iosr-JEEE   Volume-1 ~ Issue-4

Abstract: The concentration of vehicles on road is increasing day by day throughout the world particularly in large urban areas. Due to this traffic congestion problem is also increasing. To accommodate this increasing demand an optimized method for traffic control is required. As the transportation system will grow the need for an intelligent controller will have to be employed to face controlling the traffic congestion problems. Fuzzy controllers have been universally used for many industrial applications but for traffic controls it hasn't been widely applied. This paper proposes the use of fuzzy based traffic light controller using FUZZY TECH software at a complex traffic junction. The real time parameters such as traffic density and queue length are obtained by using image processing techniques. So the on and off timings for the green, red and orange lights are adjusted as per the actual road conditions. In this paper a 4 way intersection fuzzy traffic controller has been described suitable for mixed traffic. The rule base has been formulated by human experts, fuzzy inference engine and the defuzzifier.
Keywords: Traffic Control, Fuzzy Logic, membership function, Intelligent Transportation Systems (ITS), Defuzzification

17. Akçelik, R., Besley, M., Roper, R., "Fundamental Relationships for Traffic Flows at Signalized Intersections, ARRB Transport Research Ltd.," Research Report No. 340, pp. 268, ISBN 0869107968, 1999
18. Hegyi, B. De Schutter, R. Babuska, S. Hoogendoorn, H. VanZuylen, H. Shuurman, "A Fuzzy Decision Support System for Traffic Control Centers[J]", Faculty of Information Technology and Systems, Aug. 2001, pp.358-363.
19. Beauchamp-Baez,G., Rodriguez-Morales, E., Muniz Marrero, E.,L., "A Fuzzy Logic Based Phase Controller for Traffic Control", Proc. IEEE International Conference on Fuzzy Systems 1997, pp.1533-1539.
20. Dr. Devinder Kaur, Elisa Konga, Esa Konga, "Fuzzy Traffic Light Controller, Circuits and Systems" 1994, Proceedings of the 37th Midwest Symposium pp.1507-1510 Vol.2, August,1994.
21. Xu Li-qun, Jiang Fu, "Fuzzy Reasoning Algorithm for Control of Traffic in Congestion Route[J]", Information and Control, Apr.2003, Vol.32, No.2, pp. 132-141.

ABSTRACT: This paper presents a solution to Unit commitment (UC) of thermal units based on a new evolutionary algorithm known as Shuffled Frog Leaping Algorithm(SFLA). The integer coded algorithm is based on the behavior of group of frogs searching for a location that has the maximum amount of available food. This algorithm involves local search and shuffling process and these are repeated until a required convergence is reached. In this proposed method of SFLA for the UC problem, the scheduling variables are coded as integers, so that the minimum up/down time constraints can be handled directly. To verify the performance of the proposed algorithm it is applied to the IEEE 14, 30, 56,118 bus systems and 10, 20 unit systems for a one day scheduling period. The results obtained are quite encouraging for the Unit Commitment problem when compared with the existing methods. The algorithm and simulation are carried out using Matlab software.
Key words: SFLA ,Frogs, Unit Commitment

[1] A. J. Wood and B. F. Wollenberg(1984), Power Generation Operation and Control, New York: Wiley
[2] D.P. Kothari and J.S.Dhillon(2011), "Power System Optimization", Prentice Hall of India Pvt. Ltd., New Delhi.
[3] D.P.Kothari and I.J.Nagrath(2011), "Modern Power System Analysis", 4th Edition, McGraw Hill, New York.
[4] N.P. Padhy(2004), "Unit commitment- A Bibliography Survey," IEEE Trans. Power Systems, vol.19,no. 2, pp 1196-1205, May 2004.
[5] W.L. Snyder, H.D. Powell, and J.C. Rayburn(1987), "Dynamic Programming Approach to Unit Commitment", IEEE Trans. Power Systems, vol.2, no. 2,pp.339-347, May 1987.
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[10] I.G. Damousis, A.G. Bakirtzis,and P.S. Dokopolous(2004)," A Solution to Unit Commitment Problem using Integer Coded Genetic Algorithm", IEEE Trans. Power Systems, vol.19,no. 2, pp.1165-1172, May 2004.

