Special Issue No. – 3, September, 2019

Abstract:

The most concentrating area is energy sustainability across the globe due to need of energy system for different applications. An energy system in Electrical Vehicular Technology (EVT) requires high power and energy densities for achieving the long drive and acceleration respectively. Now a day’s most preferable rechargeable battery is Lithium Ion (Li-Ion) battery, to achieving the long drive of EVT, it is use for conventional vehicles (battery electric vehicles) and hybrid electric vehicles. In this paper, KIA company EV+ car specifications such as Permanent Magnet Synchronous Machine (PMSM), vehicle design parameters, drive train, and Li-Ion battery is considering. In addition to the Li-Ion battery and an ultra-capacitor bank is connected in the proposed system. Hence, the combination of energy sources is proposing a Hybrid Energy Storage System (HESS) for EVT. In this system, the conventional and proposing energy system mathematical model is developing based on Depth of Discharge (DOD) of the vehicle by using MATLAB/Simulink. Compare the both energy systems results are such as State of Charge (SOC), Life Loss, and Power for United States Simplified Federal (SFUDC) and European Union (EUDC) urban drive cycles are observing and tabulate.

Keywords:

SOC,DOD,drive cycles,energy system,Li-Ion battery,power,batteries,ultra-capacitors,SFUDC,EUDC,life loss,

Refference:

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Abstract:

In the internet along with other network applications, the requirement for security is increasing each day due to its wide usage. There are loads of algorithms which were established for the safe transmission of data. This paper offers a fresh approach for the generation of the key using the ‘Advanced Encryption Standard' (AES) algorithm along with the Flower Pollination Algorithm (FPA). This combination is termed as Modified AES (MAES). Initially, a plain text of 128 bits is the input to this algorithm. This text is transmuted to a cipher text. The key generation is important for the generation of the ‘S-Box’ (substitution box). The key generation on the proposed work is done utilizing the FPA. This step is done to make the keys in such a manner that the complexities of the S-Box enhance. This ameliorates the security of the proposed work for data transmission on a network. Then encryption is done. This is followed by decryption. Finally, the 128bit plain text is retrieved at the receiver's side. The MAES algorithm was compared with other traditional cryptographic algorithms. The proposed MAES algorithm yielded exceptional results.

Keywords:

Modified Advanced Encryption Standard Algorithm,Flower Pollination Algorithm,Security,Encryption,Decryption,Key,

Refference:

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1, pp.139-145, 2018.
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systems.” IEEE Access , Vol.: 6, pp. 20596-20608, 2018.
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and Power for Data Encryption and Decryption Using K-NN Machine
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Masked AES S-Box Based on Composite Field and Common Subexpression
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Vol.:27, Issue: 11, pp.1-11, 2018.

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Abstract:

A Gray Hole Attack (GHA) in dynamic wireless sensor networks (Dynamic WSN) is an attack that specifically drops or conveys occasion packets as the traded off hub moves. In such an attack, it is hard to recognize the traded off hub contrasted and the sending attack happening in the remote sensor arrange on the grounds that all sensor hubs move. To distinguish sending attacks in Dynamic WSN, a haze figuring based framework for a Gray hole recognition plot known as Fuzzy logic determining multi-paths in Gray hole attack (FL-MP-GHA) has been proposed. In any case, since the proposed recognition conspire utilizes a solitary way, the vitality utilization of the sensor hub for course revelation when the sensor hub moves is substantial. To take care of this issue, the manuscript utilizes fluffy rationale to decide the quantity of multi-ways expected to improve the vitality effectiveness of sensor systems. Trial results demonstrate that the vitality productivity of the sensor organize is improved.

Keywords:

Gray hole attack (GHA),dynamic wireless sensor networks (Dynamic WSN),Fuzzy logic determining multi-paths in Gray hole attack (FL-MP-GHA),

Refference:

I. A.Abduvaliyev, A.S.K.Pathan, J.Zhou, R.Roman, W.C.Wong, “On the vital
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networking: its bionic mechanisms, designing principles and
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16, Issue: 1, pp. 513-537, 2014.

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Abstract:

This work is a study of comparing different feature selection techniques on the accuracy of text classification. Text Mining or Document Categorization is a supervised learning (an Information Retrieval task which learns from labeled train data) technique where it uses labeled (set of instances with predefine labels) train instances or data to learn the categorization job and then it categorize the test text instances automatically using the system that is learnt. In the field of IR and management tasks, classification plays an important lead. The text categorization procedure includes the steps text pre-processing (cleaning, stop word removal and stemming), feature extraction or feature reduction or feature selection and then categorization. In this work, two machine learning algorithm/classifiers (Naïve Bayes and K-Nearest Neighbor) are used for classification. The analyzed experimental results show that Naïve Bayes algorithm gives more accuracy in many cases i.e. with many feature selection techniques and K-Nearest Neighbor classifier works well only in the cases, when the feature selection techniques either Information Gain (IG) or Mutual Information (MI). The results of experiments reported here were generated while Self-made corpus used for training and Reuters-21578 corpus used for testing.

