Archive

Abstract:

Abstract In this work, the all-optical plasmonic NOT logic gate was proposed using Insulator-Metal-Insulator (IMI) plasmonic waveguides Technology. The proposed all-optical NOT gate is simulated and realized using COMSOL Multiphysics 5.3a software. Recently, plasmonic technology has attracted high attention due to its wide applications in all-optical signal processing. Due to its high localization to metallic surfaces, surface plasmon (SP) may have huge applications in sub-wavelength to guide the optical signal in the waveguides which result in overcoming the diffraction limit problem in conventional optics. The proposed IMI structure is consists of dielectric waveguides plus metallic claddings, which guide the incident light strongly in the insulator region. Our design consists of symmetric nano-rings structures with two straight waveguides which based on IMI structure. The operation of all-optical NOT gate is realized by employing the constructive and destructive interface between the straight waveguides and the nano-rings structured waveguides. There are three ports in the proposed design, input, control and output ports. The activation of the control port is always ON. By changing the structure dimensions, the materials, the phase of the applied optical signal to the input and control ports, the optical transmission at the output port is changed. In our proposed structure, the insulator dielectric material is glass and the metal material is silver. The calculated contrast ratio between (ON and OFF) output states is 3.16 (dB).

Keywords:

Surface plasmon (SP),Insulator-Metal-Insulator (IMI),all-optical NOT gates,all-optical signal processing,

Refference:

I. B. Wang and G. P. Wang, “Surface plasmon polariton propagation in nanoscale metal gap waveguides,” Optics Letters, vol. 29, pp. 1992-1994, 2004.
II. C. Min, P. Wang, X. Jiao, Y. Deng, and H. Ming, “Beam focusing by metallic nano-slit array containing nonlinear material,” Applied Physics B, vol. 90, pp. 97-99, 2008.
III. Dolatabady and N. Granpayeh, “All-optical logic gates in plasmonic metal-insulator-metal nanowaveguide with slot cavity resonator,” Journal of Nanophotonics, vol. 11, p. 026001, 2017.
IV. H. J. Lezec, A. Degiron, E. Devaux, R. Linke, L. Martin-Moreno, F. Garcia-Vidal, et al., “Beaming light from a subwavelength aperture,” Science, vol. 297, pp. 820-822, 2002.
V. H. Raether, “Surface plasmons on smooth surfaces,” in Surface plasmons on smooth and rough surfaces and on gratings, ed: Springer, 1988, pp. 4-39.
VI. M. Ota, A. Sumimura, M. Fukuhara, Y. Ishii, and M. Fukuda, “Plasmonic-multimode-interference-based logic circuit with simple phase adjustment,” Scientific reports, vol. 6, p. 24546, 2016.
VII. N. Nozhat, H. Alikomak, and M. Khodadadi, “All-optical XOR and NAND logic gates based on plasmonic nanoparticles,” Optics Communications, vol. 392, pp. 208-213, 2017.
VIII. T. Birr, U. Zywietz, P. Chhantyal, B. N. Chichkov, and C. Reinhardt, “Ultrafast surface plasmon-polariton logic gates and half-adder,” Optics Express, vol. 23, pp. 31755-31765, 2015.
IX. T. Nikolajsen, K. Leosson, and S. I. Bozhevolnyi, “Surface plasmon polariton based modulators and switches operating at telecom wavelengths,” Applied Physics Letters, vol. 85, pp. 5833-5835, 2004.
X. T. S. M. Hassan Falah Fakhruldeen, “All-optical NoT Gate Based on Nanoring Silver-Air Plasmonic Waveguide ” International Journal of Engineering & Technology, vol. vol. 7, pp. pp. 2818-2821, 2018.
XI. T. S. M. Hassan Falah Fakhruldeen, Yousif I. Hammadi, “All-Optical Logic Gates Based on Graphene Interferometric Waveguide,” JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, vol. Vol.-14, pp. 98-110, 2019.
XII. X.-S. Lin and X.-G. Huang, “Tooth-shaped plasmonic waveguide filters with nanometric sizes,” Optics Letters, vol. 33, pp. 2874-2876, 2008.
XIII. Y.-D. Wu, Y.-T. Hsueh, and T.-T. Shih, “Novel All-optical Logic Gates Based on Microring Metal-insulator-metal Plasmonic Waveguides,” in PIERS Proceedings, 2013.

