Archive

Abstract:

In the present numerical investigation, the epidemic patterns of Covid-19 in India is studied from a mathematical modeling perspective. The study is based on the simple SIR (Susceptible-Infectious-Recovered) deterministic compartmental model. It is analyzed fully and then calibrated against publicly available epidemiological data from late January until 10 July 2020 for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19) in India. The purpose of this study is to give a tentative prediction of the epidemic peak and sizes in our country.

Keywords:

COVID-19,India,SIR Model,Parameter Estimation,Simulation,

Refference:

An Introduction to Mathematical Epidemiology by Maia Martcheva, Springer
II. An Introduction to Mathematical Modeling of Infectious Diseases, Michael Y. Li, Springer.
III. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 S Gupta, G S Raghuwanshi , A Chanda, Science of the Total Environment, 728 (2020)
IV. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases.
V. Kermack WO, McKendrick AG. A contribution to the mathematical theory of
VI. Rajesh Ranjan, The Ohio State University, Predictions for COVID-19 outbreak in
VII. Solving applied mathematical problems with MATLAB / DingyuXue, Chapman & Hall/CRC.
VIII. United Nations. Department of Economic and Social Affairs; Population Dynamics https://population.un.org/wpp/Download/Standard/Population/ as on 20 May 2020.
IX. Ward, Alex (24 March 2020). “India’s coronavirus lockdown and its looming crisis, explained” (http s://www.vox.com/2020/3/24/21190868/coronavirus-india-modi-lockdown-kashmir).

View Download

Abstract:

The need of proper medical diagnosis and treatment has been need of the day to deal with various infections caused by viruses and micro-organisms. To prevent the spread of the disease we need proper scientific approach and methods in place. This paper suggests one such method using Markov Process technique, in particular deciding how many patients should be allocated to respective doctors in a hospital.

Keywords:

Markov Process,Markov Decision Process,Transition Probabilities, Transition Matrix, Diagonalization of a matrix,, Equilibrium Distribution ,

Refference:

I 49(10):1021–1025, 1998.

II Amanda A. Honeycutt, James P. Boyle, Kristine R. Broglio, Theodore J. Thompson, Thomas J. Hoerger, Linda S. Geiss, and K. M. Venkat Narayan, A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management

III Behavioral Sciences, pages 9242–9250, 2004.

IV Chih-Ming Liu, Kuo-Ming Wang, and Yuh-Yuan Guh. A Markov chain model for medical

V Distribution under treatment. Mathematical and Computer Modeling, 19(11):53–66, 1994.

VI For discrete-time longitudinal data on human mixed-species infections. In Some Mathematical

VII J. E. Cohen and B. Singer. Malaria in Nigeria: Contrained continuous-time Markov models

VIII L. Billard. Markov models and social analysis, International Encyclopedia of the Social and

IX Questions in Biology, pages 69–133. Providence: American Mathematical Society, 1979.

X Record analysis. The Journal of the Operational Research Society, 42(5):357–364, 1991.

XI S. I. McClean, B. McAlea, and P. H. Millard. Using a Markov reward model to estimate

XII Science, 6:155–164, 2003.

XIII Spend-down costs for a geriatric department. The Journal of the Operational Research Society,

XIV Y. W. Tan. First passage probability distributions in Markov models and the HIV incubation

View Download

Abstract:

We proposed  [0,1] truncated Lomax –Inverted Gamma ([0,1] TLIGD) distribution build on [0,1] truncated Lomax ([0,1] TLD) distribution. General expressions for the statistical properties are obtained, also The Shannon entropy , Relative entropy functions and  Stress- Strength model of the ([0,1] TLIGD)  are presented

Keywords:

[0,1] TLIGD,stress strength model, Shannon entropy and Relative entropy functions,

Refference:

I. Abid, Salah , K. Abdulrazak, Russul, “[0, 1] truncated fréchet-gamma and inverted gam-ma distributions”, International Journal of Scientific World , 2017.
II. Eugene, N., Lee, C., & Famoye, F.,“Beta-normal distribution and its applications. Communications in Statistics-Theory and methods”,vol. 31(4), pp: 497-512, 2002.
III. Gradshteyn, I. S., & Ryzhik, I. M., “Table of integrals, series, and products”: Academic press,2014.
IV. Gupta, A. K., & Nadarajah, S., “On the moments of the beta normal distribution.Communications in Statistics-Theory and methods”, vol. 33(1), pp: 1-13, 2005.
V. Jamjoom, A., & Al-Saiary, Z., “Computing the moments of order Statistics from independent nonidentically distributed exponentiated Frechet variables”. Journal of Probability and Statistics, 2012.
VI. Jones, M., “Families of distributions arising from distributions of order statistics”. Test, vol. 13(1),pp :1- 43,2004.
VII. Maria do Carmo, S. L., Cordeiro, G. M., & Ortega, E. M., “A new extension of the normal distribution. Journal of Data Science”, vol. 13(2), pp: 385-408,2014.

View Download

Abstract:

Aadhaar based identification systems are gaining momentum and it isused in several authentication mechanisms. In many democratic countries, the electoral system is still in its juvenile stage and operating in a manual mechanism which consumes huge resources for every voting. In this work, we propose a mechanism which uses Aadhaar based identification to enable a voter to vote. The connection between the voting machine and Aadhaar database is fully secured and encrypted. To avoid intentional hacking, the whole system is computerized and does not require human intervention.

Keywords:

Refference:

I. Ankita R Kasliwal, Jaya S. Gadekar, Manjiri A. Lavadkar, Pallavi K. Thorat and Prapti Deshmukh,“Aadhar Based Election Voting System”IOSR Journal of Computer Engineering, pp.18-21, 2017.
II. K. Dinakaran, P. Aravind Kumar, E. Bagavathi, M. Kathiresh Kumar, R. Madhankumar,“Smart Electronic Voting Machine Using Raspberry Pi”, International Journal of AdvancedResearch in Electrical, Electronics and Instrumentation Engineering, Vol. No. 8, pp. 829-834, March 2019.
III. Kolluru Venkata Nagendra, Palem Chandrakala, Palicherla Anusha, Dampuru Ramesh,“Implementing Aadhar Voting System in Elections Using Raspberry Pi”, InternationalJournal of Scientific Research and Review, Vol. No.7, pp. 500-507, 2018.
IV. Latha V. and Satheesh Thirumalal, “Aadhar Based Electronic Voting System andProviding Authentication on Internet of Things”, International Journal of Engineering andManufacturing Science, Vol. No. 8, pp.102-108, 2018.
V. Lingamallu Naga Srinivas and K. Srinivasa Rao, “Aadhaar Card Voting System”, Proceedings of International Conference on Computational Intelligence and Data Engineering, Vol. No. 9, pp. 159 -172, December 2018.
VI. N. N Nagamma, M. V. Lakshmaiah and T. Narmada, “Aadhar based Finger print EVMSystem”,International Journal of Electronics Engineering Research,Vol. No. 9, pp. 923-930,2017.
VII. R. Murali Prasad, Polaiah Bojja and Madhu Nakirekanti, “Aadhar based ElectronicVoting Machine using Arduino”, International Journal of Computer Applications, Vol. No.145, pp. 39-42, July 2016.
VIII. Rakesh S. Raj, Reshma, Madhushree and Bhargavi, “An Online Voting System Using Biometric Fingerprint and Aadhaar Card”, International Journal of Computing and Technology, Vol. No. 1, pp.87-92, May 2014.
IX. Sneha S.Lad, Pranav N.Tonape, Rohit S.Bhosale, Jayesh A.Shingole, Vinayak S.Kumar, “E-Voting and Presentee Muster Using Raspberry Pi 2 Modules”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. No. 4, pp. 475-482, May 2016.
X. Syed Mahmud Hasan, Arafa Mohd Anis, Hamidur Rahman, Jennifer Sherry Alam, Sohel Islam Nabil and Md KhalilurRhaman, “Development of Electronic Voting Machine with the Inclusion of Near Field Communication ID cards and Biometric fingerprint identifier”,17th International Conference on Computer and Information Technology, pp. 383-387, 2014.
XI. Tabish Ansari, Brijesh Chaurasia, Niraj Kumar, Nilesh Yadav, SonaliSuryawanshi, “Online Voting System linked with Aadhaar Card”, International Journal of Advanced Research in Computer and Communication and Communication Engineering, Vol. No. 6, pp. 204-207, September 2017.