Keywords:

Stop word removal,stemming,feature weighting and selection,K-NN,Naïve Bayesian,

Refference:

I. “A Fuzzy Self-Constructing Feature Clustering Algorithm for Text
Classification”, IEEE transactions on knowledge and data engineering, Vol.:
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June 2009.

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Abstract:

This paper presents novel and simple scalar Pulse Width Modulation (PWM) techniques for vector control based Induction Motor (IM) drives to reduce the common mode voltage (CMV). These PWM techniques don’t require information of angle and sector. So, there is less complexity. In the proposed approach, a generalized offset time expression is derived. The modulating signals of various PWM techniques were derived by varying a constant. With these PWM techniques, 33.33% of CMV is reduced. Also, these techniques are simple to implement because, reference vector calculation and sector identification is not required. The experimental set up of v/f control based IM drive is developed. The vector control based IM drive is simulated and the proposed scalar PWM techniques are evaluated.

Keywords:

Common Mode Voltage,Vector Control,Induction Motor Drive,Active Zero State,Near State,

Refference:

I. A.M. Hava, N.O.Cetin, “A Generalized Scalar PWM Approach With Easy Implementation Features for Three-Phase,Three-Wire Voltage-Source Inverters”, IEEE Transactions on Power Electronics, Vol.: 26, Issue: 5, pp.1385-1395, 2011.
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Abstract:

In the current era, the manufacturing sector plays a vital role in the industrial growth and economy. The limited availability of resources such as land make efficient resource allocation and utilization highly necessary. Optimization of the new as well as existing facility layouts is carried out to reduce connection costs and thereby increase profits. The optimization algorithm must consider the equipment dimensions, orientation and the connection costs between each equipment. The outcome of such an algorithm would be a set of coordinates of each equipment and the floor on which the base of the equipment is to be placed. Mapping this data into a 2D layout will provide a visual understanding of the optimized plant design. In addition to this, with the development of innovative concepts such as the Industry 4.0 in the markets, companies upgrading their levels of technology. In developing countries such as India, not all plants have a huge capital. So, they need to devise a systematic plan to implement the new Industry 4.0 model and its supporting technologies. A successful conjunction between Industry 4.0 and lean concepts is the most viable option. This study aims to achieve both these targets – to devise an algorithm that optimizes the location of each equipment and a method that determines the possible upgrades in technology that are feasible for a firm.

Keywords:

Layout,Optimization,Industry 4.0,Plant design,

Refference:

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Press: San Francisco, 2015.
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subsidiaries, Technological Forecasting & Social Change.
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Automation Technology. 11(1) (2017) 4-16

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Social Change.

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Abstract:

This paper presents a novel generalized scalar Pulse Width Modulation (PWM) technique based on universal offset time expression for Multilevel Voltage Source Inverter (VSI) fed Vector Controlled Drives In this technique, by varying a constant between 0 and 1, various PWM techniques have been derived. These PWM techniques don’t require information of angle and sector. Also, these techniques are simple to implement because, reference vector calculation and sector identification is not required. So, there is less complexity. The Multilevel inverter uses level shifting carrier signals. The proposed concept is simulated and evaluated.

Keywords:

PWM Algorithm,Vector Control,Induction Motor Drive,Multilevel Inverter,Voltage Source Inverter,

Refference:

I. A.R.Beig, “Application of three-level voltage-source-inverters to voltagefed
& current-fed high-power inductionmotor drives”, Ph.D. dissertation,
Indian Institute of Science, Bengaluru,India,2004
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Schemes for Induction Motors Torque Control”, IEEE Transactions on
Power Electronics, Vol.:17, Issue:5, pp.779-787, 2002.
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generalised cascaded multi-level-inverter topology”, Proceedings of
IEEE-APEC, pp.542-548, 2000.
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Drives”, Journal of Electrical Engineering, Vol.: 11, Issue: 4, pp. 23-30,
2011.
VII. P. Rama Mohan, T. Bramhananda Reddy and M. Vijaya Kumar, “A
Simple Generalized PWM Algorithm for Three Phase Voltage Source
Inverter fed AC Drives”, International Review of Electrical Engineering,
Vol.: 10, Issue: 2,pp. 180-188, 2015.

VIII. S.Das and G.Narayanan, “Novel Switching Sequences for a Space-
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Transactions on Industry Applications,Vol.:45, Issue:3, pp.1028-1034,
2009.

