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Updated: Feb 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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稀少なデータにおける連続性情報の探求: アナモックスプロセスモデルの予測精度を高めるためのメカニズムインフォームドデータ増強戦略

Yinan Bu1, Yueqi Wang1, Yafang Fan1

  • 1Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.

Bioresource technology
|February 16, 2026
PubMed
まとめ

本研究では,無酸素アンモニアム酸化 (Anammox) プロセスにおける機械学習モデルの精度を向上させるためのメカニズムインフォメーションデータ増強 (MIDA) フレームワークを導入しています. MIDAはデータ不足を効果的に解決し,排水処理におけるプロセス制御を強化しています.

キーワード:
アナエロビックアンモニアム酸化.機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.情報化されたデータ増強のメカニズムはマイナスです.排水処理は,下水処理として行われます.

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