues in Business Research,3(4), pp: 200-210.

XIV.Zhang, H. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Working Paper, HögskolanDalarna University, Sweden.

State Estimation using Active elements for Electrical Distribution Network

Authors:

Habib Ullah, Muhammad Aamir Aman, Waleed Jan, Ehtesham-ul-Haq, Mehre Munir

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00013

Abstract:

As the world thrives for its need to complete its energy demand and supply challenges, the state estimation in distribution systems remains a key factor at online observing and controlling in Energy Management Technology. As the world is advancing towards an advance era in order to fulfill its energy supply different sources whether traditional or renewable online monitoring of Distribution of state estimation is becoming more challenging and demandable. In this letter, a concept for state estimation is offered. The accountability for SE is surrogate to indigenous means in secondary substations. By means of past statistics and probabilistic models the substation bad statistics charts knowledge is gathered. Topology and observability analysis as well as bad data identification are performed Data not performing well is identified using topology tools is accomplished with a perfunctory that crosses the secondary substations of the primary substation feeders.

Keywords:

Electrical Distribution Network,Active elements,routing packets,Secondary substation, Primary Station,

Refference:

I.Breda, Jader FD, Jose CM Vieira, and Mario Oleskovicz. “Three-phase harmonic state estimation for distribution systems by using the svd technique.” 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016.

II.D. P. Buse, P. Sun, Q. H. Wu, and J. Fitch, “Agent-based substation automation,” IEEE Power Energy, vol. 1, no. 2, pp. 50–55, Mar.–Apr. 2003.

III.D. Falcao, F.Wu, and L. Murphy, “Parallel and distributed state estimation,” IEEE Trans. Power Syst., vol. 10, no.2, pp. 724–730, May 1995.

IV.D. V. Coury, J. S. Thorp, K. M. Hopkinson, and K. P. Birman, “Agent technology applied to adaptive relay setting for multi-terminal lines,” in Proc. IEEE Power Eng. Soc. Summer Meeting, July 16–20, 2000, pp. 1196–1201.

V.M. Shahidehpour and Y. Wang, Communication and Control in Electrical Power Systems. Piscataway, NJ: IEEE Press, 2003, p. 529.

VI.M. Lehtonen, M. Jalonen, A. Matsinen, J. Kuru, and V. Haapamäki, “A novel state estimation model for distribution automation,” in Proc. PSCCConf., Jun. 24–28, 2002.

VII.M. Amin, “National infrastructure as complex interactive networks,” in Automation, Control, and Complexity: An Integrated Approach. New York: Wiley, 2000, pp. 263–286.

VIII.M. Kezunovic, X. Xu, and D. Wong, “Improving circuit breaker maintenance management tasks by applying mobile agent software technology,” in Proc. IEEE Power Eng. Soc. Asia Pacific Transm. Distrib. Conf., Oct. 6–10, 2002, pp. 782–787.

IX.Primadianto, Anggoro, and Chan-Nan Lu. “A review on distribution system state estimation.”IEEE Transactions on Power Systems32.5 (2016): 3875-3883.

X.T. Hiyama, D. Zuo, and T. Funabashi, “Multi-agent based control and operation of distribution system with dispersed power sources,” in IEEE Power Eng. Soc. Asia Pacific Transm. Distrib. Conf., Oct. 6–10, 2002, pp. 2129–2133.

XI.T. Nagata and H. Sasaki, “A multi-agent approach to power system restoration,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 457–462, May 2002.

XII.Voltage Characteristics of Electricity Supplied by Public Distribution Systems. Brussels, Belgium: Cenelec, Nov. 1999.

XIII.Y. Liang, “Simulation of Top-Oil Temperature for Transformers,” Master’s thesis, Arizona State Univ., Tempe, AZ, Feb. 2001

Power and Energy Storage of Wind Energy in Distributed Generation Network

Authors:

Alamzeb Shahzad, Waleed Jan, Muhammad Aamir Aman, Ehtesham-ul-Haq, Mehr E Munir

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00014

Abstract:

Power is a necessary tool for modern civilization. All the modern achievements and technology have made man achieve more and more day by day but use of fossil fuels tends to be limited. On other hand, technologies and techniques are being developed to use natural renewable sources in order to full fill power and energy demand. Distributed Generation is part of new renewable energy trend in which different grid resources are added to meet user end requirements. This paper presents an approach to limit the power storage from wind energy while working with voltage levels. The study is performed in mainly two levels. First the wind profile is studied with load requirements and then detailed control is performed for optimal power flow (OPF). It is found that storages can be changed via user requirement while also depending upon threshold of DG network.