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Abstract:

It can be easily understood that every relay node in traditional multi-hop (TM) communication networks only attends the previous node that is near to it, which is the difficulty in routing. Using directed graphs, the modeling of these networks is performed well in order to achieve the routing. In the networks of accumulative multi-hop (AM) communication, the routing problem is far-off from understanding and yet rather interested in it. The received data energy from earlier relay transmissions can be acquired by numerous relay nodes that assist communication between a single source and a single destination in the accumulative multi-hop network which is a simple one. At this point, in single-source single-destination accumulative multi-hop networks, the difficulty in finding the optimum paths is studied. A method of Load Balanced Energy efficient cross layer based Routing protocol for accumulative networks are implemented in this paper. The end-to-end network connectivity is enhanced as well as the faults at link or/and node level is reduced in this method. Using an energy efficient neighbor node choosing method, the establishment of a set of various paths is done from the source to the destination. Efficient load balancing is offered at the node and a constant route is discovered between the source and destination that meets the delay requirement. With respect to end to end delay, throughput, and energy consumption, the proposed system is outperformed which is demonstrated in the results of simulation.

Keywords:

Accumulative,Multi-hop,Multi-path routing,Cross layer approach,Load balancing,Energy efficiency,

Refference:

I. A. Molisch, N. Mehta, J. Yedidia, and J. Zhang, “Cooperative relay networks using fountain codes,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Nov. 2006, pp. 1.

II. Agbaria, A.; Gershinsky, G.; Naaman N. &Shagin, K. Extrapolation-based and QoS-aware real-time communication in wireless mobile ad hoc networks. In the 8th IFIP Annual Mediterranean Adhoc Networking Workshop, Med-Hoc-Net 2009. pp.21-26. doi: 10.1109/MEDHOCNET.2009.5205201.

III. Ahmed, M.; Elmoniem, Abd; Ibrahim, Hosny M.; Mohamed, Marghny H. &Hedar, Abdel Rahman. Ant colony and load balancing optimizations for AODV routing protocol. Int. J. Sensor Networks Data Commun., 2012, 1. doi: doi:10.4303/ijsndc/X110203.

IV. Cai, X., Duan, Y., He, Y., Yang, J., Li, C.: Bee-Sensor-C: an energy-efficient and scalable multipath routing protocol for wireless sensor net-works. Int. J. Distrib. Sensor Netw. 26 (2015).

V. I. Maric and R. D. Yates, “Cooperative multihop broadcast for wireless networks,” IEEE J. Sel. Areas Commun., vol. 22, no. 6, pp. 1080–1088, Aug. 2004.

VI. J. Castura and Y. Mao, “Rateless coding over fading channels,” IEEE Commun. Lett., vol. 10, no. 1, pp. 46–48, Jan. 2006.

VII. J. Chen, L. Jia, X. Liu, G. Noubir, and R. Sundaram, “Minimum energy accumulative routing in wireless networks,” in Proc. IEEE INFOCOM, vol. 3. Mar. 2005, pp. 1875–1886.

VIII. J. Gómez-Vilardebó, “Routing in Accumulative Multi-Hop Networks,” in IEEE/ACM Transactions on Networking, vol. 25, no. 5, pp. 2815-2828,Oct. 2017. doi: 10.1109/TNET.2017.2703909.
IX. J. Gomez-Vilardebo, “Heuristic routing algorithms for minimum energy cooperative multi-hop wireless networks,” in Proc. 20th Eur. Wireless Conf., May 2014, pp. 1–5.-12

X. J. Gomez-Vilardebo, “Routing in accumulative multi-hop networks,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Apr. 2015, pp. 1814–1821.

XI. L. Sobrinho, “An algebraic theory of dynamic network routing,” IEEE/ACM Trans. Netw., vol. 13, no. 5, pp. 1160–1173, Oct. 2005.

XII. Mohapatra, S., Siddappa, M.: Improvised routing using Border Cluster Node for Bee-AdHoc-C: an energy-efficient and systematic routing protocol for MANETs. In: International Conference On Advances in Computer Applications, IEEE ICACA-2016 (2016).

XIII. R. Yim, N. Mehta, A. F. Molisch, and J. Zhang, “Progressive accumulative routing in wireless networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Nov. 2006, pp. 1–6.

XIV. S. C. Draper, L. Liu, A. F. Molisch, and J. S. Yedidia, “Cooperative transmission for wireless networks using mutual-information accumulation,” IEEE Trans. Inf. Theory, vol. 57, no. 8, pp. 5151–5162, Aug. 2011.

XV. Saleem, M., Farooq, M.: Beesensor: a bee-inspired power aware routing protocol for wireless sensor networks. In: Workshops on Applications of Evolutionary Computation, pp. 81–90. Springer Berlin Heidelberg (2007).

