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• W1878757577 abstract "Advanced sensing technologies have produced a significant amount of discrete point data in the past decade. Measurement uncertainty frequently occurs at the geometric discontinuity of mechanical parts. In this paper, a genetic search algorithm is developed for optimally-constrained multiple-line fitting of discrete data points. It contains two important technical components: (a) constrained least-squares fitting of multiple lines, and (b) genetic search for optimal corner/edge points. The algorithm is designed for both two-dimensional and three-dimensional cases. Numerical experiments demonstrate the effectiveness of the proposed approach, compared to the conventional least-squares fitting method as well as exhaustive search method. A comparative study with a particle swarm method indicates that both the genetic search and particle swarm search produce similar results in terms of minimum fitting errors. It can be used for the effective determination of sharp edges or corners based on discrete data points measured for high-precision industrial inspection and manufacturing." @default.
• W1878757577 created "2016-06-24" @default.
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• W1878757577 date "2016-03-01" @default.
• W1878757577 modified "2023-10-15" @default.
• W1878757577 title "Genetic search for optimally-constrained multiple-line fitting of discrete data points" @default.
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• W1878757577 doi "https://doi.org/10.1016/j.asoc.2015.09.020" @default.
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