Volume-6 Issue-1

S. No

Volume-6 Issue-1, June 2016, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Ashish Kumar Atri

Paper Title:

Optimization of Real Time Model using Linear Programming and MATLAB

Abstract:  The Reliance Corporation is a large, fully integrated petroleum company based in India. The company produces most of its oil in its own oil field. A large distribution network is used to transport the oil to the company’s refineries and then to transport the petroleum products from the refineries to Reliance’s distribution centres. This work represents transportation problem and addresses the transportation flow of refined & processed oil from the refineries to the company’s distribution centres. The aim is to achieve the minimum cost of transportation flow, since the cost minimization directly relates to the company’s profitability of which is representing operation efficiency. The transportation model is converted into linear programming problem and is solved using MATLAB software. The models were studied based on a real time data model and as example of transportation flow of oil from various refineries to various distribution centres.

Keywords:  Transportation, Linear Programming, MATLAB, OIL Refinery.

References:
1.    Reeb, James Edmund, and Scott A. Leavengood. Transportation problem: a special case for linear programming problems. Corvallis, Or.: Extension Service, Oregon State University, 2002.
2.    Sen, Nabendu, Tanmoy Som, and Banashri Sinha. "A study of transportation problem for an essential item of southern part of north eastern region of India as an OR model and use of object oriented programming." International Journal of Computer Science and Network Security 10.4 (2010): 78-86.
3.    Chaudhuri, Arindam, and Kajal De. "A Comparative study of Transportation Problem under Probabilistic and Fuzzy Uncertainties." arXiv preprint arXiv:1307.1891 (2013).
4.    Salami, A. O. "Application of Transportation Linear Programming Algorithms to Cost Reduction in Nigeria Soft Drinks Industry." World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering 8.2 (2014): 416-422.
5.    Asase, Alfred. The transportation problem; Case study:(Guiness Ghana Limited). Diss. 2011..
6.    Lahrichi, Nadia, et al. "Strategic analysis of the dairy transportation problem." Journal of the Operational Research Society 66.1 (2013): 44-56.
7.    Bhatia, H. L., Kanti Swarup, and M. C. Puri. "A procedure for time minimization transportation problem." Indian Journal of pure and applied mathematics 8.8 (1977): 920-929.

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2.

Authors:

T. Sheeba, Reshmy Krishnan

Paper Title:

An Approach to Construct Learner Profile Using Ontology

Abstract: E-Learning is the use of technology to enable people to learn anytime and anywhere.  E-Learning depends on learner profile for the retrieval of relevant learning content to the learner. Learner profile describes the way in which a student learns best. It includes information on learner’s knowledge, interest, learning preferences and styles, goals, background etc. One of the main issues in constructing learner profile is semantic web. Ontology is used as a standard knowledge representation for the semantic web. This paper suggests an approach to construct ontology based learner profile including static and dynamic characteristics of the learner and update it automatically. Finally an efficient fuzzy semantic retrieval process is proposed for the efficient retrieval of information from learner profile.

Keywords:   E-Learning, Semantic Web, Learner Profile, Ontology, Fuzzy Semantic Retrieval.