Keywords:

Wind Energy,Energy Storage, Distributed Generation,Wind Energy Farm,Power Generation,Power flow,

Refference:

I.G. Carpinelli, G. Celli, S. Mocci, F. Mottola, F. Pilo, and D. Proto, ―Optimal integration of distributed energy storage devices in smart grids,‖ IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 985–995, 2013

.II.J. A. Martinez, F. de Leon,A. Mehrizi-Sani, M. H. Nehrir, C. Wang, and V. Dinavahi, ―Tools for analysis and design of distributed resources—Part II: Tools for planning, analysis and design of distribution networks with distributed resources,‖ IEEE Trans. Power Del., vol. 26, no. 3,pp. 1653–1662, Jul. 2011.

III.L. Alexio, G. Celli, E. Ghiani, J. Myrzik, L. F. Ochoa, and F. Pilo, ―A general framework for active distribution network planning,‖ in Proc. CIGRE Symp., 2013, pp. 1–8.

IV.L. F. Ochoa, C. Dent, and G. P. Harrison, ―Distribution network capacity assessment: Variable DG and active networks,‖ IEEE Trans. Power Syst., vol. 25, no. 1, pp. 87–95, Feb. 2010.

V.M. Nick, R. Cherkaoui, and M. Paolone, ―Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support,‖ IEEE Trans. Power Syst., vol. 29, no. 5, pp. 2300–2310, Sep. 2014.

VI.N. Etherden and M. H. J. Bollen, ―Dimensioning of energy storage for increased integration of wind power,‖ IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 546–553, 2013.

VII.N. Wade, P. Taylor, P. Lang, and J. Svensson, ―Energy storage for power flow management and voltage control on an 11 kV UK distribution network,‖ in Proc. Int. Conf. Electricity Distribution (CIRED), 2009, pp. 1–4.

VIII.R. A. F. Currie, G. W. Ault,C. E. T. Foote, and J. R. McDonald, ―Active power-flow management utilising operating margins for the increased connection of distributed generation,‖ IET Proc. Gener., Transm., Distrib., vol. 1, no. 1, pp. 197–202, 2007.

IX.J. P. Barton and D. G. Infield, ―Energy storage and its use with intermittent renewable energy,‖ IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 441–448, Jun., 2004.

X.S. Gill, I. Kockar, and G. W. Ault, ―Dynamic optimal power flow for active distribution networks,‖ IEEE Trans. Power Syst., vol. 29, no. 1, pp. 121–131, Jan. 2014.

XI.S. Carr, G. C. Premier, A. J. Guwy, R. M. Dinsdale, and J. Maddy, ―Energy storage for active network management on electricity distribution networks with wind power,‖ IET Renew. Power Gener., vol. 8, no. 3, pp. 249–259, 2014.

XII.S. W. Alnaser and L. F. Ochoa, ―Advanced network management systems: A risk-based AC OPF approach,‖ IEEE Trans. Power Syst., vol. 30, no. 1, pp. 409–418, Feb. 2015.

XIII.Y. V. Makarov, P. Du, M. C. W. Kintner-Meyer, C. Jin, and H. F. Illian, ―Sizingenergy storage to accommodate high penetration of variable energy resources,‖ IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 34–40, 2012.

XIV.Y. M. Atwa and E. F. El-Saadany, ―Optimal allocation of ESS in distribution systems with a high penetration of wind energy,‖ IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1815–1822, Nov. 2010.

Deep Learning Approach: Emotion Recognition from Human Body Movements

Authors:

R. Santhoshkumar, M. Kalaiselvi Geetha

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00015

Abstract:

Analysis of human body movements for emotion prediction is necessary for social communication. Body movements, gestures, eye movements and facial expression are some non-verbal communication method used in many applications. Among them emotion prediction from body movements is commonly used because it convey the emotional states of person from different camera view. In this paper, human emotional states predict from full body movements using feed forward deep convolution neural network architecture and Block Average Intensity Value BAIV feature. Both model can be evaluated by emotion action dataset (University of YORK) with 15 types of emotions. The experimental result showed the better recognition accuracy of the feed forward deep convolution neural network architecture.

