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21.
S. J. C. Irvine A. Stafford M. U. Ahmed A. Brown H. Kheyrandish 《Journal of Electronic Materials》1997,26(6):723-727
The first detailed comparison has been made of the metalorganic vapor phase epitaxy growth rates of CdTe, ZnTe, and ZnSe,
measured in situ with laser reflectometry. The comparison also includes the photo-assisted growth with visible radiation from
an argon ion laser. Using a standard Group II precursor (DMCd or DMZn.TEN) partial pressure of 1.5 × 10−4 atm, VI/II ratio of 1 and DIPM (M = Te, Se) the maximum growth rates are in the region of 10 to 15 AU/ s. Decrease in growth
rates of ZnTe at higher temperatures or higher laser powers have been attributed to the desorption from the substrate of unreacted
Te precursor. The behavior of DTBSe is quite different from DIPSe for both pyrolytic and photo-assisted growth. The maximum
growth rate is around 1 AU/ s with very little photo-enhancement, except at 300°C. Secondary ion mass spectroscopy analysis
of hydrogen concentration in the ZnSe layers shows high concentrations, up to 5.9 × 1019 atoms cm−3 for DTBSe grown ZnSe under pyrolytic conditions. These results show that the growth kinetics play an important part in the
incorporation of hydrogen and passivation of acceptor doped material. 相似文献
22.
The electrical properties of CdTe thin film have been studied and discussed, including, the conduction mechanism, and the effect of temperature and indium doping on the current passing through the CdTe film and hence on the film conductivity. It is observed that the CdTe film is of the modified Poole–Frenkel conduction mechanism and the resistivity of the film can be lowered by more than one order of magnitude due to indium doping. 相似文献
23.
In this article, a new extension of affine arithmetic is introduced. This technique is based on a quadratic form named general quadratic form. We focus here on the computation of reliable bounds of a function over a hypercube by using this new tool. Some properties
of first quadratic functions and then polynomial ones are reported. In order to show the efficiency of such a method, ten
polynomial global optimization problems are presented and solved by using an interval branch-and-bound based algorithm.
The work of the first author was also supported by the Laboratoire de Mathématiques Appliquées CNRS–FRE 2570, Université de
Pau et des Pays de l'Adour, France, and by the Laboratoire d'Electrotechnique et d'Electronique Industrielle CNRS–UMR5828,
Group EM3, INPT–ENSEEIHT. 相似文献
24.
Tree-based partitioning of date for association rule mining 总被引:1,自引:1,他引:0
The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds.
Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition.
Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05).
Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification. 相似文献
25.
Fahd A. Alhaidari Saleh A. Al-Dossary Ilyas A. Salih Abdlrhman M. Salem Ahmed S. Bokir Mahmoud O. Fares Mohammed I. Ahmed Mohammed S. Ahmed 《计算机系统科学与工程》2021,36(1):57-67
Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%. 相似文献
26.
The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used. 相似文献
27.
Kiani Azaz Ahmed Hafeez Yaser Imran Muhammad Ali Sadia 《The Journal of supercomputing》2021,77(8):8391-8432
The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both... 相似文献
28.
Asim Muhammad Nabeel Ghani Muhammad Usman Ibrahim Muhammad Ali Mahmood Waqar Dengel Andreas Ahmed Sheraz 《Neural computing & applications》2021,33(11):5437-5469
Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly... 相似文献
29.
Khan Muhammad Hussain Saleem Zainab Ahmad Muhammad Sohaib Ahmed Ayaz Hamail Mazzara Manuel Raza Rana Aamir 《Neural computing & applications》2021,33(21):14507-14521
Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For... 相似文献
30.
Kader Md. Abdul Zamli Kamal Z. Ahmed Bestoun S. 《Neural computing & applications》2021,33(23):15933-15953
Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold.... 相似文献