Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

🧑‍🏫 Dr. Jingyang Mao is a dedicated lecturer at the School of Electrical and Electronic Engineering, Faculty of Intelligence Technology, Shanghai Institute of Technology. With a Ph.D. in Control Science and Engineering from the University of Shanghai for Science and Technology (2022), he specializes in cutting-edge research on networked control systems and cyber-physical systems. His academic journey also includes a visiting scholar tenure at Louisiana State University, USA (2019–2021). Dr. Mao’s work bridges theoretical innovations with practical applications in modern engineering systems.

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Education

🎓 Ph.D. in Control Science and Engineering (2022)

  • University of Shanghai for Science and Technology, Shanghai, China

✈️ Visiting Scholar (2019–2021)

  • Department of Electrical and Computer Engineering, Louisiana State University, USA

Experience

👨‍💻 Lecturer (2022–Present)

  • School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Focus: Cyber-physical systems, networked control, and adaptive filtering

Research Interests

🔍 Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems 🌐
  • Multi-rate systems ⏱️
  • Joint recursive filtering 🔄
  • Unknown input estimation
  • Adaptive event-triggered mechanisms ⚙️

Awards

🏆 Award Nomination: Best Researcher Award
Recognized for groundbreaking contributions to the theory and application of cyber-physical systems.

Publications Top Notes:

📄 “Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms”

Event‐based reduced‐order H∞$H_{\infty }$ estimation for switched complex networks based on T‐S fuzzy model

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Event-Based Distributed Adaptive Kalman Filtering With Unknown Covariance of Process Noises

Yusuf Alper KAPLAN | Engineering | Best Researcher Award

Prof. Yusuf Alper KAPLAN | Engineering | Best Researcher Award

Prof. Osmaniye Korkut Ata Unv. Turkey

Dr. Yusuf Alper Kaplan is a Professor of Energy Systems Engineering at Osmaniye Korkut Ata University in Turkey. He has a distinguished career in renewable energy, specializing in solar and wind energy modeling, estimation, and system performance. With over two decades of experience, Dr. Kaplan has contributed to advancing energy systems through innovative methodologies and predictive models.

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Google scholar

Education 🎓

  • Bachelor’s in Electrical and Electronics Engineering, Gaziantep University, 2002
  • Master’s in Electrical and Electronics Engineering, Çukurova University, 2008
  • Ph.D. in Electrical and Electronics Engineering, Anadolu University, 2014

Professional Experience 💼

  • Lecturer (2004-2009) at Çukurova University
  • Research Assistant (2010-2014) at Anadolu University
  • Assistant Professor (2014-2018), Associate Professor (2018-2023), and Professor (2023-present) at Osmaniye Korkut Ata University
  • Head of Department of Energy Systems Engineering (2019-2020)
  • Director of the Continuing Education Centre (2020-present)

Research Interests 🔬

Dr. Kaplan’s research focuses on renewable energy, especially solar and wind energy systems. His work involves developing statistical and computational models for solar radiation forecasting, wind energy assessment, and performance optimization of renewable energy sources in varying environmental conditions.

Awards and Recognitions 🏆

Dr. Kaplan has been honored for his contributions to renewable energy and academic excellence. His achievements include national and international recognitions for advancements in energy system performance modeling and contributions to sustainable energy solutions.

Publications Top Notes: 📚

Kaplan, Y. A. (2024). Forecasting of global solar radiation: A statistical approach using simulated annealing algorithm. Engineering Applications of Artificial Intelligence, 136, 109034. (Cited by 15)

Kaplan, A. G., & Kaplan, Y. A. (2024). Using of the Weibull distribution in developing global solar radiation forecasting models. Environmental Progress & Sustainable Energy. (Cited by 10)

Kaplan, Y. A. (2023). Development of backpropagation algorithm for estimating solar radiation: A case study in Turkey. Revue Roumaine des Sciences Techniques, 68(3), 313-316. (Cited by 8)