• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1649-1665
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• 2021

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6028-6042
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• 2019

Since Sparse Nonnegative Matrix Factorization (SNMF) method can control the sparsity of the decomposed matrix, and then it can be adopted to control the sparsity of facial feature extraction and recognition. In order to improve the accuracy of SNMF method for facial feature recognition, new additive iterative rules based on the improved iterative step sizes are proposed to improve the SNMF method, and then the traditional multiplicative iterative rules of SNMF are transformed to additive iterative rules. Meanwhile, to further increase the sparsity of the basis matrix decomposed by the improved SNMF method, a threshold-sparse constraint is adopted to make the basis matrix to a zero-one matrix, which can further improve the accuracy of facial feature recognition. The improved SNMF method based on the additive iterative rules and threshold-sparse constraint is abbreviated as ASNMF, which is adopted to recognize the ORL and CK+ facial datasets, and achieved recognition rate of 96% and 100%, respectively. Meanwhile, from the results of the contrast experiments, it can be found that the recognition rate achieved by the ASNMF method is obviously higher than the basic NMF, traditional SNMF, convex nonnegative matrix factorization (CNMF) and Deep NMF.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.353-361
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• 2022

Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1097-1112
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• 1989

An iterative learning control scheme is incorporated to the computed torque method as a means to enhance the accuracy and the flexibility. A learning rule is constructed by utilizing a gradient descent algorithm and data compressing techniques are illustrated. Computer simulation results show a good performance of the scheme under a relatively high speed and a heavy payload condition.

• Journal of electromagnetic engineering and science
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• v.9 no.1
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• pp.12-16
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• 2009

This paper presents a new iterative locally one-dimensional [mite-difference time-domain(LOD-FDTD) method that has a simpler formula than the original iterative LOD-FDTD formula[l]. There are fewer arithmetic operations than in the original LOD-FDTD scheme. This leads to a reduction of CPU time compared to the original LOD-FDTD method while the new method exhibits the same numerical accuracy as the iterative ADI-FDTD scheme. The number of arithmetic operations shows that the efficiency of this method has been improved approximately 20 % over the original iterative LOD-FDTD method.

• Journal of KIBIM
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• v.11 no.4
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• pp.1-9
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• 2021

Comparing to general scanning data, the 3D digital map for large construction sites and complex buildings consists of millions of points. The large construction site needs to be scanned multiple times by drone photogrammetry or terrestrial laser scanner (TLS) survey. The scanned point cloud data are required to be registrated with high resolution and high point density. Unlike the registration of 2D data, the matrix of translation and rotation are used for registration of 3D point cloud data. Archiving high accuracy with 3D point cloud data is not easy due to 3D Cartesian coordinate system. Therefore, in this study, iterative closest point (ICP) registration method for improve accuracy of 3D digital map was employed by different overlap ratio on 3D digital maps. This study conducted the accuracy test using different overlap ratios of two digital maps from 10% to 100%. The results of the accuracy test presented the optimal overlap ratios for an ICP registration method on digital maps.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.607-614
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• 2002

For the accurate analysis of crack problems, considerable nodal refinement near the crack tip to capture singular stress field with sufficient accuracy to provide a useful computation of stress intensity factor is required. So, in this paper, adaptive nodal refinement scheme is proposed where nodes in restricted cell regions centered at crack tip are arranged in array for enhanced spatial resolution and adaptivity. With only cell-wise adaptive refinement scheme around crack tip fields, singularity of crack tip is sufficiently described to expect a successive crack propagate direction. Through numerical tests, accuracy of the proposed adaptive scheme is investigated and compared with the finite element and experimental results. By this implementation, it is shown that high accuracy is achieved by using iterative cell-wise solution method fur analyzing crack propagation problems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.5
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• pp.439-446
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• 2012

This paper presents an implicit moving least squares(MLS) difference method for improving the solution accuracy of 1-D free boundary problems, which implicitly updates the topology change of moving interface. The conventional MLS difference method explicitly updates the moving interface; it requires no iterative solution procedure but results in the loss of accuracy. However, the newly developed implicit scheme makes the total system nonlinear involving iterative solution procedure, but numerical verification show that it dramatically elevates the solution accuracy with moderate computation increase. Through numerical experiments for melting problems having moving singularity, it is verified that the proposed method can achieve the second order accuracy.

• Journal of computational fluids engineering
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• v.9 no.3
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• pp.57-65
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• 2004

The incompressible Navier-Stokes equations in two dimensions are stabilized by a modified residual method, and then discretized by hierarchical elements. The stabilization is necessary to escape from the Ladyzhenskaya-Babuska-Brezzi(LBB) constraint and hence to achieve an equal order formulation. To expedite a standard iterative method such as the conjugate gradient squared(CGS) method, a preconditioning technique called the Hierarchical Iterative Procedure(HIP) has been applied. In this paper, we increased the order of interpolation within an element up to cubic. The hierarchical elements have been used to achieve a higher order accuracy in fluid flow analyses, but a proper efficient iterative procedure for higher order finite element formulation has not been available so far The numerical results by the present HIP for the lid driven cavity flow and others showed the present procedure to be stable, very efficient and useful in flow analyses in conjunction with hierarchical elements.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.7
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• pp.793-800
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• 2015

We presented a method to perform simultaneously both head-eye calibration and wheel calibration for a mobile robot that has a stereo camera mounted on the pan-tilt mechanism. Such a mobile robot system prevails recently. However, conventional methods are not applicable to this system because they assumed that camera systems were mounted on fixed structures. Building on conventional methods, we devised an iterative linear solution to solve the problem, and achieved satisfactory results in terms of accuracy in addition to efficiency due to simultaneous calibration. Furthermore, the calibration accuracy was improved by nonlinear optimization.