Search Results - (( frames optimization modified algorithm ) OR ( java application max algorithm ))

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  1. 1

    Memoryless modified symmetric rank-one method for large-scale unconstrained optimization by Modarres, Farzin, Abu Hassan, Malik, Leong, Wah June

    Published 2009
    “…In this study, we present a scaled memoryless modified Symmetric Rank-One (SR1) algorithm and investigate the numerical performance of the proposed algorithm for solving large-scale unconstrained optimization problems. …”
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    Article
  2. 2

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…To address these problems, this research proposes three new distributed static batch mode inspired algorithms. The first (proposed) algorithm is based on Min-Min, called Min-Diff, the second algorithm is based on Max-Min, called Max-Average, and the third algorithm is to handle the load balancing, called Efficient Load Balancing (ELB). …”
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    Thesis
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    Shunt active power filter with modified synchronous reference frame technique and fuzzy logic current controller for harmonic mitigation by Suleiman, Musa

    Published 2017
    “…To generate the reference signals, modified synchronous reference frame (MSRF) technique is chosen. …”
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  5. 5

    Video colorization using canny optimization technique / Irwin Kevin Mueson by Irwin Kevin Mueson

    Published 2016
    “…Colorization process will be using the technique of Canny Optimization method. This is a hybrid technique from Colorization using Optimization, which was modified and improved the algorithms by adding Canny Edge Detection to improve the colorization process on the grayscale image or frames. …”
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    Development of an integrated scheduling model for handling equipment in automated port container terminals by Sadeghian, Syed Hamidreza

    Published 2014
    “…In addition, results proved that the modified meta-heuristic algorithm is able to find near optimal solutions and on average, the solutions found by the GA algorithm are only 0.2% worse than the optimal solutions and in the worst case in the test cases this difference is 2.3% which is acceptable. …”
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    Thesis