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アイテム
Task-Relevant Encoding of Domain Knowledge in Dynamics Modeling: Application to Furnace Forecasting From Video
http://hdl.handle.net/10061/0002000129
http://hdl.handle.net/10061/00020001290e077d59-c039-4c72-a73a-85df844492da
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2024-02-16 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Task-Relevant Encoding of Domain Knowledge in Dynamics Modeling: Application to Furnace Forecasting From Video | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Dynamic mode decomposition | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | forecasting | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | Fourier transforms | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | machine learning | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | video signal processing | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | waste handling | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| 著者 |
Michael, Brendan
× Michael, Brendan
× Ise, Akifumi
× Kawabata, Kaoru
× 松原, 崇充 |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Waste incineration plants are complex dynamical systems that rely on expert human operators to maintain steady combustion, by observing real-time in-chamber video feeds. Real-time plant forecasting provides vital operational support in decision making, and applying machine learning to automatically learn dynamics forecast models from video feeds is an attractive means to realise this. However, learning complex dynamics in systems that requires cost-efficiency remains an open research problem. Specifically, modelling plant dynamics in real-time is challenging due to uncertainties caused by inhomogeneous waste inputs, requiring complex learning that impedes real-time modelling. To address this, this paper presents a real-time data-driven framework for generating video forecasts, by incorporating task-relevant domain-knowledge , during learning. Specifically, this method combines dynamics modelling and forecasting using dynamic mode decomposition , with Fourier transformations informed by expert operator heuristic knowledge for encoding task-relevant frequency information inside the learning process. Experiments in this paper demonstrate that the proposed framework captures intuitive physical aspects of the underlying physiochemical process, with a greatly reduced computational runtime in comparison to standard approaches, allowing for application in real-time domains. Forecasted video predictions are accurate over short time horizons, and capture important system characteristics over longer time periods. | |||||||||||
| 書誌情報 |
en : IEEE Access 巻 10, 号 10, p. 4615-4627, 発行日 2022-01-06 |
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| 出版者 | IEEE | |||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2169-3536 | |||||||||||
| 出版者版DOI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2022.3140758 | |||||||||||
| 出版者版URI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://ieeexplore.ieee.org/abstract/document/9672193 | |||||||||||
| 権利 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | IEEE is not the copyright holder of this material. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | NA | |||||||||||
