Journal Vol – 14 No -3, June 2019

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:

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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

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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.

II.F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815

-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.

X.Sanjeev Kumar and Harpreet Kaur, “Face Recognition Techniques:Classification and Comparisons”, International Journal of Information Technology and Knowledge Management July-December, Vol

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

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XII.X. Han and Q. Du (2018). Research on face recognition based on deep learning.2018 Sixth International Conference on Digital Information, Networking, and Wireless Communications (DINWC), Beirut, pp. 53

-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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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:

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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

VII.Nixon, M. S. (2009).Model-based gait recognition.

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IX.Shaikh, S. H., Saeed, K., &Chaki, N. (2014). Gait recognition using partial silhouette-based approach.

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.

ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,

XLII-2/W4(May 2017), 207–212. https://doi.org/10.5194/isprs-archives-XLII-2-W4-207-2017

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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

To Negate The Power Losses In Grid System By Selecting Case Study Of Malakand Divison

Authors:

Hamza Mustajab, Muhammad Aamir Aman, Fazal Wahab Karam, Muhammad Mustajab Khan

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00021

Abstract:

Electricity frame has constantly changed over the past decades but there has not been quite improvement on increasing the production capability. Keeping in view of the increased movement of population from the rural to urban and ever increasing load of the industrial and agricultural areas the need for electrical power has sky rocketed. There is not only problem with the production side but also with the transmission to the consumer end which makes the problem even more serious and needs to be addressed vigilantly. The load demand of Chakdara and other grid station was calculated as 550 MV. The available source for Mardan to Chakdara could cater load = 270MW load shered and Dargai, Golan gol and Bahrain Powerhouse equal to 60MW. Load demand to be covered =550-(270+50) = 220MVA. By energizing 220 kV grid station Chakdara the load shered = 240 MW, load shedding vanished. This research paper brings us to this conclusion that the system is facing issues like identification of problems, proper management, and inability of the government to take appropriate action, lack of investing parties and also highlights and point out the areas that need improvement.

Keywords:

Power Losse, Grid System, Power Hous, Renewable Ener,Piezoelectric,Electrical Energy,

Refference:

I.B. Awan and Z. A. Khan, “Recent progress in renewable energy– remedy of energy crisis in pakistan,” Renewable and Sustainable Energy Reviews, vol. 33,pp. 236–253, 2014

II.Development of a Digital Model for Oman Electrical Transmission Main Grid Omar H. Abdalla, Senior Member, IEEE, Hilal Al-Hadi, and Hisham Al-Riyami Member, IEEE

III.Economic Survey of Pakistan, “Economic Survey of Pakistan”, Ministry of Finance, Government of Islamic Republic Pakistan

IV.G. P. Kilanc and I. Or, “A decision support tool for the analysis of pricing, investment and regulatory processes in a decentralized electricity market”.M. N.Fatemi, “Solar ready roof design for high-performing solar installation in dhaka:Potentials and strategies,” Energy Technology (ICDRET’lZ), 2012.

V.J. Matesova R. Cull, and M. Shirley, “Ownership and the Temptation to Loot:Evidence from Privatized Firms in the Czech Republic”, Journal of Comparative Economics, volume 30, no. one, (2002).

VI.Muhammad Aamir Aman*1, Muhammad Zulqarnain Abbasi2, Murad Ali3, Akhtar Khan4 Department of Electrical Engineering, Iqra National University,Pakistan Email : aamiraman@inu.edu.pk. “To Negate the influences of Un-deterministic Dispersed Generation on Interconnection to the Distributed System

considering Power Losses of the system” J.Mech.Cont.& Math. Sci., Vol.-13,No.-3, July-August (2018) Pages 117-132.

VII.N. Haq and K. Hussain, “Energy crisis in Pakistan,” IPRI Fact file, 2008.

VIII.P. Kundur, Power System Stability and Control, McGraw-Hill, Inc., 1994.S. M.Kaplan, F. Sissine, and T. Net, Smart Grid: Modernizing electric power transmission and distribution; Energy independence, Storage and security;Energy independence and security act of 2007 (EISA); Improving electrical grid efficiency communication, reliability, and resiliency; integrating new and renewable energy sources. The Capitol Net Inc, 2009.

A comparison of Seismic Behavior of Reinforced Concrete Special Moment Resisting Beam-Column Joints vs. Weak Beam Column Joints Using Seismostruct

Authors:

Usama Ali, Naveed Ahmad, Yaseen Mahmood, Hamza Mustafa, Mehre Munir

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00022

Abstract:

This paper focusses on a numerical model of a Reinforced Concrete Special Moment Resisting frame beam-column joint. The software chosen for this purpose is Seismostruct. The experimental models chosen for this purpose; referred to as Model-1 (Code Compliant), and Model-2 (Non-code complaint), are two-story twobay frames based on experimental model scaled at 1:3, and tested in University of Engineering and Technology Peshawar. Link elements that follow a distinct predefined constitutive law based on Kim (2012) and Takeda (1970) have been introduced at beam-column joint interface to simulate the failure mechanism in joint panel. Roof displacements, base shear and local damage mechanism of the numerical analysis are compared with the experimental results for the verification of the calibrated numerical models. The results showed close similarity of experimental data with numerical results with a percentage error of less than 5 percent and showed a very close resemblance of local damage mechanism. The numerical models obtained is further used to perform the seismic evaluation of code compliant and code deficient models and results like drift profile and inter-story drift ratio are calculated. Furthermore the response of both models against DBE and MCE is also determined and results shows that beam-column joints in code compliant as well as code deficient models behave in an inelastic manner and hence considering a beamcolumn joint element as a rigid panel in MCE analysis is not a valid assumption. In addition to shear cracking, bar slip mechanism was also generated in code deficient model which caused in an increased story drift, which can be prevented by adequate design of beam-column joint assemblage providing confinement for the concrete strut mechanism and proper bond anchorage to avoid bar slip mechanism.

Keywords:

Weak beam-column joint, exterior joint,Seismostruct, local mechanism,Drift ratio,Seismic loading,Frame,Special moment resisting frame,

Refference:

I.ACI-ASCE Committee 352. (1988). “Recommendations for Design of Beam-Column Connections in Monolithic Reinforced Concrete Structures.”ACI Structural Journal, 85(6), 675–696.

II. ACI Committee 318. (2008).Building Code Requirements for Structural Concrete ( ACI 318-08 )

.American Concrete Institute.

III. Altoontash, A. (2004). “Simulation and damage models for performance assessment of reinforced concrete beam-column joints.” (August), 232.

IV. Bayhan, B., Moehle, J. P., Yavari, S., Elwood, K. J., Lin, S. H., Wu, C. L.,and Hwang, S. J. (2015). “Seismic response of a concrete frame with weak beam-column joints.”Earthquake Spectra , 31(1), 293–315.

V. Biddah, A., and Ghobarah, A. (1999). “Modelling of shear deformation and bond slip in reinforced concrete joints.” (April).

VI. Broglio, S. (2009). “Critical Investigation About Bond-Slip in Beam-Column Joint Macro-Model.”

VII. Celik, O. C., and Ellingwood, B. R. (2008). “Modeling beam-column joints in fragility assessment of gravity load designed reinforced concrete frames.”Journal of Earthquake Engineering, 12(3), 357–381.

VIII. Favvata, M. J., Izzuddin, B. A., and Karayannis, C. G. (2008). “Modelling exterior beam – column joints for seismic analysis of RC frame structures ¶.”(July), 1527–1548.

IX. Kaliluthin, A. K., Kothandaraman, S., and Ahamed, T. S. S. (2014). “A Review on Behavior of Reinforced Concrete Beam-Column Joint.” 3(4), 11299–11312.

X. Kam, W. Y., Pampanin, S., and Elwood, K. (2011). “Seismic performance of reinforced concrete buildings in the 22 February Christchurch (Lyttelton)earthquake.”Bulletin of the New Zealand Society for Earthquake

Engineering, 44(4), 239–278.