XVI. Siva, K. & P. Duraiswamy, K. A QoS routing protocol for mobile ad hoc networks based on the load distribution. In the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2010, pp.1-6. doi: 10.1109/ICCIC.2010.5705724.

XVII. Srivastava, S.; Daniel, A.K.; Singh, R. &Saini, J.P. Energyefficient position based routing protocol for mobile ad hoc networks. In the IEEE International Conference on Radar Communication and Computing (ICRCC), 2012, pp.18- 23. doi: 10.1109/ICRCC.2012.6450540.

XVIII. T. Girici and A. C. Kazez, “Energy efficient routing with mutual information accumulation,” in Proc. 10th Int. Symp. Modeling Optim. Mobile, Ad Hoc Wireless Netw. (WiOpt), May 2012, pp. 425–430.

XIX. Y. Yang and J. Wang, “Design guidelines for routing metrics in Multi-hop wireless networks,” in Proc. IEEE INFOCOM, Apr. 2008, pp. 1615–1623.

XX. Yu, J.Y., Chong, P.H.J.: A survey of clustering schemes for mobile ad hoc networks. IEEE Commun. Surv. Tutorials 7(1), 32–48 (2005).

XXI. Z. Yang and A. Høst-Madsen, “Routing and power allocation in asynchronous Gaussian multiple-relay channels,” EURASIP J. Wireless Commun. Netw., vol. 2006, no. 2, p. 35, 2006.

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Abstract:

Soft-learning courses are sought-after as well as late. The need to examine understudy's presentation and anticipating their exhibition is expanding alongside it. With the developing notoriety of instructive innovation, different information digging calculations appropriate for anticipating understudy execution have been surveyed. The best calculation is based on the idea of the forecast that the staff needs to make. As the measurement of understudy information broadens the need to address and manage the complexities of the information connection, it is a test for the discovery of the understudy at risk of being short-lived.  In this paper covers the ID3 and C4.5 algorithms used for Predictive Analytics on understudy's presentation and Big Data with cloud.

Keywords:

Soft-Learning Techniques,Machine Learning Approach,Basics of Predictive Analytics,Decision Tree Techniques (C4.5 and ID3),Big Data,

Refference:

I. A. M.Shahiri, W. Hussain and N. A. Rashid. “A Review on Predicting Student’s Performance using Data Mining Techniques”, Procedia Computer Science, vol. 72, pp. 414-422, 2015.
II. B. Logica and R. Magdalena, “Using Big Data in the Academic Environment”, Procedia Economics and Finance, vol. 33, pp. 277-286, 2015.
III. C. T. Tsai, et. al., “Exchanging course content mechanism for Moodle LMS”,In: Proc. of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Huangshan, China, IEEE, pp. 464-467, 2010.
IV. D. Clanfield and J.Sivell, “Cooperative learning & social change: selected writings of CélestinFreinet. Our Schools: Canada. Firdausiah Mansur, AndiBesse, Yusof, Norazah& Othman, Mohd. Shahizan. (2011). Analysis of social learning network for Wiki in Moodle e-learning.
V. E. A. Kareem and M. G. Duaimi, “Improved Accuracy for Decision Tree Algorithm Based on Unsupervised Discretization”, International Journal of Computer Science and Mobile Computing, vol. 3, no. 6, pp. 176-183, Jun. 2014.
VI. H. Chauhan and A. Chauhan, “Implementation of decision tree algorithm C4.5”, International Journal of Scientific and Research Publications, vol. 3, no. 10, pp. 1-3, Oct. 2013.
VII. H. Gulati, “Predictive Analytics Using Data Mining Technique”,In: Proc. of 2nd International Conference on Computing for Sustainable Global Development, New Delhi, India, IEEE, 2015.
VIII. J. Han and M.Kamber, “Data Mining Conceptsandits Techniques”, Morgan Kauffmann Publishers, 2011. DOI: https://doi.org/10.1016/C2009-0-61819-5
IX. K. Kinley, “Faculty and students’ awareness and challenges of e-learning in a college of education”, Journal of the International Society for Teacher Education, vol. 14, no. 1, pp. 27-33, 2010.
X. M. A. Al-Barrak and M. Al-Razgan, “Prediction of Student’s Final GPA implementing Decision Trees: A Case Study”, International Journal of Information and Education Technology, vol. 6, no. 7, July 2016.
XI. M. G. M. Mohan, S. K. Augustin and V. S. K. Roshni,“A Big Data Approach for Classification and Prediction of Student Result Using Map Reduce”,IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, India, IEEE, 2015.
XII. W. Dai and W. Ji, “Implementing Map Reduce with C4.5 Decision Tree Algorithm”, Journal of Database Theory and Application, vo. 7, no. 1, pp. 49-60, 2014.

