Journal Vol – 15 No -2, February 2020

ADAPTIVE PI-SLIDING MODE CONTROL OF NON-HOLOMONIC WHEELED MOBILE ROBOT

Authors:

Iman Abdalkarim Hasan,Nabil Hassan Hadi,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00021

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:

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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.‏
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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.‏
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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.‏
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INVESTIGATION OF AIR INLET HEIGHT ON THE PERFORMANCE OF SOLAR TOWER SYSTEM UTILIZED WITH FLAT PLATE AND POROUS ABSORBER

Authors:

Sarmad A. Abdal Hussein,Sarmad A. Abdal Hussein,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00022

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:

I. Abdulcelil BUĞUTEKİN. An experimental investigation of the effect of periphery height and ground temperature changes on the solar chimney system. J. of Thermal Science and Technology, Isı Bilimi ve Tekniği Dergisi. 2012; 32 (1): 51-58.

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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.

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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.

XIV. Saraswat A., Verma A., Khandekar S., and Das M.K. Latent heat thermal energy storage in a heated semi-cylindrical cavity: experimental results and numerical validation. Proceedings of the 23rd National Heat and Mass Transfer Conference, India, IHMTC, 862, 2015.

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.

XVI. Wei Chen and Man Qu. Analysis of the heat transfer and airflow in solar chimney drying system with porous absorber. Renewable Energy, 2014; 63: 511-518.

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SUPPORT VECTOR MACHINE APPROACH FOR HUMAN IDENTIFICATION BASED ON EEG SIGNALS

Authors:

Shaymaaadnan Abdulrahman,Mohamed Roushdy,Abdel-Badeeh M. Salem,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00023

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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A PROPOSED ANALYTICAL SOLUTION OF CYLINDER SHELL CONTAINING A CIRCUMFERENTIAL PART-THROUGH FISSURE

Authors:

Marwah Ali Husain,Mohsin Abdullah Al-shammari,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00024

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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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.

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BRIEF REVIEW OF AUTOMATION IN AEROSPACE INDUSTRIES

Authors:

Amith A Kulkarni,Dhanush P,Chethan B S,Thammegowda C S,Prashant Shrivastava,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00025

Abstract:

This paper presents the application of automation techniques in different areas of aerospace industry such as; 4D printing prospects, automated aircraft tracking, laser marking, automated fiber placement, acoustic emission detection, analysis of the aircraft carrier for landing task, flexible and automated production, function allocation between automation and human pilot, automated selection and assembly, autonomous control reconfiguration, sensor monitoring during the process. In this study, we have explored the existing automation techniques and also find out a better way to implement in the future to minimize the human efforts and time. These technologies based on automation and artificial intelligence that will help us to make the process more efficient, stable and flexible. Moreover, aspects of the changeability and adaptiveness of the automation system have to be considered. The aim of this study to identify the opportunities and scope for future research trends in the field of aerospace industries.

Keywords:

Automation,Robotics,Artificial Intelligence,4-D Printing,3-D printing,Aerospace,

Refference:

I. Beeco JA, Joyce D. Automated aircraft tracking for park and landscape planning. Landscape and Urban Planning. 2019;186:103–11.

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III. Denkena B, Schmidt C, Weber P. Automated fiber placement head for manufacturing of innovative aerospace stiffening structures. Procedia Manufacturing. 2016;6:96–104.

IV. Dammann M, Schüppstuhl T. Automated selection and assembly of sets of blades for jet engine compressors and turbines. Procedia Manufacturing. 2018;16:53–60.

V. Eschena H, Harnischa M, Schüppstuhla T. Flexible and automated production of sandwich panels for aircraft interior. Procedia Manufacturing. 2018;18:35–42.

VI. Gao Y, Liu Y, Wang C, Li X, Ou G. Design and evaluation of a high performance distributed expert system (HPDES) for aerospace ground verification system. Procedia Computer Science. 2012;9:1380–9.