XI. Kim, J., and LaFave, J. M. (2012). “A simplified approach to joint shear behavior prediction of RC beam-column connections.”Earthquake Spectra, 28(3), 1071–1096.

XII. Lehman, D., Stanton, J., Anderson, M., Alire, D., and Walker, S. (2004).“Seismic performance of older beam-column joints.”Proc. 13th World Conf. Earthquake Engineering, (1464), 1464.

XIII. Lima, C., Martinelli, E., and Faella, C. (2012). “Capacity models for shear strength of exterior joints in RC frames : state-of-the-art and synoptic examination.” 967–983.

XIV. Lowes, L. N., Altoontash, A., and Mitra, N. (2005). “Closure to ‘Modeling Reinforced-Concrete Beam-Column Joints Subjected to Cyclic Loading’ by Laura N. Lowes and Arash Altoontash.”

Journal of Structural Engineering,131(6), 993–994.

XV. Lowes, L. N., Mitra, N., and Altoontash, A. (2004). “A Beam-Column Joint Model for Simulating the Earthquake Response of Reinforced Concrete Frames A Beam-Column Joint Model for Simulating the Earthquake Response of Reinforced Concrete Frames, PEER Report 2003/10.” (August).

XVI. Maria J. Favvata1,∗,†,‡, Bassam A. Izzuddin2, § and Chris G. Karayannis. (2008). “Modelling exterior beam–column joints for seismic analysis ofRC frame structures.” (July), 1527–1548.

XVII. Masi, A., Santarsiero, G., Lignola, G. P., and Verderame, G. M. (2013). “Study of the seismic behavior of external RC beam-column joints through experimental tests and numerical simulations.”Engineering Structures,

Elsevier Ltd, 52, 207–219.

XVIII. Mitra, N., and Lowes, L. N. (2007). “Evaluation, Calibration, and Verification of a Reinforced Concrete Beam–Column Joint Model.”Journal of Structural Engineering, 133(1), 105–120.

XIX. Monti, G., and Spacone, E. (2000). “Reinforced Concrete Fiber Beam Element with Bond-Slip.”

Journal of Structural Engineering, 126(10), 1187.

XX. Pampanin, S., Calvi, G., and Moratti, M. (2002). “Seismic behavior of RC beam-column joints designed for gravity only.” 726(June 2017), 1–10.

XXI. Pampanin, S., Magenes, G., and Carr, A. (2003). “Modeling of shear hinge mechanism in poorly detailed R.C beam-column joints.”University of Canterbury, (January), 5–8.

XXII. Park, R. (1996). “Explicit Incorporation of Element and Structure Overstrength in the Design Process (Paper No. 2130).”Eleventh World Conference on Earthquake Engineering.

XXIII. Park, S., and Mosalam, K. M. (2013). “Simulation of reinforced concrete frames with nonductile beam-column joints.”Earthquake Spectra, 29(1),233–257.

An application of Z-N Tuning method with PID controller to optimize the system performance of cardiac muscle modeland it’s practical implementation using OP AMP

Authors:

Aishwarya Banerjee, Soumyendu Bhattacharjee, Biswarup Neogi

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00023

Abstract:

The control system consisting different components mainly regulates, manages as well as senses the behavior of another system and gives the desired output. The properly tuned controllers are used widely in different industrial applications. This paper concentrates on the work based on the PID tuning by applying Z-N rule to get the optimized performance of control system based modeling of human cardiac muscle. The another aim of this paper is the addition of pole at origin in plant along with PID controller to increase the type of open loop transfer function of plant as steady state error for any input test signal reduces with the increment of type of a system. Due to addition of pole, transient part may get deteriorated as order increases with the increment of type of plant. To balance this fact, a proper PID controller has been introduced and designed by applying Z-N tuning rule to get an output with better dynamic and static performance of the total system. At the end of this work an OP-Amp based practical implementation of PID controller has been done to calculate the controller parameters in terms of resistance and capacitance for real life application.

Keywords:

PID controller, Addition of pole,Z-N tuning rule,Op-Amp.,

Refference:

I.Ang, K.H., Chong, G.C.Y., and Li, Y. (2005). PID control system analysis, design, and technology, IEEE Trans Control Systems Tech, 13(4), pp.559-576.

II.Bouallegue S, Haggege J, Ayadi M, Benrejeb M. “ID-type fuzzy logic controller tuning based on particle swarm optimization”; Vol.25 (3), pp. 484–93, 2012.

III.http://www.cds.caltech.edu/~murray/courses/cds101/fa02/caltech/astr om-ch6.pdf.

IV. K. Astrom and T. Hagglund, “The future of PID control,” Control Engineering Practice, vol. no. 11, pp. 1163 – 1175.

V. Liang Taonian, Chen Jianjun, Lei Chuang. “Algorithm of robust stability region for interval plant with time delay using fractional order PIkDl controller”. J Commun Nonlinear Sci Numer Simul Vol.17(2), pp.979–91, 2012.

VI.Luo Ying, Chen Yang Quan. “Stabilizing and robust fractional order PI controller synthesis for first order plus time delay systems”. J Automatica; Vol. 48(9): pp. 2159–67, 2012.

VII.M.J. Mahmoodabadi, H. Jahanshahi, “Multi-objective optimized fuzzy-PID controllers for Fourthorder nonlinear systems”,Engineering Science and Technology, an International, Journal, Vol.19 pp.1084–1098, 2016.

VIII.M. Sahib, “A novel optimal PID plus second order derivative controller for AVR system”, Eng. Sci. Technol. Int. J. Vol.18, pp. 194–206, 2015.

IX.Norbert Hohenbichler. “All stabilizing PID controllers for time delay systems”; Vol. 45(11): pp.2678–84, 2009.

X.Ogata, K., Third ed, Modern Control Engineering, Prentice-Hall Inc,1997.

XI.OzbayHitay, Bonnet Catherine, Fioravanti Andre Ricardo. “PID controller design for fractional-order systems with time delays”.SystContr Lett; Vol.61(1): pp.18–23, 2012.

XII.Shabib G. “Implementation of a discrete fuzzy PID excitation controller for power system damping”. Ain Shams Eng J; Vol. 3(2): pp.123–31, 2012.

XIII.Suji Prasad SJ, Varghese Susan, Balakrishnan PA. “Particle swarm optimized pid controller for second order time delayed system”. Int JSoft ComputEng; Vol. 1(2): pp. 2231–307, 2012.

XIV.Rama Reddy D, Sailaja M. “Stability region analysis of PID and series leading correction PID controllers for the time delay systems”.Int J EngSciTechnol, Vol. 4(07), pp. 1-8, 2012.

XV.Zhao YM, Xie WF, Tu XW. “Performance-based parameter tuning method of model-driven PID control systems”. ISA Trans, Vol.51(3): pp.393–9, 2012-15

XVI.Ziegler, J. G. and N. B. Nichols “Optimum settings for automatic controllers.” Transaction for the ASME, Vol.64, pp. 759–768,1942.

A Deliberate and Comprehensive Derivation from an Equation of the Special Theory of Relativity

Authors:

Prasenjit Debnath

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00024

Abstract:

A remarkable year was 1905 in physics and astronomy when Einstein first proposed the special theory of relativity. This theory is the foundation of modern astronomy and astrophysics. This theory was also the foundation of the theory of general relativity proposed by Einstein in 1915 to incorporate gravity into the system. Thus, the special theory of relativity already became of supreme importance in physics since the beginning of nineteenth century and it continues to be the right from the word go theory in modern physics. An attempt is made in this paper for a deliberate and comprehensive derivation from an equation of the special theory of relativity. The derivation is made with an aim to look deep inside of the theory of special relativity to conclude a comprehensive conclusion. Also some conceptual modifications are arranged to justify the conclusion. The physical time, mass and velocity are related in some equations in this paper.

Keywords:

The special theory of relativit, the general theory of relativit,the physical time,mass and velocity,the velocity of light,relativistic mass,

Refference:

I.Stephen Hawking, “The Beginning of Time”, A Lecture.

II.Roger Penrose, “Cycles of Time”, Vintage Books, London, pp. 50-56.

III.Stephen Hawking, “A Briefer History of Time”, Bantam Books, London, pp.1-49.

IV.Stephen Hawking, “Black holes and Baby Universes and other essays”, Bantam Press, London 2013, ISBN 978-0-553-40663-4

V.Stephen Hawking, “The Grand Design”, Bantam Books, London 2011

VI.Stephen Hawking, “A Brief History of Time”, Bantam Books, London 2011, pp. 156-157. ISBN-978-0-553-10953-5

VII.Stephen Hawking, “The Universe in a Nutshell”, Bantam Press, London 2013, pp. 58-61, 63, 82-85, 90-94, 99, 196. ISBN 0-553-80202-X

VIII.Stephen Hawking, “A stubbornly persistent illusion-The essential scientific works of Albert Einstein”, Running Press Book Publishers, Philadelphia, London 2011.

IX.Stephen Hawking, “Stephen Hawking’s Universe: Strange Stuff Explained”, PBS site on imaginary time.

Factors Affecting the Performance of Construction Projects in Pakistan

Authors:

Muhammad Iqbal, Imtiaz Khan, Fawad Ahmad, Muhammad Zeeshan Ahad, Mehre Munir

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00025

Abstract:

There is a French dictum “when the construction industry prospers everything prospers”. Construction, a term that encompasses activities related to the creation of physical infrastructure and related activities, plays a crucial role in the economy of any country with estimated share of 40-60 % in gross fixed capital formation and having linkage of more than 60 other associated industries. Today, construction is the second largest sector in Pakistan’s economy after agriculture. Roughly 30-35% of employment is directly or indirectly affiliated with the construction sector. In Pakistan, a Construction Project can be categorized as high risk as it is very complex and involves a variety of stakeholders looking after their own interests. In order to make sure that the projects are completed within the key measures of budgeted cost, allocated time and required quality, identification of causes affecting the project performance is very much necessary so that stakeholders can take proactive steps to avoid such situations and manage effectively and systematically to achieve the project performance objectives of time, cost and quality. The purpose of this Paper is to investigate the major obstacles and constraints in the performance of construction projects in Pakistan. In this paper a local construction project case study and facts which I observed during my 16+ years of experience in dealing with construction projects are taken into account to document the bottlenecks. The case study is the construction of Earth Dam Project in Tribal region of KPK, where I worked as a Resident Engineer. The project performance has been suffered adversely equally by roles played by the design consultant, the Employer, the supervisory consultant, the Contractor and country political situation and regional security. Finally, main recommendations with discussions are presented that will help to overcome the related obstacles and hindrances in project performance in construction industry of Pakistan. Similar methodology is adopted in Document of the World Bank Discussion Paper Series: Technical Note:9 LOCAL CASE STUDIES November 2007, where case studies of several past infrastructure projects in different sectors including roads, airport, motoway and irrigation were reviewed to document the bottlenecks which occurred during the various processes involved in the life cycle of infrastructure projects. Identifying such processes allows a better understanding of the capacity constraints in planning, designing, programming, procurement, contract administration, financing and budgeting, execution and other stages in a project cycle.

Keywords:

Construction Project, the project performance objectives, planning,designing programming,infrastructure projects,

Refference:

I.Assessment of Pakistan Construction Industry-Current Performance and the Way Forward”. Rizwan U.farooquui & Syed M.Ahmed* Dept of Construction Management, Florida International University, USA, Sarosh H.Lodhi, Professor/Chairman, NED Karachi.

II.An Overview Of Comparison Between Constructions Contracts In Malaysia”. Zarabizan bin Zakaria, Syuhaida binti, Ismail, Aminah binti Md Yusof, University Technology Malaysia.

III.Contract Administration and Management in Pakistan”. Engr. Iftikhar Ul Haq.

IV.CPEC & Pakistan Economy: An Appraisal”. Dr, Ishrat Hussain, former Governor State Bank of Pakistan.

V.Effects of Lowest Bidding Bid Awarding System in Public Sector Construction Projects in Pakistan”. Tariq Hussain Khan α & Abdul Qadir Khan.

VI.Economic Impact of Terrorism on Construction Industry of Pakistan”.Rehan Masood, department of civil engineering, Lahore, Rizwan Ul Haq Farooqui, department of civil engineering, NED Karachi.

VII.Fidic’s Red Book, The Conditions Of Contract For Works Of Civil Engineering Construction” Published In 1987, the 4th Edition

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IX.Study on Delaying Factors Affecting the Success of Construction Projects in Pakistan”. Sameeda Semab, Anwar Khan and Dr.Fayaz Ali Shah,journal of administrative & business Studies, Peshawar July-Dec 2017.

X.TRIBUNE, 24 MARCH, 2019”. Hasnaat Malik, Published: July 19, 2018

XI.Unethical Practices in Pakistani Construction Industry”. Dr. Tahir Nawaz,Associate Professor, Amjad Ali Ikram, Center for Advanced Studies in Engineering, Islamabad, Pakistan

Reliability Application Using Discrete Gamma Distribution

Authors:

Zainab Falih Hamza, Thaera N. Al-Ameer, Firas M. Al-Badran

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00026

Abstract:

This research dials with discrete counter-part of continuous gamma distribution. In fact, the statistical and reliability properties of this distribution are discuss and some interesting interrelationships. Furthermore, an estimation of the underlying parameter and reliability for this distribution are utilized using different samples sizes, that’s done through different simulation experiments by use (R3.5.1) program, the simulation outputs proved that the Maximum likelihood method gives small bias estimators. An application done at two Soap production machines belongs to the Vegetable Oil Plant. The results show that the second machine which follows DGD (3) is more reliable from the first one.

Keywords:

Discrete Gamma distribution, Maximum Likelihoo, Reliabilit function,

Refference:

I.Abouammoh, A. M. and Mashhour, A. F. (1981). A note on the unimodality of discrete distributions. Comm. Statistic, Ser. A, 10, 1345-1354.

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IV.Bergor, J.O (1985) ” Statistics of Decision Theory and Bayesian Analysis”, 2nd Edition, Springer Verging, New York.

V.Christian, P. Robert & Gorge Casella (2010) ” Introduction Monte Carlo Methods with R”, Springer.

VI.L .Gupta, P., Gupta, R. C. and Triatic, R. C. (1997). On the monotone property of discrete failure rate. J. of Statistical Planning and Inference, 65,255-268.

VII.Hirzebruch, F. (2008). Eulerian polynomials. Munster J. of Math, 1 , 9–14.

VIII.L .Johnson, N., Kits, S. and Kemp, A. (1992). Univariate Discrete Distribution. New York, Wiley.

IX.Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data. John Wiley and Sons, New York.

X.Law, A. and Kelton, W. (1991). Simulation Modeling and Analysis. McGraw-Hill, Inc. New York.

XI.Nakagawa, T. and Osaki, S. (1975). The discrete Weibull distributions. IEEE Trans. Reliable., 24, 300-301.

XII.Padgett, W. J. and Spurries, J. D. (1985). On discrete failure models. IEEE Trans. Reliable., 34, 253–256.

XIII.Salvia, A. A. and Bollinger, R. C. (1982). On discrete hazard function. IEEE transactions on Reliability, Vol. 31, No. 5, 458-459.

XIV.Stein, W. E. and Dater, R. (1984). A new discrete Weibull distribution. IEEE Trans. Reliable., 33, 196-107.

A Rice Quality Analysis with Image Classification using Sobel Filetr

Authors:

Nouf Saeed Alotaibi

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00027

Abstract:

In agricultural industries grain quality evaluation is very big challenge. Personal satisfaction control is extremely critical for sustenance business as a result then afterward harvesting, In view of caliber parameters nourishment results would arranged Also graded under different evaluations. Grain caliber assessment will be carried manually at it is relative, the long haul consuming, might a chance to be changing effects and expensive. With succeed these restrictions What's more deficiency image transforming systems will be the elective result can be utilized to grain personal satisfaction examination. Rice caliber may be nothing yet the blending of physical and more concoction aspects. Grain span and shape, chalkiness, whiteness, processing degree, greater part thickness and dampness content would A percentage physical qualities same time amylase content, gelatinization temperature Furthermore gel consistency are compound aspects of rice. The paper displays an answer for evaluating What's more assessment about rice grains on the foundation of grain size also state utilizing image transforming techniques. Particularly edge identification calculation may be used to figure out the area of limits for each grain. In this technology we discover those end- focuses about every grain and after utilizing caliper we could measure the length Furthermore broadness of rice. This technique obliges least time Also it will be low previously, expense.

