Lei Yao | Artificial Intelligence | Research Excellence Award

Mr. Lei Yao | Artificial Intelligence | Research Excellence Award

Ph.D. Candidate | Jilin University | China

Mr. Lei Yao is an advancing researcher whose work lies at the intersection of artificial intelligence, biomedical engineering, and intelligent monitoring systems. His research focuses on designing innovative AI-driven solutions for healthcare diagnostics, cognitive evaluation, physiological signal analysis, and smart livestock management. Through the integration of deep learning, multi-task learning, and generative models, he aims to improve the accuracy, efficiency, and scalability of real-world intelligent sensing applications.A core area of Mr. Yao’s work is biomedical signal processing, especially electrocardiogram (ECG) analysis. His contribution to MMS-Net, a multi-task learning framework, provides a transformative method for reconstructing full 12-lead ECG signals using only 3-lead inputs while simultaneously performing disease classification. This technology enhances diagnostic capabilities in low-resource settings and supports more accessible cardiology screening.Mr. Yao also investigates synthetic data generation using modern generative adversarial networks. His work on SGECG, a StarGAN-based system for ECG generation and augmentation, aids in overcoming data scarcity, a major limitation in machine-learning-based healthcare research.Beyond biomedical applications, Mr. Yao significantly contributes to smart agriculture and automaed animal health monitoring. His publication SideCow-VSS introduces a comprehensive video semantic segmentation dataset designed for intelligent dairy cow health assessment in smart ranch environments—an important advancement for precision livestock farming.His interdisciplinary research further includes cognitive assessment, demonstrated by MLCDT, a fine-grained multi-task learning model that enhances automated analysis of the clock drawing test, an essential tool in early detection of cognitive impairment.Overall, Mr. Lei Yao’s research integrates AI, signal processing, and intelligent sensing to create impactful solutions for healthcare, cognitive diagnostics, and smart agricultural systems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

  1. Yao, L., Garmash, O., Bianchi, F., Zheng, J., Yan, C., Kontkanen, J., Junninen, H., … (2018). Atmospheric new particle formation from sulfuric acid and amines in a Chinese megacity. Science, 361(6399), 278–281.
    Citations: 611

  2. Xiao, S., Wang, M. Y., Yao, L., Kulmala, M., Zhou, B., Yang, X., Chen, J. M., Wang, D. F., … (2015). Strong atmospheric new particle formation in winter in urban Shanghai, China. Atmospheric Chemistry and Physics, 15(4), 1769–1781.
    Citations: 176

  3. Zheng, J., Ma, Y., Chen, M., Zhang, Q., Wang, L., Khalizov, A. F., Yao, L., Wang, Z., … (2015). Measurement of atmospheric amines and ammonia using the high-resolution time-of-flight chemical ionization mass spectrometry. Atmospheric Environment, 102, 249–259.
    Citations: 165

  4. Yan, C., Nie, W., Äijälä, M., Rissanen, M. P., Canagaratna, M. R., Massoli, P., … Yao, L., … (2016). Source characterization of highly oxidized multifunctional compounds in a boreal forest environment using positive matrix factorization. Atmospheric Chemistry and Physics, 16(19), 12,715–12,731.
    Citations: 164

  5. Wang, X., Hayeck, N., Brüggemann, M., Yao, L., Chen, H., Zhang, C., Emmelin, C., … (2017). Chemical characteristics of organic aerosols in Shanghai: A study by ultrahigh-performance liquid chromatography coupled with Orbitrap mass spectrometry. Journal of Geophysical Research: Atmospheres, 122(21), 11,703–11,722.
    Citations: 157

Mr. Lei Yao’s research significantly advances atmospheric chemistry by uncovering the mechanisms of new particle formation, characterizing organic aerosols, and improving high-resolution chemical detection technologies. His contributions enhance scientific understanding of air pollution sources, support policymakers in designing effective climate and air-quality interventions, and strengthen industrial environmental monitoring frameworks. Through high-impact studies published in globally respected journals, his work drives innovation in atmospheric measurement, fosters healthier urban environments, and informs global strategies for mitigating particulate pollution and its effects on human and environmental health.