References:
1.       Cuncun, Chongben, Hengsong, “A Personalized Model for Ontology-driven User Profiles Mining”, International Symposium on Intelligent Ubiquitous Computing and Education, IEEE 2009.
2.       Trong, N Mohammed, L. Delong , and J. Geun. (2009), A Collaborative Ontology-Based User Profiles System, N.T. Nguyen, R. Kowalczyk, and S.-M. Chen (Eds.): LNAI 5796, pp. 540–552, Springer-Verlag Berlin Heidelberg, ICCCI 2009,.
3.       ESSI KANNINEN, “LEARNING STYLES AND E-LEARNING”, Master of Science Thesis, January, 2009.
4.       Jun Zhai, Jianfeng Li and Yan Lin, “Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce” JOURNAL OF COMPUTERS, VOL. 6, NO. 10,
OCTOBER 2011.
5.       Jun Zhai, Yan Chen, Yi Yu, Yiduo Liang and Jiatao Jiang, “Fuzzy Semantic Retrieval for Traffic Information Based on Fuzzy Ontology and RDF on the Semantic Web “, JOURNAL OF SOFTWARE, VOL. 4, NO. 7, SEPTEMBER 2009.
6.       Khaled M. Fouad, “Proposed Approach to Build Semantic Learner Model in Adaptive E-Learning”, International Journal of Computer Applications (0975 – 8887), Volume
58– No.17, November 2012.
7.       Khaled M. Fouad, Adaptive E-Learning System based on Semantic Web and Fuzzy Clustering”, International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 .
8.       Khaled M. Fouad Ibrahim, Semantic Retrieval and Recommendation in Adaptive E-Learning System, ICCIT, 2012.
9.       L. Han and G. Chen, "A Fuzzy Clustering Method of Construction of Ontology-based User Profiles", Advances in Engineering  Software, vol. 40(7), 2009, pp. 535-540.
10.    Lixin Han, Guihai Chen, “A fuzzy clustering method of construction of ontology-based user profiles”, Advances in Engineering Software 40, 535–540, December 2008.
11.    Marek, “Updating User Profile using Ontology-based Semantic Similarity”, FUZZ- IEEE, August 20-24, 2009.
12.    Maria, Akrivi, Costas,George,Constantin,”Creating an Ontology for the User Profile: Method and Applications”, 2006.
13.    Mateus, Francisco, Victor, Alfredo, Manuel, “A Fuzzy Ontology Approach to represent User Profiles in E-Learning Environments” IEEE, 2010.
14.    Mateus Ferreirs-Satler, “Fuzzy ontologies-based user profiles applied to enhance e-learning activities”, Springler-Verlag, November 2011.
15.    Moshe Leiba, “WEB USAGE PATTERNS AND LEARNING STYLES IN AN ACADEMIC COURSE IN ENGINEERING”, accessed July 2013.
16.    Qiu Baishuang,”Student Model in Adaptive Learning System based on Semantic Web”,  First International Workshop on Education Technology and Computer
Science,2009.
17.    Xin Li and Shi-Kuo Chang, ” A Personalized E-Learning System Based on User Profile Constructed Using Information Fusion”, 200418.    Yasser A. Nada,” An Approach to Improve the Representation of the User Model in the Web-Based Systems”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, 2011.
19.    Zhiwen Yu, Yuichi Nakamura, Seiie Jang, Shoji Kajita, and Kenji Mase, “Ontology-Based Semantic Recommendation for Context-Aware E-Learning”, Ubiquitous Intelligence and Computing, volume 4611 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2007.

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3.

Authors:

Sonal M. Wange, Shiv K. Sahu, Amit Mishra

Paper Title:

An Efficient Random Iterative Based Particle Swarm Optimization for Intrusion Detection

Abstract:  In this paper, an efficient intrusion classification has been proposed by the help of association rule and random iterative based particle swarm optimization NSL-KDD dataset has been used for the experimentation. This is done by the separation of nodes by receiving and sending.  Then it is examined for malicious behavior. RIPSO is applied then to examine the approved threshold value for the detection of different intrusion types defined. If the value obtained after RIPSO iteration passed the threshold assigned, then it will be categorized as the specific intrusion and type will identified. Denial of Service (DoS), User to Root (U2R), Remote to User (R2L) and Probing (Probe) attacks is considered in this paper for intrusion detection. The results show the improvement in detection as compared to the previous method. The average accuracy obtained by our approach is 91.3 %.Index

Keywords:    RIPSO, Intrusion Detection, DoS, U2R, R2L and Probe.