Keywords:

Grain Evaluation, Image processing,MATLAB,Rice characteristics,Grain quality,

Refference:

I.Bhavesh B. Prajapati, Sachin Patel, “Classification of Indian Basmati Rice Using Digital Image Processing as per Indian Export Rules”, International Research Journal of computer Science Engineering and Applications, Vol.2 Issue 1, January 2013.

II.Bhupinder Verma, “Image Processing Techniques for Grading & Classification of Rice”, International Confer- ence on Computer and Communication Technology (ICCCT), pp. 220-223, 2012.

III.Chetana V. Maheshwari, Kavindra R. Jain, Chintan K. Modi, “Non- destructive Quality Analysis of Indian Gu- jarat-17 Oryza Sativa SSP Indica (Rice) Using Image Processing”, International Journal of Computer Engi- neering Science (IJCES) , Vol. 2 Issue 3, March 2012.

IV.G.Ajay, M.Suneel, K.Kiran Kumar, P.Siva Prasad, “Qual- ity Evaluation of Rice Grains Using Morphological Methods”, International Journal of Soft Computing and Engineering (IJSCE), pp. 35-37, Vol. 2, Issue 6, January

2013.

V.Harpreet Kaur, Baljit Singh, “Classification & Grading Rice using Multi- Class SVM ”, International Journal of Scientific and Research Publications (IJSRP), Vol. 3, Is- sue 4, April 2013.

VI.S. Kanchana, S. Lakshmi Bharati, M. Ilamran and K. Singaravadivel, “Physical Quality of Selected Rice Veri- ties”, World Journal of Agriculture Sciences, pp. 468-472, 2012.

VII.Shilpa J. Bhonsle, “Grain Quality Evaluation and Or- ganoleptic Analysis of Aromatic Rice Varieties of Goa, India”, Journal of Agricultural Science, pp. 99- 107, Vol. 2, No. 3; September 2010.

Mechanical behavior of concrete having springs at different zones

Authors:

Imtiaz khan, Intikhab Ahmad, Fawad Ahmed, Muhammad Zeeshan Ahad

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00028

Abstract:

Concrete durability, strength, manageability, and economy have made it the world's most generally utilized development material. The term Concrete alludes to a blend of totals, normally sand, and either (rock or smashed stone) coarse aggregates, held together by a binder of cementitious paste. Concrete has great compressive quality however almost no rigidity, subsequently constraining its utilization in development. . Thus it needs assistance in opposing pliable anxieties caused by twisting forces from connected burdens which would bring about breaking and at last disappointment. Due to the increasing demands of concrete strength & ductility in our modern day construction, increases the demand to address the importance of this concept once again. As many techniques/researches has been carried to improve the strength of concrete prior which has been successful to some extent but still increase in the strength of plain cement concrete considered to be a future challenge. In this research the focus on increasing the strength of concrete is by embedding steel springs phenomena in the different zones of concrete samples are studied. Tension and compression steel springs attached to base plate embedded in the concrete samples, and are tested for compressive and tensile strength of the concrete. Results shows that steel springs can be effective in the strength of concrete at specified zones. Hence it is recommended using steel springs in the concrete at effective zones to increase the strength of concrete.

Keywords:

Mechanical behavior of concrete,twisting forces, concrete strength & ductility,Tension and compression steel springs,

Refference:

I.Al-Oraimi, S.K. and Seibi, A.C., 1995. Mechanical characterisation and impact behaviour of concrete reinforced with natural fibres. Composite Structures, 32(1-4), pp.165-171.