Keywords:

Emotion Recognition,Non-verbal communication,Body Movement,Human Computer Interaction (HCI),Deep Convolutional Neural Networks (DCNN),BAIVfeature,

Refference:

I.A.Krizhevsky, I. Sutskever, and G. E. Hinton, (2014),“Imagenet Classification With Deep Convolutional Neural Networks,” In Advances in neural information processing systems, pp. 1097–1105.

II.D.Tran, L. Bourdev, R. Fergus, L.Torresani and M. Paluri, (2015), “Learning Spatiotemporal features with 3d Convolutional networks”,IEEE International Conference on Computer Vision (ICCV), pp. 4489-4497.

III.Damel Rucha, Gurjar Aditya, Joshi Anuja, Nagre Kartik, (2015), “Human Body Skeleton detection and Tracking”, International Journal of Technical Research and Applications, Volume 3, Issue 6, pp.222-225.

IV.Daniel Holden, Jun Saito, Taku Komura. (2016) “A Deep Learning Framework for Character Motion Synthesis and Editing” SIGGRAPH ’16 Technical Paper, July 24 -28, Anaheim, CA, ISBN: 978-1-4503-4279-7/16/07.

V.Enrique Correa, Arnoud Jonker, Michael Ozo, Rob Stolk. (2016) “Emotion Recognition using Deep Convolutional Neural Networks”

VI.F. Zhu and L. Shao, (2014),“Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition,” International Journal of Computer Vision, Vol. 109, No. 1-2, pp. 42–59.

VII.F. Zhu and L. Shao, (2015),“Correspondence-Free Dictionary Learning for Cross-View Action Recognition,” In ICPR, pp. 4525–4530.

VIII.F. Zhu, L. Shao, J. Xie, and Y. Fang, (2016),“From Handcrafted to Learned Representations for Human Action Recognition: A Survey,” Image and Vision Computing.

IX.Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kami ́nska, Tomasz Sapi ́ nski, Sergio Escalera, and Gholamreza Anbarjafari (2015) “Survey on Emotional Body Gesture Recognition” Journal of IEEE Transactions on Affective Computing.

X.Gavrilescu, M., (2015) “Recognizing emotions from videos by studying facial expressions, body postures and hand gestures”, 23rdTelecommunication fourm TELFOR,pp. 720-723.

XI.H.Wang, C. Yuan,W. Hu, and C. Sun,(2012), “Supervised Class-Specific Dictionary Learning for Sparse Modeling in Action Recognition,” Pattern Recognition, Vol. 45, No. 11,pp. 3902–3911.

XII.Hatice Gunes, Caifeng Shan, Shizhi Chen, YingLi Tian. (2015) “Bodily Expression for Automatic Affect Recognition. Emotion Recognition: A Pattern Analysis Approach” Published by John Wiley & Sons, Inc.

XIII.Hazel Rose Markus, Shinobu Kitayama.(1991) “Culture and the self: Implementations for cognition, emotion, and motivation” Psychological Review,pp. 224-253.

XIV.Heike Brock. (2018) “Deep learning -Accelerating Next Generation Performance Analysis Systems” 12th Conference of the International Sports Engineering Association, Brisbane, Queensland, Australia, pp. 26–29

.XV.Hiranmayi Ranganathan, Shayok Chakraborty, Sethuraman Panchanathan.(2017) “Multimodal Emotion Recognition using Deep Learning Architectures” http://emofbvp.org/

XVI.J. Arunnehru, M. Kalaiselvi Geetha. (2017) “Automatic Human Emotion Recognition in Surveillance Video” Intelligent Techniques in Signal Processing for Multimedia Security, Springer-Verlag,pp. 321-342.

XVII.Lei Zhang, Shuai Wang, Bing Liu. (2018) “Deep Learningfor Sentiment Analysis: A Survey” https://arxiv.org/pdf/1801.07883.XVIII.Nourhan E, Pablo B, Parisi, Stefan Wermter, (2017),”Emotion recognition from body expressions with Neural Network Architecture”, Algorithm and Learning, HAI 2017, pp. 143-149.

XIX.Pablo Barros, Doreen Jirak, Cornelius Weber, Stefan Wermter. (2015) “Multimodal emotional state recognition using sequence-dependent deep hierarchical features” Neural Networks. 72, pp. 140–151.

XX.Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie Dagli, Thomas S. Huang. (2017) “How Deep Neural Networks Can Improve Emotion Recognition on Video Data” https://arxiv.org/pdf/1602.07377.pdf.

XXI.Prinzie, A., Van den Poel, D., (2012), Random Forests for multiclass classification: Random MultiNomial Logit. Expert Systems with Applications. Vol.34, 3, pp.1721–1732.

XXII.Samira Ebrahimi Kahou, Vincent Michalski, Kishore Konda, Roland Memisevic, Christopher Pal. (2015) “Recurrent Neural Networks for Emotion Recognition in Video” ICMI 2015, Seattle, WA, USA.

XXIII.Shirbhate Neha, Talele Kiran, (2016), “Human Body Language Understanding for Action detection using Geometric Features”,2ndInternational Conference on Contemporary Computing and Informatics, IEEE, pp.603-607.

XXIV.T. Guha and R. K.Ward, (2012),“Learning Sparse Representations for Human Action Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 34, No. 8, pp. 1576–1588.

XXV.Y. Du,W.Wang, and L.Wang, (2015),“Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110–1118.

XXVI.Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel,(1989), “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural computation,Vol. 1, No. 4, pp. 541–551.

XXVII.Yann LeCun, Yoshua Bengio, Geoffrey Hinton.(2015) “Deep learning” Nature, Vol. 521, pp. 436-444.

Approximation of large-scale dynamical systems for Bench-mark Collection

Authors:

Santosh Kumar Suman, Awadhesh Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00016

Abstract:

In this contribution,We present a benchmark collection Inclusive of some needful real-world examples, which can be used to assessment and compare numerical methods for model reduction. In this paper the reduction method is explored for getting structure preserving reduced order model of a large-scale dynamical system, we have considered model order reduction of higher orderLTIsystems) with SISO and MIMO [XXXII] that aims at finding Error estimation using Approximation of both system. This enables a new evaluation of the error system Provided that the Observability Gramian of the original system has once been considered, an H∞and H2 error bound can be computed with negligible numerical attempt for any reduced model attributable to The reduced order model (ROM) of a large-scale dynamical system is necessary to effortlessness the analysis of the system using approximation Algorithms. The response evaluation is considered in terms ofresponse constraints and graphical assessments.the application of Approximation methodsis offered for arisingROMof the large-scaleLTI systems which includes benchmark problems. It is reported that the reduced order model using compare numerical methods is almost alike in performance to that of with original systems.all simulation resultshave been obtained via MATLAB based software (sssMOR toolbox).

Keywords:

Benchmarks Example,Order reduction,Error estimation,Krylov,Balanced Truncation,Modal method,

Refference:

I.Antoulas, A. C. (2004). Approximation of large-scale dynamical systems: An overview. IFAC

Proceedings Volumes (IFAC-PapersOnline).https://doi.org/10.1016/S1474-6670(17)31584-7

II.Antoulas, A. C. (2005). An overview of approximation methods for large-scale dynamical systems.

Annual Reviews in Control.https://doi.org/10.1016/j.arcontrol.2005.08.002

III.Antoulas, A. C., Benner, P., & Feng, L. (2018). Model reduction by iterative error system approximation.

Mathematical and Computer Modelling of Dynamical Systems. https://doi.org/10.1080/13873954.2018.1427116

IV.Antoulas, A. C., Sorensen, D. C., & Gugercin, S. (2012).A surveyof model reduction methods for

large-scale systems. https://doi.org/10.1090/conm/280/04630

V.Antoulas, Athanasios C. (2011a). 8. Hankel-Norm Approximation. En Approximation of Large-Scale

Dynamical Systems.https://doi.org/10.1137/1.9780898718713.ch8

VI.Antoulas, Athanasios C. (2011b). Approximation of Large-Scale Dynamical Systems. En Approximation of Large-Scale Dynamical Systems.https://doi.org/10.1137/1.9780898718713

VII.Antoulas, Athanasios C., Beattie, C. A., & Gugercin, S. (2010). Interpolatory model reduction of large-

scale dynamical systems. En Efficient Modeling and Control of Large-Scale Systems. https://doi.org/10.1007/978-1-4419-5757-3_1

VIII.Antoulas, Athanasios C., & Sorensen, D. C. (2001). Approximation of large-scale dynamical systems: An overview.Int.J. Appl. Math. Comput. Sci.