View Download

Abstract:

Frequent failure in production systems is one of the most important problems facing maintenance planners. In this paper, the methodology for estimating failure in an electrical energy production system has been proposed.Consisting of a number of related sub-systems, respectively, failure of any one causes the rest to stop producing.Operating data were collected and the type of failure identified, which was classified into three types (mechanical failure, electrical failure, and control failure). The software (Matlab) was used in generating and training an artificial neural network (ANN) to estimate the type of failure, through the data collected for each sub-system of the unit under study, use 90% of the data for training, 5% for testing, and 5% for valuation. The target matrix was built and trained, with a mean square error (MSE) its(6.54 E-16), and regression (91%), and adopted to estimate the type of future failure for subsequent years(2019),conformance results were for the subsequent year between (82%-87%) for all the subsystems. Using the artificial neural network, failure types were estimated for another subsequent year (2020), the failure ratios were for subsystems for every ten days during the year of estimation, were (33%) for the generator, (22%) for the boiler, (31%) for the turbine, and (13%) for the condenser. High percentages, which can be reduced by taking advantage of the proposed methodology that gave an understanding of the type of failure, the time it occurred, and the location of the failure, by building an overlapping preventive maintenance plan whose application is approved in reducing the failuretimes of the unit under study.The proposed methodology can also be applied to all other systems of different production

Keywords:

Matlab software, Generator,Artificial Intelligent (AI),

Refference:

I. Devika Chhachhiya, Amita Sharma, Manish Gupta “Case Study on Classification of Glass Using Neural Network Tool in MATLAB” International Journal of Computer Applications, 0975 – 8887),(2014).
II. D. Bose, G. Ghosh, K. Mandal, S.P. Sau4 and S. Kunar “Measurement and Evaluation of Reliability, Availability and Maintainability of a Diesel Locomotive Engine” International Journal of Engineering Research and Technology, Volume 6, Number 4,pp. 515-534, 2003.

III. Emilia Sipos, Laura-Nicoleta Ivanciu”Failure Analysis and Prediction Using Neural Networks in the Chip Manufacturing Process “ResearchGate, DOI: 10.1109/ISSE.2017.8000931, May 2017

IV. Erdi Tosun, Ahmet C¸ alık”Failure load prediction of single lap adhesive joints using artificial neural networks”Alexandria Engineering Journal vol. 55, pp1341–1346,2016
V. Farhad Hooshyaripora, Ahmad Tahershamsib, and Kourosh Behzadian”Estimation of Peak Outflow in Dam Failure Using Neural Network Approach under Uncertainty Analysis” Pleiades Publishing, Vol. 42, No. 5, 2015
VI. Gustavo Scalabrini Sampaio, Arnaldo Rabello de Aguiar Vallim Filho,Leilton Santos da Silva and Leandro Augusto da Silva” Prediction of Motor Failure Time Using An Artificial Neural Network” Sensors, 19, 4342; doi:10.3390/s19194342, 2019
VII. Laurene V. Fausett, “Fundamentals of Neural Networks: Architecture, Algorithm, and Application”, Florida Institute of Technology, First Edition, December, 1993.
VIII. Mahdi Saghafi , Mohammad B. Ghofrani “Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network” Nuclear Engineering and Technology doi.org/10.1016/j.net.2018.11.017
IX. M. Goya-Martinez, “The Emulation of Emotions in Artificial Intelligence,” Emotions, Technology, and Design. pp. 171–186, 2016
X. Walter, E.; Pronzato L., “Identification of Parametric Models from Experimental Data”, London, England: Springer-Verlag, 1997.

View Download

Abstract:

IoT is a globally accepted smart technology that has the ability to connect each and almost every physical devices through the network. It acts as a bridge between cloud environment and physical environment. It is mainly used to connect the hardware devices like sensors, actuators, storage, hardware, and software to acquire or exchange data. These devices collect the information from the physical world and convert this into useful information that can help in decision making. Since IoT connects everything to the network, so it may face the problem of a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing of an energy efficient data transfer scenario between cloud and IoT devices. For this reason, a Complex Proportional Assessment (COPRAS) based clustering approach has been proposed in this work to select the cluster premier effectively and form the set of best clusters for maximizing the network lifetime. The proposed work deals with data transmission model between IoT and cloud that confirms the improvement in energy efficiency, network lifetime, and latency. Furthermore, the sensitivity analysis has also been carried out and satisfactory results has been obtained.

Keywords:

Cloud Computing, Clustering, MCDM, IoT,

Refference:

