Prof. Keon Baek | Data analysis | Best Researcher Award

Keon Baek | Data analysis | Best Researcher Award

Keon Baek | Chosun University | South Korea

Keon Baek is a dedicated Data Scientist and Electrical Engineer based in Gwangju, South Korea 1 πŸ‡°πŸ‡·. With a strong academic background and practical experience, he focuses on power market analysis, policy design, and technology development through insightful data analysis πŸ“Š. His research interests include consumer behavior πŸ’‘, demand flexibility πŸ”„, market and policy implications πŸ›οΈ, and the growing field of vehicle electrification πŸš—βš‘. Keon’s passion lies in leveraging data to shape the future of sustainable energy.

Professional profile :Β 

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Summary of Suitability :Β 

Keon Baek, a dedicated Data Scientist and Electrical Engineer from Gwangju, South Korea, is an excellent candidate for the Best Researcher Award. With a robust academic foundation and a wealth of hands-on experience, Keon has demonstrated significant contributions to the fields of power market analysis, policy design, and technology development. His expertise lies in using data to inform decisions around sustainable energy, which aligns perfectly with the award’s criteria for groundbreaking research that drives innovation and societal impact.

Education :

  • Ph.D. (Power System & Economics) – Gwangju Institute of Science and Technology (2020.03 – 2023.02) βš‘πŸ’°
  • M.S. (Power System & Economics) – Gwangju Institute of Science and Technology (2018.03 – 2020.02) πŸ’‘πŸ“ˆ
  • B.S. (Electrical Engineering) – Korea Advanced Institute of Science and Technology (2004.03 – 2011.02) βš™οΈπŸ”Œ

Experience :

  • Assistant Professor, Dept. of Electrical Engineering – Chosun University (2023. 09 – 2023. 08) πŸ‘¨β€πŸ«πŸ’‘
  • Post-doc., Research Institute for Solar and Sustainable Energies (RISE) – Gwangju Institute of Science and Technology (2023. 02 – 2023.08) β˜€οΈπŸŒ±
  • Electric Engineer, Distribution Transformer Division – Hyundai (2017. 04 – 2018. 07) 🏭⚑
  • Engineer, Offshore Plant Engineering Center – Korea Shipbuilding & Offshore Engineering (2015. 02 – 2017. 03) 🚒🌊
  • Associate Researcher, Wind Power System Research Center – Korea Shipbuilding & Offshore Engineering (2011. 02 – 2015. 01)
  • Publication Top NOTES :
    Resident Behavior Detection Model for Environment Responsive Demand Response :
    • Authors: K. Baek, E. Lee, J. Kim

    • Published in: IEEE Transactions on Smart Grid, 2021, Vol. 12, Issue 5, Pages 3980-3989

    • Citations: 35

    • Summary: This paper proposes a model for detecting resident behavior in smart grid environments, aiming to optimize demand response (DR) mechanisms. The approach focuses on adjusting electricity usage patterns by predicting and responding to residents’ behavior, enhancing both energy efficiency and grid reliability. This model is crucial for increasing the responsiveness and flexibility of demand response programs in residential areas.

    Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach :
    • Authors: E. Lee, K. Baek, J. Kim

    • Published in: Energies, 2020, Vol. 13, Issue 23, Article 6355

    • Citations: 28

    • Summary: This study assesses the potential flexibility of demand response programs in industrial settings using a data-driven approach. It evaluates how various industrial processes can be adjusted to provide flexibility in energy consumption without negatively impacting production efficiency. The research also explores the use of real-time data to enhance decision-making in demand response strategies, enabling more effective integration of renewable energy sources.

    Multi-Objective Optimization of Home Appliances and Electric Vehicles Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment :
    • Authors: Y. Kwon, T. Kim, K. Baek, J. Kim

    • Published in: Energies, 2020, Vol. 13, Issue 11, Article 2852

    • Citations: 22

    • Summary: This paper discusses a multi-objective optimization approach for managing home appliances and electric vehicles (EVs) while considering customer benefits and photovoltaic (PV) energy curtailment. It focuses on maximizing the benefits to consumers by coordinating the use of home appliances and EVs with the availability of solar energy while reducing the waste of excess PV power. The study is significant for improving the efficiency of residential energy management systems.

    Stochastic Optimization-Based Hosting Capacity Estimation with Volatile Net Load Deviation in Distribution Grids :Β 
    • Authors: Y. Cho, E. Lee, K. Baek, J. Kim

    • Published in: Applied Energy, 2023, Vol. 341, Article 121075

    • Citations: 13

    • Summary: The research proposes a stochastic optimization method to estimate hosting capacity in distribution grids, accounting for the volatile nature of net load deviation. The study addresses challenges related to integrating renewable energy sources, such as solar and wind, into existing power grids. It develops a model that quantifies the grid’s capacity to absorb additional renewable energy without compromising stability, providing valuable insights for grid operators managing increasing renewable penetration.

