134 research outputs found
Multicomponent image segmentation using a genetic algorithm and artificial neural network
No full textImage segmentation is an essential process for image analysis. Several methods were developed to segment multicomponent images, and the success of these methods depends on several factors including 1) the characteristics of the acquired image and 2) the percentage of imperfections in the process of image acquisition. The majority of these methods require a priori knowledge, which is difficult to obtain. Furthermore, they assume the existence of models that can estimate its parameters and fit to the given data. However, such a parametric approach is not robust, and its performance is severely affected by the correctness of the utilized parametric model. In this letter, a new multicomponent image segmentation method is developed using a nonparametric unsupervised artificial neural network called Kohonen's self-organizing map (SOM) and hybrid genetic algorithm (HGA). SOM is used to detect the main features that are present in the image; then, HGA is used to cluster the image into homogeneous regions without any a priori knowledge. Experiments that are performed on different satellite images confirm the efficiency and robustness of the SOM-HGA method compared to the Iterative Self-Organizing DATA analysis technique (ISODATA). © 2007 IEEE.ARIA EH, 2004, P 20 ISPRS C IST TUR, P117; AWAD M, IN PRESS INT J REMOT; BACAO F, 2005, P ICCS 2005 C, P476; Baker J. E., 1987, P 2 INT C GEN ALG, P14; CHEN Q, 2004, LECT NOTES COMPUT SC, V33, P621; Chun DN, 1996, PATTERN RECOGN, V29, P1195, DOI 10.1016-0031-3203(95)00148-4; Fauzi M., 2003, P BRIT MACH VIS C, P519; HOLLLAND J, 1975, ADAPT NATURAL ARTIFI; HUAPT R, 2004, PRACTICAL GENETIC AL; Jensen J. R., 1996, INTRO DIGITAL IMAGE; Kohavi R., 1998, APPL MACHINE LEARNIN, V30, P271; Levine M. D., 1985, VISION MAN MACHINE; NEVATIA R, 1980, COMPUT VISION GRAPH, V13, P257, DOI 10.1016-0146-664X(80)90049-0; Ng SC, 1996, IEEE SIGNAL PROC MAG, V13, P38, DOI 10.1109-79.543974; PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214-aoms-1177704472; PERKINS S, 2000, FUZZY SYST EVOL COMP, V3, P52; Pina P, 2003, INT GEOSCI REMOTE SE, P3516; PRATT W, 1991, DIGITA IMAGE PROCESS; Tou J.T., 1974, PATTERN RECOGNITION; Wang X., 2004, P IEEE C ROB AUT MEC, P991; Xiaoying Jin, 2003, Proceedings of the 12th IEEE International Conference on Fuzzy Systems (Cat. No.03CH37442); Xu BG, 2002, AATCC REV, V2, P42; Yao KC, 2000, PATTERN RECOGN, V33, P1575, DOI 10.1016-S0031-3203(99)00135-1; YIN HJ, 1995, NEURAL COMPUT, V7, P1178, DOI 10.1162-neco.1995.7.6.117834232
Attractive educational strategies in teaching and learning chemistry
No full textThe main objectives of this article is to find attractive and appropriate educational strategies and methodologies that could be used in teaching and learning chemistry in order to attract new generations to appreciate studying the most important discipline in science; chemistry. Chemistry is considered as the central backbone for science since its concepts and theories can explain all the scientific phenomena. Since science is the core of the human sustainability, therefore improvement of chemical education would definitely result in improvement of social sustainability. Attractive educational strategies in teaching and learning chemistry can be achieved by using attractive and interactive appropriate methodologies such as Systemic Approach (SATLC), E-learning, M-learning, and any other tools in which modern technologies are integrated