I. A. Majumdar, T. Debnath, S. K. Sood, K. L. Baishnab, “Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment”, Journal of medical systems, Springer, vol. 42, no.10, pp.187, 2018.
II. A. Majumdar, A., Biswas, K. L. Baishnab, S. K. Sood, “DNA Based Cloud Storage Security Framework Using Fuzzy Decision Making Technique”, KSII Transactions on Internet & Information Systems, vol.13, no.7, pp. 3794-3820, 2019.
III. A. Majumdar, N. M. Laskar, A. Biswas, S. K. Sood, K. L. Baishnab, “Energy efficient e-healthcare framework using HWPSO-based clustering approach”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 36, no. 5, pp. 3957-3969, 2019.
IV. A. Biswas, A. Majumdar, S. Nath, A. Dutta, K. L. Baishnab, “LRBC: a lightweight block cipher design for resource constrained IoT devices”, Journal of Ambient Intelligence and Humanized Computing, Springer pp.1-15, 2020.
V. A. V. Dhumane and R. S. Prasad, “Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT”, Wireless Personal Communications, Springer, vol. 102, no. 1, pp. 411-36, 2018.
VI. A. V. Dhumane, R. S. Prasad, and J. R. Prasad, “An optimal routing algorithm for internet of thing enabling technologies”, International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 4, no. 3, pp. 1-16, 2017.
VII. A. Orsino, G. Araniti, L. Militano, J. Alonso-Zarate, A. Molinaro, A. Iera,. “Energy efficient IoT data collection in smart cities exploiting D2D communications”, Sensors, vol. 16, no. 6, p.836, 2016.
VIII. D. Wei, S. Kaplan, H.A. Chan, “Energy efficient clustering algorithms for wireless sensor networks”, In Communications Workshops, 2008. ICC Workshops’ 08. IEEE International Conference on, pp. 236-240, 2008.
IX. G. L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, and L.D.P. Mendes, “Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things,” in International Workshop on Telecommunications (IWT), pp. 1–7, 2015.
X. I. Yaqoob, E. Ahmed, I.A.T. Hashem, A.I.A. Ahmed, A. Gani, M. Imran, M. Guizani, “Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges”, IEEE wireless communications, vol. 24, no. 3, pp.10-16, 2017.
XI. J. H. Kwon, M. Cha, S. B. Lee, and E. J. Kim, “Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks”, Multimedia Tools and Applications, Springer, vol. 78, no.3, pp. 2963-82, 2019.
XII. J. A. Martins, A. Mazayev, N. Correia, G. Schütz, and A. Barradas, “GACN: Self-clustering genetic algorithm for constrained networks”, IEEE Communications Letters, vol. 21, no. 3, pp. 628-31, 2017.
XIII. J.M. Liang, J.J. Chen, H.H. Cheng, Y.C. Tseng, “An energy-efficient sleep scheduling with qos consideration in 3gpp lte-advanced networks for internet of things,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 1, pp.13-22, 2013.
XIV. J. Tang, Z. Zhou, J. Niu, Q. Wang, “An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things”, Journal of Network and Computer Applications, vol. 40, pp.1-11, 2014.
XV. L. Song, K. K. Chai, Y. Chen, J. Loo, S. Jimaa, and J. Schormans, “QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems”, in Wireless Communications and Networking Conference, IEEE, April 2016, pp. 1-6.
XVI. L. Song, K.K. Chai, Y. Chen, J. Schormans, J. Loo, A. Vinel, “QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems”, IEEE Systems Journal, vol. 11, no. 3, pp.1447-1455, 2017.
XVII. M. P. K. Reddy and M. R. Babu, “Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things”, Cluster Computing, Springer, pp. 1-12, 2018.
XVIII. M. P. K. Reddy and M. R. Babu, “Energy Efficient Cluster Head Selection for Internet of Things”, New Review of Information Networking, Taylor & Francis, vol. 22, no. 1, pp. 54-70, 2017.
XIX. M. P. K. Reddy and M. R. Babu, “An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things”, International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 337-46, 2017.
XX. N. T. Van, T. T. Huynh, and B. An, “An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 35, no. 6, pp. 5845-5852, 2018.
XXI. N. Kaur, and S.K. Sood, “An Energy-Efficient Architecture for the Internet of Things (IoT)”, IEEE Systems Journal, vol.11, no.2, pp.796-805, 2017.
XXII. Ö.U. Akgül, B. Canberk, “Self-Organized Things (SoT): An energy efficient next generation network management,” Computer Communications, vol. 74, pp.52-62, 2016.
XXIII. S. K. Singh, M.P. Singh, D.K. Singh, “Energy-efficient homogeneous clustering algorithm for wireless sensor network”, International Journal of Wireless & Mobile Networks (IJWMN), vol. 2, no. 3, pp.49-61, 2010.
XXIV. S. Rani, R. Talwar, J. Malhotra, S.H. Ahmed, M. Sarkar, H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks”, Sensors, vol. 15, no. 11, pp.28603-28626, 2015.
XXV. S. D. Muruganathan, D. C. Ma, R. I. Bhasin, A. O. Fapojuwo, “A centralized energy-efficient routing protocol for wireless sensor networks”, IEEE Communications Magazine, vol. 43, no. 3, pp. S8-13, 2005.
XXVI. T. Ayesha, S. Sadaf, D. Sinha, and A. K. Das. “Secure Anti-Void Energy-Efficient Routing (SAVEER) Protocol for WSN-Based IoT Network”, In Advances in Computational Intelligence, pp. 129-142. Springer, Singapore, 2020.
XXVII. Z. Zhou, J. Tang, L.J. Zhang, K. Ning, Q. Wang, “EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things”, Personal and Ubiquitous computing, vol.18, no.4, pp.951-966, 2014.
XXVIII. A. Majumdar, T. Debnath, K. L. Baishnab, S. K. Sood, “An Energy Efficient e-Healthcare Framework Supported by HEO-µGA (Hybrid Extremal Optimization Tuned Micro-GeneticAlgorithm)”, Information System Frontiers, Springer, 2020, DoI: https://doi.org/10.1007/s10796020-10016-5.
XXIX. A. Majumdar, M. Sharma, “Enhanced information security using DNA cryptographic approach. International Journal of Innovative Technology and Exploring Engineering”, vol.4, no.2, pp. 72-76, 2020.

View Download

Abstract:

Despite the world’s 20th largest economy, Thailand's innovation ecosystem is questionable, ranked the world’s 43rd in Global Innovation Index 2019 report.  The paper aims at investigating the innovation performance and trend of Thailand based on 7 aspects of innovation inputs and outputs.  Referred to the data dated back to 2011, knowledge and technology outputs, human capital and research, institutions, and creative inputs are considered Thai strengths with progressive improvement.  Market sophistication is strong but there has been no significant improvement.  Business sophistication is considerably weak but there is a sign of improvement.  Infrastructure is the most concerning issue.

Keywords:

Thailand,Global Innovation Index ,innovation performance and trend,

Refference:

I. A. Limcharoen, V.Jangkrajarng, W.Wisittipanich, S. Ramingwong, “Thailand logistics trend: Logistics performance index”. International Journalof Applied Engineering Research, Vol: 12, Pages: 4882-4885, 2017.
II. A. Sopadang, N. Chonsawat, S. Ramingwong, “Smart SME 4.0 Implementation Toolkit”. in Industry 4.0 for SMEs. Palgrave Macmillan, Cham, 2020.
III. B. Å.Lundvall, “Why study national systems and national styles of innovation?”. Technology Analysis & Strategic Management, Vol: 10, Issue: 4, Pages: 403-422, 1998.
IV. B. Mercan, D. Goktas, “Components of innovation ecosystems: a cross-country study”. International Research Journal of Finance and Economics, Vol: 76, Issue: 16, Pages: 102-112, 2011.
V. C. Chaminade, P.Intarakumnerd, K. Sapprasert, “Measuring systemic problems in national innovation systems”. An application to Thailand. Research Policy, Vol: 41, Issue: 8, Pages: 1476-1488, 2012.
VI. C. Jones, P.Pimdee, “Innovative ideas: Thailand 4.0 and the fourth industrial revolution”. Asian International Journal of Social Sciences, Vol: 17, Issue: 1, Pages: 4-35, 2017.
VII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2013: The Local Dynamics of Innovation”. Geneva, Ithaca, and Fontainebleau, 2013.
VIII. Cornell University, INSEAD, WIPO,“The Global Innovation Index 2014: The Human Factor In innovation”. Fontainebleau, Ithaca, and Geneva, 2014.
IX. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2015: Effective Innovation Policies for Development”. Fontainebleau, Ithaca, and Geneva, 2015.
X. Cornell University, INSEAD,WIPO, “The Global Innovation Index 2016: Winning with Global Innovation”. Ithaca. Fontainebleau, and Geneva, 2016.
XI. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2017: Innovation Feeding the World”. Ithaca, Fontainebleau, and Geneva, 2017.
XII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2018: Energizing the World with Innovation”. Ithaca, Fontainebleau, and Geneva, 2018.
XIII. Cornell University, INSEAD, WIPO, “The Global Innovation Index 2019: Creating Healthy Lives – The Future of Medical Innovation”. Ithaca, Fontainebleau, and Geneva, 2019.
XIV. D. J. Jackson, “What is an innovation ecosystem”. National Science Foundation, Vol: 1, Issue: 2. 2011.
XV. D. Schiller, “Nascent innovation systems in developing countries: University responses to regional needs in Thailand”. Industry and Innovation, Vol: 13, Issue: 4, Pages: 481-504, 2006.
XVI. D. Schiller, “The potential to upgrade the Thai innovation system by university‐industry linkages”. Asian Journal of Technology Innovation, Vol: 14, Issue: 2, Pages: 67-91, 2006.
XVII. E. G. Carayannis, D. F. J. Cambell, “’Mode 3’and’Quadruple Helix’: toward a 21st century fractal innovation ecosystem”. International Journal of technology management, Vol: 46, Issue: 3-4, Pages: 201-234, 2009.
XVIII. E. Rauch, P. Dallasega, M. Unterhofer, “Requirements and Barriers for Introducing Smart Manufacturing in Small and Medium-Sized Enterprises”. IEEE Engineering Management Review, Vol: 47, Issue: 3, Pages: 87-94, 2019.
XIX. H. Zsifkovits, M.Woschank, S. Ramingwong, W. Wisittipanich, “State-of-the-Art Analysis of the Usage and Potential of Automation in Logistics”. In Industry 4.0 for SMEs (pp. 193-212). Palgrave Macmillan, Cham, 2020.
XX. INSEAD, CII,“Global Innovation Index 2008-2009”, 2008.
XXI. INSEAD, CII, “Global Innovation Index 2009-2010”, 2009.
XXII. INSEAD, “The Global Innovation Index 2011: Accelerating Growth and Development”. Fontainebleau, 2011.
XXIII. INSEAD, WIPO, “The Global Innovation Index 2012: Stronger Innovation Linkages for Global Growth”. Fontainebleau, 2012.
XXIV. J. Jintana, A. Limcharoen, Y. Patsopa, S. Ramingwong, “Innovation Ecosystem of ASEAN Countries”. Amazonia Investiga, Vol: 9, Issue: 28, Pages: 356-364, 2020.
XXV. J.R. Bessant, J. Tidd. “Innovation and Entrepreneurship”. John Wiley & Sons, 2001.
XXVI. J. Wonglimpiyarat, “Government programmes in financing innovations: Comparative innovation system cases of Malaysia and Thailand”. Technology in Society, Vol: 33, Issue: 1-2, Pages: 156-164, 2011.
XXVII. K. B. Kahn, “Understanding innovation”. Business Horizons, Vol: 61, Issue: 3, Pages: 453-460, 2018.
XXVIII. L.M. Branscomb, J.H. Keller, “Investing in innovation: Creating a research and innovation policy that works”. MIT Press, 1999.
XXIX. M. A. R. Garcia, R. Rojas, L. Gualtieri, E. Rauch, D. Matt, “A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing”. Procedia CIRP, Vol: 81, Pages: 600-605, 2019.
XXX. M. Lindberg, M. Lindgren, J.Packendorff, “Quadruple Helix as a Way to Bridge the Gender Gap in Entrepreneurship: The Case of an Innovation System Project in the Baltic Sea Region”. Journal of the Knowledge Economy, Vol: 5, Pages: 94-113, 2014.
XXXI. M. Woschank, E. Rauch, H. Zsifkovits, “A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics”. Sustainability, Vol: 12, Issue: 9, Pages: 3760, 2020.
XXXII. N. Chonsawat, A. Sopadang, “The Development of the Maturity Model to evaluate the Smart SMEs 4.0 Readiness”. In Proceedings of the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, 2019.
XXXIII. NESDB.“The Twelfth National Economic and Social Development Plan (2017-2021)”, 2017.
XXXIV. NESDB, “Gross Domestic Product, Chain Volume Measures: Q1/2019”, 2019.
XXXV. P. Cooke, M.G. Uranga, G.Etxebarria, “Regional innovation systems: Institutional and organizational dimensions”. Research Policy, Vol: 26, Issue: 4-5, Pages: 475-491, 1997.
XXXVI. P. Intarakumnerd, P. A.Chairatana, T. Tangchitpiboon, “National innovation system in less successful developing countries: the case of Thailand”. Research Policy, Vol: 31, Issue: 8-9, Pages: 1445-1457, 2002.
XXXVII. R. R. Nelson, “National innovation systems: a comparative analysis”. Oxford University Press on Demand, 1993.
XXXVIII. S. Durst, P. Poutanen, “Success factors of innovation ecosystems-Initial insights from a literature review”. Co-create,Pages: 27-38, 2013.
XXXIX. S. Dutta, INSEAD, S. Caulkin, “The World’s Top Innovators”. World Business, Vol: 8, Pages: 26-37, 2007.
XL. S. J. Kline, N. Rosenberg, “An overview of innovation”. in Studies On Science And The Innovation Process: Selected Works of Nathan Rosenberg, Pages: 173-203, 2010.
XLI. S. Klaus, “The Global Competitiveness Report 2019”. World Economic Forum, Geneva, 2019.
XLII. S. Ramingwong, W.Manopiniwes, “Supportment for organization and management competences of ASEAN community and European Union toward Industry 4.0”. International Journal of Advanced and Applied Sciences, Vol: 6, Issue: 3, Pages: 96-101, 2019.
XLIII. S. Ramingwong, W.Manopiniwes, V.Jangkrajarng, “Human Factors of Thailand Toward Industry 4.0”. Management Research and Practice, Vol: 11, Issue: 1, Pages: 15-25, 2019.
XLIV. S. Santiteerakul, K. Y.Tippayawong, P.Dallasega, K.Nimanand, S. Ramingwong, “Logistics performance review: European Union and ASEAN community”. Journal of Applied Economic Sciences, Vol: 13, Pages: 1175-1180, 2018.
XLV. S. Tiwong, S. Ramingwong, K. Y. Tippayawong, “On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs”. Sustainability, Vol: 12, Issue: 6, Pages: 2394, 2020.
XLVI. SDPD, “NESDC Economic Report: Thai Economic Performance in Q3 and Outlook for 2019 – 2020”, 2019.
XLVII. W. Manopiniwes, K. Y.Tippayawong, J.Numkid, S.Santiteerakul, S. Ramingwong, P.Dallasega, “On Logistics Potential of Thai Industry in Identifying Gap to Logistics 4.0”. Journal of Engineering and Applied Sciences, Vol: 14, Pages: 1608-1613, 2019.

View Download

Abstract:

In this work, the effect of electromagnetic field on natural fluid flow within the absorbent tube in the parabolic solar collector was numerical investigated.Where a solar collector with parabolic reflector was used. Water was used in the first and the flow was free as the results showed high efficiency of the device. Then a magnetic iron oxide (Fe₃O₄) nanoparticle was added to make the fluid subject to influence in the electromagnetic field, where three concentrations (0.9%, 0.5%, and 0.3%) were used to study the effect of magnetic flux on each concentration and to make a comparison. The results showed a slight effect of the electromagnetic field in the case of water use, as the efficiency of the solar collector improved by (8.8%) in the case of using the concentration (0.9%) and an electromagnetic overflow (7970 Gauss).