VII. Holford KM, Eaton MJ, Hensman JJ, Pullin R, Evans SL, Dervilis N, et al. A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview. Progress in Aerospace Sciences. 2017;90:1–11.

VIII. Hess RA. Analysis of the Aircraft Carrier Landing Task, Pilot+ Augmentation/Automation. IFAC-PapersOnLine. 2019;51(34):359–65.

IX. Han W, Bai X, Xie J. Assessment Model of the Architecture of the Aerospace Embedded Computer. Procedia Engineering. 2015;99:991–8.

X. Idris H, Enea G, Lewis TA. Function Allocation between Automation and Human Pilot for Airborne Separation Assurance. IFAC-PapersOnLine. 2016;49(19):25–30.

XI. Jun X, Junjia H, Chunyan Z. Dynamic analysis of contact bounce of aerospace relay based on finite difference method. Chinese Journal of Aeronautics. 2009;22(3):262–7.

XII. Möller C, Schmidt HC, Koch P, Böhlmann C, Kothe S-M, Wollnack J, et al. Machining of large scaled CFRP-Parts with mobile CNC-based robotic system in aerospace industry. Procedia manufacturing. 2017;14:17–29.

XIII. Ntouanoglou K, Stavropoulos P, Mourtzis D. 4D Printing Prospects for the Aerospace Industry: a critical review. Procedia Manufacturing. 2018;18:120–9.

XIV. Pei C, Zongji C, Rui Z, Chen W. Autonomous control reconfiguration of aerospace vehicle based on control effectiveness estimation. Chinese Journal of Aeronautics. 2007;20(5):443–51.

XV. Velotti C, Astarita A, Leone C, Genna S, Minutolo FMC, Squillace A. Laser marking of titanium coating for aerospace applications. Procedia CIRP. 2016;41:975–80.

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AN OVERVIEW OF BIOMASS CONVERSION MATERIALS AND METHODS

Authors:

Akshay M N,Anand V,Abhilash S,Deepak G,Prashant Shrivastava,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00026

Abstract:

In this present study, we have discussed the different types of waste materials from different industries like mechanical, electrical, electronics, automobiles, medical and agriculture industries. Moreover, industrial waste is one of the biggest problems in INDIA. Thereafter, the waste management process is required to recycle waste materials. The main problem is in the recycling process is to higher cost and time-consuming. The main aim of this study to identify the biomass converted material like pyrolysis of biomass, biomass-waste, gasification of harmful gases, carbon-based supercapacitor, silica, macadamia shell waste, lithium Sulphur battery and rise husk etc. from the different industries and try to convert that materials into a useful materials by using different types of conversion approaches. There are different types of conversion techniques are available like CVD, hydrothermal process, Thermolysis, Pyrolysis, combustion, and chemical treatments. However, this process applied on the basis of types of waste material. Moreover, in this study, we have discussed the different types of biomass converted waste materials and their conversion approaches.

Keywords:

Graphene,Waste,Bio precursors,Biomass,Glucose,Rice husk,Hemp,

Refference:

I. B. Ruiz, R. P. Girón, I. Suárez-Ruiz, and E. Fuente, “From fly ash of forest biomass combustion (FBC) to micro-mesoporous silica adsorbent materials,” Process Safety and Environmental Protection, vol. 105, pp. 164–174, 2017.

II. G. V. Nivea Raghavan, Sakthivel Thangavel, “A short review on preparation of graphene from waste and bioprecursors,” Applied Energy, vol. 7, pp. 246–254, 2017.

III. J. A. S. Costa and C. M. Paranhos, “Systematic evaluation of amorphous silica production from rice husk ashes,” Journal of Cleaner Production, vol. 192, pp. 688–697, 2018.

IV. K. Yang et al., “Biomass‐Derived Porous Carbon with Micropores and Small Mesopores for High‐Performance Lithium–Sulfur Batteries,” Chemistry–A European Journal, vol. 22, no. 10, pp. 3239–3244, 2016.

V. L. H. Nguyen and V. G. Gomes, “High efficiency supercapacitor derived from biomass based carbon dots and reduced graphene oxide composite,” Journal of Electroanalytical Chemistry, vol. 832, pp. 87–96, 2019.

