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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.

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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).

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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

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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

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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

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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.

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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)

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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

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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).

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Abstract:

This paper deals with accurate decoupling capacitance estimation which is commonly used for suppression of power supply noise in modern day system-on-chip. Supply noise is a major issue needs to be addressed for proper functioning which may lead to logic failure in digital integrated circuit. Capacitors directly effects the power consumption and delay parameters and hence the overall performance of integrated circuits.  In this work design verification has been done between the pre-layout stage and post-layout stage. Simulation results show that the difference in results between pre-layout and post-layout stages is marginal. This early detection of errors can be helpful for the designers in the latter stages of the system design. This CAD flow can also be used on any system-on-chip design.

Keywords:

Computer Aided Design (CAD),White Space (WS),System-on-chip (SoC),Power Distribution Network (PDN),Decoupling capacitor (decap),

Refference:

I C. Tirumurti, S. Kundu, S. Sur-Kolay, Y.Chang, “A modeling approach for addressing power supply switching noise related failures of integrated circuits”, Proceedings of Design, Automation and Test in Europe Conference and Exhibition (DATE) pp.: 1078- 1083, 2004.
II D. Kang, C. Yiran, K. Roy, “ Power_ supply Noise- aware Scheduling and Allocation for DSP Synthesis”, Proceedings of Sixth International Symposium on Quality Electronic Design (ISQED’05), 2005
III K. Shah, “Power Grid Analysis in VLSI Designs”, Dissertation in Master of Science (Engineering),Super Computer Education and Research Centre, Indian Institute of Science Bangalore, 2007.
IV K. Shimazaki, T. Okumura, “A Minimum Decap Allocation Technique Based on Simultaneous Switching for nano-scale SoC”. Proceedings of IEEE Custom Integrated Circuits Conference, 2009.
V M. Khellah, D. Khalil, D. Somasekhar, Y. Ismail, T. Karnik, V.De, “Effect of power supply noise on SRAM dynamic stability”, Proceedings of Symposium on VLSI Circuits 2007.
VI M. Saint-Laurent, M. Swaminathan,“Impact of power-supply noise on timing in high frequency microprocessors”, IEEE Transactions on Advanced Packaging, Vol.:27, pp.: 135-144, 2004.
VII P. Mitra, J. Bhaumik, “Pre-Layout Decap Allocation for Noise suppression and Performance Analysis for 512-Point FFT core”, Proceedings of 2017 Devices for Integrated Circuits (DevIC), pp.: 341-345, 2017.
VIII P. Mitra, J.Bhaumik, “A CAD Approach for Suppression of Power Supply Noise and Performance Analysis of Some Multi-core Processors in Pre-layout Stage”, Microsystem Technologies, Springer, Vol.: 25, Issue: 5, pp.: 1977-1986, 2019.
IX S. Pant, “Design and analysis of Power Distribution Networks in VLSI Circuits”, Ph.D. Dissertation, University of Michigan, 2008.
X S. Zhao, K. Roy, C.K. Koh, “Decoupling Capacitance Allocation and Its Application to Power-Supply Noise-Aware Flooring”, IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol.: 21, Issue: 1, pp.: 81-92, 2002.
XI T.C. Hsueh, F. O’Mahony, M. Mansuri, B. Casper, “An On-Die All-Digital Power Supply Noise Analyzer with Enhanced Spectrum Measurements”, IEEE Journal of Solid-State Circuits, Vol.: 50, Issue: 7, pp.:1711-1721, 2015.
XII T. Karim, “On-Chip Power Supply Noise: Scaling, Suppression and Detection”, Ph.D. Dissertation, University of Waterloo, 2012.
XIII Y.L. Chuang, P.W. Lee, Y.W. Chang, “Voltage-Drop Aware Analytical Placement by Global Power Spreading for Mixed-Sized Circuits Design”, IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol.: 30, Issue: 11, pp. 1649-1662, 2011.
XIV Y. Shi, J. Xiong, C. Liu, L. He,“Efficient Decoupling Capacitance Budgeting Considering Operation and Process Variations”, IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol.: 27, Issue: 7, pp.: 1253-1263, 2008.

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