Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Yiliao Song"'
Publikováno v:
Energy Reports, Vol 1, Iss C, Pp 8-16 (2015)
The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind powe
Externí odkaz:
https://doaj.org/article/985313403e5b4fc0812010856cc1c90b
Publikováno v:
Energies, Vol 9, Iss 8, p 618 (2016)
The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecastin
Externí odkaz:
https://doaj.org/article/56c80bf0e6ab465084775d9c36c4b415
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Publikováno v:
Nanoscale Advances. 4:30-38
The emerging optical multiplexing within nanoscale shows super-capacity in encoding information by using lifetime fingerprints from luminescent nanoparticles. However, the optical diffraction limit compromises the decoding accuracy and throughput of
Publikováno v:
The Journal of Physical Chemistry Letters. 12:10242-10248
Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the poten
Existing concept drift adaptation (CDA) methods aim to continually update outdated classifiers in a single-labeled stream scenario. However, real-world data streams are massive, with hybrids of labeled and unlabeled streams. In this paper, we discuss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da8e66009ce76f8a5066338516c779b9
https://hdl.handle.net/10453/168220
https://hdl.handle.net/10453/168220
As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic data streams, which suffer from concept drift problems where the data dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57560ccd3652f4fb620e77dadb9c7dc3
https://hdl.handle.net/10453/167777
https://hdl.handle.net/10453/167777
Publikováno v:
Proceedings of the European Conference on Information Systems (ECIS); 2023, p1-12, 12p
Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6294dd03d5befb63a16e8b114152aea7
https://hdl.handle.net/10453/167779
https://hdl.handle.net/10453/167779
Publikováno v:
IEEE transactions on neural networks and learning systems.
In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus o