2021
DOI: 10.3390/mi12030316
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Kuhn–Munkres Algorithm-Based Matching Method and Automatic Device for Tiny Magnetic Steel Pair

Abstract: The tiny magnetic steel pair (TMSP), composed by two tiny magnetic steel blocks (TMSBs), is critical for some precision instruments. Incorrect matching of TMSP may result in insufficient instrument performance. Herein, the matching method of TMSP based on the Kuhn–Munkres algorithm is proposed. Further, an automatic TMSP matching device is developed. Especially, an ingenious clamp for multiple constraints of TMSB is presented and a visual/magnetism/force hybrid control strategy is realized for the safe and eff… Show more

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Cited by 3 publications
(2 citation statements)
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“…The Hungarian theory hypothesizes that vertices and edges graph exist, G = {P, R, E} as illustrated in Figure 1, where P and R are node sets in each graph partition, and E is the set of weights. The Hungarian algorithm [39] is used to transform Kuhn Munkres (6) to a problem of assignment in figure 1, its ultimate solution is optimum allocation, and the solution is more sensitive to practical requirements. 1 if P i is assigned to R j.…”
Section: Matrix Derivationmentioning
confidence: 99%
“…The Hungarian theory hypothesizes that vertices and edges graph exist, G = {P, R, E} as illustrated in Figure 1, where P and R are node sets in each graph partition, and E is the set of weights. The Hungarian algorithm [39] is used to transform Kuhn Munkres (6) to a problem of assignment in figure 1, its ultimate solution is optimum allocation, and the solution is more sensitive to practical requirements. 1 if P i is assigned to R j.…”
Section: Matrix Derivationmentioning
confidence: 99%
“…Specifically, for an initial similarity metric of patches generated from images s i and s i+1 , setting r i m and r n i+1 as elements of graph vector X and graph vector Y in the similarity metrix, respectively, then the weight of the connection between elements can be denoted as weight (r i m ,r n i+1 ), and the corresponding connected edge can be denoted as edge (r i m ,r n i+1 ) [51]. In the process of optimal matching using the Kuhn-Munkres algorithm, similarity scores of patches from the similarity matrix are considered as the weights of the edges between elements of vector X and vector Y for bipartite graph matching [52]. By updating the top marks of the elements, the number of viable edges between elements is continuously gained, and all these top marks would be assured to be viable top marks until the optimal matching is completed.…”
Section: Fruit Matching Modelmentioning
confidence: 99%