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Abstract:

Since forty years of computing history, cloud computing has made revolutionary changes. The daily life of human beings is completely depended on this advancement. Data centres are the backbone for the cloud computing. During the time of peak hours, load will be heavy on data center. Load balancing is needed. It provides better services to the end-user. Existing load balancing algorithms have their drawbacks. Hybrid algorithm approach is also a way to balance the load in cloud computing. Many efforts are made by several researchers in this direction. Combination of Round robin, Throttled, Equally Spread Current Execution, and Artificial Bee Colony Optimization algorithms as a hybrid algorithm (RTEAH) has shown improved results, hence it can be considered. 

Keywords:

Cloud computing,Distributed Computing,Virtual Machine,Data Center,Downtime,

Refference:

I. A. Addison and C. Andrews, “Low-Latency Trading in Cloud Environment ”, Conf. Comp. Science and Eng. and Embed. & Ubiquitous Computing, NewYork, USA, pp.272 282, 2019.

II. A. Sharma and S. K. Peddoju, “Response time-based load balancing in Cloud Comp.”, Conf. on Contrl, Instrumt., Comm. and Comp. Tech., Kanyakumari District, India,pp.1287 1293,2014.

III. Chen, X., “Decentralized Computation Offloading Game For Mobile Cloud Comp. ”, Decentralized Comp. Offload. Game for Mob.Cloud Comp. IEEE Trans. on Parl. and Dist. Sys., Vol. 26, No.4, pp. 974 983. 2015.

IV. K. Ha, P. Pillai,“The Impact of Mobile Multimedia Appli. on Data Center Consolidation”, IEEE Intl. Conf. on Cloud Eng.,California, USA, pp.166 176 , 2013.

V. Linthicum, D. S., “ Understanding Complex Cloud Patterns ” , IEEE Cloud Comp., Vol. 3,No. 1, pp.8 11, 2016.

VI. Mavrogeorgi, N., Gogouvitis, S., “ Dynamic Rule Based SLA Management in Clouds”. IEEE Sixth Intl. Conf. on Cloud Comp., Santa Clara, CA, USA, pp. 964 965, 2013.

VII. Rani, E., &Kaur, H., “ Study on fundamental usage of Cloud Simsimu. And algo. of resource allocation in cloud comp. ”, 8th Intl. Conf. on Comp., Communic. and Network. Tech., IEEE Conference, Delhi, India, pp.2 7,2017.

VIII. Ritu, S. Jain, “ A Trust Model in Cloud Computing Based on Fuzzy Logic ”,IEEE Intl. Conf. On Recent Trends InEle. Info. Comm. Tech., Bangalore, India, 47 52, 2016

IX. S. A. Narale and P. K. Butey, “IEEE Intl. Conf. 2nd Intl. Conf. on Inventive Comm. and Comp. Tech.”, Coimbatore, India, pp.1464 1467,2018.

X. Shakeel, F., & Sharma, S. “ Green cloud computing: A review on efficiency of data centres and virtualization of servers”, Intl. Conf. on Comp., Comm. and Automation ,Greater Noida, India,pp.1264 1267,2017.

XI. Wang, Z., Zeng, J., “ Cloud Auditor: A Cloud Auditing Framework Based on Nested Virtualization”, IEEE 3rd Intl Conf. on Cyber Security and Cloud Comp., Beijing, China, pp.50 53, 2016.

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Abstract:

The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of this paper is to suggest a new hybrid estimator obtained by an ad-hoc algorithm which relies on data driven strategy that overcomes outliers. While the minor goal is to introduce a new employment of an unweighted estimation method named "winsorization"  which is a good method to get robustness in regression estimation via special technique to reduce the effect of the outliers. Another specific contribution in this paper is to suggest employing "Kernel" function as a new weight (in the scope of the researcher's knowledge).Moreover, two weighted estimations are based on robust weight functions named "Cauchy" and "Talworth". Simulations have been constructed with contamination levels (0%, 5%, and 10%) which associated with sample sizes (n=40,100). Real data application showed the superior performance of the suggested method compared with other methods using RMSE and R² criteria.

Keywords:

Data-driven strategy,kernel,multiphase regression,robustness,threshold point,winsorization,

Refference:

I. Acitas, S. and Senoglu, B., (2020). “Robust change point estimation in two-phase linear regression models: An application to metabolic pathway data”. Journal of Computational and Applied Mathematics, Vol. 363, pp 337–349.

