LAB NOTICE
- [Vahid 졸업생][Building and Environment (JCR5%] 환경 결측 데이타 DL 모델기반 보정 및 복원력, 예측 비교(Deep Learning Data Imputation for IAQ
- I am pleased to inform you that Vahid (ExEMSELIAN)'s paper has been accepted for publication in Building and Environment (SCI, JCR TOP 5%)Great journal indeed. Congratulations Vahid for your acceptance.-Title: Evaluating Deep Learning Data Imputation for Subway Indoor Air Quality: Accuracy, Efficiency, and Implications for Downstream Tasks-Keywords: Particulate matter; Built environment; Tele-monitoring system; Time-series, imputation; Forecasting accuracy.1) determine how different classes of deep learning models perform in addressing the problem of missing data in multi-zone indoor air quality 2) understand the extent to which the choice of imputation method affects downstream applications that rely on reconstructed data, including forecasting, ventilation response modeling, and source apportionment. - Nine transformers-type deep learning algorithms for data imputation (SAITS, Transformer, BRITS, MICN, StemGNN, FreTS, CSDI,
USGAN and GPVAE)- Python 3.7 using the PyPOTS toolbox 연구실 졸업생 Vahid Vahid 논문이 JCR TOP 5%로 매우 좋은 저널인 Building and Environment (SCI, Top 5% journal: Civil Eng.)에 게재되었습니다. 축하합니다.-연구주제 : 실내공기질 지하역사 측정 데이타중 결측데이타를 DL 모델을 이용한 보정 방법론 비교 및 복원력, 예측등 downstream task 비교-9 개 transformers-type deep learning algorithms for data imputation , SAITS, Transformer, BRITS, MICN, StemGNN, FreTS, CSDI,
USGAN and GPVAE-Python 3.7 using the PyPOTS toolbox * 특허 출원 예정 (정찬혁/김상윤 박사과정)Vahid Ghorbani, Amir Ghorbani, ChangKyoo Yoo*, Evaluating Deep Learning Data Imputation for Subway Indoor Air Quality: Accuracy, Efficiency, and Implications for Downstream Tasks, Building and Environment (ISSN: 0360-1323, SCIE, Top 5% journal: Civil Eng.), Elsevier, pp. (2025.09) (Ack NRF: NRF중견2021R1A2C2007838)---------- Forwarded message ---------보낸사람: Building and Environment <em@editorialmanager.com>Date: 2025년 9월 17일 (수) 오후 9:30Subject: Your Submission - [EMID:5186c508b0af8466]To: ChangKyoo Yoo <ckyoo@khu.ac.kr>CC: bertolesie@cardiff.ac.ukMs. Ref. No.: BAE-D-25-04837R1Title: Evaluating Deep Learning Data Imputation for Subway Indoor Air Quality: Accuracy, Efficiency, and Implications for Downstream TasksBuilding and EnvironmentDear ChangKyoo Yoo,I am pleased to inform you that your above paper has been accepted for publication in Building and Environment. Now that the review process is complete, the publication process begins. Elsevier will contact you soon for any questions during the publication process. After you receive an acknowledgement e-mail from Elsevier's OASIS, you can further track your paper from http://authors.elsevier.com/<wbr>TrackPaper.html.In order to encourage the publishing of high-quality papers in Building and Environment, the journal establishes annually:* Three best paper awards* One best paper award to a first author younger than 35 years oldYour paper will be automatically considered for the above awards.
2025-09-18
- [VSI] [JWPE] GenAI for Water Process Engineering
- 08 July 2025Generative Artificial Intelligence for Sustainable Water Process EngineeringSubmission deadline: 31 March 2026Journal of Water Process Engineering | ScienceDirect.com by Elsevier - Journal of Water Process Engineering | ScienceDirect.com by Elsevier Recent advancements in Generative Artificial Intelligence (AI) offer transformative potential for water management and process engineering. Generative AI refers to systems capable of creating new content or solutions by learning patterns from vast datasets. Among these, Large Language Models (LLMs) stand out for their exceptional abilities in natural language understanding, reasoning and problem-solving. Generative AI can facilitate synthesis, prediction, and optimization in unprecedented ways, providing novel tools for advancing water process engineering.While AI adoption in the water sector is growing, the application of generative AI is still in its early stages, requiring further exploration and rigorous research to unlock its full potential. Challenges such as developing new methods, ensuring transparency, mitigating biases, and aligning AI systems with domain-specific needs must be addressed to maximize their impact on water and wastewater operations.This Special Issue aims to showcase cutting-edge research and applications of generative AI and LLMs in addressing water challenges. We invite full research, literature review and short communication papers in the following (but not limited to) key topics:• Development and application of new algorithms and tools for water process and systems management, including both natural and engineered systems• System monitoring, modelling, optimisation, predictive maintenance• Workflow automation, human-computer interaction, and AI system development• Data challenges in generative AI applications• Ethics, transparency, and governance of AI in water management• Case studies and pilot projectsBy showcasing groundbreaking research and addressing challenges in this emerging field, this special issue aims to shape a roadmap for integrating advanced AI technologies into sustainable water management—one of the most pressing global challenges of our time.Guest editors:Professor Guangtao FuUniversity of Exeter, Exeter, UKProfessor Lina SelaThe University of Texas at Austin, Austin, USAProfessor Shuming LiuTsinghua University, Beijing, ChinaProfessor Zhugen YangCranfield University, Melton Keynes, UKManuscript submission information:
2025-09-15
PUBLICATIONS
- Sustainable energies and machine learning: an organized review of recent applications and challenges
- Pouya Ifaei, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, ChangKyoo Yoo*, Sustainable energies and machine learning: an organized review of recent applications and challenges, Energy (ISSN: 0360-5442, SCI, Top 5% journal – THERMODYNAMICS), 266(1), pp.126432 (2023.3) (Ack 3개: Brain Pool Program2019H1D3A1A02071051 & NRF중견2021R1A2C2007838 & Collabo R&D of SMEs and Startups in 2022.(Project No. S3301144)
- Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach
- Shahzeb Tariq+, Jorge Loy-Benitez+, KiJeon Nam, SangYoun Kim, MinJeong Kim*, ChangKyoo Yoo*, Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach, Tunnelling and underground space (ISSN: 0886-7798, SCIE, JCR Top 10%-ENGINEERING, CIVIL, DOI), Elsevier, 131(1), pp.104843 (Ack: 2021R1A2C2007838 & Fine Dust Reduction Technology(21QPPW-B152306-03)
- Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving
- Abdulrahman H. Ba-Alawi, KiJeon Nam, SungKu Heo, TaeYong Woo, Hanaa Aamer, and Chang Kyoo Yoo*, Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving, Chemical Engineering Journal (IF>16.4, JCR TOP 3% journal/Top 1 journal-Chemical Engineering), 452(1), pp.139220 (2023.1) (Ack 3개:NRF2021R1A2C2007838, “Prospective green technology innovation project (2020003160009), SMEs and Startups in 2022 (Project No. S3301144)
