Xiaopeng Han | Computer Science | Best Industrial Research Award

Dr. Xiaopeng Han | Computer Science | Best Industrial Research Award

Researcher at Purple Mountain Laboratories, China

Dr. Xiaopeng Han is a dedicated researcher currently serving as an Assistant Research Fellow at the Endogenous Security Research Center, Purple Mountain Laboratories. With a strong foundation in photogrammetry, remote sensing, and cyber-physical systems security, Dr. Han bridges geospatial technology and security innovation. His career has been marked by a blend of academic rigor and real-world application, particularly in the fields of high-resolution remote sensing image interpretation and network security. Over the past few years, he has contributed to numerous national and provincial research projects, including high-value initiatives like the National Key R&D Program and the Jiangsu Province Doctoral Innovation Program. Dr. Han has also played pivotal roles in multi-disciplinary collaborative research, publishing extensively in leading international journals. Notably, his work integrates machine learning, deep learning, and sensor network control with applications in smart cities and industrial cybersecurity. Through his academic endeavors and contributions to national strategy documents and patents, he has established himself as a well-rounded scientist pushing the boundaries of both remote sensing and cybersecurity. His robust profile and consistent academic engagement reflect a passion for scientific innovation, talent cultivation, and technological transformation.

Profile

ORCID

Education

Dr. Xiaopeng Han began his academic journey at Central South University, where he pursued a Bachelor of Engineering in Surveying and Mapping Engineering from September 2010 to June 2014. This program provided him with a solid grounding in geospatial science, data acquisition, and engineering applications. Motivated by a desire to further specialize, he continued his education at Wuhan University—one of China’s leading institutions in the field of photogrammetry and remote sensing—where he earned a Ph.D. between September 2014 and June 2019. His doctoral studies involved deep analytical work in remote sensing technologies, image classification, and environmental modeling. During this time, he developed a strong foundation in high-resolution image analysis and multi-source data fusion, skills that have been integral to his subsequent research. The academic rigor and innovative environment at Wuhan University equipped Dr. Han with the tools to thrive in cross-disciplinary research areas, paving the way for his transition into more security-focused technological research. Though he has not pursued postdoctoral studies, his educational background has enabled him to take on high-impact research roles in both academic and industry-aligned settings, bridging theory with practice.

Professional Experience

Dr. Xiaopeng Han’s professional journey reflects a well-rounded progression from industry roles to academic research positions. From July 2019 to July 2022, he worked as an Engineer in the System Research Department at the 14th Research Institute of China Electronics Technology Group Corporation (CETC). Here, he engaged in research and development activities focused on system integration, high-tech innovations, and security frameworks. This experience grounded his technical knowledge in practical, large-scale applications, particularly in cybersecurity systems and smart infrastructure. Since July 2022, Dr. Han has been serving as an Assistant Research Fellow at the Endogenous Security Research Center of Purple Mountain Laboratories. In this role, he has continued his work on network security, remote sensing, and data-driven system optimization. His professional portfolio includes collaborations on significant national projects, involving cutting-edge topics such as semi-supervised learning for remote sensing and cloud-edge industrial security technologies. He has also led and participated in provincial-level and talent development programs. These experiences have allowed him to blend the rigor of academic research with the urgency of real-world problem-solving. Dr. Han’s current position enables him to mentor junior researchers, drive innovative studies, and contribute to China’s evolving cybersecurity and geospatial technology landscapes.

Research Interest

Dr. Xiaopeng Han’s research interests span across multiple interdisciplinary domains, with a strong emphasis on high-resolution remote sensing, intelligent image interpretation, urban spatial analysis, and cybersecurity systems. His early academic work focused on photogrammetry and remote sensing, particularly in developing frameworks for image classification and environmental modeling using machine learning. Over time, his research evolved to address more complex challenges in smart city planning, environmental monitoring, and urban morphology analysis. Recently, Dr. Han has concentrated on cybersecurity, especially in relation to cloud-edge industrial systems and the development of endogenous security strategies. He is particularly interested in semi-supervised learning approaches for pixel-to-scene image interpretation, which allows for greater precision in automated data processing. Additionally, he investigates the application of artificial intelligence and deep learning in both remote sensing and network threat detection systems. His integrative research perspective allows him to develop solutions that link earth observation data with national defense and network security concerns. This convergence of disciplines places him at the forefront of innovation, where data science meets geospatial intelligence and cyber-physical security.