VI. M. Chen et al., “Honeycomb‐like Nitrogen and Sulfur Dual‐Doped Hierarchical Porous Biomass‐Derived Carbon for Lithium–Sulfur Batteries,” ChemSusChem, vol. 10, no. 8, pp. 1803–1812, 2017.

VII. N. Zhou et al., “Silicon carbide foam supported ZSM-5 composite catalyst for microwave-assisted pyrolysis of biomass,” Bioresource technology, vol. 267, pp. 257–264, 2018.

VIII. N. Raghavan, S. Thangavel, and G. Venugopal, “A short review on preparation of graphene from waste and bioprecursors,” Applied Materials Today, vol. 7, pp. 246–254, 2017.

IX. R. Zhong and B. F. Sels, “Sulfonated mesoporous carbon and silica-carbon nanocomposites for biomass conversion,” Applied Catalysis B: Environmental, vol. 236, pp. 518–545, 2018.

X. S. Maroufi, M. Mayyas, and V. Sahajwalla, “Waste materials conversion into mesoporous silicon carbide nanocermics: Nanofibre/particle mixture,” Journal of Cleaner Production, vol. 157, pp. 213–221, 2017.

XI. S. Imtiaz et al., “Biomass-derived nanostructured porous carbons for lithium-sulfur batteries,” Science China Materials, vol. 59, no. 5, pp. 389–407, 2016.

XII. S. S. Shams, L. S. Zhang, R. Hu, R. Zhang, and J. Zhu, “Synthesis of graphene from biomass: a green chemistry approach,” Materials Letters, vol. 161, pp. 476–479, 2015.

XIII. V. S. Samane Maroufi, Mohannad Mayyas, “Waste Materials Conversion into Mesoporous Silicon Carbide Nanoceramics: Nanofiber/Particle Mixture,” Journal of cleaner production, 2017.

XIV. W. Wang, L. Tian, W. Song, L. Lv, and Z. Tu, “Twisted angle effects in the absorption spectra of carbon nanotube,” Optik, vol. 171, pp. 845–849, 2018.

XV. W. Du, X. Wang, X. Sun, J. Zhan, H. Zhang, and X. Zhao, “Nitrogen-doped hierarchical porous carbon using biomass-derived activated carbon/carbonized polyaniline composites for supercapacitor electrodes,” Journal of Electroanalytical Chemistry, vol. 827, pp. 213–220, 2018.

XVI. W. Zhang et al., “Hierarchical porous carbon prepared from biomass through a facile method for supercapacitor applications,” Journal of colloid and interface science, vol. 530, pp. 338–344, 2018.

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SYNTHESIS AND CHARACTERIZATION OF HYBRID BIO-EPOXY COMPOSITE

Authors:

Rakshit S,Vaishnavi Shrivatsa V,Shamanath K,Prashant Shrivastava,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00027

Abstract:

A composite material is a combination of two or more materials arranged in the form of layer one on the other layer using binding material through some prescribed methods. The Bio-epoxy composite is prepared by hand layup method using Bio-epoxy, natural fiber, and particulate here we discuss the properties and characterization of the composite such as reaction with moisture content in soil, degradability and reaction with water. In this paper, the moisture absorption rate as a function of time is discussed with reference to the Bio-epoxy prepared. The matrix structure is the same as that of any composite but the reinforced material used is the hybrid being the natural epoxy. The bio epoxy hybrid composite is having high moisture absorbing capacity which leads to low flexibility in the specimen. The natural fiber used here as well as the particulate is readily degradable in soil when exposed to a specific time. The matrix reinforced hybrid composite used here is flexible in its very nature indicating its adaptability to various uses. The composite is eco-friendly. There is also a comparison between the traditional epoxy and the bio composites to check the time required for degradation. The usage of these bio composites makes the surrounding less harmful and it also cost-effective. There was also a test conducted for bio composites with water and soil for 72 hours. The natural fibers used havea high affinity for water therefore degradation easily takes place along with reinforced particulate material that is Tulsi seeds. In this article synthesis and characterization of hybrid bio-epoxy composite, and the reaction of these composites in wet environmental conditions are discussed.