References:
1.       Alexander O. Tarakanov, Sergei V. Kvachev, Alexander V. Sukhorukov ,” A Formal Immune Network and Its Implementation for On-line Intrusion Detection”, Lecture Notes in Computer Science Volume 3685, pp 394- 405, 2005.
2.       Ranjna Patel, DeepaBakhshi and TriptiArjariya, " Random Particle Swarm Optimization (RPSO) based Intrusion Detection System " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-2, Issue-5, April-2015 ,pp.60-66.
3.       MengJianliang,ShangHaikun,Bian Ling,” The Application on Intrusion Detection Based on K-means Cluster Algorithm”, International Forum on Information Technology and Applications, 2009.
4.       Lundin, E. and Jonsson, E. “Survey of research in the intrusion detection area”, Technical Report, Department of Computer Engineering, Chalmers University of Technology, Göteborg, Sweden. January 2002.
5.       R.Venkatesan, R. Ganesan, A. Arul Lawrence Selvakumar, " A Comprehensive Study in Data Mining Frameworks for Intrusion Detection " , International Journal of Advanced Computer Research (IJACR), Volume-2, Issue-7, December-2012 ,pp.29-34.
6.       S.Devaraju, S.Ramakrishnan:,”Analysis of Intrusion Detection System Using Various Neural Network classifiers, IEEE 2011.
7.       Moriteru Ishida, Hiroki Takakura and Yasuo Okabe,” High-Performance Intrusion Detection Using OptiGrid Clustering and Grid-based Labelling”, IEEE/IPSJ International Symposium on Applications and the Internet, 2011.
8.       S. T. Brugger, “Data mining methods for network intrusion detection”,pp. 1-65, 2004.
9.       W. Lee, S. J. Stolfo, “Data Mining Approaches for Intrusion Detection”,Proceedings of the 1998 USENIX Security Symposium, 1998.
10.    KaminiNalavade, B.B. Meshram, “Mining Association Rules to Evade Network Intrusion in Network Audit Data” , International Journal of Advanced Computer Research (IJACR), Volume-4, Issue-15, June-2014 ,pp.560-567.
11.    W. Lee, S. J. Stolfo, “Data mining approaches for intrusion detection” Proc. of the 7th USENIX Security Symp.. San Antonio, TX, 1998.12.    ReyadhNaoum, Shatha Aziz, FirasAlabsi, “An Enhancement of the Replacement Steady State Genetic Algorithm for Intrusion Detection”, International Journal of Advanced Computer Research (IJACR), Volume-4, Issue-15, June-2014, pp.487-493.
13.    AdityaShrivastava, MukeshBaghel, Hitesh Gupta, " A Review of Intrusion Detection Technique by Soft Computing and Data Mining Approach " , International Journal of Advanced Computer Research (IJACR), Volume-3, Issue-12, September-2013 ,pp.224-228.
14.    LI Yin–huan , “Design of Intrusion Detection Model Based on Data Mining Technology”, International Conference on Industrial Control and Electronics Engineering, 2012.
15.    P. Prasenna, R. Krishna Kumar, A.V.T RaghavRamana and A. Devanbu “Network Programming And Mining Classifier For Intrusion Detection Using Probability Classification”, Pattern Recognition, Informatics and Medical Engineering, March 21-23, 2012.16.    LI Han, ”Using a Dynamic K-means Algorithm to Detect Anomaly Activities”, Seventh International Conference on Computational Intelligence and Security, 2011.
17.    Z. Muda, W. Yassin, M.N. Sulaiman, N.I. Udzir,” Intrusion Detection based on K-Means Clustering and Naïve Bayes Classification”, 7th International Conference on IT in Asia (CITA), 2011.
18.    Deshmukh, D.H.; Ghorpade, T.; Padiya, P., "Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset," Electronics and Communication Systems (ICECS), 2014 International Conference on , vol., no., pp.1,7, 13-14 Feb. 2014.
19.    Benaicha, S.E.; Saoudi, L.; BouhouitaGuermeche, S.E.; Lounis, O., "Intrusion detection system using genetic algorithm," Science and Information Conference (SAI), 2014 , vol., no., pp.564,568, 27-29 Aug. 2014.
20.    Kiss, I.; Genge, B.; Haller, P.; Sebestyen, G., "Data clustering-based anomaly detection in industrial control systems," Intelligent Computer Communication and Processing (ICCP), 2014 IEEE International Conference on , vol., no., pp.275,281, 4-6 Sept. 2014.
21.    Thaseen, I.S.; Kumar, C.A., "Intrusion detection model using fusion of PCA and optimized SVM," Contemporary Computing and Informatics (IC3I), 2014 International Conference on , vol., no., pp.879,884, 27-29 Nov. 2014.
22.    Wagh, S.K.; Kolhe, S.R., "Effective intrusion detection system using semi-supervised learning," Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on , vol., no., pp.1,5, 5-6 Sept. 2014.
23.    Masarat, S.; Taheri, H.; Sharifian, S., "A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems," Computer and Knowledge Engineering (ICCKE), 2014 4th International eConferenceon , vol., no., pp.165,170, 29-30 Oct. 2014.
24.    Yan C. Intelligent Intrusion Detection Based on Soft Computing. InMeasuring Technology and Mechatronics Automation (ICMTMA), 2015 Seventh International Conference on 2015 Jun 13 (pp. 577-580). IEEE.