Keywords:

Magnetic field, ,solar collector,solar collecto,Solar energy,Ferrfluid, Nano Particles,Nanofluid Properties,Nanofluid,

Refference:

I Abu-Nada, E, “Application of nanofluids for heat transfer enhancement of separated flows encountered in a backward-facing step”, International Journal of Heat and Fluid Flow.; 242-24,. (2008).
II Aminfar H., Mohammad P. M., Mohseni F., “Two-phase mixture model simulation of the hydro-thermal behavior of an electrical conductive ferrofluid in the presence of magnetic fields”, Journal Magn. Magn. Mater.;324, 830-842, (2012).
III Duffie J A., Beckman W A., “Solar energy thermal processes”, in, University of Wisconsin- Madison, Solar Energy Laboratory, Madison, WI, (1974).
IV Hussein A. K., Ashorynejad H. R., Sheikholeslami M., Sivasankaran S., “Lattice Boltzmann simulation of natural convection heat transfer in an open enclosure filled with Cu–water nanofluid in a presence of magnetic field”,Nucl. Eng. Des.;268,10-17, (2014).
V Maiga S. E. B., Cong T. N., “Heat transfer enhancement in turbulent tube flow using Al2O3 nanoparticle suspension”, International Journal of Numerical Methods for Heat and Fluid Flow.; 275-29, (2006).
VI Mohsen S., Mofid G. B., Ellahibc A. Z., “Simulation of MHD CuO–water nanofluid flow and convective heat transfer considering Lorentz forces”, Journal of Magnetism and Magnetic Materials.; 369, 69-80, (2014).
VII Nagarajan P K., Subramani J., Suyambazhahan S., Sathyamurthy R., “Nanofluids for solar collector applications: A Review”, Energy Procedia; 61: 2416 – 2434,(2014).
VIII Sheikhzadeh G A, Sebdani1 M S, Mahmoodi M, Elham S, Hashemi S E. “Effect of a Magnetic Field on Mixed Convection of a Nanofluid in a Square Cavity”, Journal of Magnetics.;18, 321-325, (2012).
IX Titan C., Morshed A. M., Jamil A.K., “Nanoparticle enhanced ionic liquids (NEILS) as working fluid for the next generation solar collector”, Procedia Engineering, 5th BSME International Conference on thermal engineering.; 56, 631-636, (2013).
X Tyagi H., Phelan P., Prasher R., “Predicted Efficiency of a Low-Temperature Nanofluid–Based Direct Absorption Solar Collector”, Journal of Solar Energy Eng. 131, 041004, (2009).
XI Zhang Z., Gu H., Fujii M., “Effective thermal conductivity and thermal diffusivity of nanofluids containing spherical and cylindrical nanoparticles”, Exp. Therm. Fluid Sci.; 31, 5593-5599, (2007).

View Download

Abstract:

The main purpose of segmentation is to partition an image based on features into different regions. Unsupervised classification algorithms K means, K-nearest neighbor, neural networks can be used to perform efficient image segmentation. Image segmentation is an important step to perform classification of images. Segmentation algorithms such as watershed segmentation, support vector machines can be used to find the region of interest. A genetic algorithm based image segmentation algorithm, ant colony optimization algorithm is proposed and we compare it with k-means segmentation. We apply some segmentation algorithms in industry standard datasets and view the results of our segmentation algorithms. Segmentation is a basic task in image processing and can be applied in large number of domains. We emphasize on how a segmentation algorithm can be developed to segment out tum ours from medical magnetic resonance images. We have used the open CV python package for our image processing tasks.

Keywords:

Magnetic Resonance Imaging,K-means algorithm,Genetic Algorithms,Ant Colony Optimization ,Image segmentation,unsupervised classification,support vector machine,Medical Image processing,

Refference:

I A Markov random field image segmentation model for color textured images Zoltan Kato a,*, Ting-Chuen Pong b,1
II Bradski, G., 2000. The Open CV Library. Dr. Dobb Journal of Software Tools.
III Colour Image Segmentation Using SVM Pixel Classification Image K. Sakthivel, R. Nallusamy, C. Kavitha TW World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:8, No:10, 2014
IV Dorigo, Marco & Birattari, Mauro & Stützle, Thomas. (2006). Ant Colony Optimization. Computational Intelligence Magazine, IEEE. 1. pp 28-39. 10.1109/MCI.2006.329691.
V Digital Image Processing and Analysis by Bhabatosh Chanda and Dwijesh Dutta Majumder PDF Online. ISBN 9788120343252 from PHI Learning.
VI Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. Upper Saddle River, N.J.: Prentice Hall, 2002.pp700-809
VII M. Haseyama, M. Kumagai and H. Kitajima, “A genetic algorithm based image segmentation for image analysis,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA, 1999,
VIII Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011

View Download

Abstract:

There are serious obstacles in resolving a people’s present position and movement state inside an indoor situation. Position and movement action report of people becomes a business. For particular, it can resort movement accelerometer information to scan how patients are adapted to practices, for example, strolling or standing. Position following data can be for ensuring the preservation of mature consideration cases. The designed system applied for patient’s localization, tracking and investigation services within healthcare institutes through a wireless sensor network based on IoT. The personal monitoring module based on optional sensors which analyzes the movements of the patients is detecting hazardous incidents, and the wireless communication framework to send the data. Two methodologies are contrasted with the usage of the limitation and following motor a unified execution where confinement is executed halfway out of data gathered at the local area and a result where the localization is observed at nodes and the result is given to the central administrator connected through IOT which provides global accesses monitoring to the authorized personnel at anytime and anywhere. It displays strong and poor positions of the both the results from a system viewpoint in calls of localization efficiency, energy performance and traffic capacities. These sensor systems are examined in a specific situation using testing kits. The key outcomes are average localization faults fewer than 2 m in 80% of the experiments and an operation’s analysis efficiency as significant as 90%. This paper presents patient localization, tracking and information services within healthcare institutes through a WSN based on IoT. Particle Swarm Optimization Adaptive Extended Kalman Filter (PSO-AKF) have been recommended for localization and having a path of victim’s position. A particular observation module based on optional sensors that analyzes the actions of the patients eventually detecting hazardous incidents, and a wireless communication framework to transmit the data remotely.

Keywords:

Localization, E-Health,Particle Swarm Optimization Adaptive Extended Kalman Filter (PSO-AKF), IoT,Wireless Sensor Networks,

Refference:

I. E.K. Antonsson, R.W. Mann, The frequency content of gait, Journal of Biomechanics 18 (1) (1985) 39–47, http://dx.doi.org/10.1016/0021929 (85)90043-0.

II. G. Currie, D. Rafferty, G. Duncan, E. Bell, A. Evans, Measurement of gait by accelerometer and walkway: a comparison study, Medical & Biological Engineering & Computing 30 (1992) 669670.

III. J. Ko, C. Lu, M. Srivastava, J. Stankovic, A. Terzis, M. Welsh, Wireless sensor networks for healthcare, Proceedings of the IEEE 98 (11) (2010) 1947–1960, http://dx.doi.org/10.1109/JPROC.2010.2065210.

IV. Janapati, Ravichander, and K. Soundararajan. “Enhancement of Indoor Localization in WSN using PSO tuned EKF.” International Journal of Intelligent Systems and Applications 9.2 (2017): 10.

V. Janapati, Ravichander, et al. “Indoor localization of cooperative WSN using PSO assisted AKF with optimum references.” Procedia Computer Science 92 (2016): 282-291.

VI. L. Klingbeil and T. Wark, “A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring“ in International Conference on Information Processing in Sensor Networks, 2008.

VII. M. Mathie, A. Coster, B. Celler, N. Lovell, Classification of basic daily movements using a triaxial accelerometer, Medical and Biological Engineering and Computing 42 (2004) 670687.

VIII. M. McCarthy, P. Duff and H. L. Muller, C. Randell, C. “Accessible Ultrasonic Positioning”, IEEE Pervasive Computing, Vol 5, pp 86-93, 2006

IX. M. Sugano, T. Kawazoe, Y. Ohta, M. Murata, Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard, in: Wireless and Optical Communications, IASTED/ ACTA Press, 2006, pp. 1–6.

X. Prasad, C. R., & Bojja, P. (2020). The energy-aware hybrid routing protocol in WBBSNs for IoT framework. International Journal of Advanced Science and Technology, 29(4), 1020–1028.

XI. Pravalika, V., & Rajendra Prasad, C. (2019). Internet of things based home monitoring and device control using Esp32. International Journal of Recent Technology and Engineering, 8(1 Special Issue 4), 58–62.