Keywords:

Green Composites,Areca fiber,Ocimumtenuiflorum (Tulsi) seeds,bio epoxy,degradation,

Refference:

I. A. D. La Rosa, G. Recca, J. Summerscales, A. Latteri, G. Cozzo, and G. Cicala, “Bio-based versus traditional polymer composites. A life cycle assessment perspective,” Journal of cleaner production, vol. 74, pp. 135–144, 2014.

II. A. Shakeri and M. Raghimi, “Studies on mechanical performance and water absorption of recycled newspaper/glass fiber-reinforced polypropylene hybrid composites,” Journal of Reinforced Plastics and Composites, vol. 29, no. 7, pp. 994–1005, 2010.

III. A. Ashori and S. Sheshmani, “Hybrid composites made from recycled materials: Moisture absorption and thickness swelling behavior,” Bioresource technology, vol. 101, no. 12, pp. 4717–4720, 2010.

IV. B. Szolnoki et al., “Development of natural fibre reinforced flame retarded epoxy resin composites,” Polymer Degradation and Stability, vol. 119, pp. 68–76, 2015.

V. E. Munoz and J. A. García-Manrique, “Water absorption behaviour and its effect on the mechanical properties of flax fibre reinforced bioepoxy composites,” International Journal of Polymer Science, vol. 2015, 2015.

VI. H.-S. Yang, H.-J. Kim, H.-J. Park, B.-J. Lee, and T.-S. Hwang, “Water absorption behavior and mechanical properties of lignocellulosic filler–polyolefin bio-composites,” Composite Structures, vol. 72, no. 4, pp. 429–437, 2006.

VII. H.-S. Yang, H.-J. Kim, H.-J. Park, B.-J. Lee, and T.-S. Hwang, “Water absorption behavior and mechanical properties of lignocellulosic filler–polyolefin bio-composites,” Composite Structures, vol. 72, no. 4, pp. 429–437, 2006.

VIII. K. G. Satyanarayana, G. G. C. Arizaga, and F. Wypych, “Biodegradable composites based on lignocellulosic fibers—An overview,” Progress in polymer science, vol. 34, no. 9, pp. 982–1021, 2009.

IX. K. L. Pickering, M. G. A. Efendy, and T. M. Le, “A review of recent developments in natural fibre composites and their mechanical performance,” Composites Part A: Applied Science and Manufacturing, vol. 83, pp. 98–112, 2016.

X. K. P. Ashik and R. S. Sharma, “A review on mechanical properties of natural fiber reinforced hybrid polymer composites,” Journal of Minerals and Materials Characterization and Engineering, vol. 3, no. 05, p. 420, 2015.

XI. M. Jawaid, H. P. S. A. Khalil, P. N. Khanam, and A. A. Bakar, “Hybrid composites made from oil palm empty fruit bunches/jute fibres: Water absorption, thickness swelling and density behaviours,” Journal of Polymers and the Environment, vol. 19, no. 1, pp. 106–109, 2011.

XII. N. Saba, M. Jawaid, O. Y. Alothman, and M. T. Paridah, “A review on dynamic mechanical properties of natural fibre reinforced polymer composites,” Construction and Building Materials, vol. 106, pp. 149–159, 2016.

XIII. P. B. Van Putten, P. J. Coenraads, and J. P. Nater, “Hand dermatoses and contact allergic reactions in construction workers exposed to epoxy resins,” Contact Dermatitis, vol. 10, no. 3, pp. 146–150, 1984.

XIV. S. Ma et al., “Synthesis and properties of a bio‐based epoxy resin with high epoxy value and low viscosity,” ChemSusChem, vol. 7, no. 2, pp. 555–562, 2014.

XV. S. M. Sapuan, M. Harimiand, and M. A. Maleque, “Mechanical properties of epoxy/coconut shell filler particle composites,” Arabian Journal for Science and Engineering, vol. 28, no. 2, pp. 171–182, 2003.

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UNDERWATER SENSOR NETWORKS: OVERVIEW OF APPLICATIONS AND RESEARCH CHALLENGES

Authors:

Krishnapriya J,Krithika Sharma N,Linitha Marina Pinto,Kavyashree B,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00028

Abstract:

A review on the challenges in underwater wireless network systems is discussed in this paper. In underwater network systems different methodologies have to be adopted in comparison to the overland network systems. Acoustic signals are used instead of electromagnetic signals. One of the issue is the propagation of electromagnetic signals through water. The marine environment also poses serious challenges in deploying the underwater wireless systems. The architecture and the applications of underwater network systems is also discussed.

Keywords:

Underwater wireless sensor networks,Acoustic sensor networks,oceanographic data collection,

Refference:

I. Barbosa, P.; White, N.M.; Harris, N.R., “Wireless Sensor Network for Localized Maritime Monitoring”, Proceedings of the 22nd International Conference on Advanced Information Networking and Applications, Okinawa, Japan, pp. 681–686, 2008.
II. Cayirci, E, Tezcan Hakan, DoganYasar, Coskun Vedat, “Wireless sensor networks for underwater survelliance systems”, Ad Hoc Networks., Vol. 4, pp 431-446, 2006.
III. Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., and Qaisar, S. B., “Underwater Sensor Network Applications: A Comprehensive Survey”. International Journal of Distributed Sensor Networks, 2015.
IV. G. A. Hollinger, S. Choudhary, P. Qarabaqi, Christopher Murphy, UrbashiMitra, Gaurav s. Sukhatme, Milica Stojanovic, Hanumant Singh and Franz Hover, “Underwater data collection using robotic sensor networks,” IEEE Journal on Selected Areas in Communications, Vol. 30, no. 5, pp. 899–911, 2012.
V. J.-H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless networks for aquatic applications,” IEEE Network, Vol. 20, no. 3, pp. 12–18, 2006.
VI. John Heidemann, Yuan Li, Affan Syed, Jack Wills and Wei Ye, “Underwater Sensor Networking: Research Challenges and Potential Applications”. USC/ISI Technical Report ISI-TR-2005-603.
VII. Liu, K.; Yang, Z.; Li, M.; Guo, Z.; Guo, Y.; Hong, F.; Yang, X.; He, Y.; Feng, Y.; Liu, Y. “Oceansense: Monitoring the sea with wireless sensor networks”. Mob. Comput. Commun. Rev., Vol. 14, pp 7–9, 2010.
VIII. Lu, K.; Qian, Y.; Rodriguez, D.; Rivera, W.; Rodriguez, M., “Wireless Sensor Networks for Environmental Monitoring Applications: A Design Framework”, Proceedings of the Global Communications Conference, Washington, DC, USA, pp. 1108–1112, 2007.
IX. M. C. Domingo and R. Prior, “Energy analysis of routing protocols for underwater wireless sensor networks,” Computer Communications, Vol. 31, no. 6, pp. 1227–1238, 2008.
X. Perez, C.A. Jimenez, M. Soto, F. Torres, R. López, J.A. Iborra, A., “A system for monitoring marine environments based on Wireless Sensor Networks”, In Proceedings of the IEEE Conference on OCEANS, Santander, Spain, pp. 1–6, 2011.
XI. Saha, S.; Matsumoto, M., “A Framework for Disaster Management System and WSN Protocol for Rescue Operation”, Proceedings of the IEEE Region 10 Conference on TENCON 2007, Taipei, Taiwan, pp. 1–4, 2007.
XII. Sharif-Yazd M., Khosravi M. R. and Moghimi M. K., “A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing”, Journal of Computer and Communications, Vol. 5, pp 12-23, 2017.
XIII. S. Premkumar Deepak and M. B. M. Krishnan, “Intelligent sensor based monitoring system for underwater pollution,” 2017 International Conference on IoT and Application (ICIOT), Nagapattinam, pp. 1-4, 2017.
XIV. S. Yoon, A. K. Azad, H. Oh, and S. Kim, “AURP: an AUV-aided underwater routing protocol for underwater acoustic sensor networks,” Sensors, Vol. 12, no. 2, pp. 1827–1845, 2012.
XV. U. Devee Prasan and S. Murugappan, “Underwater Sensor Networks: Architecture, Research Challenges and Potential Applications”. International Journal of Engineering Research and Applications, Vol. 2, Issue 2, pp.251-256, 2012.