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4.

Authors:

Datar Singh Nathawat, Vishnu Goyal

Paper Title:

A Symmetric 9 - Level Multilevel Inverter with Minimum Number of Device

Abstract:   In this paper, an improved technique for Multilevel Inverter. The improved technique using less number of switches than conventional Cascade H- Bridge topology which enhances system performance decreases system complexity and also reduces total cost of the inverter. The main objective of this paper is to increase figure of output level by reducing number of power switches without any complexity in the circuit. The merit of this improved modified technique is to reduce THD and High output voltage level. Multicarrier PWM based techniques used for controlling, firing circuit of switching device. In this paper comparison between proposed improved technique and conventional cascaded H-Bridge inverter done. The number of output voltage level is nine. Simulation is done in MATLAB 2010b environment and the waveforms are obtained. The results are analysed using MATLAB/SIMULINK software.

Keywords: Multicarrier PWM (MC-PWM), Cascaded H-Bridge, THD, reduced switches.

References:
1.    Krishna Kumar Gupta and Shailendra Jain “A Novel Multilevel Inverter Based on Switched DC Sources”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 7, JULY
2.    Rodriguez, J.-S. Lai, and F. ZhengPeng, “Multilevel inverters: A survey of topologies, controls, applications,” IEEE Trans. Ind. Electron., vol. 49, no. 4, pp. 724–738, Aug. 2002.
3.    J. Ebrahimi, E. Babaei, and G. B. Gharehpetian, “A new multilevel converter topology with reduced number of power electronic components,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 655–667, Feb. 2012.
4.    Leon M. Tolbert, Senior Member, IEEE, and Thomas G. Habetler, “Novel Multilevel Inverter Carrier-Based PWM Method”, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 35, NO. 5, SEPTEMBER/OCTOBER 1999
5.    Blasko, “A novel method for selective harmonic elimination in power electronic equipment,” IEEE Trans. Power Electron., vol. 22, no. 1, pp. 223–228, Jan. 2007
6.    S. De, D. Banerjee, K. Siva Kumar, K. Gopakumar, R. Ramchand, and C. Patel, “Multilevel inverters for low-power application,” IET Power Electronics, vol. 4, no. 4, pp. 384–392, Apr. 2011.
7.    Vasanth V1 , Prabu M2 ,“Optimal Low Switching Frequency Pulse width Modulation of Fifteen Level Hybrid Inverter”, International Journal of Engineering Research and General Science Volume 2, Issue 6, October-November, 2014 ISSN 2091-2730
8.    M. Malinowski, K. Gopakumar, J. Rodriguez, and M. A. Pérez, “A survey on cascaded multilevel inverters,” IEEE Trans. Ind. Electron., vol. 57, no. 7, pp. 2197–2206, Jul. 2010.
9.    S. Kouro, M. Malinowski, K. Gopakumar, J. Pou, L. Franquelo, B. Wu, J. Rodriguez, M. Perez, and J. Leon, “Recent advances and industrial applications of multilevel converters,” IEEE Trans. Ind. Electron., vol. 57, no. 8, pp. 2553–2580, Aug. 2010

15-18

5.