XII. V. Otsason, A. Varshavsky, A. LaMarca and E. de Lara, “Accurate GSM indoor localization.”, in Ubiquitous Computing 7th International Conference, Proceedings (Lecture Notes in Computer Science Vol. 3660) . Springer-Verlag, pp 141-58, 2005

View Download

Abstract:

In this article we introduce A*-involution in additively inverse semirings. This involution have potential to extend the striking results of B*-algebras, C*- algebras and involutory rings in the domain of semirings. The remarkable result due to Herstein[XII] states that every Jordan derivation on a 2-torsion free prime ring is a derivation. In the present paper, we shall study the above mentioned result for Jordan left derivations in semirings with A* -Involution.

Keywords:

Jordan left derivation,Involution,Prime semirings,Additive Inverse semirings,

Refference:

I. Awtar, R, Lie ideals and Jordan derivations of prime rings, Proc. Amer. Math. Soc.90 (1984), 9-14.
II. Ashraf, M. and N. Ur. Rehmann, On Lie ideals and Jordan left derivations of prime rings. Arch. Math. (Brno) 36 (2000), 201-206
III. Bandlet H.J. and M. Petrich, Subdirect products of rings and distributive lattices Proc. Edin Math.Soc. 25 (1982) 135-171.
IV. Beidar K.I, WS Martindale On Functional Identities in Prime Rings with Involution,Journal of Algebra Volume 203. Issue 2, 15 May 1998, 491-532.
V. Bergen, J., Herstein, I.N. and Ker, J.W., Lie ideals and derivations of prime rings,J. Algebra 71 (1981), 259-267.
VI. Bresar. M. and Vukman, J., On left derivations and related mappings, Proc. Ameer. Math. Soc. 110 (1990), 7-16.
VII. C. Lanski, Commutation with skew elements in rings with involution, Pacific J. Math. Volume 83, Number 2(1979), 393-399.
VIII. Chadja. I , H. LANGER , Near Semirings and Semirings with Involution, Miskolc Mathematical Notes, Vol.17 (2017) No. 2, 801810
IX. Fadaee. B and H.Ghahramani, Continuous linear maps on reflexive algebras behaving like Jordan left derivations at idempotent- product elements,ar Xiv:1312.6953
X. Ghahramani. H., Characterizations of left derivable maps at non- trivial idempotents on nestalgebras, arXiv:1312.6959.
XI. Golan. J.S., The theory of semirings with applications in mathematics and theoretical computer science (John Wiley and Sons . Inc, New York, 1992). doi:10.1007/978- 94-015-9333-5-13.
XII. Herstein, I.N, Jordan derivations of prime rings, Proc. Amer. Math. Soc. 8(1957), 1104- 1110.
XIII. J. Li and J. Zhou, Jordan left derivations and some left derivable maps, Oper. Matrices 4(2010),127-138.
XIV. J.V. Markov, Pierce Sheaf for semirings with involution, Russian Mathmatics (Iz. VUZ), 2014, Vol.58, No. 4, 1419.
XV. Javed. M. A, Aslam M. Hussain M., On condition (A 2) of Bandlet and Petrich for inverse semirings, Int. Math. Forum, 2012,7, 2903-2914.
XVI. Javed. M. A., Aslam M., Some commutativity conditions in prime MA-semirings, Ars Combin., 2014,114,373-384.
XVII. Karvellas P.H, Inversive semirings, J. Austral. Math. Soc., 1974,18,277-288.
XVIII. Kill-Wong Jun and Byung-Do Kim, A note on Jordan left derivations, Bull. Korean Math. Soc.33 (1996) No.2,221-228.
XIX. M. Bresar, Characterizing homomorphisms, multopliers and derivations in rings with idempotents, Proc. Roy,Soc.Edinburgh Sect. A137(2007), 9-21.
XX. M. Burgos, J. Cabello-Sanchezanda. M . Peralta, Linear maps between C*-algebras that are*- homomorohism at a fixed point, arXiv: 1609.07776.
XXI. M.A. Chebotar, W.F. Ke and P.H .Lee , Maps characterized by action on zero products, Pacific J. Math.216 (2004), 217-2278.
XXII. Oukhtite.L., S. Salhi , On commutativity of – prime rings. Glas. Math. Ser. III Vol.41, no.1 (2006), 57-64.
XXIII. Yaqoub Ahmed, W.A. Dudek, Stronger Lie derivations on MA-semirings, Afrika Mat., doi.org/10.1007/s13370-020-00768-3.
XXIV. Yaqoub Ahmed, W.A. Dudek. Left Jordan derivation on certain semirings,. Hacepette J. Math. (accepted).
XXV. Vukman. J. On left Jordan derivations of rings and Banach algebras, Equations Math .75 (2008), no. 3, 260-266
XXVI. Y. Ahmed, M. Nadeem, M. Aslam, On Centralizers of MA semirings, JMCMS, Vol 15 (4), 47-57
XXVII. Y. Ahmed, Wieslaw Dudek, M. Aslam, Asian European journal of Mathematics (accepted), DOI: 10.1142/S1793557121500674

View Download

Abstract:

In wide area monitoring system, phasor measurement units (PMUs) plays a vital role in providing synchronized measurements with the help of Global Positioning System (GPS). In conventional optimal PMU placement methodology these PMUs are placed optimally across the power system network ensuring completely observable.  It is found in literature, that most of them neglect the PMU channel limitations, variable PMU costs, and measurement redundancy improvement. To address this problem, in the present paper an optimal PMU problem is addressed by optimizing the two objective functions that are conflicting in nature, namely, minimization of PMU installation cost and maximization of measurement redundancy at the same time. In order to allocate PMUs, both channel limitation and variable cost of PMUs has been considered. A non-dominated sorting genetic algorithm-II (NSGA-II)based methodology is proposed to solve the combinatorial optimization problem. The Pareto optimal solution obtained using the concept of crowding distance and non-dominated sorting. A multi-criteria decision making technique based on VIKOR method is utilized for finding the best compromise solution from the set of Pareto-optimal solution obtained through NSGA-II. To verify the effectiveness and reliability, the proposed approach is tested on IEEE 14-bus, 30-bus, and 57-bus systems.

Keywords:

PMU placement, VIKOR method, NSGA-II,Power System,

Refference:

I. A. Enshaee, R. A. Hooshmand, and F. H. Fesharaki, “A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies,” Electr. Power Syst. Res., vol. 89, pp. 1–10, 2012.

II. A. Mahari and H. Seyedi, “Optimal PMU placement for power system observability using BICA, considering measurement redundancy,” Electr. Power Syst. Res., vol. 103, pp. 78–85, 2013.

III. B. Milošević and M. Begović, “Nondominated sorting genetic algorithm for optimal phasor measurement placement,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 69–75, 2003.

IV. C. Chang, “A modified VIKOR method for multiple criteria analysis,” Environ. Monit. Assess., vol. 168, pp. 339–344, Sep. 2010.

V. C. Peng, H. Sun, and J. Guo, “Multi-objective optimal PMU placement using a non-dominated sorting differential evolution algorithm,” Int. J. Electr. Power Energy Syst., vol. 32, no. 8, pp. 886–892, 2010.

VI. C. Ruben, S. C. Dhulipala, A. S. Bretas, Y. Guan, and N. G. Bretas, “Multi-objective MILP model for PMU allocation considering enhanced gross error detection: A weighted goal programming framework,” Electr. Power Syst. Res., vol. 182, p. 106235, 2020.

