In this paper, the well-known cholesky algorithm (for solving simultaneous linear equations, or SLE) is re-visited, with the ultimate goal of developing a simple, user-friendly, attractive, and useful Java Visualizati...
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In this paper, the well-known cholesky algorithm (for solving simultaneous linear equations, or SLE) is re-visited, with the ultimate goal of developing a simple, user-friendly, attractive, and useful Java Visualization and Animation Graphical User Inter-face (GUI) software as an additional teaching tool for students to learn the cholesky factorization in a step-by-step fashion with computer voice and animation. A demo video of the cholesky Decomposition (or factorization) animation and result can be viewed online from the website: http://***/~imako001/cholesky/demo/***. The software tool developed from this work can be used for both students and their instructors not only to master this technical subject, but also to provide a dynamic/valuable tool for obtaining the solutions for homework assignments, class examinations, self-assessment studies, and other coursework related activities. Various transportation engineering applications of SLE are cited. Engineering educators who have adopted “flipped classroom instruction” can also utilize this Java Visualization and Animation software for students to “self-learning” these algorithms at their own time (and at their preferable locations), and use valuable class-meeting time for more challenging (real-life) problems’ discussions. Statistical data for comparisons of students’ performance with and without using the developed Java computer animation are also included.
Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squa...
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Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the crossvalidation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LSSVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLSSVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.
3G standards such as TD-SCDMA use multi-user detection(MUD) at the base station to combat multi address interference(MAI) and inter-symbol interference(ISI). We review cholesky factorization and Schur algorithm for Ze...
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3G standards such as TD-SCDMA use multi-user detection(MUD) at the base station to combat multi address interference(MAI) and inter-symbol interference(ISI). We review cholesky factorization and Schur algorithm for Zero Forcing Block Linear Equalization. We compare the solutions of different algorithm for ZF-BLE by computation, and analyze the complexity of those methods.
The radiological characterization of a steam generator consists of evaluating the global radiological activity in the tube bundle. In this paper, we present a non-destructive method and the results analysis of the gam...
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ISBN:
(纸本)9781479910465;9781479910472
The radiological characterization of a steam generator consists of evaluating the global radiological activity in the tube bundle. In this paper, we present a non-destructive method and the results analysis of the gamma sampling measurements from a sample of U-tubes in the bundle. On site, the implementation of the methodology is fairly easy. But the analysis of the results is more complicated due to the long path of the gamma ray (from Co-60 quite penetrating), and also the heterogeneous activity of U-tubes bundle, which have not the same life cycle. We explain why the periodic spatial arrangement complicates also the analysis. Furthermore, we have taken into account the environment of all tubes measured because of all the external influence activity of others U-tubes (the nearest, the most distant and potential hot spot). A great amount of independent influence coefficient had to be considered (roughly 18 million). Based on a physical and numerical modeling, and using a cholesky algorithm solving the problem and saving time machine.
Using the domain decomposition method we give an application of a block version of the interval cholesky algorithm. We give an example where this version accelerates the calculation of bounds for the solutions of line...
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Using the domain decomposition method we give an application of a block version of the interval cholesky algorithm. We give an example where this version accelerates the calculation of bounds for the solutions of linear systems of equations with symmetric matrices and right-hand sides both of which are varying within given intervals. (C) 2002 Elsevier Science B.V. All rights reserved.
One must first make sure that a given matrix subject to factorization is symmetric, and then use it in cholesky algorithm, in MATLAB. This may cost machine time to check for symmetry, however, saves much more of it by...
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One must first make sure that a given matrix subject to factorization is symmetric, and then use it in cholesky algorithm, in MATLAB. This may cost machine time to check for symmetry, however, saves much more of it by preventing errors due to inherent structure of the built-in algorithm.
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