II. Chen, C.W.S., Chan, J. S.K., Gerlach, R., and Hsieh, W. Y.L., (2011). “A comparison of estimators for regression models with change points”. Stat Comput, Vol. 21, pp 395–414.

III. Dehnel, G., (2016). “M-Estimators in Business Statistics”.Statistics in Transition new series, Vol. 17, No. 4, pp 1–14.

IV. Fearnhead, P. and Rigaill, G., (2017). “Changepoint Detection in the Presence of Outliers”.Journal of the American Statistical Association, Vol. 114, No. 525, pp 169-183.

V. Ganocy, S. J. and Sun, J., (2015). “Heteroscedastic Change Point Analysis and Application to Footprint Data”.Journal of Data Science, Vol. 13, pp 157-186.

VI. Hernandez, E.L., (2010). ” Parameter Estimation in Linear-LinearSegmentedRegression. M.Sc. thesis, Department of Statistics, Brigham Young University,

VII. Julious, S.A., (2001). “Inference and Estimation in a Change point Regression Problem”. The Statistician, Vol. 50, Part 1, pp 51-61.

VIII. Klotsche, J. and Gloster, A. T., (2012). “Estimating a Meaningful Point of Change:A Comparison of Exploratory Techniques Based on Nonparametric Regression”. Journal of Educational and Behavioral Statistics Vol. 37, pp 579-600.

IX. Liu, Z., (2011). “Empirical Likelihood Method for Segmented Linear Regression”.Ph.D. Dissertation, Faculty of the Charles E. Schmidt, College of Science, Florida Atlantic University, USA.

X. Muggeo, V. M. R., (2003). “Estimating regression models with unknown break-points”, Statist.Med., Vol. 22, pp 3055–3071.

XI. Muggeo, V. M. R., (2017).”Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach”. Aust. N. Z. J. Stat. Vol. 59, No.3, pp 311–322.

XII. Pusparum, M., (2017). “Winsor Approach in Regression Analysis with Outlier”.Applied Mathematical Sciences, Vol. 11, No. 41, pp 2031-2046.

XIII. Ryan, S.E. and Porth, L. S., (2007). “A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data”. General Technical Report RMRS-GTR-189. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 41 p.

XIV. Whitehead, N., Hill, H.A., Brogan, D.J. and Blackmore-Prince, C., (2002). Exploration of threshold analysis in the relation between stressful life events and preterm delivery”. American Journal of Epidemiology Vol. 155, pp 117–124.

XV. Yale, C. and Forsythe, A.B., (1976). “Winsorized Regression”, Technometrics, Vol.18 No.3, pp 291-300.

XVI. Zhang, F., Li, Q.,(2017).”Robust bent line regression”. J. Statist. Plann. Inference, Vol.185,pp41-55.

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Abstract:

Development has been achieved to the road vehicle industry so as to manufacture types of automobiles with high ride passenger comfort. One requirement needed to obtain good quality of drive handling efficient operating characteristics of the model system which is equipped for road automobile. The suspension systems are mainly used to restrain externally disturbance from affecting ride tripper rest .Our research had been presented (Fuzzy PID) control for investigate active road vehicle suspension controller. The Fuzzy logic function is used to improve tuning and performance the gain of the road vehicle suspension with PID controller. Undesired displacements of the road vehicle body during dynamic process are presented and compared for two road vehicle models with PID controller and FUZZY PID controller. The final simulated results show the influence of the active road vehicle suspension controller on the efficiency of ride road vehicle handling however raising the strength and execute slick driving. Then, a robust control is executed to optimize these operating characteristics of the suspension systems to improve the road vehicle.

Keywords:

Fuzzy PID controller,active suspension,quarter vehicle model,MATLAB simulation,

Refference:

I. Alleyne, A., & Hedrick, J. K. (1995). Nonlinear adaptive control of active suspensions. IEEE transactions on control systems technology, 3(1), 94-101.
II. Agharkakli, A., Sabet, G. S., & Barouz, A. (2012). Simulation and analysis of passive and active suspension system using quarter car model for different road profile. International Journal of Engineering Trends and Technology, 3(5), 636-644.‏
III. Changizi, N., & Rouhani, M. (2011). Comparing PID and fuzzy logic control a quarter car suspension system. The journal of mathematics and computer science, 2(3), 559-564.‏
IV. Dukkipati, R. V. (2007). Solving vibration analysis problems using MATLAB. New Age International.‏
V. Ghasemalizadeh, O., Taheri, S., Singh, A., & Goryca, J. (2014). Semi-active Suspension Control using Modern Methodology: Comprehensive Comparison Study. arXiv preprint arXiv:1411.3305.‏
VI. Gordon, T. J., Marsh, C., & Milsted, M. G. (1991). A comparison of adaptive LQG and nonlinear controllers for vehicle suspension systems. Vehicle System Dynamics, 20(6), 321-340.‏
VII. Gysen, B. L., Paulides, J. J., Janssen, J. L., & Lomonova, E. A. (2009). Active electromagnetic suspension system for improved vehicle dynamics. IEEE Transactions on Vehicular Technology, 59(3), 1156-1163.‏
VIII. Hatch, M. R. (2000). Vibration simulation using MATLAB and ANSYS. CRC Press.‏
IX. Tiwari, P., & Mishra, G. (2014). Simulation of quarter-car model. JOSR Journal of Mechanical and Civil Engineering, 11(2), 85-88.‏
X. Yagiz, Nurkan, and Yuksel Hacioglu. “Backstepping control of a vehicle with active suspensions.” Control Engineering Practice16.12 (2008): 1457-1467.‏

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Abstract:

Tracking wheeled mobile robot control is a complicated problem encounter in robotic science. Many issues occurring that are affecting the control of nonlinear robot in actual application. The applications would include uncertainties parameter and internal disturbances. The factors restrict the study of mobile robot tracing control.  In this study we modified adaptive sliding mode controller for nonholonomic wheeled mobile robot. The kinematic controller used to produce the desired tracking velocities as input term after that used suggested of the dynamic controller to overcome the uncertainties, disturbance and chattering effect of the sliding controller. according to stability of Lyapunov, the final controlled system is proven to be globally asymptotically stable. Proposed control system is verified and validated using MATLAB\SIMULINK to track the required WMR trajectory. A comparison between PI adaptive sliding mode and PI sliding mode is done. Simulated result portrays that in the presence of continuous disturbances and uncertainties and presented work with very good accuracy and fast error convergence and robustness.

Keywords:

Wheeled mobile robot,kinematic control,dynamic control,sliding mode control,adaptive control,

Refference:

I. A .Bloch, & Drakunov, S. (1994, December). Stabilization of a nonholonomic system via sliding modes. In Proceedings of 1994 33rd IEEE Conference on Decision and Control (Vol. 3, pp. 2961-2963). IEEE.‏
II. B S .Park., Yoo, S. J., Park, B J.., & Choi, Y. H. (2008). Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Transactions on Control Systems Technology, 17(1), 207-214.‏
III. B .d’Andréa-Novel., Campion, G., & Bastin, G. (1995). Control of nonholonomic wheeled mobile robots by state feedback linearization. The International journal of robotics
IV. BeloboMevo, B., Saad, M. R., & Fareh, R. (2018, May). Adaptive Sliding Mode Control of Wheeled Mobile Robot with Nonlinear Model and Uncertainties. In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) (pp. 1-5). IEEE.‏
V. D .Young, K., Utkin, V. I., & Ozguner, U. (1996, December). A control engineer’s guide to sliding mode control. In Proceedings. 1996 IEEE International Workshop on Variable Structure Systems.-VSS’96- (pp. 1-14). IEEE.‏
VI. Das, T., & Kar, I. N. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE Transactions on Control Systems Technology, 14(3), 501-510.‏
VII. D. Chwa,Seo, J. H., Kim, P., & Choi, J. Y. (2002, May). Sliding mode tracking control of nonholonomic wheeled mobile robots. In Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301) (Vol. 5, pp. 3991-3996). IEEE.‏
VIII. F. Hamerlain, K .Achour., T. Floquet., & Perruquetti, W. (2005, December). Higher order sliding mode control of wheeled mobile robots in the presence of sliding effects. In Proceedings of the 44th IEEE Conference on Decision and Control (pp. 1959-1963). IEEE.‏