Superconducting properties of zinc substitution in Tl-2223 phase
No full textThe effect of partial replacement of copper by zinc in Tl2Ba2Ca2Cu3O10-δ superconductor phase is studied. Superconducting samples of the nominal composition Tl2Ba2Ca2Cu3-xZnx O10-δ with x ranging from 0 to 0.6 are prepared under normal pressure by a one step of solid-state reaction technique. The samples are characterized by using X-ray powder diffraction, scanning electron microscope (SEM) and EDX. The X-ray data indicate that the partial replacement of Cu2+ions by Zn2+ions does not influence the tetragonal structure of the samples, and the lattice parameters a and c vary according to the difference in the ionic radii of Cu and Zn. The superconducting parameters, such as superconducting transition temperature Tc, critical current density Jc and irreversibility field Bir are calculated from electrical resistivity and AC-magnetic susceptibility measurements. © 2007 Elsevier B.V. All rights reserved.Abou-Aly A.I., 2002, INT C RES TRENDS SCI, V91; ADACHI S, 1990, PHYSICA C, V111, P543; Awad R, 2000, PHYSICA C, V341, P685, DOI 10.1016-S0921-4534(00)00650-X; Awad R, 2007, SUPERCOND SCI TECH, V20, P401, DOI 10.1088-0953-2048-20-4-017; Awad R, 2001, PHYSICA B, V307, P72, DOI 10.1016-S0921-4526(01)00971-1; Batista-Leyva AJ, 2003, SUPERCOND SCI TECH, V16, P857, DOI 10.1088-0953-2048-16-8-305; BEAN CP, 1964, REV MOD PHYS, V36, P31, DOI 10.1103-RevModPhys.36.31; BERKLEY DD, 1993, PHYS REV B, V47, P5524, DOI 10.1103-PhysRevB.47.5524; CHEN DX, 1990, PHYSICA C, V167, P317, DOI 10.1016-0921-4534(90)90349-J; Chu SY, 2000, PHYSICA C, V337, P229, DOI 10.1016-S0921-4534(00)00107-6; Fradina IA, 1999, PHYSICA C, V311, P81, DOI 10.1016-S0921-4534(98)00563-2; Glowacki BA, 1997, CRYOGENICS, V37, P609, DOI 10.1016-S0011-2275(97)00053-2; HAZEN RM, 1988, PHYS REV LETT, V60, P1657, DOI 10.1103-PhysRevLett.60.1657; Isber S, 2005, SUPERCOND SCI TECH, V18, P311, DOI 10.1088-0953-2048-18-3-018; Isber S, 2006, J PHYS CONF SER, V43, P450, DOI 10.1088-1742-6596-43-1-112; Kayed TS, 2003, CRYST RES TECHNOL, V38, P946, DOI 10.1002-crat.200310118; Kuhberger M, 2003, PHYSICA C, V390, P263, DOI 10.1016-S0921-4534(03)00706-8; LEE MW, 1995, PHYSICA C, V245, P6, DOI 10.1016-0921-4534(95)00100-X; Mezzetti E, 2000, PHYSICA C, V332, P115, DOI 10.1016-S0921-4534(00)00008-3; MOHAMMED NH, 2005, ARAB INT C REC ADV P, P9; Nishida A, 2003, PHYSICA C, V392, P349, DOI 10.1016-S0921-4534(03)00848-7; Pavard S, 1999, PHYSICA C, V316, P198, DOI 10.1016-S0921-4534(99)00259-2; Ravi S, 2000, PHYSICA C, V330, P58, DOI 10.1016-S0921-4534(99)00611-5; REN ZF, 1991, PHYSICA C, V184, P24, DOI 10.1016-0921-4534(91)91496-Q; RUCKENSTEIN E, 1989, MATER LETT, V8, P421, DOI 10.1016-0167-577X(89)90065-7; Tang H, 1997, PHYSICA C, V282, P2111, DOI 10.1016-S0921-4534(97)01171-4; Triscone G, 1996, PHYSICA C, V264, P233, DOI 10.1016-0921-4534(96)00262-6; VANDERAH TA, 1992, CHEM SUPERCONDUCTOR, P90; WANG YB, 1993, J LOW TEMP PHYS, V15, P169; WESTERHOLT K, 1989, PHYS REV B, V39, P11680, DOI 10.1103-PhysRevB.39.11680; Wisniewski A, 2000, PHYS REV B, V61, P791, DOI 10.1103-PhysRevB.61.791; XU YW, 1990, PHYSICA C, V169, P205, DOI 10.1016-0921-4534(90)90177-G; Yamauchi H, 1998, SUPERCOND SCI TECH, V11, P1006, DOI 10.1088-0953-2048-11-10-022; Yang Li, 1994, Physics Letters A, V18543
