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dc.contributor.authorTao, Feng
dc.contributor.authorSuresh, Rengan
dc.contributor.authorVotion, Johnathan
dc.contributor.authorCao, Yongcan
dc.date.accessioned2021-04-19T15:26:37Z
dc.date.available2021-04-19T15:26:37Z
dc.date.issued3/16/2021
dc.identifierdoi: 10.3390/s21062069
dc.identifier.citationSensors 21 (6): 2069 (2021)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/550
dc.description.abstractIn this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve the data-target association problem when targets move on a constrained space and minimal information of the targets can be obtained by sensors. Instead of employing the traditional data-target association methods that are based on statistical probabilities, the G-MLKM solves the problem via data clustering. We first develop the multi-layer k-means++ (MLKM) method for data-target association at a local space given a simplified constrained space situation. Then a p-dual graph is proposed to represent the general constrained space when local spaces are interconnected. Based on the p-dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association, extracting cross-local data-target association mathematically, and then analyzing the data association at intersections of that space. To exclude potential data-target association errors that disobey physical rules, we also develop error correction mechanisms to further improve the accuracy. Numerous simulation examples are conducted to demonstrate the performance of G-MLKM, which yields an average data-target association accuracy of 92.2%.
dc.titleGraph Based Multi-Layer K-Means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
dc.date.updated2021-04-19T15:26:38Z


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