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Abstract:

Grid-connected PV systems (GPV) with one stage of conversion method, high performance and effectiveness can be caused by some control objectives. Objectives like current control, output current harmonics, maximum power tracking algorithm with synchronized grid connections. These objectives are merely controlled in one stage GPV systems with two-level inverter topology. The proposed paper, a medium scale variable PV single stage 3-ɸ power is associated with the grid is presented. The basic Perturb and Obseve type of MPPT tracking technique is used to abstract tremendous energy of PV system. This can be done by using a novel technique called Voltage Oriented Control (VOC). To validate the proposed method, solar irradiation and temperature of a solar PV cell are considered as input for the simulation process. The VOC based GPV system performance can be evaluated with the simulation results, the percentage THD estimations of electrical parameters like voltage and currents are verified at the point of common coupling. The presented results will identify that the VOC based GPV gives the better and high dynamic performance of the system at various irradiation conditions.

Keywords:

Perturb and observe MPPT,VOC,Grid-Connected PV system,VSI,

Refference:

I. A.Yazdani, “Modeling guidelines and a benchmark for power system
simulation studies of three-phase, single-stage photovoltaic systems”,
IEEE Trans. Power Del., Vol.: 26, Issue: 2, pp. 1247–1264, April
2011.
II. B.Tamimi, C.Canizares, K.Bhattacharya, “Modeling and performance
analysis of large solar photo-voltaic generation on voltage stability and
inter-area oscillations”, in Proc. IEEE Power Energy Soc. Gener. Meet,
pp. 1–6, July 2011.
III. F.Blaabjerg, Z.Chen, S.Kjaer, “Power electronics as an efficient
interface in dispersed power generation systems”, IEEE Trans. Power
Electron., Vol.: 19, Issue: 5, pp. 1184–1194, Sep. 2004.
IV. F.Delfino, G.Denegri, M.Invernizzi, R.Procopio, “A control algorithm
for the maximum power point tracking and the reactive power injection
from grid-connected PV systems”, in Proc. IEEE Power Energy Soc.
Gener.Meet, pp.1–7, Jul. 2010
V. G.S.Reddy, T.B.Reddy, M.V.Kumar, “Investigations on grid connected
PV system under Variable irradiation conditions”, International Journal
of Engineering & Technology, Vol.: 7 Issue:3.29, pp:253-258, 2018.
VI. G.Petrone, G.Spagnuolo, R.Teodorescu, M.Veerachary, M.Vitelli,
“Reliability issues in photovoltaic power processing systems”, IEEE
Trans. Ind. Electron., Vol.: 55, Issue: 7, pp. 2569–2580, Jul. 2008.
VII. J.Carrasco, “Power-electronic systems for the grid integration of
renewable energy sources: A survey”, IEEE Trans. Ind. Electron.,
Vol.: 53, no. 4, pp. 1002–1016, Jun. 2006.
VIII. L. Wang and Y.H Lin, “Dynamic stability analyses of a photovoltaic
array connected to a large utility grid”, in Proc.IEEE Power Eng. Soc.
Winter Meet, Vol.: 1, pp. 476–480, Jan. 2000

IX. R.Mastromauro, M.Liserre, A.Dell’Aquila, “Control issues in singlestage
photovoltaic systems: MPPT, current and voltage control”, IEEE
Trans. Ind. Informat., Vol.: 8, Issue: 2, pp. 241– 254, May 2012.
X. R.Varma, B.Das, I.Axente, T.Vanderheide, “Optimal 24-hr utilization
of a PV solar system as STATCOM (PV-STATCOM) in a distribution
network”, in Proc. IEEE Power Energy Soc.Gener.Meet., pp. 1–8, Jul.
2011.
XI. S.Jain, V.Agarwal, “A single-stage grid connected inverter topology
for solar PV systems with maximum power point tracking”, IEEE
Trans.Power Electron., Vol.: 22, Issue: 5, pp. 1928–1940, Jul.2007.
XII. S.H.Ko, S.Lee, H.Dehbonei, C.Nayar, “Application of voltage and
current-controlled voltage source inverters for distributed generation
systems”, IEEE Trans. Energy Convers., Vol.: 21, Issue: 3, pp. 782–
792, Sep. 2006.
XIII. S.Jain, V.Agarwal, “Comparison of the performance of maximum
power point tracking schemes applied to single-stage grid-connected
photovoltaic systems,” IET Electr. Power Appl., Vol.:1, Issue: 5, pp.
753–762, Sep. 2007.
XIV. T.Esram, P.Chapman, “Comparison of photovoltaic array maximum
power point tracking techniques”, IEEE Trans. Energy Convers, Vol.:
22, Issue: 2, pp. 439–449, Jun. 2007.
XV. W.Du, H.Wang, R.Dunn, “Power system small-signal oscillation
stability as affected by large-scale PV penetration”, in Proc. IEEE
Int.Conf. SUPERGEN, pp. 1–6, Apr. 2009.

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Abstract:

The use of composite materials is increasing day by day due to their less weight and high strength. Some of the natural fiber reinforced composites are competing with the artificial fiber reinforced composites. The most significant parameters that affect properties of composites are fiber loading, fiber length, fiber orientation, method of fabrication, etc. In the present research work, an attempt has been made to produce composite materials reinforced with teak fiber in epoxy resin (Araldite LY556). In this investigation, fiber lengths of10mm, 30mm and 50 mm and fiber loading of 2%, 3% and 4% w/w were used. The composite specimens were fabricated by hand layup technique. Experiments were scheduled as per L9 orthogonal array using Taguchi’s design of experiments. The effect of fiber loading and fiber length on tensile and flexural strengths has been analyzed.

Keywords:

Teak fiber,Epoxy composites,Tensile strength,Flexural strength,

Refference:

I. https://en.wikipedia.org/wiki/Teak
II. J. Holbery and D. Houston, “Natural fiber reinforced polymer composites
in automotive applications”,‖ J Miner Meta Mater Soc., Vol. 58, 2006,
pp. 80-86.
III. J. K. Pandey, S. H. Ahn, C. S. Lee, A. K. Mohanty and M. Misra “Recent
advances in the application of natural fiber based composites”,
Micromolecular Materials and Engineering, 295(11), 2010, pp.975-89.
IV. M. Ganesan, and T.N. Valarmathi, “Tensile strength of teak wood saw
dust-cashew nut shell liquid resin composites”. IJEDR, Vol.2, (1), 2014,
pp.196-199.
V. M. J. John and R. D. Anandjiwala, “Recent developments in chemical
modification and characterization of natural fiber-reinforced composites”,
J Polym Compos., Vol. 29, 2008, pp.187-207.
VI. M. J. John and S. Thomas, “Biofibres and biocomposites”, J
CarbohydPolym., Vol. 71, 2008 pp. 343-364.
VII. M. N.Belgacem, P. Bataille and S. Sapieha, “Effect of corona
modification on the mechanical properties of polypropylene/cellulose
composites”, J ApplPolym Sci., Vol. 53, 1994, pp. 379-385.
VIII. MohiniSaxena, Asokanpappu, Anushasharm, RuhiHaque and
SonalWankhede, CSIR- Advanced Materials and Processes research
Institute, Counsil of Scientific & Industrial Research, Habibganj Naka,
Bhopal.
IX. S.V. Joshi, L.T. Drzal, A.K. Mohanty and S. Arora, “Are natural fiber
composites environmentally superior to glass fiber reinforced
composites”,‖ Compos Part A., Vol. 35, 2004, pp. 371-376.
X. V.Anandarao, Aloksatapathy, S.Mishra, Proceedings of international and
INCCOM-6 Conference Future Trends in Composite materials and
processing Dec12-14,2007, Indian Institute of Technology Kanpur.

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Abstract:

In this paper, proposes Image Encryption using chaotic crypto algorithm for improving the cyber security levels of Images and videos, the inherent characteristics and Properties of digital images, enormously using all properties of chaos being the natural superiorities of chaotic systems in secret transmissions and information encryption, able to provide the way to solve major issues of cyber security.

Keywords:

Image Encryption,Chaotic crypto,Cyber Security,Image Decryption,

Refference:

I. A.Akhshani, A., Behnia, S., Akhavan, A., Hassan, H.A. and Hassan, Z, “A
novel scheme for image encryption based on 2D piecewise chaotic maps”,
Optics Communications, Vol.: 283, pp.3259-3266, 2010.
II. A.Awad, “A New Chaos-Based Cryptosystem for Secure Transmitted
Images”, IEEE Trans. on Computers, 2011.
III. Ahmad, Musheer, and M. ShamsherAlam. “A new algorithm of encryption
and decryption of images using chaotic mapping.” International Journal on
computer science and engineering, Vol.: 2, Issue: 1, pp. 46-50, 2009.
IV. A.Z. Tirkel, R.G. Van Schyndel, C.F. Osborne, A digital watermark,
Proceedings of ICIP 1994, Austin Convention Center, Austin, Texas, Vol.: II,
pp. 86–90, 1994.
V. Banik, B.Gupta, S.K.Bandyopadhyay. “Secret Sharing Using 3 Level DWT
Method of Image Steganography Based on Lorenz Chaotic Encryption and
Visual Cryptography.” Computational Intelligence and Communication
Networks (CICN), 2015 International Conference on. IEEE, 2015.
VI. Bauer, Lujo, “Device-enabled authorization in the Grey
system.”InternationalConference on Information Security. Springer Berlin
Heidelberg, 2005.
VII. Francois, Michael “A new pseudo-random number generator based on two
chaotic maps”, Informatica, Vol.: 24, Issue: 2, pp.181-197, 2013.