Ordinal optimization for dynamic network reconfiguration
No full textMotivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the computation more efficient. The successful application of ordinal optimization to such a complex optimization problem required the investigation of several algorithmic parameters. The solution algorithm was implemented in a software package, where an acceptable solution is considered good enough if it is in the top mpercent of the solutions with a probability P. Testing it on 33- and 136-bus systems, minimal power loss results were obtained on the 33-bus system that are in the top 0.03percent of the search space. Comparing the experimental results with other recently published methods showed the effectiveness of ordinal optimization for minimum loss calculations and motivated further studies in smart-grid-like scenarios, where the results obtained for different load levels were in the top 0.13percent of the search space. © 2011 Copyright Taylor and Francis Group, LLC.Abdelaziz A. Y., 2009, IEEE POW EN SOC M CA; Abdelaziz AY, 2010, ELECTR POW SYST RES, V80, P943, DOI 10.1016-j.epsr.2010.01.001; Baran M. E., 1989, IEEE T POWER DELIVER, V4, P101; Braverman M., 2007, 22 ANN IEEE C COMP C, P225; BUNCH JB, 1982, IEEE T POWER AP SYST, V101, P284, DOI 10.1109-TPAS.1982.317104; Carreno EM, 2008, IEEE T POWER SYST, V23, P1542, DOI 10.1109-TPWRS.2008.2002178; CASTRO CA, 1990, ELECTR POW SYST RES, V19, P137, DOI 10.1016-0378-7796(90)90064-A; CHIANG HD, 1990, IEEE T POWER DELIVER, V5, P1568, DOI 10.1109-61.58002; CIVANLAR S, 1988, IEEE T POWER DELIVER, V3, P1217, DOI 10.1109-61.193906; Debs A. S., 1987, MODERN POWER SYSTEM, P180; de Oliveira LW, 2010, INT J ELEC POWER, V32, P840, DOI 10.1016-j.ijepes.2010.01.030; Dogrusoz U., 1994, INT C COMP INF APR, V6, P46; Fusheng Li, 2009, P 6 ANN IEEE COMM SO, P1, DOI 10.1109-ICUT.2009.5405702; GOSWAMI SK, 1992, IEEE T POWER DELIVER, V7, P1484, DOI 10.1109-61.141868; Ho Y. C., 1992, DISCRETE EVENT DYN S, V2, P61, DOI 10.1007-BF01797280; Ho Y. C., 1994, P 33 IEEE C DEC CONT, V2, P1470; Ho Y.C., 2007, ORDINAL OPTIMIZATION, P7; Kachem M. A., 2000, ELECT POWER ENERGY S, V22, P269; Kashem MA, 1999, IEE P-GENER TRANSM D, V146, P563, DOI 10.1049-ip-gtd:19990694; Lau TWE, 1997, J OPTIMIZ THEORY APP, V93, P455, DOI 10.1023-A:1022614327007; Mantovani JRS, 2000, SBA CONTROLE AUTOMAC, V11, P150; MAYEDA W, 1965, IEEE T CIRCUITS SYST, VCT12, P181; Merlin A, 1975, P 5 POW SYST COMP C, P1; Morton AB, 2000, IEEE T POWER DELIVER, V15, P996, DOI 10.1109-61.871365; NARA K, 1992, IEEE T POWER SYST, V7, P1044, DOI 10.1109-59.207317; Ravibabu P, 2008, IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering. SIBIRCON 2008, DOI 10.1109-SIBIRCON.2008.4602603; SHERMAN J, 1950, ANN MATH STAT, V21, P124, DOI 10.1214-aoms-1177729893; SHIRMOHAMMADI D, 1989, IEEE T POWER DELIVER, V4, P1492, DOI 10.1109-61.25637; Sivanagaraju S, 2006, ELECTR POW COMPO SYS, V34, P249, DOI 10.1080-15325000500240854; Sivanagaraju S, 2008, ELECTR POW COMPO SYS, V36, P513, DOI 10.1080-15325000701735389; Swarnkar A, 2011, ELECTR POW SYST RES, V81, P1619, DOI 10.1016-j.epsr.2011.03.020; Yu Y., 2002, IEEE T POWER SYST, V3, P172953
