LAB NOTICE
- 2nd Application for 2025 Fall Semester KHU Graduate International Admission (Jun. 13th 2025 (Fri) 10:00 ~ Jun. 20th 2025 (Fri)
- 2nd Application for 2025 Fall Semester KHU Graduate International Admission (Jun. 13th 2025 (Fri) 10:00 ~ Jun. 20th 2025 (Fri) 공지 Notice 외국인 입학(대학원) Graduate Admission 국제처 글로벌입학팀 (Jun. 13th 2025 (Fri) 10:00 ~ Jun. 20th 2025 (Fri) Greetings from Global Admissions, KHU. We are pleased to announce the schedule and online application process for the Fall 2025 Graduate International Student Additional Recruitment. We look forward to your applications. ※ Please note that the additional recruitment is conducted only for selected departments.All document submission standards and evaluation methods follow the 2025 Fall Graduate International Student Recruitment Guidelines.(Please ensure to check the attached file for detailed information on the specifics of each recruitment unit and the evaluation methods.) [Admission Schedule] ProcessDate and DeadlinesOnline ApplicationJun. 13th 2025 (Fri) 10:00 ~ Jun. 20th 2025 (Fri) 17:00(KST)Submission of DocumentsJun. 13th 2025 (Fri) ~ Jun. 20th 2025 (Fri) [Postmark Deadline]Admission Interview(Audition)Jul. 4th 2025 (Fri)Departments Conducting Remote Interviews: Interview time will be individually notified.Result AnnouncementJul. 10th 2025 (Thu) 15:00(KST)Registration(Tuition Payment)Jul. 11th 2025 (Fri) 10:00(KST) ~ Jul. 18th 2025 (Fri) 16:00(KST) 1. Online Application : June 13, 2025 (Fri) 10:00 AM ~ June 20, 2025 (Fri) 5:00 PM (KST) 2. Online Application URL :Click Here※Applicants must sign up for the website first. 3. Document Submission- Submission Period :June 13, 2025 (Mon) ~ June 20, 2025 (Fri) [Postmark Deadline]- How to submit : All documents should be submitted to the Global Admissions Team at Global Campus, KHU.- Address : Room 108, Woojungwon, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Korea※ Only documents postmarked by June 20, 2025 (Fri) will be accepted. 4. Contact to : Division of Global Admissions- Tel. : +82-31-201-3961~4 녕하세요. 경희대학교 글로벌입학팀입니다. 2025학년도 후기 일반대학원 정원외 외국인 신입학전형 수시모집 일정 및 온라인 원서접수를 아래와 같이 안내드리니 많은 지원 바랍니다. ※수시모집의 경우 일부 학과만 시행되며, 모든 서류 제출의 기준과 전형평가 방법은 2025학년도 후기 일반대학원 정원외 외국인전형 모집요강을 따릅니다.(모집단위 세부사항 및 전형평가 방법 등 자세한 사항은 첨부파일을 반드시 확인해주시기 바랍니다.) [주요 일정] 구분전형일정원서 접수2025.06.13(금) 10:00 ~ 06.20(금) 17:00(KST)까지서류 제출2025.06.13(금) ~ 06.20(금) [우편소인분까지]전형일2025.07.04.(금)비대면 면접 시행 학과: 면접 시간 개별 공지 예정합격자 발표2025.07.10.(목) 15:00(KST)합격자 등록2025.07.11.(금) 10:00(KST) ~ 07.18(금) 16:00(KST)까지 1.온라인 접수 기간 : 2025년 6월 13일(금) 10시 ~ 6월 20일(금) 17시 마감 2.인터넷 접수 : 이곳을 클릭하세요※유웨이 홈페이지에 회원 가입 후 지원 가능 3.지원 서류 제출 안내-제출기간 : 2025년 6월 13일(금) ~ 6월 20일(금) [우편소인분까지]-제출방법 : 모든 서류는 국제캠퍼스 글로벌입학팀으로 제출-우편주소 : (17104) 경기도 용인시 기흥구 덕영대로 1732 경희대학교 우정원 1층 108호 국제처※2025년 6월 20일(금) 우편소인까지만 접수 가능 4.문의 : 경희대학교 글로벌입학팀-전화 : +82-31-201-3961~4-이메일 : ciss_gc@khu.ac.kr
2025-06-12
- [Prof. Tariq/usama학생,김상윤학생, Energy/JCR5%] PPO 강화학습 에이젼트기반 multi-agent control under load variations
- [Prof. Tariq/usama학생,김상윤학생, Energy/JCR5%] PPO 강화학습 에이젼트기반 multi-agent control under load variations Good afternoon and good news.Prof. Tariq at Dongguk Univ (eX Emselian), Usama Ali (Ph. D student) , Sangyoun Kim(Ph. D student) 's paper has been accepted at Energy journal (JCR Top 5% journal ).Great paper indeed. Congratulations Prof. Tariq.Topic : PPO-based Multiagent Reinforcement Learning control under diverse occupancy patterns ( Useful for load or peak variations situation)작년 연구실 박사졸업하고 3월 동국대 외국인 교원으로 임용된 Tariq교수님 논문이 Energy journal (JCR Top 5% journal )에 게재 승인되었습니다.박사과정인 Usama Ali학생 , 김상윤학생이 공저자로 참여하였습니다.연구주제는 PPO 강화학습 에이젼트기반 운전 제어 전략으로 특히 일일 변화 또는 주간 변화, 계절별 load변화가 심한경우 유용한 방법). 연구실 박사후 연구원인 mohammad박사가 서울 사립대 화학공학과 외국인 전임교원으로 임용 예정입니다. 