Lei Tian | Embedded Systems | Best Paper Award

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Assoc Prof. Dr. Lei Tian | Embedded Systems | Best Paper Award

Laboratory Director at Xi’an University of Posts and Telecommunications | China

Lei Tian is a laboratory director at Xi’an University of Posts & Telecommunications whose work spans embedded systems, new semiconductor materials, and optoelectronic interconnection. He has focused on the analysis, modeling, and design of photoelectric coupling systems, including conversion‑efficiency optimization and noise‑reduction modeling. He has led and completed provincial and municipal R&D projects, contributed to State Grid initiatives, and authored both a monograph and a ministry‑planned textbook. His publication record includes more than sixty papers across SCI, EI, and core journals, with recent articles in the International Journal of Hydrogen Energy, Diamond & Related Materials, Physica Status Solidi B, and on power‑management circuits. Tian’s recent research advances 2D/Janus heterostructures for water splitting and gas sensing, and investigates device‑level co‑design strategies where materials inform embedded hardware architectures. His work targets sustainable energy, intelligent sensing, and robust, low‑noise, high‑efficiency systems suitable for real‑world deployment.

Professional Profile

Scopus

Education 

Lei Tian earned a Ph.D. in Circuits and Systems from Xidian University, emphasizing the intersection of signal integrity, noise modeling, and device‑level architectures for mixed‑signal and optoelectronic systems. Postdoctoral training at the Institute of Modern Physics, Northwest University, strengthened his first‑principles and multi‑physics modeling toolkit, including density‑functional workflows that bridge material properties to circuit‑level specifications. This background shaped a research style that connects quantum‑scale material parameters with embedded‑system requirements such as power budgets, spectral response, and noise floors. Coursework and mentoring activities have centered on semiconductor devices, optoelectronic interfaces, embedded firmware for instrumentation, and algorithm‑hardware co‑optimization. Tian’s graduate and postdoctoral path fostered collaborations across materials science, device physics, and systems engineering, informing a translational approach from theory to prototypes. The resulting expertise supports end‑to‑end pipelines—from ab initio predictions and sensor stack design to embedded control, calibration routines, and system‑level validation for power, reliability, and real‑time performance.

Experience 

As Laboratory Director at Xi’an University of Posts & Telecommunications, Lei Tian leads a group focused on optoelectronic interconnection and embedded hardware–software co‑design. The team develops modeling frameworks for photoelectric conversion efficiency, designs low‑noise coupling schemes, and validates concepts through simulations and targeted prototypes. He has steered key provincial R&D programs and municipal science projects, as well as multiple State Grid engagements, delivering deployable insights for power and sensing infrastructure. Tian’s portfolio extends from novel 2D/Janus heterostructures and graphene‑based stacks to practical power‑management ICs such as high‑voltage, low‑quiescent‑current LDOs with stability‑oriented impedance buffers. He regularly collaborates with materials scientists and circuit designers to translate computed properties into embedded constraints, addressing latency, energy, thermal limits, and field robustness. Alongside publications and books, his experience includes curriculum and lab development, fostering hands‑on training that connects material innovation with firmware, drivers, diagnostics, and system bring‑up.

Research Focus

Tian’s research targets the convergence of embedded systems with novel semiconductor and 2D materials. The thrusts include first‑principles discovery of van der Waals and Janus heterojunctions optimized for hydrogen evolution and gas sensing  photoelectric conversion analysis and noise‑reduction modeling for optoelectronic coupling embedded co‑design, where device physics informs circuit topologies, firmware routines, and on‑board diagnostics; and power‑management solutions such as high‑voltage LDOs with ultra‑low quiescent current for edge instrumentation. A defining feature is the “materials‑to‑metrics” pipeline—mapping band alignments, excitonic effects, and defect physics to embedded KPIs like SNR, dynamic range, and power efficiency. This enables predictive selection of sensor stacks and control algorithms prior to fabrication, accelerating time‑to‑prototype. Recent studies on MoSSe‑based heterostructures for water splitting exemplify this approach, linking catalytic descriptors to embedded monitoring strategies and stability management for scalable, field‑ready hydrogen‑generation systems.

Publication Top Notes

Title: Z-scheme WSTe/MoSSe van der Waals heterojunction as a hydrogen evolution photocatalyst: First-principles predictions
Year: 2025

Title: First-principles exploration of hydrogen evolution ability in MoS₂/hBNC/MoSSe vdW trilayer heterojunction for water splitting
Year: 2025

Title: Research of Power Inspection Based on Intelligent Algorithm
Year: 2025.

Conclusion

Lei Tian’s research exhibits high originality, technical depth, and relevance to global energy challenges, making the candidate a strong contender for the Best Paper Award. The contributions to hydrogen evolution photocatalysts using novel van der Waals heterojunctions represent valuable advancements in computational materials science. With further emphasis on experimental validation and broader impact demonstration, the works could achieve even greater recognition. Overall, the candidate’s publications align well with the award’s objectives, and the research output shows significant promise for long-term influence in sustainable energy technologies.

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

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Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same Award, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.