Erratum: Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt (Open Chem. (2022) 20: 1 (254–256) DOI: 10.1515/chem-2022-0131)
No full textCorrigendum to: Awad H, Abu El-Leil I, Nastavkin A, Tolba A, Kamel M, El-Wardany R, et al. Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt. Open Chem. 2022;20(1):254 6. https://doi.org/10.1515/chem-2022-0131. After publishing the article, the authors noticed that there is a mistake in the authors contributions section. It was given as: Author contributions: H.A., I.A., A.N.conception of the study; A.T, M.K.experiment; R.E., A.R.analysis and manuscript preparation; H.Z., H.A., H.T.data analysis and writing the manuscript; S.I., H.Z.analysis with constructive discussions. It should be given as: Author contributions: H.A., I.A., A.N.conception of the study; A.T, M.K.experiment; R.E., A.R.analysis and manuscript preparation; H.Z., H.A., H.T.data analysis and writing the manuscript; H.A., A.E., S.I., H.Z.analysis with constructive discussions. © 2022 De Gruyter. All rights reserved
Entropy-based and weighted selective sift clustering as an energy aware framework for supervised visual recognition of man-made structures
No full textUsing local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3percent of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1percent on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand. © 2013 Ayman El Mobacher et al.Awad M., 2009, P 5 INT C SOFT COMP; Bonaiuto J. J., 2005, P 3 INT WORKSH ATT P; Gao K, 2008, 7TH IEEE-ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE-ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS, P191, DOI 10.1109-ICIS.2008.24; Kennedy Lyndon S., 2008, P 17 INT C WORLD WID, P297, DOI 10.1145-1367497.1367539; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023-B:VISI.0000029664.99615.94; Pass G, 1999, MULTIMEDIA SYST, V7, P234, DOI 10.1007-s005300050125; Quack T., 2008, P INT C CONT BAS IM, P47, DOI DOI 10.1145-1386352.1386363; Shao H, 2003, LECT NOTES COMPUT SC, V2728, P71; Shao H, 2003, 260 SWISS FED I TECH; Zhang W., 2005, IEEE COMP SOC C COMP, P21; Zhang W., 2004, GMUCSTR20043; Zheng YT, 2009, PROC CVPR IEEE, P10850
Multicomponent image segmentation: A comparative analysis between a hybrid genetic algorithm and self-organizing maps
No full textImage segmentation is an essential process in image analysis. Several methods have been developed to segment multicomponent images and the success of these methods depends on the characteristics of the acquired image and the percentage of imperfections in the process of its acquisition. Many of the segmentation methods are parametric, which means that many parameters need to be computed or provided before the segmentation process, and any method that works on one type of multicomponent image cannot necessarily work on another. In addition, many segmentation methods are supervised, where a priori knowledge is needed, such