IX.Beattie, C. A., & Gugercin, S. (2011). Weighted model reduction via interpolation.IFAC Proceedings

Volumes(IFAC-PapersOnline).https://doi.org/10.3182/20110828-6-IT-1002.03419

X.Benner, P. (2007). A MATLAB repository for model reduction based on spectral projection.Proceedings of the 2006 IEEE Conference on Computer Aided Control Systems Design, CACSD.https://doi.org/10.1109/CACSD.2006.285438

XI.Benner, P., & Faßbender, H. (2011). On the numerical solution of large-scale sparse discrete-time Riccati equations.Advances in Computational Mathematics. https://doi.org/10.1007/s10444-011-9174-7

XII.Benner, P., Gugercin, S., & Willcox, K. (2015). A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems.SIAM Review. https://doi.org/10.1137/130932715

XIII.Castagnotto, A., Cruz Varona, M., Jeschek, L., & Lohmann, B. (2017). Sss &sssMOR: Analysis and reduction of large-scale dynamic systems inMATLAB.At-Automatisierungstechnik. https://doi.org/10.1515/auto-

2016-0137

XIV.Castagnotto, A., Hu, S., & Lohmann, B. (2018). An Approach for GlobalizedH2-Optima lModel Reduction.

IFAC-PapersOnLine.https://doi.org/10.1016/j.ifacol.2018.03.034

XV.Castagnotto, A., Panzer, H. K. F., & Lohmann, B. (2017). Fast H 2-optimalmodel order reduction exploiting the local nature of Krylov-subspace methods.2016 European Control Conference,ECC 2016.

https://doi.org/10.1109/ECC.2016.7810578

XVI.Chahlaoui, Younès. (2011). Two efficient SVD/Krylov algorithms for model order reduction of large scale systems.Electronic Transactions on Numerical Analysis.

XVII.Chahlaoui, Younes, & Dooren, P. Van. (2002). A collection of Benchmark examples for model reduction of linear time invariant dynamical systems.SLICOT Working Notes. https://doi.org/10.1007/3-540-27909-1_24

XVIII.Chahlaoui, Younes, & Van Dooren, P. (2005).Benchmark Examples for Model Reduction of Linear Time

-Invariant Dynamical Systems.https://doi.org/10.1007/3-540-27909-1_24

XIX.Chidambara, M. R. (1967). Further Remarks on Simplifying Linear Dynamic Systems.IEEET ransactions

on Automatic Control.https://doi.org/10.1109/TAC.1967.1098557

XX.Davison, E. J. (1966). A method for simplifying linear dynamic systems.IEEE Transactionson AutomaticControl.https://doi.org/10.1109/TAC.1966.1098264

XXI.Dax, A. (2013). From Eigenvalues to Singular Values: A Review.Advances in Pure Mathematics

. https://doi.org/10.4236/apm.2013.39a2002

XXII.Ferranti, F., Deschrijver, D., Knockaert, L., & Dhaene, T. (2011). Data-driven parameterized model order reduction using z-domain multivariate orthonormal vector fitting technique.Lecture Notes in Electrical

Engineering. https://doi.org/10.1007/978-94-007-0089-5_7

XXIII.Grimme, E. (1997). Krylov projection mezhods for model reduction.Vasa.

XXIV.Gugercin, S., Antoulas, A. C., & Beattie, C. (2008). $\mathcal{H}_2$Model Reduction for Large-

Scale Linear Dynamical Systems.SIAM Journal on Matrix Analysis and Applications. https://doi.org/10.1137/060666123

XXV.Gugercin, Serkan, & Antoulas, A. C. (2006). Model reduction of large-scalesystems by least squares.

Linear Algebra and Its Applications.https://doi.org/10.1016/j.laa.2004.12.022

XXVI.Korvink, J. G., & Rudnyi, E. B. (2005). Oberwolfach Benchmark Collection.En Dimension Reduction of Large-Scale Systems. https://doi.org/10.1007/3-540-27909-1_11

XXVII.Litz, L. (1979). Ordnungsreduktion linearer zustandsraummodelle durch beibehaltung der dominanten eigenbewegungen.At-Automatisierungstechnik.https://doi.org/10.1524/auto.1979.27.112.80

XXVIII.Model Order Reduction: Theory, Research Aspects and Applications. (2008).https://doi.org/10.1007/978

-3-540-78841-6

XXIX.Mohamed, K. S. (2018). Machine learning for model order reduction. En Machine Learning for Model Order Reduction. https://doi.org/10.1007/978-3-319-75714-8

XXX.Moore, B. C. (1981). Principal Component Analysis in Linear Systems: Controllability, Observability, and Model Reduction.IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.1981.1102568