VIII. Liu, Ye, “Image encryption algorithm based on chaotic system and dynamic
S-boxes composed of DNA sequences”, Multimedia Tools and
Applications, Vol.: 75, Issue: 8, pp. 4363-4382, 2016.
IX. Liu, Hongjun, A.Kadir, Pijuan, Gong “A Fast Color Image Encryption
Scheme using one-time S-Boxes based on complex chaotic system and
Random noise”, Optics Communications , Vol.: 338, pp.340-347, 2015.
X. M.Brindha, N.A.Gounden. “A chaos based image encryption and lossless
compression algorithm using hash table and Chinese Remainder
Theorem.” Applied Soft Computing, Vol.: 40 (2016): 379-390.
XI. N.Bourbakis, and Christos Alexopoulos. “Picture data encryption using scan
patterns.” Pattern Recognition, Vol.: 25, Issue: 6, pp. 567-581, 1992.
XII. Norouzi, Benyamin, “A novel image encryption based on row-column,
masking and main diffusion processes with hyper chaos”, Multimedia Tools
and Applications, Vol.: 74, Issue: 3, pp.781-811, 2015.
XIII. R. Guesmi, “A novel chaos-based image encryption uses DNA sequence
operation and Secure Hash Algorithm SHA-2 and Nonlinear Dynamguesmi,
Ramzi, et al., Hash key-based image encryption using crossover operator and
chaos”, Multimedia tools and applications, Vol.: 75, Issue: 8, pp.1123-1136,
2016.
XIV. Uhl, Andreas, A.Pommer, “Image and video encryption: from digital rights
management to secured personal communication” , Vol.: 15. Springer
Science and Business Media, 2004.
XV. X.H.Q.Zhang “Image Encryption Based on Chaotic Modulation of Wavelet
Coefficients”, Congress on IEEE Image and Signal Processing (CISP’08),
Sanya, Hainan, Vol.: 1, pp.622-626, 27- 30, May 2008.
XVI. Ying, Wang, “The spatial-domain encryption of digital images based on
high-dimension chaotic system”, Cybernetics and Intelligent Systems, IEEE
Conference on, Vol.: 2. IEEE, 2004.
XVII. Y.Wang, X.Liao, T.Xiang, K.W.Wong, D.Yang, “Cryptanalysis and
Improvement on a Block Cryptosystem based on Iteration a Chaoticmap”,
Physics Letters A, Vol.: 363, pp. 277-281, 2007.

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Abstract:

Photovoltaic (PV) systems are most commonly used renewable energy source to obtain electrical energy economically as photovoltaic systems are simple, precise and economical but it effects with temperature and irradiance which means that the photovoltaic system is a non-linear source. It is possible to supply photovoltaic power to the utility grid while the power demand increases. Grid integrated photovoltaic system has the advantage of effective utilization of generated power.This paper presents an overview of single phase single stage grid integrated photovoltaic system with maximum power point tracking. The proposed system embraces a PV array, MPPT controller, DC/AC inverter, LCL filter, and electrical grid. In this paper, grid synchronization is provided through phase locked loop which improves the quality of power supplied to the grid. The proposed system is validated through MATLAB/ Simulink.

Keywords:

Photovoltaic Systems,Maximum Power Point Tracking (MPPT),Grid integrated,Phase Locked Loop (PLL),Grid Synchronization,

Refference:

I. A.D.Martin, J.R.Vazquez, “MPPT Algorithms Comparison in PV Systems P
& O, PI, Neuro-Fuzzy and Backstepping Controls”, IEEE Transactions, 2015.
II. A.F.Cupertino, J.T.Resende, H.A.Pereira, S.I.Seleme, “A Grid-Connected
Photovoltaic System with a Maximum Power Point Tracker using Passivitybased
Control applied in a Boost Converter”, in Proc. IEEE International
Conference on Industrial Application, pp. 1-8, 2012.
III. A.Rajapakse, D.Muthumuni, N.Perera, “Grid integration of Renewable
Energy Systems”, in Renewable Energy, InTech, pp. 109-131, 2009.
IV. G.Marcelo, J.Gazoli, E.Filho, “Comprehensive Approach to Modeling and
Simulation of Photovoltaic Arrays”, IEEE Transactions on Power
Electronics, Vol.: 24, Issue: 5, pp: 1198-1208, May 2009.
V. IEEE Recommended Practice for Utility Interface of Photovoltaic (PV)
Systems. IEEE Std 929-2000; 2000.
VI. L.Hassaine, E.Olias, J.Quintero, V.Salas, “Overview of Power Inverter
Topologies and Control Structures for Grid Connected Photovoltaic
Systems”, Renewable and Sustainable Energy Reviews, Vol.: 30, pp: 796-
807, 2014.
VII. M.A.Elgendy, B.Zahawi, D.J.Atkinson, “Assessment of Perturb and Observe
Algorithm Implementation Techniques for PV Pumping Applications”, IEEE
Transactions on Sustainable Energy, Vol.:3, pp: 21-33, 2012.
VIII. M.C.D.Piazza, G.Vitale, “Photovoltaic Sources: Modeling and Emulation”,
Springer, London, 2013.
IX. M.G.Villalva, J.R.Gazoli, E.R.Filho, “Comprehensive Approach to Modeling
and Simulation of Photovoltaic arrays”, IEEE Transactions, Power Electron,
Vol.:94, Issue: 5, pp: 1198-1208, 2009.
X. M.N.Hossain, “Design and Development of a Grid Tied Solar Inverter in
Informatics”, Electronics &Vision (lCIEV), 2012.
XI. M.Reznik, G,Simöes , A,Al-Durra, S.M.Muyeen, “LCL Filter Design and
Performance Analysis for Grid Interconnected Systems”, IEEE Transactions
on Industry Applications, Vol.: 50, Issue: 2, pp: 1225-1232, 2013.
XII. P.Burns, N.Anani, “Modelling and Simulation of Photovoltaic arrays under
varying conditions”, 9th International Symposium on Communication
Systems, Networks & Digital Sign (CSNDSP), 2014.
XIII. P.Chowdhury, I.Koley, S.Sen, P.K.Saha, G.K.Panda, “Modelling, Simulation
and Control Of a Grid Connected Non Conventional Solar Power Generation
System using MATLAB”, International Journal of Advanced Research in
Electrical, Electronics and Instrumentation Engineering, Vol.: 2, Issue: 4,
April 2013.

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Abstract:

The study focuses the Overall Equipment Efficiency (OEE), one of the tools which is used by an organisation to measure that how efficiently the equipment is working in comparison to its installed capacity and the benchmark set by the organisation. OEE could easily be calculated by the organisation if the collected data is accurate. The data accuracy could be increased by using the Industrial Internet of Things (IIoT) tools. Operators tend to make a mistake sub-consciously which results in invalid data and inaccurate solutions. To overcome the problems, it is required to use some devices to record all the data. The OEE monitored by deploying the IIoT tools gives a better result. This work shows a possible way to implement IIoT tools and a pathway towards Industry 4.0 in manufacturing plant. The primary objective and goal of this study is to increase the current state OEE to the World Class OEE as up to 85%, and after achieving it, increasing the production and sustaining it.

Keywords:

Industrial Internet of Things,Overall Equipment Efficiency,Total Productive Maintenance,Lean Manufacturing,Minor Losses,Industry 4.0,

Refference:

I. A. Rymaszewska, P. Helo and A. Gunasekaran (2017), IoT powered servitization
of manufacturing – an exploratory case study, Int. J. Production Economics,
192:92-105.
II. A. Uriarte, H.C.Amos and M. Moris (2018), Supporting the lean journey with
simulation and optimization in the context of Industry 4.0, Procedia
Manufacturing 25: 586–593.

III. A.S. Jabbour, C. Jose Jabbour, C. Foropon and M. Filho (2018), Can Industry 4.0
revolutionise the environmentally- sustainable manufacturing wave, Technological
Forecasting & Social Change
IV. B.M. Kariuki, (2013), Role of Lean manufacturing on organization
competitiveness, Industrial Engineering Letters, 3 :81-91.
V. F. Shrouf, J. Ordieres, G. Miragliotta (2014), Smart Factories-Energy
Management Review, IEEE
VI. J. Junior, C.M. Busso, S.Gobbo and H. Carreão (2018), Making the links among
environmental protection, process safety, and industry 4.0, Process Safety and
Environmental Protection
VII. M.P. Taylor, P.Boxall, J.J. Chen, X. Xu, A. Liew and A. Adeniji (2018), Operator
4.0 or Maker 1.0? Exploring the implications of Industrie 4.0 for innovation,
safety and quality of work in small economies and enterprises, Computers &
Industrial Engineering
VIII. R.Y. Zhong, X. Xu, E. Klotz and S.T. Newman (2017), Intelligent Manufacturing
in the Context of Industry 4.0- A Review, Engineering 3: 616–630.
IX. S. F. Miranda, M. Marcos, M.E. Peralta and F. Aguayo (2017), The challenge of
integrating Industry 4.0 in the degree of Mechanical Engineering, Manufacturing
Engineering Society International Conference 2017, MESIC 2017
X. S. Vaidya, P. Ambad and Santosh Bhosle (2018), Industry 4.0 Glimpse, 2nd
International Conference on Materials Manufacturing and Design Engineering
XI. T. Stock and G. Seliger (2016), Opportunities of Sustainable Manufacturing, 13th
Global Conference on Sustainable Manufacturing – Decoupling Growth from
Resource Use