as the number of classes. To overcome these obstacles, a self-organizing map (SOM), which is an unsupervised nonparametric method, was used to segment four different types of multicomponent images (Landsat, SPOT, IKONOS and CASI), and the results compared to those of a new nonparametric unsupervised genetic algorithm (GA) for image segmentation. To improve the performance of the GA, a hill-climbing process and another random heuristic module were added to escape the local-minima trap and to improve the speed of the GA; the new algorithm is called the hybrid genetic algorithm (HGA). Verification of the results was performed using two different techniques: field verification and the functional model. These verification techniques show that the HGA is more accurate in multicomponent image segmentation than the SOM.ARIA E, 1973, P 20 INT SOC PHOT RE, P117; BAKER EB, 1987, P 2 INT C GEN ALG L, P14; BHANU B, 1995, IEEE T SYST MAN CYB, V25, P1543, DOI 10.1109-21.478442; BRICE CR, 1970, ARTIF INTELL, V1, P205, DOI 10.1016-0004-3702(70)90008-1; CHANG YL, 1994, IEEE T IMAGE PROCESS, V3, P868; Chun DN, 1996, PATTERN RECOGN, V29, P1195, DOI 10.1016-0031-3203(95)00148-4; Cohen J, 1960, EDUC PSYCHOL MEAS, V20, P46; COLLET C, 1995, GRESTI STUDY RES GRO, V2, P569; CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016-0034-4257(91)90048-B; Cormen T., 2001, INTRO ALGORITHMS; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Haupt R L, 2004, PRACTICAL GENETIC AL; Holland J. H., 1975, ADAPTATION NATURAL A; Jiang T., 2001, ELECT NOTES THEORETI, V46, P1; KHUNKAY S, 1997, P 1997 INT C INF COM, V2, P713; Kim EY, 2000, IEEE SIGNAL PROC LET, V7, P301, DOI 10.1109-97.873564; KIM HJ, 1998, ELECTRON LETT, V34, P1394; Kohavi R., 1998, APPL MACHINE LEARNIN, V30, P271; Kohonen T., 2001, SPRINGER SERIES INFO, V30; Levine M. D., 1985, VISION MAN MACHINE; Lo Bosco G, 2001, 11TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, P262; Ng SC, 1996, IEEE SIGNAL PROC MAG, V13, P38, DOI 10.1109-79.543974; OHLANDER R, 1978, COMPUT VISION GRAPH, V8, P313, DOI 10.1016-0146-664X(78)90060-6; OJOLA T, 1998, PATTERN RECOGN, V19, P1213; PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214-aoms-1177704472; Pham DL, 2000, ANNU REV BIOMED ENG, V2, P315, DOI 10.1146-annurev.bioeng.2.1.315; Pratt WK, 1991, DIGITAL IMAGE PROCES; Schalkoff R.J, 1992, PATTERN RECOGNITION; Shapiro L., 2001, COMPUTER VISION; Xu BG, 2002, AATCC REV, V2, P42; Yao KC, 2000, PATTERN RECOGN, V33, P1575, DOI 10.1016-S0031-3203(99)00135-1; YIN HJ, 1995, NEURAL COMPUT, V7, P1178, DOI 10.1162-neco.1995.7.6.1178; Yoshimura M, 1999, PATTERN RECOGN, V32, P2041, DOI 10.1016-S0031-3203(99)00004-7; ZHANG P, 2003, P IEEE C EV COMP CEC, P634; Zouagui T, 2004, PATTERN RECOGN, V37, P1785, DOI 10.1016-j.patcog.2003.12.01462
Superconducting properties of Tl-2223 phase substituted by iron