XXXI.Pinnau, R. (2008). Model Reduction via Proper Orthogonal Decomposition.https://doi.org/10.1007/978

-3-540-78841-6_5

XXXII.Samba riya, D. K., & Sharma, O. (2016). Routh Approximation: An Approach of Model Order Reduction in SISO and MIMO Systems.Indonesian Journal of Electrical Engineering and Computer Science.

https://doi.org/10.11591/ijeecs.v2.i3.pp486-500

XXXIII.Schilders, W. (2008). Introduction to Model Order Reduction.https://doi.org/10.1007/978

-3-540-78841-6_1

XXXIV.Segalman, D. J. (2007). Model Reduction of Systems With Localized Nonlinearities.Journal of Computational and Nonlinear Dynamics.https://doi.org/10.1115/1.2727495

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AIAA Journal. https://doi.org/10.2514/2.1570

XXXIX.Yogarathinam, A., Kaur, J., & Chaudhuri, N. R. (2019). A New H-IRKA Approach for Model Reduct

ion with Explicit Modal Preservation:Application on Grids with Renewable Penetration.IEEE Transactions on

Control Systems Technology. https://doi.org/10.1109/TCST.2017.2779104

Face Recognition using Machine Learning Algorithms

Authors:

Amirhosein Dastgiri, Pouria Jafarinamin, Sami Kamarbaste, Mahdi Gholizade

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00017

Abstract:

Face recognition is one of the most challenging issues in analyzing images. Face recognition technology is one of the fastest technologies that do the identification process without having the slightest disturbance to the person. Face recognition today has found many applications that can be used for faces recognition, military issues, legal issues, image retrieval, identification of protagonists, video images, and so on. Face recognition is considered as one of the smart computer analysis scenarios. There are always improvements in this area that make these improvements accurate in identifying facial expressions. Accordingly, the present paper seeks to study facial recognition using machine learning algorithms. Time information has useful features for recognizing facial expressions. However, a lot of effort is needed to manually design features. In this paper, to reduce these factors, a machine learning technique is selected, which is an automated tool that extracts useful features from raw data. Using machine learning methods can be considered as a more effective way. In this paper, a method based on machine learning algorithms for face recognition is presented. The proposed algorithms perform the unknown image by comparing it with known and stored images in databases and also obtaining information from a person familiar with the process of face recognition. The results show that the proposed method has high accuracy compared to other previous methods.

Keywords:

face recognition, machine learning al gorithms,image process,

Refference:

I.E. García Amaro, M. A. Nuño-Maganda and M. Morales-Sandoval, “Evaluationof machine learning techniques for face detection and recognition,”CONIELECOMP 2012, 22nd International Conference on Electrical

Communications and Computers, Cholula, Puebla, pp. 213-218, 2012.

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-823, 2015.

III.G. Zeng, J. Zhou, X. Jia, W. Xie and L. Shen, “Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition,”2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018),Xi’an, pp. 423-430, 2018.

IV.Kong, X., Gong, S., Su, L., Howard, N., & Kong, Y. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods. EBioMedicine,27, 94-102, 2017.

V.Kortylewski, B. Egger and A. Schneider. Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems.Computer Vision and Pattern Recognition (CVPR), 2018.

VI.M.Saraswathi and Dr. S. Sivakumari, “Evaluation of PCA and LDA techniques for Face recognition using ORL face database”, (IJCSIT) International Journal of Computer Science and Information Technologies,

Volume 6 (1), pp. 810-813,2015.

VII.P. Jonathon Phillips, Amy N. Yates, Ying Hu, Carina A. Hahn, Eilidh Noyes,Kelsey Jackson, Jacqueline G. Cavazos, Géraldine Jeckeln, Rajeev Ranjan,Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa, David White, and Alice J. O’Toole. Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. PNAS June 12, 115 (24) 6171-6176, 2018.

VIII.Praahas Amin, Prithvi, Roshni Fernandes, Shivaraj S B,Sneha P. Machine Learning based Face Recognition System for Virtual Assistant. International Research Journal of Engineering and Technology(IRJET). Volume: 05 Issue: 05,pp. 33-55, 2015.

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g faces across pose and age,” arXiv preprintarXiv:1710.08092, 2017.

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ume 5, No. 2, pp.361-363, 2012.

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-58, 2018.

XIII.Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: Adataset and benchmark for large-

scale face recognition,” inEuropeanConference on Computer Vision. Springer, pp. 87–102, 2016.