또 다른 좋은 소식 ..Shahzeb Tariqa+, Usama Alib+, Sangyoun Kimb, ChangKyoo Yoob* Multi-agent distributed reinforcement learning for energy-efficient thermal comfort control in multi-zone buildings with diverse occupancy patterns, Energy (ISSN: 0360-5442, SCI, Top 5% journal - THERMODYNAMICS), Elsevier, , pp. (Ack.: 2021R1A2C2007838) (<wbr>2025.06)Keywords : Multizone building; Energy efficient HVAC control; Transfer reinforcement learning; Decentralized control; thermal comfort management.Research HighlightsØ Decentralized multi-agent thermal comfort control in multizone buildings.Ø Transfer reinforcement learning enables control policy transfer across buildings.Ø Compared to rule-based control, the proposed method reduced grid import by 51%.Ø A floor-specific reward structure can further reduce energy consumption by 23%.Ø Transfer learning reduced grid imports by 14.4% in limited data scenario.
2025-06-11
- [SI/Applied Soft Computing] Interpretable Reinforcement Learning
- [SI/Applied Soft Computing] Interpretable Reinforcement Learninghttps://www.sciencedirect.com/special-issue/322834/interpretable-reinforcement-learningCall for papers01 June 2025Interpretable Reinforcement LearningSubmission deadline: 31 December 2025Reinforcement Learning (RL) has achieved significant successes in a variety of domains, from game playing to autonomous driving, control systems, and decision-making problems. However, the interpretability of RL models remains a critical challenge. Interpretable Reinforcement Learning (IRL) focuses on creating models that not only perform well but are also understandable to humans. Enhancing the interpretability of RL models could also significantly aid in addressing the reality gap—the performance difference between simulations and real-world applications—as more transparent models provide better insights into decision-making processes and facilitate smoother transitions from simulations to real environments. This field has recently gained significant attention from both the academic and industrial communities, leading to various initiatives such as the Interpretable Control Competition at GECCO 2024. IRL has also been identified as one of the main areas where soft computing techniques, such as evolutionary algorithms, may be an enabling factor. This special issue seeks to gather cutting-edge research that advances the theory, methodologies, and applications of interpretable reinforcement learning, with particular emphasis on approaches based on soft computing (such as, but not limited to, evolutionary computation). Guest editors: Dr. Leonardo Lucio Custode University of Trento, Trento, Italy Research Interests: Interpretable and Explainable Artificial Intelligence, Reinforcement Learning, Machine Learning, Large Language Models, and Optimization. Email: leonardo.custode@gmail.com Prof. Giovanni Iacca University of Trento, Trento, Italy Research Interests: Computational Intelligence, Distributed Systems, Explainable AI, and Analysis of Biomedical Data Email: giovanni.iacca@unitn.it Prof. Eric Medvet University of Trieste, Trieste, Italy Research Interests: Evolutionary Computation (with a focus on genetic programming and grammar-guided genetic programming), Artificial Life, and the Application of Machine Learning Techniques to engineering and computer security problems, including robotics. Email: emedvet@units.it Dr. Giorgia Nadizar University of Trieste, Trieste, Italy Research Interests: Explainable AI, Evolutionary Machine Learning, Interpretable Control, Evolutionary Robotics Email: giorgia.nadizar@phd.units.it Dr. Erica Salvato University of Trieste, Trieste, Italy Research Interests: Control system, Artificial Intelligence, Reinforcement Learning, Robotics