Abstract : The Power transformers are one of the most vital as well as expensive equipment in an electric power system. Therefore, it is the responsibility of utilities to decrease the transformer lifecycle costs and to increase the usable service time. The damage may cause a change in the physical condition of transformer which would be reflected in the electrical parameters- resistance, inductance and capacitance. The insulation performance is influenced by thermal, electrical and mechanical stresses. The displacement of windings can occur during transportation of transformers or during a short circuit near the transformer in the power system. The Frequency Response Analysis (FRA) can detect the type of fault and the exact location of fault. The result obtained for the various fault condition is compared with the reference set and the conclusion are drawn.
Keywords - Fra,Ehv,Sfra,Lvi,Hv.

[1] Ryder (2003) "Diagnosing transformer faults using frequency response analysis‟- IEEE Electrical Insulation Magazine, pp 16-22.
[2] P.T.M. Vaessen, E. Hanique (January 1992) "A New Frequency Response Analysis Method for Power Transformers‟- Transactions on power delivery, vol.7, No.1, PP 384-389.
[3] K. Feser, J. Christian, C. Neumann, U. Sundermann, T. Leibfried, A. Kachler, M. Loppacher- "The Transfer Function Method for Detection of Winding Displacements on Power Transformers after Transport, Short Circuit or 30 Years‟.

Abstract: Power transformers are key component because power system operation depends on it. The reliability of Power Transformer is essential and hence the monitoring of such equipment is necessary at substation level. The assessment techniques of transformer include various methods. Dissolved Gas Analysis (DGA) is a universally accepted and highly recommended technique for fault diagnosis of Power Transformer. There are several DGA methods for detection of faults to measure the concentration in particle per million in oil sample. Gas concentrations indicate fault type and accordingly health of Power Transformer can be judge. This paper introduces Grey Theory approach in which analysis is carried out based on partial information to help in standardizing DGA interpretation techniques and to identify transformer state assessment through it. Grey Theory is perfectly matched to the said problem as the DGA samples are less. The keystone of Grey Theory is to find target heart degree and from that making a decision. The key gases used for Grey analysis. Grey model built in soft computing tool Adaptive Neuro-Fuzzy Inference system (ANFIS). The results of soft computing tools compared with Grey Model output. The soft computing technique shows certain degree of success to validate benchmarking of Grey Model.
Keywords: Anfis, Dga, Grey Model, State Assessment

[1] T. K. Saha, "Review of modern diagnostic techniques for assessing insulation condition in aged transformers," Dielectrics and Electrical Insulation, IEEE Transactions on [see also Electrical Insulation, IEEE Transactions on], vol. 10, pp. 903-917, 2003.
[2] A. Abu-Siada, M. Arshad and S. Islam, "Fuzzy Logic Approach to Identify Transformer Criticality using Dissolved Gas Analysis", Power and Energy Society General Meeting, 2010 IEEE, 25-29 July 2010
[3] X. Liu, F. Zhou, and F. Huang, "Research on on-line DGA using FTIR [power transformer insulation testing]," 2002, pp. 1875-1880 vol.3.
[4] Jianpo Li, Xiaojuan Chen, Chunming Wu; Information Engineering College Northeast Dianli University Jilin, China "Power Transformer State Assessment Based on Grey Target Theory" IEEE, 2009 International Conference on Measuring Technology and Mechatronics Automation, 11-12 April 2009, pp 664-667.
[5] Sikun Yang, School of Electrical Engineering, Beijing Jiaotong University, "Application of Grey Target Theory for Handling Multi-criteria Vague Decision Making Problems",2008 ISECS International Colloquium on Computing, Communication, Control and Management, 3-4 Aug. 2008, pp 467-471.
[6] N.A. Muhamad, B.T. Phung, T.R. Blackburn, K.X Lai, "Comparative Study and Analysis of DGA Methods for Transformer Mineral Oil", The University of New South Wales, School of Electrical Engineering and Telecommunications, Sydney 2052, Australia.