XIV.Y. Li, B. Sun, T. Wu, and Y. Wang, “Face detection with end-to-endintegration of a convnet and a 3d model,”European ConferenceonComputer Vision (ECCV), 2016.

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shops on Automatic Face and Gesture Recognition, pp. 1–8, Shanghai, 2013.

Human Gait Recognition using Neural Network Multi-Layer Perceptron

Authors:

Faisel Ghazi Mohammed, Waleed khaled Eesee

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00018

Abstract:

The wide separation of using camera video surveillance and increasing the depending on these video to identify human identity. One of trending method to achieve this task is human gait recognition. In this paper, human gait recognized using three features include gait energy image (GEI) human body height and width. Features are easy to extract and archived high correlation to target class. Neural network Multi-Layer Perceptron used to build a recognition model to achieve 90 % accuracy.

Keywords:

human gait recognition,gait energy image,Neural network Multi-Layer Perceptron,

Refference:

I.Arora, P., & Srivastava, S. (2015). Gait Recognition using Gait Energy Image. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN),4(3), 316–322.

II.Dolatabadi, E., Mansfield, A., Patterson, K. K., Taati, B., &Mihailidis, A.(2017). Mixture-Model Clustering of Pathological Gait Patterns.IEEE Journal of Biomedical and Health Informatics,21(5), 1297–1305.https://doi.org/10.1109/JBHI.2016.2633000

III.El-Alfy, H., Mitsugami, I., & Yagi, Y. (2018). Gait Recognition Based on Normal Distance Maps.IEEE Transactions on Cybernetics,48(5), 1526–1539. https://doi.org/10.1109/TCYB.2017.2705799

IV.Kumar, H. P. M., &Nagendraswamy, H. S. (2013). Gait recognition: An approach based on interval valued features.2013 International Conference on Computer Communication and Informatics, ICCCI 2013

, 0–5.https://doi.org/10.1109/ICCCI.2013.6466243

V.Li, X., & Chen, Y. (2013).Gait Recognition Based on Structural Gait Energy Image.1, 121–126.

VI.Mohualdeen, M., & Baker, M. (2018). Gait recognition based on silhouettes sequences and neural networks for human identification.Indonesian Journal of Electrical Engineering and Informatics,6(1), 110–117. https://doi.org/10.11591/ijeei.v6i1.303

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101–106.https://doi.org/10.1109/spin.2014.6776930

X.Shirke, S., Pawar, S. S., & Shah, K. (2014). Literature review: Model free human gait recognition.Proceedings – 2014 4th International Conference on Communication Systems and Network Technologies, CSNT 2014, 891–895. https://doi.org/10.1109/CSNT.2014.252

XI.Sokolova, A., &Konushin, A. (2017). Gait Recognition Based on Convolutional Neural Networks.

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Engineering Applications of Artificial Intelligence,23(8),1237–1246.https://doi.org/10.1016/j.engappai.2010.07.004

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Journal of Physics A: Mathematical and Theoretical,44(8), 56. https://doi.org/10.1088/1751-8113/44/8/085201

Control System Based Modeling and Simulation of Cardiac Muscle With Optimization Using Performance Index

Authors:

Soumyendu Bhattacharjee, Aishwarya Banerjee, Biswarup Neogi

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00019

Abstract:

Because of the prolong use of the system, the performance (Output parameters of the system) can change and output of the system may start deteriorating from the desired value. If the performance of a system, based on control theory is not up to the expectations as per the desired specification, then some changes in the system are required to obtain the desired performance. The control system can be represented with a set of mathematical equations called system model which are used to answer questions via analysis and simulation. A model is a precise representation of a system dynamics which are the arrangement of physical elements and that physical elements are analyzed to make governing equations. Cardiovascular muscle senses the force generated due to the contraction and expansion of muscle wall .This can be well understood by the analytical approach of the transfer function generated by using a mechanical model of force displacement analogy. The efficiency of the work also lies in the measure of the movement of cardiovascular factors in the system. The mass of heart muscle varies with different age groups both for male and female. This work is based on the glimpses of changing transfer function with different age groups due to the variation of mass of heart muscle. Viscous drag has also been calculated considering different values of damping coefficient for a particular value of mass. For attending the optimality in the performance of the system one designed controller is used along with the derived transfer function in cascade arrangement. To get more stability of the system, damping coefficient is chosen for the system model considering less settling time and steady state error. The open loop transfer function in the forward path is simply the product of derived transfer function and designed transfer function of controller. The design emphasizes on the optimality in operation of the control process which has been determined by the performance index (PI) of the total process using integral square method.

