Open PhD Presentation (in Japanese 日本語で発表)
We are pleased to announce the open presentation of a PhD thesis. Join us to explore innovative research on quality prediction in batch processes under small data conditions, a topic with significant implications for industries like pharmaceuticals, chemicals, and advanced manufacturing. 東京農工大学大学院 工学府 応用化学専攻では、博士論文の公開発表会を開催いたします。スモールデータ環境におけるバッチプロセスの品質予測に関する革新的な研究をご紹介します。この研究は、医薬品、化学製品、電子機器などの産業に大きな影響を与える可能性を秘めています。
Event Details
- Date: Monday, February 17, 2025
- Time: 13:00 AM – 14:30 PM (JST)
- Location: 3F meeting room, Building 4, TUAT Koganei Campus
- Presenter: Takashi Yamaguchi 発表者： 山口 貴史（博士課程学生）
- Advisor: Professor Yoshiyuki Yamashita 指導教員： 山下 善之 教授
- Thesis Commitee: 滝山, Lenggoro, 利谷, 金
Thesis Title
“A Study on Quality Prediction under Small Data Conditions for Batch Processes”「バッチプロセスにおけるスモールデータ環境での品質予測に関する研究」
About the Presentation
This research tackles the challenges of predicting product quality in batch processes where data availability is limited. The thesis introduces novel machine learning methods—Sparse Flexible Clustered Multi-Task Learning (FCMTL) and Multi-Target Regression (MTR)—to address these challenges effectively. Applications include medical balloon catheter manufacturing and OLED production. 本研究では、データが限られた環境下でのバッチプロセスにおける製品品質予測の課題に取り組みます。提案された革新的な機械学習手法（Sparse Flexible Clustered Multi-Task Learning（FCMTL）およびMulti-Target Regression（MTR））は、医療用バルーンカテーテル製造やOLED製造などへの応用が期待されています。
Key highlights include:
- Development of innovative methods for small-data environments.
- Applications in real-world industrial settings like medical balloon catheter manufacturing and OLED production.
- Comparisons with existing techniques to demonstrate superior prediction accuracy.
This research bridges the gap between theoretical advancements and practical applications, making it relevant for scientists, engineers, and industry professionals.
どなたでもご参加いただけます：
高校生・大学生：最先端研究への興味を深めたい方。
大学院生・研究者：機械学習の応用に関心がある方。
産業界の方々：品質管理の実践的な洞察を得たい方。
教授・学術関係者：革新的な手法について議論したい方。
Who Should Attend?
This event is open to everyone:
- High School Students and Undergraduates: Discover how advanced research impacts real-world industries.
- Graduate Students and Researchers: Gain insights into cutting-edge machine learning techniques for process optimization.
- Industry Professionals: Learn about practical solutions for quality control in manufacturing.
- Professors and Academics: Engage in discussions on the future of data-driven engineering.
No prior registration is required.