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Abstract:

Transport system in now a day’s play a major role in our day to day life. In today’s world, road and transport has become an integral part of every human being. In India, for every 4 minutes one person is died on the road accidents. Locating the points in the road network that are particularly dangerous and where more accidents occur is called as accident prone area or Black spot. This survey is conducted on SH-27 from Nandyal to Koilakuntla. The accident data has been collected from near by police station available for consequent 3 years from 2015 to 2017. Preliminary analysis reveals that there are 5 blackspots in the given corridor line. The detailed analysis been carried out on these 5 locations in order to study accidents at this locations. Based on the analysis the improvement measures have been recommended. The identified blackspots are outskirts of Koilakuntla, Kaanala village, Julepalli village, Joladharasi village, and Rythunagar.The important factors considered for analysis includes the classification of accidents, types of vehicles involved in the accident, type of collision occurred, month wise distribution of accidents, time wise distribution of accidents, analysis of data based on Accident Severity Index method.

Keywords:

Transport system,fatalities,Black spots,accident severity index Introduction,

Refference:

I. A.SD.Selvasofia, P.G.Arulraj, “Accident and traffic analysis using GIS”,
Biomedical Research, Computational Life Sciences and Smarter
Technological Advancement, 2016.
II. A.K.Upadhyay, “Highway engineering”, S.K. Kataria & Sons Publishers,
India, 2014.
III. https://www.kgm.gov.tr (Blackspot manual).
IV. https://www.researchgate.net; (Identification and analysis of blackspot on
NH-5).
V. J.O.Olusina, W.A.Ajanaku, “Spatial Analysis of Accident Spots Using
Weighted Severity Index (WSI) and Density-Based Clustering
Algorithm”, J. Appl. Sci. Environ. Manage, Vol.: 21, Issue: 2,pp: 397-
403 April. 2017.
VI. L. R. Kadiyali, “Traffic Engineering and Transport Planning”, Khanna
Publishers, India, 1983.
VII. M.M.Fayaz, S.P.Mrudula, S.J.George, S.P.Yoyak, S.S.Roy, “Blackspot
identification using the accident severity index method”, International
Journal of Current Engineering and Scientific Research, Vol.: 5, Issue: 3,
2018
VIII. S.K.Khanna, C.E.G.Justo, A.Veeraragavan, “Highway Engineering”,
Nem Chand & Bros Publishers, Roorkee, India, 2001.
IX. Vivek, R.Saini, “identification and improvement of accident blackspots
on highway”, International Journal of core Engineering and Management,
Vol.: 2, Issue: 3, June 2015.

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Abstract:

DDF is the most valuable measure among various cluster performance techniques to evaluate the perfectness of any cluster mechanism. Normally, best clusters are evaluated by computing the number of data points within a cluster. When this count is equivalent to the number of required data points then this cluster is considered to be perfect. The excellence of the cluster methodology is essential not only to find the data count inside a cluster but also to examine it by totaling the data points these are (i) present within a cluster where it should not be and vice versa and (ii) not clustered i.e. outliers (OL). The main functionality of DDF is that all cluster points can be grouped in similar clusters without outliers, the present paper highlights on how compared to DDF more efficient Clusters can be formed through the Modern DDF. Further, we evaluate the performance of some clustering algorithms, K-Means. Recently we developed the Modified K-Means Algorithm and Hierarchical Algorithm by using the Data Discrepancy Factor (DDF).

Keywords:

K-Means,Modified K-Means,Hierarchical Clustering,DDF,Modern DDF,

Refference:

I. B.Giovanni, “AClAP, Autonomous hierarchical agglomerative Cluster
Analysis based protocol to partition conformational datasets.” Bioinformatics
Vol: 22, Issue: 14, pp: e58-e65, 2006.
II. M.Ujjwal, S.Bandyopadhyay. “Performance evaluation of some clustering
algorithms and validity indices.” IEEE Transactions on Pattern Analysis and
Machine Intelligence Vol:24, Issue: 12, pp: 1650-1654, 2002.
III. N.Shi, L.Xumin, G.Yong. “Research on k-means clustering algorithm: An
improved k-means clustering algorithm.”Intelligent Information Technology
and Security Informatics (IITSI), Third International Symposium on. IEEE,
2010.
IV. O.J.Oyelade, , O.Oladipupo, I.C.Obagbuwa. “Application of k Means
Clustering AlgorithmFor prediction of Students Academic Performance.”
arXiv preprint arXiv:1002.2425, 2010.
V. R.P.Vaishali, R.G.Mehta. “Modified k-means clustering algorithm.”
Computational Intelligence and Information Technology. Springer, Berlin,
Heidelberg, pp: 307-312, 2011.
VI. S.E.Brian, “Hierarchical clustering.” Cluster Analysis, 5th Edition, pp: 71-
110, 2011.

