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951.
Mapping applications onto parallel platforms is a challenging problem, that becomes even more difficult when platforms are heterogeneous — nowadays a standard assumption. A high-level approach to parallel programming not only eases the application developer’s task, but it also provides additional information which can help realize an efficient mapping of the application. 相似文献
952.
Hermite spectral methods using Sobolev orthogonal/biorthogonal basis functions for solving second and fourth-order differential equations on unbounded domains are proposed. Some Hermite–Sobolev orthogonal/biorthogonal basis functions are constructed which lead to the diagonalization of discrete systems. Accordingly, both the exact solutions and the approximate solutions can be represented as infinite and truncated Fourier series. The convergence is analyzed and some numerical results are presented to illustrate the effectiveness and the spectral accuracy of this approach. 相似文献
953.
胡振 《计算机测量与控制》2014,22(11):3662-3665
针对计算机超频结果测量值的估计问题,提出一种优化的LS-SVM回归模型解决方案;首先对CPU和显示卡超频的参数设置及主要结果进行了分析,并探讨了常用预测算法的不足,据此选择LS-SVM回归模型进行超频结果测量值的预测;然后设计了一种LCQPSO算法寻找模型参数的最优值,使模型的预测精度和泛化能力得以提高;经采集AMD FX-8350CPU的50组超频样本验证,该算法的预测误差比RBF神经网络降低了80%,表明了该算法的有效性。 相似文献
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《遥感技术与应用》2013,28(5):766-772
The glacier is an important natural and great potential of the fresh water resources,and plays a vital role in the regional ecological environment balance and stability.This study acquired the airborne hyperspectral data over Zhongxi-1 Glacier in August,2011.Firstly,the data preprocessing,including radiation calibration,atmospheric correction and geometric correction was performed on the hyperspectral data;secondly,using principal component analysis (PCA)and minimum noise transformation (MNF) for data dimensionality reduced respectively;thirdly,six classification methods,i.e.maximum likelihood method,minimum distance,Mahalanobis distance method,spectral angle method binary encoding,and spectral information divergence,were applied in the two datasets,and also the comparison results of the different classification methods were conducted to determine the optimal method of data dimensionality reduction and the optimal classification method;finally,the hyperspectral data for glacierclassification was compared with the HJ satellite multispectral data.The results show that: the classification accuracy of the PCA transform data from hyperspectral data is higher than that of MNF transform data;for the PCA transformed dataset of hyperspectraldata,the Mahalanobis distance method,maximum likelihood method,minimum distance method produced better classification results with the comparison to others,while for the MNF transformed dataset from hyperspectral data,the spectral angle method and spectral information divergence method is better than others. 相似文献
959.
A well-known result due to J.T. Stafford asserts that a stably free left module M over the Weyl algebras D=An(k) or Bn(k)–where k is a field of characteristic 0–with is free. The purpose of this paper is to present a new constructive proof of this result as well as an effective algorithm for the computation of bases of M. This algorithm, based on the new constructive proofs [Hillebrand, A., Schmale, W., 2001. Towards an effective version of a theorem of Stafford. J. Symbolic Comput. 32, 699–716; Leykin, A., 2004. Algorithmic proofs of two theorems of Stafford. J. Symbolic Comput. 38, 1535–1550] of J.T. Stafford’s result on the number of generators of left ideals of D, performs Gaussian elimination on the formal adjoint of the presentation matrix of M. We show that J.T. Stafford’s result is a particular case of a more general one asserting that a stably free left D-module M with is free, where denotes the stable rank of a ring D. This result is constructive if the stability of unimodular vectors with entries in D can be tested. Finally, an algorithm which computes the left projective dimension of a general left D-module M defined by means of a finite free resolution is presented. It allows us to check whether or not the left D-module M is stably free. 相似文献
960.
Empirical evaluation of optimization algorithms when used in goal-oriented automated test data generation techniques 总被引:2,自引:0,他引:2
Man Xiao Mohamed El-Attar Marek Reformat James Miller 《Empirical Software Engineering》2007,12(2):183-239
Software testing is an essential process in software development. Software testing is very costly, often consuming half the
financial resources assigned to a project. The most laborious part of software testing is the generation of test-data. Currently,
this process is principally a manual process. Hence, the automation of test-data generation can significantly cut the total
cost of software testing and the software development cycle in general. A number of automated test-data generation approaches
have already been explored. This paper highlights the goal-oriented approach as a promising approach to devise automated test-data
generators. A range of optimization techniques can be used within these goal-oriented test-data generators, and their respective
characteristics, when applied to these situations remain relatively unexplored. Therefore, in this paper, a comparative study
about the effectiveness of the most commonly used optimization techniques is conducted.
Man Xiao received a B.S. degree in Space Physics and Electronics Information Engineering from the University of Wuhan, China; and a M.S. degree in Software Engineering, from the University of Alberta, Canada. She is now a Software Engineer at a small start-up company in Edmonton, Alberta, Canada. Mohamed El-Attar is a Ph.D. candidate (Software Engineering) at the University of Alberta and a member of the STEAM laboratory. His research interests include Requirements Engineering, in particular with UML and use cases, object-oriented analysis and design, model transformation and empirical studies. Mohamed received a B.S. Engineering in Computer Systems from Carleton University. Marek Reformat received his M.S. degree from the Technical University of Poznan, Poland, and his Ph.D. from the University of Manitoba, Canada. His interests are related to simulation and modeling in time-domain, and evolutionary computing and its application to optimization problems. For 3 years he worked for the Manitoba HVDC Research Centre, Canada where he was a member of a simulation software development team. Currently, he is with the Department of Electrical and Computer Engineering at the University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, and probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. He was a member of program committees of several conferences related to computational intelligence and evolutionary computing. James Miller received his B.S. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship and subsequently a senior lectureship in Computer Science. Initially, during this period, his research interests were in computer vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests were in software and systems engineering. In 2000, he joined the Department of Electronic and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate verification and validation issues of software, embedded and ubiquitous computer systems. He has published over one hundred refereed journal and conference papers on software and systems engineering (see for details for recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献
James Miller (Corresponding author)Email: |
Man Xiao received a B.S. degree in Space Physics and Electronics Information Engineering from the University of Wuhan, China; and a M.S. degree in Software Engineering, from the University of Alberta, Canada. She is now a Software Engineer at a small start-up company in Edmonton, Alberta, Canada. Mohamed El-Attar is a Ph.D. candidate (Software Engineering) at the University of Alberta and a member of the STEAM laboratory. His research interests include Requirements Engineering, in particular with UML and use cases, object-oriented analysis and design, model transformation and empirical studies. Mohamed received a B.S. Engineering in Computer Systems from Carleton University. Marek Reformat received his M.S. degree from the Technical University of Poznan, Poland, and his Ph.D. from the University of Manitoba, Canada. His interests are related to simulation and modeling in time-domain, and evolutionary computing and its application to optimization problems. For 3 years he worked for the Manitoba HVDC Research Centre, Canada where he was a member of a simulation software development team. Currently, he is with the Department of Electrical and Computer Engineering at the University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, and probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. He was a member of program committees of several conferences related to computational intelligence and evolutionary computing. James Miller received his B.S. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship and subsequently a senior lectureship in Computer Science. Initially, during this period, his research interests were in computer vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests were in software and systems engineering. In 2000, he joined the Department of Electronic and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate verification and validation issues of software, embedded and ubiquitous computer systems. He has published over one hundred refereed journal and conference papers on software and systems engineering (see for details for recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献