Authors:

M.V. Dhivya Lakshmi, S.P.G.Bhavani

Paper Title:

A Fuzzy Based Power Factor Corrected Bridgeless Converters Fed Bldc Motor

Abstract: This paper presents a power factor corrected (PFC) bridgeless (BL) converters-fed brushless direct current (BLDC) motor drive as a cost-effective solution for low-power applications. An approach of speed control of the BLDC motor is by controlling the dc link voltage of the voltage source inverter (VSI).This facilitates the operation of VSI at fundamental frequency switching by using the electronic commutation of the BLDC motor which offers reduced switching losses. A Bridgeless configuration of the various non-isolated converters such as (Buck-boost, CUK, SEPIC) are proposed which offers the elimination of the diode bridge rectifier thus reducing the conduction losses associated with it. The performance of the proposed drive is studied and simulated in MATLAB/Simulink environment.

Keywords: Bridgeless (BL) converters, Power factor correction (PFC), Brushless dc motor, PI controller, Fuzzy controller.

References:
1.       “An Adjustable-Speed PFC Bridgeless Buck–Boost  Converter-Fed BLDC Motor Drive”, Vashist Bist, Student Member, IEEE, and Bhim Singh, IEEE transactions on industrial electronics, vol. 61, no. 6, june 2014.
2.       “Performance Analysis of BLDC Motor Using Basic Switching Converters”,Bikram Das, Suvamit Chakraborty, Abanishwar Chakraborti, Prabir Ranjan Kasari, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-1, December 2012
3.       Somanatham.R, Prasad.P.V.N, Rajkumar.A.D, “Modelling and Simulation of Sensorless Control of PMBLDC Motor Using Zero-Crossing Back EMF Detection” IEEE SPEEDAM 2006 International Symposium on Power Electronics, Drives, Automotive and Motion.
4.       Bimal K Bose, “Modern Power Electronics and AC Drives”, Pearson Education Asia 2002.
5.       Miller. T.J.E., “Brushless permanent magnet and reluctance motor drives ", Clarendon Press, Oxford, 1989.
6.       “ Buck-Boost Converter for BLDC Motor Drive to Improve Power Factor”, Besten, Stepanov, Research Script International Journal of Research in Electrical Engineering.
7.       “A Novel Approach of Position Estimation, PFC based Buck Boost Converter and Energy generation in BLDC Motor Drive”, Selamparasan. S, Shyamalagowri.M, International Journal of Emerging Technology and Advanced Engineering , Volume 4, Issue 4, April 2014.
8.       “PFC Bridge Converter for Voltage-controlled Adjustable-speed  PMBLDCM Drive”, Sanjeev Singh,Bhim Singh, Journal of Electrical Engineering & Technology Vol. 6, No. 2, 2011.
9.       “A Comparative Analysis of PI & Fuzzy PFC CUK Converter Based PMBLDCM Drive for Air-Conditioner Application”, I.Lakshmana Rao, S.Srikanth, IJEAR Vol. 4, Issue Spl-1, Jan - June 2014
10.    “High efficiency Bridgeless Unity Power factor CUK converter Topology”, Aysha Kemaidesh AL-Kaabi, Abbas A. Fardoun and Esam H. Ismail, International Conference on Renewable Energies and Power Quality (ICREPQ’13).
11.    “Simulation Of Bridgeless SEPIC Converter With Power Factor Correction Fed DC Motor”, Dr.T. Govindaraj, H.Ashtalakshmi,    International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 2, Issue 1, January 2014.
12.    “An Efficient Closed Loop  Controlled Bridgeless CUK Rectifier For PFC Applications”, Shalini.K, Murthy.B, International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 2, Issue 2, February 2014 .
13.    “An Efficient Bridgeless PFC  Converter Based PMBLDCM Drive”, Jomy Joy, Amal M.R, Rakesh R, Kannan S.A, Anna Raina, International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 2, Issue 2, February 2014
14.    “A Voltage-Controlled PFC CUK Converter-Based PMBLDCM Drive Using Fuzzy Logic      Controller”, Dr.B.SANJIVA RAO, Phd, Mr.M.KONDALU,(Phd).
15.    “ Bridgeless Discontinuous Conduction Mode SEPIC Power Factor Correction Rectifier”,Saravanan..S, P. Usha Rani, Vargheese.A, International Journal of Automation and Power Engineering, 2012,.
16.    Ramesh.M.V, Amarnath.J, Kamakshaiah.S and Rao.G.S, “Speed control of Brushless DC Motor by using Fuzzy Logic PI Controller”, ARPN Journal of Engineering and Applied Sciences, Vol.6, No.9, September 2011.