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ANALYZING DIFFERENT ALGORITHMS AND TECHNIQUES TO FIND OPTICAL CHARACTER RECOGNITION FOR TAMIL SCRIPTS

Authors:

Rajkumar N,A. B. Rajendra,Janhavi V,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00029

Abstract:

Tamil is one of the world’s ancient languages. This paper focuses mainly in particular on OCR for the digitalization and conservation of texts and inscriptions in the Tamil language. A system that does not include obtaining either Standard size and shape or the color difference between background and foreground to recognize Palm Leaf Manuscript and stone inscriptions and obtaining information. A variety of algorithms have been analyzed for OCR texts for Tamils, and ancient letter conversion still has a big challenge to convert ancient Tamils into today’s digital text format for Tamils.

Keywords:

Tamil,OCR,Manuscript,Script,Optical Character Recognition,Tamil Language,Tamil Script,

Refference:

I. A. V. S. Rao, N. V. Rao, L. P. Reddy, G. Sunil,T.S.K.Prabhu, and A. S. C. S. Sastry, “Adaptive binarization of ancient documents,” in Proceedings of the 2nd International Conference on Machine Vision (ICMV ’09), pp. 22–26, December 2009.
II. B. Gangamma, K. Srikanta Murthy, and A. V. Singh, “Restoration of degraded historical document image,” Journal of Emerging Trends in Computing and Information Sciences, vol. 3, no. 5,pp. 36–39, 2012.
III. C. L. Tan, R. Cao, and P. Shen, “Restoration of archival documents using a wavelet technique,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1399–1404, 2002.
IV. E. Nadernejad, S. Sharifzadeh, and H. Hassanpour, “Edge detection techniques:evaluations and comparisons,” Applied Mathematical Sciences, vol. 2, no. 31, pp. 1507–1520, 2008.
V. G. R. M. Babu, P. Srimayee, and A. Srikrishna, “Heterogenous images using mathematical morphology,” Journal ofTheoretical and Applied Information Technology, vol. 15, no. 5, pp. 795–825, 2008.
VI. G. Raju and K. Revathy, “Wavepackets in the Recognition of Isolated Handwritten Characters”, Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 – 4, 2007, London, U.K.
VII. G. Rama mohanbabu,, P. Srimaiyee, A. Srikrishna , “Text extraction from hetrogenous images using mathematical morphology”, Journal of Theoretical and Applied Information Technology, vol. 16, no. 1, pp. 39 – 47 2010.
VIII. G.Y.Chen, T.D.Bui, A.Krzyzak. “Contour based handwritten numeral recognition using multiwavelets and neural networks”,Pattern Recognition, vol. 36, no. 7, pp. 1597 – 1604, 2003.
IX. H.K.Chethan, G.Hemantha Kumar, “ A Comparative Analysis of Different Edge based Algorithms for Mobile/Camera Captured Images”, International Journal of Computer Applications, vol. 7, no. 3, pp. 36-41, 2010.
X. H.K.Chethana, G.HemanthaKumar , “Image Dewarping and Text Extraction from Mobile Captured Distinct Documents”, Procedia Computer Science, vol. 2, pp. 330 – 337, 2010.
XI. Jian Yuan, Yi Zhang, KokKiong Tan, Tong Heng Lee, “Text Extraction from Images Captured via Mobile and Digital Devices”, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 566 – 571,2009.
XII. K. C. Kim, H. R. Byun, Y. J. Song et al., “Scene text extraction in natural scene images using hierarchical feature combining and verification,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR ’04), vol. 2, pp. 679–682, gbr, August 2004.
XIII. L. Agnihotri and N. Dimitrova, “Text detection for video analysis,” in Proceedings of IEEE International Workshop onContent-Based Access of Image and Video Libraries, pp. 109–113, June 1999.
XIV. M. Seeger and C. Dance, Binarising Camera Images for OCR, Xerox Research Centre, Meylan, France, 2000.
XV. P Wunsch, A F Laine. “Wavelet descriptors for multiresolution recognition of handprinted characters”. Pattern Recognition, vol. 28, no. 8, pp. 1237–1249, 1995.