    Datasets on South Korean Manufacturing Factories’ Electricity Consumption and Demand Response Participation :
    • Authors: E. Lee, K. Baek, J. Kim

    • Summary: This dataset publication presents detailed information on electricity consumption patterns and the participation of South Korean manufacturing factories in demand response programs. It provides real-world data that can be used to evaluate the effectiveness of demand response strategies and analyze consumption behaviors in industrial sectors. Researchers and energy managers can leverage this dataset to optimize industrial demand response programs and improve grid reliability.

Fan Wang | Data Analysis | Best Researcher Award

Mrs. Fan Wang | Data Analysis | Best Researcher Award

Mrs. Fan Wang, Xi’an Shiyou University, China .

Mrs. Fan Wang is a Lecturer at Xi’an Shiyou University, China, specializing in imaging, image processing, data analysis, and machine learning. She earned her Ph.D. and Master’s degrees in Graphic and Image Processing from Northwestern Polytechnical University, Xi’an, China. With a strong academic foundation, Dr. Wang is passionate about advancing methodologies in image processing and applying machine learning to solve complex visual data challenges. Her expertise in data-driven approaches continues to inspire innovation and impactful contributions to the field of computational imaging. πŸ’»πŸ“Š

Publication Profile

ScopusΒ πŸ“š

Education and Experience

Education
  • Ph.D. in Theory and Methods of Graphic and Image Processing, Northwestern Polytechnical University, Xi’an, China (2018–2022)Β πŸŽ“
  • M.S. in Theory and Methods of Graphic and Image Processing, Northwestern Polytechnical University, Xi’an, China (2015–2018)

Experience

  • Lecturer, Xi’an Shiyou University, Xi’an, China (2022–present)Β πŸŽ“πŸ“

Suitability for the Award

Mrs. Fan Wang, a dedicated researcher and Lecturer at Xi’an Shiyou University, specializes in imaging, image processing, data analysis, and machine learning. With a Ph.D. and M.S. in Theory and Methods of Graphic and Image Processing from Northwestern Polytechnical University, she has demonstrated expertise in advanced computational techniques. Her contributions to innovative research and academic excellence make her a strong contender for the Best Researcher Award.Β πŸ†

Professional Development

Mrs. Fan Wang is a researcher and educator specializing in cutting-edge techniques in imaging and machine learning. With a Ph.D. in Graphic and Image Processing, she has developed advanced skills in data analysis and the application of AI algorithms to enhance image interpretation and processing. Currently a Lecturer at Xi’an Shiyou University, Dr. Wang is committed to fostering innovation and knowledge dissemination through teaching and collaborative research. Her work integrates computational intelligence with visual data, advancing impactful solutions in imaging technologies. πŸŒ±πŸ’‘

Research Focus

Mrs. Fan Wang’s research lies at the intersection of imaging and artificial intelligence. She focuses on developing innovative methods for image processing, leveraging data analysis to optimize the extraction of meaningful information from complex visual datasets. Her work also involves applying machine learning techniques to automate and enhance image interpretation for diverse applications. Dr. Wang aims to address challenges in computational imaging by combining theory with practical solutions, driving advancements in visualization technologies for academic and industrial use.Β πŸ”πŸ€–

Awards and Honors

  • Ph.D. Scholarship Award, Northwestern Polytechnical University (2022)Β πŸ…
  • Recognized for Excellence in Research during Graduate Studies (2018–2022)
  • Best Presentation Award in Machine Learning Symposium (2021)Β πŸ†
  • Published high-impact research in top-tier journals on imaging and AI methods
  • Contributor to innovative methodologies in graphic and image processing

Publication Highlights

  • πŸ“–Β Intensifying graph diffusion-based salient object detection with sparse graph weightingΒ (2023) – Cited by: 0
  • πŸ“–Β Graph construction by incorporating local and global affinity graphs for saliency detectionΒ (2022) – Cited by: 3
  • πŸ“–Β Saliency detection based on color descriptor and high-level priorΒ (2021) – Cited by: 3
  • πŸ“–Β Graph-based saliency detection using a learning joint affinity matrixΒ (2021) – Cited by: 4
  • πŸ“–Β Saliency detection via coarse-to-fine diffusion-based compactness with weighted learning affinity matrixΒ (2021) – Cited by: 1
  • πŸ“–Β Salient object detection via cross diffusion-based compactness on multiple graphsΒ (2021) – Cited by: 4
  • πŸ“œΒ Salient Object Detection via Quaternionic Local Ranking Binary Pattern and High-Level PriorsΒ (2019, Conference Paper) – Cited by: 0
  • 🌊 Underwater Image Restoration Based on Background Light Estimation and Dark Channel PriorΒ (2018) – Cited by: 25