II.Dixon, J.C., 2008. The shock absorber handbook. John Wiley & Sons.

III.David, R., 1960. Shock absorbing connections for building constructions. U.S. Patent 2,950,576.

IV.Damping Technologies for Tall Buildings: New Trends in Comfort and Safety

V.Hollaway, L.C. and Leeming, M. eds., 1999. Strengthening of reinforced concrete structures: Using externally-bonded FRP composites in structural and civil engineering. Elsevier.

VI.Kareem, A., Kijewski, T., & Tamura, Y. (1999). Mitigation of motions of tall buildings with specific examples of recent applications. Wind and Structures, 2(3), 201–251.

VII.Lai, S.S., Will, G.T. and Otani, S., 1984. Model for inelastic biaxial bending of concrete members. Journal of structural engineering, 110(11), pp.2563-2584.

VIII.Park, R.L., Park, R. and Paulay, T., 1975. Reinforced concrete structures. John Wiley & Sons.

IX.Shedbale, N. and Muley, P.V., 2017. Review on Viscoelastic Materials used in Viscoelastic Dampers.

X.Yau, C.Y. and Chan, S.L., 1994. Inelastic and stability analysis of flexibly connected steel frames by springs-in-series model. Journal of Structural Engineering, 120(10), pp.2803-2819.

Energy Efficient Backend Cluster Head and Fault Tolerance model for Wireless Sensor Networks

Authors:

Ch. Rambabu, V.V.K.D.V. Prasad, K. Satya Prasad

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00029

Abstract:

Fault Tolerance and energy consumption are the constraints in designing Wireless Sensor Network’s (WSN’s) while most of the Base station’s designed are based on energy usage whereas medical care systems need fault tolerant systems. In previous works, numerous clustering procedures are intended for network clustering. There doesn’t exist any recovery methods in the clustering procedures in case if CH node fails. Since the load is enlarged at the CH nodes, the energy depletion occurs more quickly which results in CH failure. Centered on the proposed technique, distance and residual energy are the two parameters that are considered to select BKCH. This BKCH which act as CH aggregates the data and send them back to the BS when there occurs a failure in the elected CHs. For solving the problems produced by faults of Cluster Heads, an Energy efficient Backup Cluster Head Fault tolerance (EE-BKCH-FT) is offered. We present two strategies to find the optimal position of BS’s: 1) low energy usage while transmission 2) low energy usage when a CH fails. Considering weight factor and the above conditions the position of a CH is decided as fault tolerance is highly recommended as to increase network lifetime which can sustain in any environmental conditions even if CH failures happen. Simulation results given by NS2 software are utilized to verify the efficiency of proposed method compared to the surviving approaches.

Keywords:

WSN, Routing,Fault Tolerance,Clustering, Backup cluster head, Node failure,Relay node,Coverage, Network lifetime,

Refference:

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The Natural Logarithmic Transformation and its Applications

Authors:

Emad Kuffi, Elaf Sabah Abbas

DOI NO:

https://doi.org/10.26782/jmcms.2019.06.00030

Abstract:

In this paper, a new integral transformation is proposed, where the transformation kernel is the natural logarithmic function 𝑙𝑛(∝ 𝑥), ∝> 0, 𝑥 > 0 , the transformation interval is the closed interval 􁉂 􀬵 ∝ , 1􁉃, and the range of its kernel 𝑙𝑛(∝ 𝑥) is the entire set of the real numbers (−∞ < 𝑙𝑛(∝ 𝑥) < ∞). The wide range of the kernel for the proposed transformation giving it a wider usage from the other transformations such as Laplace transformation which its kernel is (𝑒􀬿􀰈􀯫 )and its range includes the natural numbers only ((𝑒􀬿􀰈􀯫) > 0). The proposed integral transformation is called “the logarithmic integral transformation” based on the kernel of the transformation. Some properties and theorems are presented for this new transformation.

Keywords:

Boundary value, changing the measurement theorem,derivative transformation theorem, existence theorem, first transition theore,logarithmi integral transformation,

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