VII. H. Manoharan, S. Srikrishna, G. Sivarajan, and A. Manoharan, “Economical placement of PMUs considering observability and voltage stability using binary coded ant lion optimization,” Int. Trans. Electr. Energy Syst., vol. 28, no. 9, pp. 1–19, 2018.
VIII. J. Aghaei, A. Baharvandi, A. Rabiee, and M. A. Akbari, “Probabilistic PMU Placement in Electric Power Networks: An MILP-Based Multiobjective Model,” IEEE Trans. Ind. Informatics, vol. 11, no. 2, pp. 332–341, 2015.

IX. J. Aghaei, A. Baharvandi, M. A. Akbari, K. M. Muttaqi, M. R. Asban, and A. Heidari, “Multi-objective Phasor Measurement Unit Placement in Electric Power Networks: Integer Linear Programming Formulation,” Electr. Power Components Syst., vol. 43, no. 17, pp. 1902–1911, 2015.

X. K. Arul jeyaraj, V. Rajasekaran, S. K. Nandha Kumar, and K. Chandrasekaran, “A multi-objective placement of phasor measurement units using fuzzified artificial bee colony algorithm, considering system observability and voltage stability,” J. Exp. Theor. Artif. Intell., vol. 28, no. 1–2, pp. 113–136, Mar. 2016.

XI. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.

XII. K. Jamuna and K. S. Swarup, “Multi-objective biogeography based optimization for optimal PMU placement,” Appl. Soft Comput. J., vol. 12, no. 5, pp. 1503–1510, 2012.

XIII. M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II,” Int. J. Electr. Power Energy Syst., vol. 30, no. 2, pp. 140–149, 2008.

XIV. N. P. Theodorakatos, N. M. Manousakis, and G. N. Korres, “A sequential quadratic programming method for contingency-constrained phasor measurement unit placement,” Int. Trans. Electr. Energy Syst., vol. 25, no. 12, pp. 3185–3211, Dec. 2015.

XV. R. Babu and B. Bhattacharyya, “Strategic placements of PMUs for power network observability considering redundancy measurement,” Meas. J. Int. Meas. Confed., vol. 134, pp. 606–623, 2019.

XVI. S. M. Mazhari, H. Monsef, H. Lesani, and A. Fereidunian, “A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2136–2146, 2013.

XVII. S. Opricovic and G.-H. Tzeng, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS,” Eur. J. Oper. Res., vol. 156, no. 2, pp. 445–455, Jul. 2004.

XVIII. S. P. Singh and S. P. Singh, “A Multi-objective PMU Placement Method in Power System via Binary Gravitational Search Algorithm,” Electr. Power Components Syst., vol. 45, no. 16, pp. 1832–1845, 2017.

XIX. S. Prasad and D. M. V. Kumar, “Robust meter placement for active distribution state estimation using a new multi-objective optimization model,” IET Sci. Meas. Technol., vol. 12, no. 8, pp. 1047–1057, 2018.

XX. V. Basetti and C. Ashwani Kumar, “Optimal Multi-Objective Hybrid Measurement Placement Using NSGA-II,” i-manager’s J. Power Syst. Eng., vol. 2, no. 3, p. 28, 2014.

View Download

Abstract:

The leaf spring is one of the vital components of an automobile which absorbs vibration from shocks generated due to uneven road surface. It is made up of conventional materials like plain carbon steel are heavy and add weight to vehicle which reduces mileage. This necessitates new material which is light in weight and could provide adequate strength to leaf spring along with higher strain energy absorption to absorb shocks.The current research investigates the application of Graphite Aluminum MMC on leaf spring for mass reduction using Finite Element Method.  The CAD model is developed in ANSYS design modeler and analyzed in workbench.  The design is then optimized using Taguchi Response Surface method using Central Composite Design scheme. The RSM optimization generated specific set values for optimization variables (inner radius and outer radius) along with sensitivity plot and goodness of fit curve. The application of Graphite Aluminum MMC resulted in 56.1% of mass reduction without increase in stress as compared to conventional steel material.

Keywords:

Graphite Aluminum MMC,stress,Leaf Spring,Response Surface,FEA,

Refference:

I. Abbas MKG, Niakan A, Ming CC, Singh R, Teo P, “Design and numerical analysis of leaf spring using composite materials”, International Conference on Material Science and Engineering, pp: 305–310, (2017)
II. Agarwal A, Pitso I, “Modelling & Numerical Exploration of Pulsejet Engine Using Eddy Dissipation Combustion Model”, Material Today Proceedings, Volume:27, part:2, (2020)
III. Akshat Jain, Arun Jindal, Prateek Lakhiani, Sheelam Mishra, “Mathematical Approach to Helical and Wave Spring: A Review”, International Journal of Mechanical and Production Engineering, volume: 5, issue:6, (2017)
IV. Al-Qureshi HA, “Automobile leaf springs from composite materials”, Journal of Materials Processing Technology, volume:118, Issues:1–3, pp: 58-61, (2001)
V. Deshmukh BB, Jaju SB.,“Design and analysis of glass fiber reinforced polymer (GFRP) leaf spring”,IEEE 4th International Conference on Emerging Trends in Engineering and Technology, pp: 82-7(2011)
VI. Goette T, Jakobi R, Puck A,“Fundamentals of the dimensioning of fibre/plastics composite leaf springs for commercial vehicle application”, Kunststoffe – German Plastics,75,pp:17–19 (1985)
VII. Goette T, Jakobi R, Puck A, “On the development of fibre/plastics composite leaf springs for commercial vehicle application”,Kunststoffe – German Plastics,75, pp:20–24(1985)
VIII. Hameed MI, Alazawi DA, Hammoudi ZS,“Finite element analysis of steel and composite leaf springs under static loading”, International Scientific Conference of Engineering, Iraq, pp: 181-185 (2018)
IX. Jenarthanan MP, Ramesh Kumar S, Venkatesh G, Nishanthan S,“Analysis of leaf spring using carbon/glass epoxy and EN45 using ANSYS-A comparison”, Materials Today Proceedings, Volume:5, Issue:6, Part:2, pp: 14512-14519, (2018)
X. J-tJ Kueh, Faris T,“Finite element analysis on the static and fatigue characteristics of composite multi-leaf spring”, J Zhejiang Univ. Sci A,Volume:13,pp 159–164 (2012)
XI. M. Parwani, V. Jain, V. Sharma, “optimization of leaf spring using composite material,” Int. J. Recent Technol. Sci. Manag.,volume 2, pp: 17–26 (2017)
XII. N. Lavanya, P. S. Rao, M. P. Reddy, “Design and Analysis of A Suspension Coil Spring For Automotive Vehicle,” Int. J. Eng. Res. Appl., volume: 4, no. 9, pp. 151–157 (2014)
XIII. Rajendran I., S. Vijayarangan, “Design, analysis, fabrication and testing of a composite leaf spring,” volume: 82, pp: 180–187(2002)
XIV. Shamsaei N, D. Rezaei, “Comparing Fatigue Life Reliability of a Composite Leaf Spring With a Steel Leaf Spring”, Proceedings of the 7th Biennial Conference on Engineering Systems Design and Analysis, ESDA, pp: 371–374 (2004)
XV. Seretse O.M, Agarwal A., Letsatsi M.T., “Exploratory investigation of vibrational characteristics of the Un-damped and Damped Spring Mass Systems”, Journal of Mechanical Engineering Research & Developments, Volume: 4, issue: 3, pp: 96-103 (2018)
XVI. Singh H, Brar GS,“Characterization and investigation of mechanical properties of composite materials used for leaf spring”, Material Today Proceedings, Volume:5, Issue:2,pp: 5857–5863(2018)
XVII. Soner M. et al., “Design and Fatigue Life Comparison of Steel and Composite Leaf Spring”, in SAE 2012 World Congress & Exhibition(2012)
XVIII. Suraj Rawal, “Metal-Matrix Composites for Space Applications”, JOM, Volume:53,Issue:4, pp: 14-17 (2001)
XIX. Thippesh L, “Fabrication of Hybrid Composite Mono-Leaf Spring with Unidirectional Glass Fibers”, Materials Today Proc., volume 5, no. 1, pp: 2980–2984(2018)
XX. Yinhuan Z., X. Ka, H. Zhigao, “Finite element analysis of composite leaf spring,” in 2011 6th International Conference on Computer Science Education (ICCSE), pp: 316–319 (2011)
XXI. Yu W.J, H. C. Kim, “Double tapered FRP beam for automotive suspension leaf spring”, Composite Structure, volume 9, no. 4, pp: 279–300(1988)