Keywords:

Transfer Function, Steady State Error, Performance Index,Integral Square Error,

Refference:

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Hydrological Modeling of Upper Indus Basin Using HEC -HMS.

Authors:

Muhammad Ismail Khan, Saqib Shah, Jowhar Hayat, Faisal Hayat Khan, Mehre Munir

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00020

Abstract:

One of the most frequently used mechanism to estimate the basin’s hydrological response due to precipitation is Hydrological modeling. In the paper to pretend the rainfall-runoff processing Upper Indus Basin, Pakistan the HEC-HMS model is used. The development of most of hydrological methodologies is done before 1990. But the advancement in GIS technologies and the management of spatial data has assisted repetitious processing tasks for high resolution datasets improving efficiency and spatial variations. Although large amount of data are often required but the preprocessors in GIS helps hydrological modeling through the semi-automated spatial analysis. We used HEC-HMS for hydrological modeling of Upper Indus Basin. To estimate the watershed discharge over time, the HEC-HMS routes a runoff hydrograph through the stream network. It produces the event based storm hydrograph which can be used in urban drainage design, water management, reservoir design, land use impact studies, flood forecasting and floodplain mapping To cover seven years (2005-2011) data Rainfall-runoff simulation is done using random rainstorm measures of these years. Selected events are used for model calibration and the remaining are used for model validation. The statistical tests of error function like root mean square error (RMSE), Nash Sutcliffe efficiency (NASH), mean biased efficiency (MBE), R-square between the observed and simulated data are conducted for calibration of the model. The results show values of R-square of 0.855, and the values of RMSE between the observed and simulated data were indicated as 7.83 and the value of NASH is 0.91 similarly, the value of MBE is 1.5 between the observed and simulated discharge for calibration of the model, respectively. The results indicate values of R-Square is 0.856 and value of RMSE is 9.54 and value of NASH is 1.5 and, the value of MBE is 1.5 among the experimental and simulated discharge for validation of the model, respectively. For validation and calibration of model select sub-basin of Upper Indus Basin, precipitation and discharge data from 2005 to 2011 is used in modeling.

Keywords:

Hydrological m.odeli.ng,Rainfall-runoff simulation,Upper Indus Basin,model calibration,model validation,

Refference:

I. Ateeq-ur Rauf and Abdul Razzaq Ghumman (June 2018) Impact Assessment of Rainfall-Runoff Simulations on the Flow Duration Curve of the Upper Indus River-A comparison of Data-Driven and Hydrologic Models.
II. Abushandi, Eyad & Merkel, Broder (2014). Modelling Rainfall Runoff Relations Using HEC-HMS and IHACRES for a Single Rain Event in an Arid Region of Jordan.
III. Brooks, K.N., N.B., 2003. Hydrology and the Management of Watersheds. 3rd Edition, Wiley-Blackwell, Ames, ISBN-10: 0813829852 pp: 574.
IV. Cunderlik, Juraj & Simonovic, Slobodan. (2004). Inverse Modeling of Water Resources Risk and Vulnerability to Changing Climatic Conditions.
V. D.R Archer, H.J.Fowler(August 2004) Hydrology and Earth System Sciences
VI. France, R.L., 2002. Handbook of Water Sensitive Planning and Design. 1st Edition, Lewis Publishers, Boca Raton, ISBN-10: 1566705622, pp: 699.
VII. Hassan A.K.M Bhuivan, Heather McNairn, Jarett Powers and Amine Merzouki (September 2017)Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 SoilMoisture in Initializing the Model.
VIII. KimhuySok and Chantha OeurngApplication of HEC-HMS Model to Assess Streamflow and water Resources Availability in Stung Sangker Catchment of Mekong’ Tonle Sap Lake Basin in Cambodia.
IX. MeilingWang, LeiZhang, Thelma D Baddoo(September 2016) Hydrological Modeling in a Semi-Arid Region Using HEC-HMS.
X. Mahmood, R.; Jia, S.; Babel, M.S. Potential Impacts of Climate Change on Water Resources in the Kunhar River Basin, Pakistan. Water 2016, 8, 23
XI. Mukhopadhyay, Biswajit & Khan, Asif. (2014). A quantitative assessment of the genetic sources of the hydrologic flow regimes in Upper Indus Basin and its significance in a changing climate. Journal of Hydrology. 509. 549–572