Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Tommy W S Chow"'
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 72:1-9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::302cf59fb3b269837939db4d6bc5b6bb
https://doi.org/10.1109/tim.2022.3227552
https://doi.org/10.1109/tim.2022.3227552
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:6038-6046
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::693a96e918cc887e5d1b587450bb8062
https://doi.org/10.1109/tii.2022.3141783
https://doi.org/10.1109/tii.2022.3141783
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:4477-4487
Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling stage or/and t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65b23d83fb20aa1be2a6dd06463086ac
https://doi.org/10.1109/tii.2021.3119377
https://doi.org/10.1109/tii.2021.3119377
Autor:
Tommy W. S. Chow, Jianghong Ma
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:315-329
Multilabel learning has been extensively studied in the past years, as it has many applications in different domains. It aims at annotating the labels for unseen data according to training data, which are often high dimensional in both instance and f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ca69b09c711f8e71fc13bc4136b6277
https://doi.org/10.1109/tnnls.2020.3027745
https://doi.org/10.1109/tnnls.2020.3027745
Autor:
Tommy W S Chow, Xiaolei Lu
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-10
Existing partial sequence labeling models mainly focus on a max-margin framework that fails to provide an uncertainty estimation of the prediction. Furthermore, the unique ground-truth disambiguation strategy employed by these models may include wron
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd32435bdfc7230a3b8dfd3824512f0b
https://doi.org/10.1109/tnnls.2022.3191726
https://doi.org/10.1109/tnnls.2022.3191726
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d2e5280f8b77899df1219f0f11bc073
https://doi.org/10.1109/tim.2022.3214490
https://doi.org/10.1109/tim.2022.3214490
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a41da2cc45e7be99144d9409f8aaaff
https://doi.org/10.1109/tim.2022.3196742
https://doi.org/10.1109/tim.2022.3196742
Publikováno v:
Entropy, Vol 17, Iss 3, Pp 1535-1548 (2015)
Traditional centroid-based clustering algorithms for heterogeneous data with numerical and non-numerical features result in different levels of inaccurate clustering. This is because the Hamming distance used for dissimilarity measurement of non-nume
Externí odkaz:
https://doaj.org/article/5466143994154ec7bfda375da9b9f934
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:6272-6281
In recent years, the estimation of the remaining useful life (RUL) has become an increasingly important topic. Existing RUL estimation studies mainly focus on linear degradation cases or degradation processes that can be linearized. A few nonlinear d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef2507fac5cf980b705819269f7054e2
https://doi.org/10.1109/tii.2021.3051285
https://doi.org/10.1109/tii.2021.3051285
Autor:
Xiaolei Lu, Tommy W. S. Chow
Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7970dbe636bbc2094e192fd45084d56
http://arxiv.org/abs/2209.09410
http://arxiv.org/abs/2209.09410