VII. S.G.Rao, A.Govardhan. “Assessing h-and g-Indices of Scientific Papers using
k-MeansClustering.” International Journal of Computer Applications Vol:
100, Issue: 11, 2014.
VIII. S.G.Rao, A.Govardhan. “Investigation of Validity Metrics for Modified KMeans
Clustering Algorithm.” i-Manager’s Journal on Computer Science
Vol: 3, Issue: 2, pp: 33, 2015.
IX. S.G.Rao, A.Govardhan. “Performance Validation of the Modified K-Means
Clustering Algorithm Clusters Data.” International Journal of Scientific &
Engineering Research Vol: 6, Issue: 10, pp: 726-730, 2015.
X. X.Juanying, “An Efficient Global K-means Clustering Algorithm.” JCP
Vol:6, Issue: 2, pp:271-279, 2011.

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Abstract:

Natural fibers taken from the bark of ligno-cellulosic fibers are now a day’s using as reinforcement in composite materials, also used as an alternative for synthetic fibers. These are environmentally friendly materials used in many applications like engineering, space, construction, sports etc. In the present study, a new ligno-cellulosic fiber extracted from Hardwickia Binata fiber hybridized with banana fiber was reinforced with epoxy for fabricating hybrid composite material. Studies on mechanical, degradation temperatures and features of the uniaxial cellulosic alkali treated Hardwickia Binata banana fibers were carried out. HBF and banana fibers reinforced hybrid epoxy samples were prepared varying fiber loading (10, 20, 30, 40 and 50%). Tensile strength variation with respect to the banana fiber loading is analyzed. The removal of the amorphous cellulose on alkali treatment may be the reason for the improved properties.

Keywords:

Natural fiber,Hardwickia Binata,Banana,epoxy,hybrid,Tensile,

Refference:

I. A.Chauhan, P.Chauhan, B.Kaith, “Natural Fiber Reinforced Composite”, J.
Chem Eng Process Techno, Vol.:3:132, 2012.
II. A.F.Michael, S.Huo, A.C.Ulven, “Natural Fiber Reinforced Composites”,
Polymer Reviews, Vol.: 52, Issue: 3, pp. 259-320, 2012.
III. A.Shahwad, F.Habib, M.Irfan, “Effect of Orientation of glass fiber on
Mechanical properties of GRP Composites”, Journal of Chem. Soc. Pak,
Vol.: 32, 2010.
IV. A.Wazzan, “Effect of fiber orientation on the mechanical properties and
fracture characteristics of date palm fiber reinforced composites”,
International Journal of Polymeric Materials and Polymeric Biomaterials,
Vol.: 54, Issue:3, pp.213-225, 2005.
V. D.N.Saheb, J.P.Jog, “Natural fiber polymer composites: A review”,Adv.
Polym. Technol., Vol.: 18, pp. 351–363, 1999.
VI. Kalam, M.N.Berhan, H.Ismail, “Physical and mechanical characterizations of
oil palm fruit bunch fiber filled polypropylene composites”,J Reinforc Plast
Compos, Vol.: 29, pp. 3173–3184, 2010.
VII. Sathishkumar, P.Navaneethakrishnan, S.Shankar, R.Rajasekar, N.Rajini,
“Characterization of natural fiber and composites. A review”,Journal of
Reinforced Plastics and Composites, Vol.: 32, pp. 1457, 2013.
VIII. Satyanarayana, K.Sukumaran, P.S.Mukherjee, “Natural fiber–polymer
composite”,Cement Compos, Vol.: 12, pp. 117–136, 1990.
IX. M.AshokKumar, G.Ramachandra Reddy, A.Ramesh, “Performance of
Coconut shell particulate filled polyester composites”,pak .j .sci. ind. res. Ser.
A: phys. Sci., Vol.: 3, pp.142-148, 2012.
X. M.Ramesh, S.Nijanthan, K.Palanikumar, “Processing and Mechanical
Property Evaluation of Kenaf-Glass Fiber Reinforced Polymer
Composites”,Applied Mechanics and Materials, pp. 187-192, 2015
XI. N.Venkateshwaran, A.ElayaPerumal, M.S.Jagatheeshwaran, “Effect of fiber
length and fiber content on mechanical properties of banana fiber/epoxy
composite”,Journal of Reinforced Plastics and Composites,Vol.: 34(2) 156-
168, 2011.
XII. V.K.Mathur, “Composite materials from local resources”,Construct Build
Mater, Vol.: 20, pp. 470–477, 2006.

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