• Title/Summary/Keyword: Approximate computing

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ON A SYSTEM OF GENERALIZED NONLINEAR VARIATIONAL INEQUALITIES

  • Li, Jingchang;Guo, Zhenyu;Liu, Zeqing;Kang, Shin-Min
    • Communications of the Korean Mathematical Society
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    • v.22 no.2
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    • pp.247-258
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    • 2007
  • In this paper a new class of system of generalized nonlinear variational inequalities involving strongly monotone, relaxed co coercive and relaxed generalized monotone mappings in Hilbert spaces is introduced and studied. Based on the projection method, an equivalence between the system of generalized nonlinear variational inequalities and the fixed point problem is established, which is used to suggest some new iterative algorithms for computing approximate solutions of the system of generalized nonlinear variational inequalities. A few sufficient conditions which ensure the existence and uniqueness of solution of the system of generalized nonlinear variational inequalities are given, and the convergence analysis of iterative sequences generated by the algorithms are also discussed.

A Computational Improvement of Otsu's Algorithm by Estimating Approximate Threshold (근사 임계값 추정을 통한 Otsu 알고리즘의 연산량 개선)

  • Lee, Youngwoo;Kim, Jin Heon
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.163-169
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    • 2017
  • There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

Effect of Gas Beaming in the Calculation of Tube Conductance (Beaming Effect를 고려한 Tube 연결부의 Conductance 계산)

  • 이진원;이영규
    • Journal of the Korean Vacuum Society
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    • v.1 no.1
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    • pp.16-23
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    • 1992
  • Conductance calculation is essential in designing a vacuum system. Since mathematical solution is nearly impossible except for a few simple cases, it has been so usual to calculate the conductance for a series connection of components using an approximate formula by Oatley. However, Oatley's formula has a fundamental flaw of totally neglecting the effect of gas beaming. In this study, a new technique is suggested for calculating the conductance with the same accuracy as the Monte Carlo method but with much less computing time, and applied to a system of tube connections in series. The effects of gas beaming in the calculation of conductance is analyzed quantitatively by comparing the conductance calculated by this method with those from Oatley's formula.

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Feature curve extraction from point clouds via developable strip intersection

  • Lee, Kai Wah;Bo, Pengbo
    • Journal of Computational Design and Engineering
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    • v.3 no.2
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    • pp.102-111
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    • 2016
  • In this paper, we study the problem of computing smooth feature curves from CAD type point clouds models. The proposed method reconstructs feature curves from the intersections of developable strip pairs which approximate the regions along both sides of the features. The generation of developable surfaces is based on a linear approximation of the given point cloud through a variational shape approximation approach. A line segment sequencing algorithm is proposed for collecting feature line segments into different feature sequences as well as sequential groups of data points. A developable surface approximation procedure is employed to refine incident approximation planes of data points into developable strips. Some experimental results are included to demonstrate the performance of the proposed method.

A MULTILEVEL BLOCK INCOMPLETE CHOLESKY PRECONDITIONER FOR SOLVING NORMAL EQUATIONS IN LINEAR LEAST SQUARES PROBLEMS

  • Jun, Zhang;Tong, Xiao
    • Journal of applied mathematics & informatics
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    • v.11 no.1_2
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    • pp.59-80
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    • 2003
  • An incomplete factorization method for preconditioning symmetric positive definite matrices is introduced to solve normal equations. The normal equations are form to solve linear least squares problems. The procedure is based on a block incomplete Cholesky factorization and a multilevel recursive strategy with an approximate Schur complement matrix formed implicitly. A diagonal perturbation strategy is implemented to enhance factorization robustness. The factors obtained are used as a preconditioner for the conjugate gradient method. Numerical experiments are used to show the robustness and efficiency of this preconditioning technique, and to compare it with two other preconditioners.