View Download

Abstract:

Bus travel is gaining importance during the last decade. Due to its rapidness, easiness in booking and sophisticated travel, ticket booking is slowly shifting from manual to cloud based due to increase in technology awareness. In this paper a ticketing system is designed by using image captured which will be used to predict and update available vacancy which can be further used for booking. This is a fully cloud based system linked to QR code-based wallet linked ticket booking mechanism which is connected to a secured payment gateway. Passenger availability inside the bus will be validated by using QR code which should be scanned near entrance; this validates the current availability and updates the system. The whole system is designed in a way such that it is fully automated and seat vacancy updates dynamically. The whole framework including software and tools will operate from cloud-based servers for increased stability and reliability.  

Keywords:

QR-code,Cloud-based servers,Cloud-based system,Ticketing system,

Refference:

I. Asha P, Albert Mayan J, Canessane A, “Efficient Mining of Positive and Negative Itemsets Using K-Means Clustering to Access the Risk of Cancer Patients”, Communications in Computer and Information Science, ICSCS 2018, Kollam, 2018, pp.373-382

II. C.Upendra Reddy , D.L.S.Vara Prasad Reddy “Bus Ticket System For Public Transport Using Qr Code”, Department Of Cse, Sathyabama Institute of Science and Technology, Chennai, India

III. Jafrul Islam Sojol, Nayma Ferdous Piya, ShalimSadman, “Tamanna Motahar An Automated Passenger Counting System”. Department of Electrical and Computer Engineering, North South University Dhaka, Bangladesh.

IV. Janewit“Vehicle Seat Vacancy Identification using ImageProcessing Technique” Wittayaprapakorn School of Information Technology Mae Fah Luang University Chiangrai, Thailand

V. M.K.Dharani M.Priadarsini K.Tamilselvi “Nifty system for tracking bus and seat availability” Department of CSE Kongu Engineering College,Erode-638 0522 dharani.cse@kongu.edu priadarsini.cse@kongu.edu tamilselvik.cse@kongu.edu

VI. Mohini,Pooja M. Chinchole,Vaishnavi R. Mahajan, S. Shirsath ,Varsha G. Moga “A Review on Smart Bus Ticketing System using QR-Code”. Department of Information Technology Engineering Matoshri College of Engineering and Research Centre

VII. Prof. Balram Timande, “Public Transport System Ticketing system using RFID and ARM processor Perspective Mumbai bus facility”, VLSI & Embedded System design Electronics and Telecommunication Engineering, DIMAT, Associate Professor Department of Electronics and Telecommunication Engineering

VIII. Xiao-Lei, M et al “Transit Smart Card Data Mining for Passenger Origin Information Extraction”, Journal of Zhenjiang University Science (2012),Vol. 13(10), pp.750-760

View Download

Abstract:

Data mining is the process of discovering interesting patterns from the transactional database. In the past decade, numerous techniques have been proposed for mining frequent patterns using single minimum support threshold for all items from the transactional database which results in “rare item issue”. While fixing the minimum support to higher level, it results frequent patterns where rare item are missed. While fixing the minimum support to lower level, it results in too many frequent patterns which is known as combinatorial explosion. To confront the rare item problem, an effort has been made in the literature to find frequent patterns with “multiple minimum supports thresholds”. In this approach, minimum item support (MIS) is given to each item for mining frequent patterns. In this article, comparative analysis is done between MISFP-Growth and MISLP-Growth algorithm for mining frequent patterns using multiple minimum support threshold. In MISLP-Growth algorithm array based structure is adopted which is the major advantage and in MISFP-Growth algorithm pointer based structure is adopted which is the disadvantage. For this, the experiments are conducted using benchmark databases to find the efficient algorithm.From the results produced by these algorithms, it is found that the MISLP-Growth algorithm outperforms MISFP-Growth algorithm for all the databases in the criteria of consumption of runtime and memory.

Keywords:

Data Mining,Multiple Minimum Support, Minimum support, LP-Growth,Frequent Patterns,

Refference:

I. Agrawal, R., and Srikant, R, (1994) ‘Fast algorithms for mining association rules in large databases’, In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pp.487–499.
II. Borah, A., and Nath, B, (2019) ‘Rare Pattern Mining: Challenges and Future Perspectives.’ Complex Intell. Syst. 5,pp. 1-23.
III. Chee, C., Jaafar, J., Aziz, I.A. (2019)’ Algorithm for Frequent Itemset Mining: A Literature Review’ Arificial Intelligence,52,pp.2603-2621.
IV. Han, J., Pei, Y., Yin, (2000) ‘Mining frequent patterns without candidate generation’, Proceedings ACM-SIGMOD International Conference on Management of Data (SIGMOD’ 00), Dallas.
V. Han, J., Pei, J., Yin, and Mao, R, (2004) ‘Mining frequent patterns without candidate generation: A frequent- pattern tree Approach, Data Mining in Knowledge Discovery 8(1): pp.53–87.
VI. Hoque, F.A., Easmin, N., and Rashed, K. (2012) ‘Frequent pattern mining for multiple minimum supports with support tuning and tree maintenance on incremental database’, Research of Information Technology J., 3(2): pp.79-90.
VII. Hu, Y.H., and Chen, Y, (2006) ‘Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism’, Decis. Support Syst., 42(1):pp.1–24.
VIII. Kiran, and Reddy, P.K. (2010) ‘Mining rare association rules in the datasets with widely varying items’ frequencies’, In DASFAA (1), pp. 49–62.
IX. Liu, B., Hsu, W., and Ma. Y. (1999) ‘Mining association rules with multiple minimum supports’. In KDD ’99: Proceedings of the Fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pages 337–341.
X. Sinthuja, M, Puviarasan, N, and Aruna, P, (2018) ‘Proposed Improved FP-Growth Algorithm with Multiple Minimum Support Threshold Value (MISIFP-Growth) For Mining Frequent Itemset’, International Journal of Research in Advent Technology, Vol.6, May, pp.471-476.
XI. Sinthuja, M, Puviarasan, N, and Aruna, P, (2018) ‘Mining frequent Itemsets Using Top Down Approach Based on Linear Prefix tree’, Springer, Lecture Tabs on Data Engineering and Communications Technologies, Vol.(15), September, pp.23-32.

XII. Sinthuja, M, Puviarasan, N, and Aruna, P, (2018) ‘Geo Map Visualization for Frequent Purchaser in Online Shopping Database Using an Algorithm LP-Growth for Mining Closed Frequent Itemsets’, Elsevier, procedia computer science, Vol.132, pp.1512-1522.
XIII. Sinthuja, M. Puviarasan, N. and Aruna, P, (2019) Frequent Itemset Mining using LP-Growth algorithm based on Multiple Minimum Support Threshold Value (MIS-LP-Growth), Journal of Computational and Theoretical Nanoscience,Volume 16, No(4), pp. 1365-1372(8).

View Download