Abstract: Silicon carbide (SiC) is the perfect cross between silicon and diamond. The crystal lattice of SiC is identical to silicon and diamond, but, exactly half the lattice sites are occupied by silicon atoms and half by carbon atoms. Like-diamond SiC has electronic properties superior to silicon, but, unlike diamond it is also manufacturable. The thermal leakage current (dark current) in SiC is sixteen orders-of magnitude lower as well. As temperature increases, the leakage current increases, but, the temperature where the leakage current would disrupt circuit operation is over 1000 °C in SiC, compared to about 250 °C in silicon. The SiC electronic revolution began in the early 1990's when single-crystal wafers became commercially available for the first time. During the intervening years, many different electronic devices have been demonstrated in SiC, with performance often exceeding the theoretical limits of silicon. These include pin diodes, MOS field-effect transistors (MOSFETs), metal-semiconductor field-effect transistors (MESFETs), and bipolar transistors (BJTs), as well as specialized devices such as CCD imagers, Schottky diodes, static induction transistors (SITS) and impact-ionization- avalanche-transit-time (MATT) microwave oscillators. These early digital logic gates and linear elements are based on n-channel MOS technology, but, quickly followed by more sophisticated CMOS integrated circuits.
Key words: Silicon Schottky diode, Silicon Carbide Schottky diode, MOSFET

[1] I. Batarseh "Power Electronic Circuits", University of Central Florida, John Wiley &Sons, Inc., 2004.
[2] A.P. Malvino, "Transistor Circuit Approximation", McGraw-Hill, Inc., 3rd Ed., 2007
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Abstract : Emerging techniques for composite power system reliability evaluation mainly focus on
conventional generation and transmission facilities. Reliability analysis of composite power system is
determined by applying the FACTS devices like UPFC & TCSC in IEEE 6 Bus RBTS system at different buses &
transmission lines. In this paper, a comparison is carried out between the systems using UPFC, TCSC in order
to describe which FACTS device is best suitable for the system. The comparison is made in different parameters
via. availability & unavailability, System Indices, Probability of Failure & Expected Energy Not Supplied
(EENS), by considering different modules & bus numbers of the composite power system.
Keywords - UPFC, TCSC, Reliability, EENS, Probability of Failure, System Indices.

[1] Roy Billinton, Yu Cui, Reliability Evaluation of Composite Electric Power Systems Incorporating FACTS, Proceeding of the IEEE Canadian Conference on Electrical and Computer Engineering, 2002, pp: 1-6.
[2] Ajit Kumar Verma, A. Srividya, Bimal C. Deka, Impact of a FACTS controller on reliability of composite power generation and transmission system, Elsevier, Electric Power Systems Research, 72(2), Dec 2004, pp: 125-130.
[3] Mario V. F. Pereira, Neal J. Balu, Composite Generation / Transmission Reliability Evaluation, Proceedings of the IEEE, 80(4), April-1992, pp: 470-491.
[4] T. Suresh Kumar, V. Sankar, Reliability Improvement of Composite Electric Power System using Unified Power Flow Controller, IEEE Conference, INDICON 2011, BITS-Pilani, Hyderabad, 16th – 18th Dec 2011.
[5] Hamid R. Bay, Ahad. Kazemi, Reliability evaluation of composite electric power systems incorporating STATCOM & UPFC, IEEE International Conference, Canada, 2009.
[6] Sayed-Mahdi Moghadasi, Ahad Kazemi, Mahmud Fotuhi-Firuzabad, Abdel-Aty Edris, Composite system reliability assessment incorporating an Interline Power Flow Controller, IEEE Transactions on Power Delivery, 23(2), April 2008, pp: 1191-1199.
[7] Sreten Skuletic, Adis Balota, Reliability assessment of composite power systems, IEEE CCECE/CCGEI, Saskatoon, May 2005, pp: 1718-1721.
[8] T. Suresh Kumar, V. Sankar, Reliability Analysis of Unified Power Flow Controllers & Series Compensator for a Transmission System, i-manager's journal on Electrical Engineering, 2(2), Oct-Dec 2008, pp:47-54.
[9] Klaus Habur, Donal O'Leary, FACTS – For Cost Effective and Reliable Transmission of Electrical Energy, BHEL Paper, 2008, pp: 1-11.
[10] T. Suresh Kumar, D. V. Ashok Kumar, V. Sankar, K. V. Desikachar, Transmission System Reliability Analysis using Thyristor Controlled Series Capacitor & Series Compensator, Proceedings of National Power Electronics Conference (NPEC-2007), IISc, Bangalore, 17th – 19th Dec. 2007.