IX. G. Klančar, Matko, D., & Blažič, S. (2009). Wheeled mobile robots control in a linear platoon. Journal of Intelligent and Robotic Systems, 54(5), 709-731.‏ research, 14(6), 543-559.‏
X. Gu, D., & Hu, H. (2002). Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems, 39(2), 73-86.‏
XI. H .Mehrjerdi, & M. Saad, (2011). Chattering reduction on the dynamic tracking control of a nonholonomic mobile robot using exponential sliding mode. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(7), 875-886.‏
XII. Ibrahim, A. E. S. B. (2016). Wheeled Mobile Robot Trajectory Tracking using Sliding Mode Control. JCS, 12(1), 48-55.‏
XIII. Ibari, Benaoumeur, et al. “Backstepping approach for autonomous mobile robot trajectory tracking.” Indonesian Journal of Electrical Engineering and Computer Science 2.3 (2016): 478-485.‏
XIV. J. Wu,Xu, G., & Yin, Z. (2009). Robust adaptive control for a nonholonomic mobile robot with unknown parameters. Journal of Control Theory and Applications, 7(2), 212-218.‏
XV. Kanayama, Y., Kimura, Y., Miyazaki, F., & Noguchi, T. (1990, May). A stable tracking control method for an autonomous mobile robot. In Proceedings., IEEE International Conference on Robotics and Automation (pp. 384-389). IEEE.‏
XVI. Liyong, Y., & Wei, X. (2007, July). An adaptive tracking method for non-holonomic wheeled mobile robots. In 2007 Chinese Control Conference (pp. 801-805). IEEE.‏
XVII. M .Yang, J., & H. Kim, J. (1999). Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Transactions on robotics and automation, 15(3), 578-587.‏
XVIII. Normey-Rico, Julio E., et al. “Mobile robot path tracking using a robust PIDcontroller.” Control Engineering Practice 9.11 (2001): 1209-1214.
XIX. R .Rashid., Elamvazuthi, I., Begam, M., & Arrofiq, M. (2010). Fuzzy-based navigation and control of a non-holonomic mobile robot. arXiv preprint arXiv:1003.4081.‏
XX. R. Dhaouadi., & Hatab, A. A. (2013). Dynamic modelling of differential-drive mobile robots using lagrange and newton-euler methodologies: A unified framework. Advances in Robotics & Automation, 2(2), 1-7.‏
XXI. R .Fierro., & Lewis, F. L. (1997). Control of a nonholomic mobile robot: Backstepping kinematics into dynamics. Journal of robotic systems, 14(3), 149-163.‏
XXII. T .Fukao., Nakagawa, H., & Adachi, N. (2000). Adaptive tracking control of a nonholonomic mobile robot. IEEE transactions on Robotics and Automation, 16(5), 609-615.‏

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Abstract:

The performance of the solar updraft tower system (SUTS) investigates numerically by comparing between two quarters circular thermal solar collectors (with and without porous absorber plate). The porous copper foam 10 PPI and porosity 0.9 is used as an absorber plate. The present work aims to study the effect of variation the heights of the air inlet (3, 5, and 8) cm respectively utilized conventional flat and porous metal foam absorber plate. The physical quantities inside flat and porous absorber plate are simulated. A set of assumptions are adopted such as a steady state condition, three dimensional, Darcy  and energy equations. The numerical simulation are approximated k- ϵ turbulent model by a Re-Normalization Group (RNG) and discrete ordinates (DO) radiation model equations. The numerical study is analyzed by using ANSYS FLUENT program (version 18.2) to solve the governing equations. The results showed that variation in the heights of the air inlet with  the presence of the porous absorber plate is more effective than the conventional flat plate on the performance of the SUTS. The maximum performance of the system is predicted with the height of the air inlet of 3 cm by using the porous metal foam absorber plate

Keywords:

Solar tower,porous metal foam,performance of the solar tower,ANSYS FLUENT,renewable energy,

Refference:

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VI. Dr.Hamza D and Salman Al.Obaidi. Expermental research to evaluate solar chimney power plant in Baghdad/Iraq and determine its performance. SYLWAN.English Edition, Printed in Poland, 2017.

VII. Elizabeth Marie Heisler. Exploring alternative designs for solar chimneys using computational fluid dynamics. Thesis, the Virginia Polytechnic Institute and State University, 2014.

VIII. Guo C.X., Zhang W.J., and Wang D.B. Numerical investigation of heat transfer enhancement in latent heat storage exchanger with paraffin/graphite foam. 10th Int. Conf. on Heat Transfer, Fluid Mechanics and Thermodynamics 2014; July: 14 – 26.

IX. Holman, J.P.,” Heat Transfer”, 10th Edition, McGraw-Hill Higher Education, 2010.

X. Ming T., Liu W., and Xu G. Analytical and numerical investigation of the solar chimney power plant systems. International Journal of Energy Research, 2006; 30 (9) : 861–873.

XI. M.M. Rahman, S. Mojumder, S. Saha, S.Mekhilef, R. Saidur. Effect of solid volume fraction and tilt angle in a quarter circular solar thermal collectors filled with CNT–water nanofluid. International Communications in Heat and Mass Transfer, 2014; 57: 79–90.

XII. Prof. Dr. Arkan khilkhal Husain, Asst.Prof.Dr Waheeds Shate Mohammad, and Lecturer. Abbas JassimJubear. Numerical simulation of the influence of geometric parameter on the flow behavior in a solar chimney power plant system. Journal of Engineering, 2014; 20 (8): 88-108.

