_:n2dc3a6bc540845f1bb519fc70e49d65bxb26
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"In the era of big data, data-driven based classification has become an\nessential method in smart manufacturing to guide production and optimize\ninspection. The industrial data obtained in practice is usually time-series\ndata collected by soft sensors, which are highly nonlinear, nonstationary,\nimbalanced, and noisy. Most existing soft-sensing machine learning models focus\non capturing either intra-series temporal dependencies or pre-defined\ninter-series correlations, while ignoring the correlation between labels as\neach instance is associated with multiple labels simultaneously. In this paper,\nwe propose a novel graph based soft-sensing neural network (GraSSNet) for\nmultivariate time-series classification of noisy and highly-imbalanced\nsoft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series\nand intra-series dependencies jointly in the spectral domain; 2) exploit the\nlabel correlations by superimposing label graph that built from statistical\nco-occurrence information; 3) learn features with attention mechanism from both\ntextual and numerical domain; and 4) leverage unlabeled data and mitigate data\nimbalance by semi-supervised learning. Comparative studies with other commonly\nused classifiers are carried out on Seagate soft sensing data, and the\nexperimental results validate the competitive performance of our proposed\nmethod."
<https://cdnjs.deepai.org/profile/xiaoye-qian>