Abstract : Image pre-processing is an important and challenging factor in the computer-aided diagnostic systems. In medical image processing and especially in tumor segmentation task it is very important to pre-process the image so that segmentation and feature extraction algorithms work correctly. Proper detection and segmentation of the tumor leads to exact extraction of features and classification of those tumors. The accurate tumor segmentation is possible if image is pre-processed as per image size and quality. This paper describes the pre-processing method consisting of two phases. In the first phase we remove the film artifact by using median filter. In the second phase we introduce an algorithm that uses morphological operations to remove unwanted skull/ribcage portion. This reduces the false positive results in the later stages of processing in the computer-aided diagnostic systems .Both algorithms are applied on MR/CT images of Brain having tumors and, CT images of Thorax and Abdomen having tumors. The second algorithm has an effect of skull portion removal in Brain images and, effect of ribcage portion removal in CT images of Thorax and Abdomen. The results are superior to those of tracking algorithms considering the retention of regions of interests and the film artifacts removal. This preprocessing will also reduce the over segmentation problem in further processing still retaining the tumors.
Keywords: Pre-processing, Medical Imaging, Tumors, Median Filter, Morphological Operators

[1] Sigurd Angenent, Eric Pichon, Allen Tannenbaum, Mathematical Methods in Medical Image Processing, BULLETIN (New Series)OFTHEAMERICANMATHEMATICALSOCIETY,00(0),Pages000–000. www.math.wisc.edu/~angenent/preprints/medicalBAMS.pdf)
[2] Jaya , K.Thanushkodi , M.Karnan, Tracking Algorithm for De-Noising of MR Brain Images, International Journal of Computer Science and Network Security, 9(11), November 2009, 262-267
[3] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing (Prentice Hall, Aug 21, 2007)
[4] Pan Lin ,Yong Yang, Chong-Xun Zheng, Jian-Wen Gu,.An Efficient Automatic Framework for Segmentation of MRI Brain Image, Proceedings of the Fourth International Conference on Computer and Information Technology(CIT'04),IEEE,2004.
[5] K.Selvanayaki, Dr. M. Karnan, CAD System for Automatic Detection of Brain Tumor through Magnetic Resonance Image-A Review, International Journal of Engineering Science and Technology, 2(10), 2010, 5890-5901.
[6] Harman Kataria, Alka Jindal, Brain MRI: Region based Segmentation Techniques, IJCST, 3(1), Jan. - March 2012,527-529.
[7] Wang Rongfu, PET/CT Tumour Diagonsis, Peking University Medical Press,1st ed., Beijing, 2007, pp. 3-13.(in Chinese)
[8] Csaba Barcsák, Péter Mileff, Preprocessing and Real Time Visualization of CT and MRI Data, microCAD 2011, 25th International Scientific Conference, University of Miskolc Hungary, pp 7-12
[9] José Silvestre Silva, Augusto Silva and Beatriz Sousa Santos, Lung Segmentation Methods in X-ray CT Images, SIARP2000, 5TH IBEROAMERICAN SYMPOSIUM ON PATTERN RECOGNITION, 583-598.