No full textBulk superconducting samples of type Tl2Ba2Ca 2Cu3-xFexO10-δ; with 0 x 0.4, have been prepared using a single step of solid-state reaction. The prepared samples have been characterized using X-ray powder diffraction (XRD), scanning electron microscope (SEM) and microprobe analysis (MPA). The tetragonal structure of Tl-2223 did not change with the partial replacement of Cu 2+ by Fe3+ ions, whereas the lattice parameters were found to vary as function of Fe-content. The superconducting transition temperature Tc determined from electrical resistivity and ac magnetic susceptibility measurements shows suppression in its value as Fecontent increases. The suppression in Tc was attributed to the magnetic disorder and Cooperpairs breaking. The critical current density Jc and field irreversibility Bir were calculated as function of Fe-content. © 2006 IOP Publishing Ltd.Abou-Aly A. I., 2002, INT C RES TRENDS SCI, P91; Awad R, 2000, PHYSICA C, V341, P685, DOI 10.1016-S0921-4534(00)00650-X; Awad R, 2001, PHYSICA B, V307, P72, DOI 10.1016-S0921-4526(01)00971-1; BEAN CP, 1964, REV MOD PHYS, V36, P31, DOI 10.1103-RevModPhys.36.31; ESKES H, 1988, PHYS REV LETT, V61, P1415, DOI 10.1103-PhysRevLett.61.1415; GOTO T, 1997, PHYSICA C, V263, P8750; Isber S, 2005, SUPERCOND SCI TECH, V18, P311, DOI 10.1088-0953-2048-18-3-018; Koo JH, 2003, J PHYS-CONDENS MAT, V15, pL729, DOI 10.1088-0953-8984-15-46-L03; Li Y, 1999, PHYSICA C, V315, P129, DOI 10.1016-S0921-4534(99)00209-9; SIEGAD MP, 1997, J MATER RES, V12, P1421; WESTERHOLT K, 1989, PHYS REV B, V39, P11680, DOI 10.1103-PhysRevB.39.1168022
Energy-Aware Discrete Probabilistic Localization of Wireless Sensor Networks
No full textLocalizing sensor nodes is critical in the context of wireless sensor network applications. It has been shown that, for some applications, low-overhead discrete localization achieves results comparable to costly fine localization. This research presents a hybrid energy-aware discrete localization method that requires no transmission overhead from the sensor nodes. The proposed method, E-KalmaNN, is a combination of a Kalman-inspired localization and Artificial Neural Networks estimation that updates the position of a node with respect to a mobile reference. E-KalmaNN runs on the sensor nodes and supports different listening-wakeup frequencies for different nodes to balance power requirements with localization accuracy for each node. Simulation results show that the method converges to the correct position of the node in a relatively short time with high average location accuracy. Compared to the localization methods found in the literature, E-KalmaNN localizes with comparable accuracy, lower transmission costs and-or fewer motion restrictions. © 2013 Copyright TSI® Press.Amro A., 2008, IEEE ASME INT C ADV; Bulusu N, 2000, IEEE PERS COMMUN, V7, P28, DOI 10.1109-98.878533; Elhajj I.H., 2006, IEEE RSJ INT C INT R; Galstyan A., 2004, P 3 INT S INF PROC S, P61, DOI 10.1145-984622.984632; Gorski J., 2005, IEEE ASME INT C ADV, P735; Hu L., 2004, P 10 ANN INT C MOB C; Karl H, 2005, PROTOCOLS AND ARCHITECTURES FOR WIRELESS SENSOR NETWORKS, P1, DOI 10.1002-0470095121; Kecman V., 2001, LEARNING SOFT COMPUT; Khan Haseebulla M., 2006, P 2 IEEE WORKSH DEP; Moore D., 2004, P 2 ACM C EMB NETW S; Priyantha N. B., 2003, 892 MIT LAB COMP SCI; Priyantha Nissanka B., 2000, P 6 ACM MOBICOM BOST; Ramadurai V., 2007, P ACM SIGMOBILE MOB, V11, P53; Reichenbach F., 2006, P 9 EUROMICRO C DIG; Savarese C., 2001, P INT C AC SPEECH SI; Want R., 1992, ACM T INFORM SYSTEMS, V10; Welch G., 2001, INTRO KALMAN FILTER; Xiao B., 2007, P IEEE INT C ICC 070
- …