XIII. Richard A. H. Performance evaluation of a solar chimney power plant. M.Sc. Thesis, Mechanical Engineering Department, University of Stellenboch, 2000.

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XV. Sarmad A. Abdul Hussein and Dr. Mohammed A. Nima. Numerical analysis of solar tower system utilized with flat plate and porous absorber. Journal of Mechanical Engineering Research and Developments (JMERD), 2019; 42(5): 216-223.

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XVII. Wei Chen, Wei Liu. Numerical analysis of the heat transfer in solar composite wall collector system with porous absorber. Journal of Solar Energy, 2005; vol.26, Journal Issue 6, pp.882-6.

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Abstract:

The signals of the electroencephalogram (EEG) have been applied for detecting as well as registering the electrical efficiency in the human brain.  In this paper, EEG signals have been utilized for human identification. The reliability regarding a lot of biometric systems aren’t adequate due to the possibility of being copied or faked. Thus the brain signatures have been applied as potential biometric identifiers. The aim of this paper is to apply sample entropy and graph entropy as feature extraction. While in classification Support vector machine (SVM) and K-Nearest Neighbor (KNN) have achieved. Machine Learning Repository (UCI) used as dataset. Experimental consequences on this dataset demonstrate substantial enhancement in the classification accuracy as compared with other testified results in the literature. Results showed that the classification accuracy with SVM for biometric identification is 90.8% while with K-NN is 83.7% .Our study using13channels to feature extraction.

Keywords:

Electroencephalogram (EEG),Support vector machine,K-Nearest Neighbor,Machine learning,

Refference:

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III. Damastuti Natalia, Aisjah Aulia Siti , Masroeri Agoes A,Cassification of Ship-Based Automatic Identification Systems Using K- Nearest Neighbors”International Seminar on Application for Technology of Information and Communication, IEEE, pp 331-335, 2019.
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VIII. G. Paul and J. Irvine, “Fingerprint authentication is here but are we ready for what it brings?,” IEEE Consumer Electronics Magazine, 2015.
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Abstract:

This study proposes an analytical solution method for investigating vibrational characteristics for a tubular cylindrical shell of a finite-length and bares a circumferential part-through fissure. The effect of different  parameters i,e, length, depth and the fissure's location, on the vibrational characteristics, were also investigated. The equations for motion, that are founded on the classical shell theory  for the fissured shell were  transformed into simpler equations via Donell–Mushtari–Vlasov (DMV) hypothesis. The equivalent bending stiffness of the shell (D) was calculated by an exponential function while taking into consideration the effect of the fissure. The analytical approach gave us results for a structure with simply supported (S-S) at both ends boundary conditions. The natural frequencies were obtained by solving the general equations on a program built for "MATLAB" SOFTWARE. The results that were obtained from the suggested modal were confirmed by the use of a modal created by ANSYS APDL ver.15 in addition to the results that were attained from literature. There was a passable agreement between the results of the analytical and FE model. The results set forth that as fissure's parameters, length & depth, Increasing them reduces the natural frequency, In addition to this, the natural frequency will also decrease if the fissure is located in the middle of the shell is larger than  if it were in other locations.

Keywords:

Cylindrical shell,vibration characteristics,Part-Through fissure,natural frequency,

Refference:

I. Aruna Rawat, Vasant Matsagar, and A K Nagpal, “Finite element analysis of thin circular cylindrical shells,” Proc Indian NatnSciAcad 82 June pp. 351, 2016.

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III. A. Vaziri, and H. E. Estekanchi, “Buckling of cracked cylindrical thin shells under combined internal pressure and axial compression,” Thin-Walled Structures, Vol.44, pp. 141–151, 2006.

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V. H. J. Abbas, M. J. Jweeg, Muhannad Al-Waily, Abbas Ali Diwan “Experimental Testing and Theoretical Prediction of Fiber Optical Cable for Fault Detection and Identification’ Journal of Engineering and Applied Sciences, Vol. 14, No. 02, pp. 430-438, 2019.

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XII. Mohsin Abdullah Al-Shammari “Experimental and FEA of the Crack Effects in a Vibrated Sandwich Plate’ Journal of Engineering and Applied Sciences, Vol. 13, No. 17, pp. 7395-7400, 2018.

XIII. Muhannad Al-Waily, Maher A.R. Sadiq Al-Baghdadi, RashaHayder Al-Khayat “Flow Velocity and Crack Angle Effect on Vibration and Flow Characterization for Pipe Induce Vibration’ International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 17, No. 05, pp.19-27, 2017.

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