XVI. P. Chevalier, L. Albera, P. Comon, and Ferreol, “Comparative performance analysis of eight blind source separation methods on radio communications signals,” in Proceedings of the International Joint Conference on Neural Networks, vol. 8, pp. 251–276, July 2004.
XVII. P. Tichavsk´y, Z. Koldovsk´y, and E. Oja, “Performance analysis of the FastICA algorithm and Cram´er-Rao bounds for linear independent component analysis,” IEEE Transactions on Signal Processing, vol. 54, no. 4, pp. 1189–1203, 2006.
XVIII. S P Chowdhury S DharA K Das B Chanda K McMenemy, “Robust Extraction of Text from Camera Images “, 2009 10th International Conference on Document Analysis and Recognition, pp. 1280 – 1284, 2009.
XIX. S W Lee, C H Kim, H Ma, Y Y Tang. “Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network”. Pattern Recognition, vol. 29, pp. 1953–1956, 1996.
XX. S. Buzykanov, “Enhancement of poor resolution text images in the weighted sobolev space,” in Proceedings of the 19th International Conference on Systems, Signals and Image Processing (IWSSIP ’12), pp. 536–539, Vienna, Austria, April 2012.
XXI. S. Cherala and P. Rege, “Palm leaf manuscript/color document image enhancement by using improved adaptive binarization method,” in Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP ’08), pp. 687–692, December 2008.
XXII. S. Choi, “Independent component analysis,” in Proceedings of the 12th WSEAS International Conference on Communications, pp. 159–162, July 2008.
XXIII. S. Choi, A. Cichocki, and S. I. Amari, “Flexible independent component analysis,” Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, vol. 26, no. 1, pp. 25–38, 2000.
XXIV. Sachin Grover, KushalArora,Suman K. Mitra , “Text Extraction from Document Images using Edge Information”, 2009 Annual IEEE India Conference, 2009.
XXV. Seethalakshmi R, Sreeranjani TR., Balachandar T, “Optical Character Recognition for printed Tamil text using Unicode”, Journal of Zhejiang University Science, vol. 6A, no. 11, pp. 1297-1305, 2005
XXVI. U. Garain, A. Jain, A. Maity, and B. Chanda, “Machine reading of camera-held low quality text images: an ICA-based image enhancement approach for improving OCR accuracy,” in Proceedings of the 19th International Conference on Pattern Recognition (ICPR ’08), pp. 1–4, December 2008.
XXVII. Vinoth R, Rajesh R, Yoganandhan P, “Intelligence System for Tamil Vattezhuttu Optical Character Recognition”, International Journal of Computer Science & Engineering Technology, vol. 8, no. 04, pp. 22 -27 Apr 2017
XXVIII. X. S. Hua, P. Yin, and H. J. Zhang, “Efficient video text recognition using multiple frame integration,” in Proceedings of the International Conference on Image Processing, vol. 2, pp. 22–25, September 2004.

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A SURVEY ON THE COLLECTIVE BEHAVIOUR OF SWARM ROBOTICS

Authors:

Jeevan J Murthy,Irshad T Y,Harshit P S,Harshith M,Kavya A P,

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00030

Abstract:

In nature many social animals follow a cooperative behaviour for the common good of their colony. Swarm robotics is a method in which a collection of similar or dissimilar robots follow an organized behaviour pattern to perform some specific tasks. The robots interact and follow simple rules to coordinate a large number of robots. Here we focus on the recent developments in swarm robotics as applied to real world problems. Swarm robotics deals with the defining the rules for the cooperative behaviour and designing, modelling, validating, operating and maintaining the robotics system. Swarm robotics can be classified as per the design and analysis or as per the collective behaviour. The limitations and the future research directions for swarm robotics is also discussed.