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6.

Authors:

Derya Demirkol, Tamer Dag, Taner Arsan

Paper Title:

Comparison of Various Indoor Positioning Systems Techniques

Abstract:  Localization is one of most important topic. GPS is perfectly using outside environment. However it is not possible to use indoor environment. In this paper, Triangulation, Maximum Likelihood and Fuzzy Logic algorithms were developed. Algorithms work with same environment and same conditions. Algorithms were compared each other in order to find better accuracy.

Keywords:  Indoor positioning, triangulation, maximum likelihood, fuzzy logic, received signal strength, wireless network.

References:
1.    Hakan Koyuncu, Shuang Hua Yang, “A Survey of Indoor Positioning Systems and Object Location Systems” IJCSNS International Journal of Computer Science and Network Security, vol. 10, no. 5 May 2010
2.    RSS Based WLAN Indoor Positioning and Tracking System Using Compressive and Its implementation on Mobile Devices [Online] Available: http://www.wirlab.utoronto.ca/wirlab/thesis/Au-Anthea-WS-201011-MASc-thesis.pdf
3.    Xiaoyi Ye, “WiFiPoz – An Accurate Indoor Positioning System”, Eastern Washington University, EWU Digital Commons, Master Thesis
4.    Thomas Fagerland Wiig, “Assesment of Indoor Positioning (IPS) technology”, University of Oslo Department of Informatics, Master Thesis, May 3, 2010 [Online] Available: https://www.duo.uio.no/bitstream/handle/10852/8740/Wiig.pdf?sequence=4
5.    Fazli Subhan, Halabi Hasbullah and Khalid Ashraf “Kalman Filter-Based Hybrid Indoor Positioning Estimation Technique in Bluetooth Networks”, International Journal of Navigation and Observation Volume 2013,Article ID 5709664 [Online]. Available: http://www.hindawi.com/journals/ijno/2013/570964/#B26
6.    Jianwei Zhang, “Applied Informatics and Communication, Part III”, International Conference, ICAIC 2011, Xi’an China August 20-21, 2011, page: 219-220 [E-Book]
7.    L.A. Zadeh, “Fuzzy Set”, Department of Electrical Engineering and Electronics Research Laboratory, University of California, 1965. [Online] [Available] http://www.cs.berkeley.edu/~zadeh/papers/Fuzzy%20Sets-Information%20and%20Control-1965.pdf
8.    Andreas Teuber, Bern Eissfeller, “WLAN Indoor Positioning Based on Euclidean Distances and Fuzzy Logic”, Institute of Geodesy and Navigation, University FAF [Online] [Available] http://wpnc.net/fileadmin/WPNC06/Proceedings/31_WLAN_Indoor_Positioning_Based_on_Euclidean_Distances_and_Fuzzy_Logic.pdf
9. Chih-Yung Chen, Jen-Pin Yang, Guang-Jeng Tseng, Yi-Huan Wu, Rey-Chue Hwang, “An Indoor Positioning Technique Based on Fuzzy Logic”, IMECS 2010, March 17-19, 2010

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