 

Qingyun Yang | Robotics and Automation | Best Researcher Award

Assoc. Prof. Dr. Qingyun Yang | Robotics and Automation | Best Researcher Award

Qingyun Yang | Zaozhuang University | China

Assoc. Prof. Dr. Qingyun Yang is a distinguished academic and researcher at the College of Mechanical and Electrical Engineering, Zaozhuang University, China, where he has served as Lecturer and Associate Professor since 2016. He earned his B.S. degree in Electrical Engineering and Automation from Nanjing Normal University in 2008, followed by an M.S. degree in Pattern Recognition and Intelligent Systems in 2011 and a Ph.D. in Control Theory and Control Engineering in 2016, both from the Nanjing University of Aeronautics and Astronautics. His professional experience is marked by a steady trajectory of excellence in teaching, research, and project leadership, especially in the fields of nonlinear systems and control, constrained control, and advanced flight control systems for near-space vehicles and UAVs. Dr. Yang’s primary research interests include robust adaptive control, prescribed performance tracking, reinforcement learning-based control strategies, fault-tolerant systems, and aerospace engineering applications. He has developed strong research skills in nonlinear dynamics modeling, control algorithm design, simulation, and experimental validation, complemented by expertise in advanced optimization, adaptive neural networks, and machine intelligence approaches. Over the years, he has authored and co-authored multiple peer-reviewed publications in high-quality SCI and EI journals such as Neurocomputing, Journal of Applied Mathematics, Control Theory and Applications, and Machines, alongside IEEE conference proceedings, demonstrating both breadth and depth in control engineering research. His notable book Robust Constrained Flight Control for Near Space Vehicles (National Defense Industry Press, 2017) further highlights his contribution to the field. Professionally, he has led several significant research projects, including funding from the National Natural Science Foundation of China (Youth Fund) and the Shandong Provincial Natural Science Foundation, as well as a Key R&D Program of Shandong Province (2025–2028) with a major focus on industrial machine tool innovation. His recognition includes multiple project leadership roles and scholarly contributions that advance both theory and practice in engineering systems control. According to Scopus, Dr. Yang has received 142 citations, 15 documents, and an h-index of 5, reflecting his growing impact in the international research community. In conclusion, Assoc. Prof. Dr. Qingyun Yang is an accomplished researcher and academic whose sustained contributions to control theory, aerospace systems, and intelligent engineering mark him as an emerging leader in his field. With his strong foundation in robust adaptive methods, active involvement in funded projects, and expanding global visibility, he is well-positioned to make transformative contributions to control engineering and interdisciplinary automation research in the future.

Profile: Scopus

Featured Publications

Cai, L., Yang, Q., & Chen, M. (2016). Robust tracking control of near space vehicles with input and output saturation. Revista de la Facultad de Ingeniería, 31(3), 282–299.

Yang, Q., & Chen, M. (2013). Composite nonlinear control for near space vehicles with input saturation based on disturbance observer. In Proceedings of the 32nd Chinese Control Conference (pp. 2763–2768). IEEE.

Yang, Q., & Chen, M. (2014). Anti-windup control for near space vehicles subject to input saturation. In Proceedings of the 33rd Chinese Control Conference (pp. 3760–3765). IEEE.

Shao, S., Chen, M., & Yang, Q. (2016). Sliding mode control for a class of fractional-order nonlinear systems based on disturbance observer. In Proceedings of the 2016 IEEE International Conference on Industrial Technology (pp. 1790–1795). IEEE.

Min, H., & Yang, Q. (2018). Study on modeling and simulation of production logistics system based on Flexsim. Academic Journal of Manufacturing Engineering, 16(2).