Keywords:

Swarm Robotics,Social behaviour,Collective behaviour,Robots,

Refference:

I. Ampatzis, C., Tuci, E., Trianni, V., &Dorigo, M.,“Evolution of signaling in a multi-robot system: categorization and communication”, Adaptive Behavior, Vol. 16(1), pp. 5–26, 2008.
II. Bachrach, J., Beal, J., &McLurkin, J.,“Composable continuous-space programs for robotic swarms”, Neural Computing & Applications, Vol. 19(6), pp. 825–847, 2010.
III. Beni, G.,“From swarm intelligence to swarm robotics”, In Lecture notes in computer science:Swarm robotics, Berlin: Springer, Vol. 3342,pp. 1–9, 2005.
IV. Bonabeau, E., Dorigo, M., &Theraulaz, G.,“Swarm intelligence: from natural to artificial systems”, New York: Oxford University Press, 1999.
V. Brambilla, M., Pinciroli, C., Birattari, M., &Dorigo, M.,“Property-driven design for swarm robotics”, In Proceedings of 11th international conference on autonomous agents and multiagent systems (AAMAS 2012), Richland: IFAAMAS, pp. 139–146,2012.
VI. Dorigo, M., & ¸ Sahin, E., Guest editorial. “Autonomous Robots”, Vol. 17, pp. 111–113, 2004.
VII. Dorigo, M., &Birattari, M.,“Swarm intelligence”,Scholarpedia, Vol. 2(9), pp. 1462, 2007.
VIII. Ferrante, E., Brambilla, M., Birattari, M., &Dorigo, M.,“Socially-mediated negotiation for obstacle avoidance in collective transport”, In Springer tracts in advanced robotics: Vol. 83. Proceedings of the international symposium on distributed autonomous robotics systems (DARS 2010), Berlin: Springer, pp. 571–583,2013.
IX. Gazi, V., &Passino, K. M.,“Stability analysis of social foraging swarms: combined effects of attractant/repellent profiles”, In Proceedings of the 41st IEEE conference on decision and control, Piscataway: IEEE Press, Vol. 3, pp. 2848–2853,2002.
X. Pugh, J., &Martinoli, A.,“Parallel learning in heterogeneous multi-robot swarms”, In Proceedings of the IEEE congress on evolutionary computation, Piscataway: IEEE Press, pp. 3839–3846,2007.
XI. Riedmiller, M., Gabel, T., Hafner, R., & Lange, S.,“Reinforcement learning for robot soccer”, Autonomous Robots, Vol. 27(1), pp. 55–73, 2009.
XII. Sahin, E.,“Swarm robotics: from sources of inspiration to domains of application”, In Lecture notes in computer science: Berlin: Swarm robotics, Springer, Vol. 3342. pp. 10–20,2005.
XIII. Seeja G, ArockiaSelvakumar A, Berlin Hency V, “A Survey on Swarm Robotic Modeling, Analysis and Hardware Architecture”, Procedia Computer Science,Vol. 133, pp. 478–485, 2018.
XIV. Soysal, O., & ¸ Sahin, E.,“Probabilistic aggregation strategies in swarm robotic systems”, In Proceedings of the IEEE swarm intelligence symposium, Piscataway: IEEE Press, pp. 325–332,2005.
XV. Spears, W. M., & Spears, D. F.,“Physics-based swarm intelligence”, Berlin: Springer, 2012.
XVI. Waibel, M., Keller, L., &Floreano, D.,“Genetic team composition and level of selection in the evolution of cooperation”, IEEE Transactions on Evolutionary Computation, Vol. 13(3), pp. 648–660, 2009.

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