A Vertical File Partitioning Method Using SOFM in Database Design (데이터베이스 설계에서 SOFM 을 이용한 화일 수직분할 방법)

  • Shin, K.H.;Kim, J.Y.
    • Journal of Korean Institute of Industrial Engineers
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    • v.24 no.4
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    • pp.661-671
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    • 1998
  • It is important to minimize the number of disk accesses which is necessary to transfer data in disk into main memory when processing transactions in physical database design. A vertical file partitioning method is used to reduce the number of disk accesses by partitioning relations vertically and accessing only necessay fragments. In this paper, SOFM(Self-Organizing Feature Maps) network is used to solve vertical partitioning problems. This paper shows that SOFM network is efficient in solving vertical partitioning problem by comparing approximate solution of SOFM network with optimal solution of N-ary branch and bound method. And this paper presents a heuristic algorithm for allocating duplicate attributes to vertically partitioned fragments. As branch and bound method requires particularly much computing time to solve large-sized problems, it is shown that SOFM network is able to overcome this limitation of branch and bound method and solve large-sized problems efficiently in a short time.

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Modal Analysis of Large Scale Multi-Machine Power System using Rayleigh Quotient and Deflation (Rayleigh Quotient와 Deflation을 이용한 대형다기(多機)전력계통의 고유치 해석)

  • Shim, Kwan-Shik;Nam, Hae-Kon
    • Proceedings of the KIEE Conference
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    • 1993.07a
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    • pp.76-78
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    • 1993
  • This paper describes an efficient method of computing any desired number of the most unstable eigenvalues and eigenvectors of a large scale multi-machine power system. Approximate eigenvalues obtained by Hessenberg process are refined using Rayleigh quotient iteration with cubic convergence property. If further eigenvalues and eigenvectors are needed, the procedure described above are repeated with deflation. The proposed algorithm can cover all the model types of synchronous machines, exciters, speed governing system and PSS defined in AESOPS. The proposed algorithm applied to New England test system with 10 machines and 39 buses produced the results same with AESOPS in faster computation time. Also eigenvectors computed in Rayleigh quotient iteration makes it possible to make eigen-analysis for improving unstable modes.

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STOCHASTIC GRADIENT METHODS FOR L2-WASSERSTEIN LEAST SQUARES PROBLEM OF GAUSSIAN MEASURES

  • YUN, SANGWOON;SUN, XIANG;CHOI, JUNG-IL
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.25 no.4
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    • pp.162-172
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    • 2021
  • This paper proposes stochastic methods to find an approximate solution for the L2-Wasserstein least squares problem of Gaussian measures. The variable for the problem is in a set of positive definite matrices. The first proposed stochastic method is a type of classical stochastic gradient methods combined with projection and the second one is a type of variance reduced methods with projection. Their global convergence are analyzed by using the framework of proximal stochastic gradient methods. The convergence of the classical stochastic gradient method combined with projection is established by using diminishing learning rate rule in which the learning rate decreases as the epoch increases but that of the variance reduced method with projection can be established by using constant learning rate. The numerical results show that the present algorithms with a proper learning rate outperforms a gradient projection method.

New approach method of finite difference formulas for control algorithm (제어 알고리즘 구현을 위한 새로운 미분값 유도 방법)

  • Kim, Tae-Yeop
    • Journal of IKEEE
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    • v.23 no.3
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    • pp.817-825
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    • 2019
  • Difference equation is useful for control algorithm in the microprocessor. To approximate a derivative values from sampled data, it is used the methods of forward, backward and central differences. The key of computing discrete derivative values is the finite difference coefficient. The focus of this paper is a new approach method of finite difference formula. And we apply the proposed method to the recursive least squares(RLS) algorithm.

Some Approximation Results by Bivariate Bernstein-Kantorovich Type Operators on a Triangular Domain

  • Aslan, Resat;Izgi, Aydin
    • Kyungpook Mathematical Journal
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    • v.62 no.3
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    • pp.467-484
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    • 2022
  • In this work, we define bivariate Bernstein-Kantorovich type operators on a triangular domain and obtain some approximation results for these operators. We start off by computing some moment estimates and prove a Korovkin type convergence theorem. Then, we estimate the rate of convergence using the partial and complete modulus of continuity, and derive a Voronovskaya-type asymptotic theorem. Further, we calculate the order of approximation with regard to the Peetre's K-functional and a Lipschitz type class. In addition, we construct the associated GBS type operators and compute the rate of approximation using the mixed modulus of continuity and class of the Lipschitz of Bögel continuous functions for these operators. Finally, we use the two operators to approximate example functions in order to compare their convergence.