Email: erica.salvato@dia.units.it Special issue information: Full scope of the Special Issue: Reinforcement Learning (RL) has achieved significant successes in a variety of domains, from game playing to autonomous driving, control systems, and decision-making problems. However, the interpretability of RL models remains a critical challenge [1]. Interpretable Reinforcement Learning (IRL) focuses on creating models that not only perform well but are also understandable to humans. Enhancing the interpretability of RL models could also significantly aid in addressing the reality gap—the performance difference between simulations and real-world applications—as more transparent models provide better insights into decision-making processes and facilitate smoother transitions from simulations to real environments. This field has recently gained significant attention from both the academic and industrial communities, leading to various initiatives such as the Interpretable Control Competition at GECCO 2024 [2]. IRL has also been identified as one of the main areas where soft computing techniques, such as evolutionary algorithms, may be an enabling factor [3]. This special issue seeks to gather cutting-edge research that advances the theory, methodologies, and applications of interpretable reinforcement learning, with particular emphasis on approaches based on soft computing (such as, but not limited to, evolutionary computation). We invite high-quality submissions on topics including, but not limited to: Theoretical Foundations of Interpretable RL: New frameworks for interpretable decision-making in RL.Formal definitions and metrics for interpretability in RL contexts.Analytical and empirical studies on the trade-offs between interpretability and performance.Methods and Techniques: Techniques for extracting interpretable policies from complex RL models.Novel algorithms that inherently produce interpretable solutions, such as evolutionary and swarm intelligence techniques.Visualization tools and methods for RL models and policies.Use of symbolic, rule-based, or other interpretable models in RL.Applications: Case studies demonstrating the application of interpretable RL in real-world scenarios.Interpretable RL in healthcare, robotics, finance, and other critical domains.Comparative studies showing the impact of interpretability on user trust and system usability.Human-in-the-Loop Systems: Techniques for incorporating human feedback into RL systems to improve interpretability.Studies on the effectiveness of human-in-the-loop approaches for developing interpretable RL systems.Evaluation and Validation: Benchmarks and datasets for evaluating interpretability in RL.User studies assessing the interpretability of RL models and their decisions.Validation frameworks and experimental protocols for interpretable RL.Manuscript submission information: Important Dates: Submission deadline: December 31, 2025 Final Decision: June 01, 2026 Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Editorial Manager®. All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance, and contributions, as well as their suitability to the special issue. Each submission must contribute to soft computing related methodology. Papers that either lack originality or clarity in presentation or fall outside the scope of the special issue will be desk-rejected and will not be sent for review. Authors should select “VSI:ASOC_Interpretable Reinforcement Learning” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor is currently under review in other venues. Keywords: ((interpretable) OR (interpretability)) AND ((RL) OR (Reinforcement Learning ------
2025-06-06
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)