Research Skills

Dr. Xiaopeng Han possesses a diverse and advanced skill set, positioning him as a key contributor in both geospatial and cybersecurity research. His core competencies include high-resolution remote sensing image processing, data fusion techniques, and machine learning-based image classification methods. He is proficient in implementing multi-classifier learning frameworks that preserve edge features in complex remote sensing data. Beyond remote sensing, Dr. Han is also skilled in designing resilient control strategies for mobile sensor networks under adversarial conditions, including input delay and Sybil attacks. His work often involves semi-supervised and sparse representation learning, reflecting his deep understanding of AI model optimization for real-world scenarios. Furthermore, he has experience developing system-level threat detection and risk assessment methodologies, which are crucial for next-generation industrial and smart grid environments. His skills extend into software programming and system modeling, making him capable of conducting end-to-end experimentation and algorithm development. With the ability to cross traditional disciplinary boundaries, Dr. Han brings computational, analytical, and theoretical expertise to the table, supported by practical engagement in multi-million-yuan national and provincial projects. His research capabilities are complemented by his familiarity with cutting-edge platforms and security protocols in cloud-edge computing environments.

Awards and Honors

Dr. Xiaopeng Han has received several prestigious recognitions that underscore his academic excellence and innovative contributions. One of his most notable honors is the inclusion in the Jiangsu Province Dual-Innovation Doctoral Talent Program, administered by the Jiangsu Provincial Organization Department in 2020. This competitive award recognizes outstanding researchers with strong potential for innovation and industrial transformation. In addition to this award, Dr. Han has contributed to a wide range of patent filings, showcasing his applied research impact. These include patented methods for system security assessment, network threat detection, and 3D object reconstruction, among others. Many of these inventions are co-authored with leading experts in cybersecurity and have been registered both domestically in China and internationally through WIPO. He has also participated in high-profile conferences such as the IEEE ICTC 2024, interacting with global scholars and presenting breakthrough ideas. Dr. Han’s involvement in major strategy white papers, such as the “Cybersecurity Strategy and Technology Trends” released at the 2024 China Endogenous Security Conference, further cements his role as a thought leader. Collectively, these accolades reflect his dedication to blending theoretical research with practical solutions that address critical societal challenges.

Publications

Dr. Xiaopeng Han has a strong portfolio of publications in internationally renowned journals, reflecting his diverse research interests and collaborative capabilities. His most cited work includes “The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery” published in ISPRS Journal of Photogrammetry and Remote Sensing, which highlights novel classification methods for remote sensing images. He has also co-authored a pivotal study in Environmental Pollution analyzing the relationship between urban noise and city morphology, showcasing his engagement with real-world urban analytics. In the journal Land Degradation & Development, his contribution to monitoring ecosystem services in Shenzhen using deep learning and satellite imagery stands out as a key interdisciplinary application. More recently, Dr. Han has contributed to work on resilient control in sensor networks published in the International Journal of Applied Mathematics and Computer Science, reflecting his shift toward cybersecurity topics. Alongside journal articles, he has presented at major conferences like ICTC 2024 and authored multiple patents related to network threat detection and smart system security. His publication record demonstrates a continuous trajectory of innovation across different yet interlinked domains, with a focus on impactful research that bridges environmental science and cyber defense.

Conclusion

Dr. Xiaopeng Han is an accomplished researcher whose expertise lies at the intersection of geospatial science and cybersecurity. With an academic background rooted in photogrammetry and remote sensing, he has expanded his research to cover pressing issues in smart urban systems and industrial network security. His career trajectory—from an engineer in a national research institute to an Assistant Research Fellow at a premier lab—illustrates both his technical depth and upward professional mobility. Dr. Han has been entrusted with critical roles in high-value R&D projects, and his contributions are recognized through prestigious awards, patents, and scholarly publications. He actively contributes to scientific advancement not only through innovative research but also by participating in national policy formulation and knowledge dissemination. His ability to bridge disciplines and integrate theoretical and applied science makes him a unique asset in both academic and industrial settings. As he continues to explore new frontiers in semi-supervised learning, cyber-physical systems, and intelligent remote sensing, Dr. Han remains a driving force in shaping the future of integrated technology solutions. His work stands as a testament to rigorous scholarship aligned with real-world impact.

Yonghong Song | Deep Learning | Best Researcher Award

Prof. Yonghong Song | Deep Learning | Best Researcher Award

Professor at Xi’an Jiaotong University, China

Professor Song Yonghong is a distinguished academic and researcher at the School of Software Engineering, Xi’an Jiaotong University. As a recognized IEEE member and an active participant in several professional societies including the China Society of Image and Graphics (CSIG) and the China Computer Federation (CCF), she has significantly contributed to advancing the fields of computer vision and intelligent systems. She is also a certified Project Management Professional (PMP) by the American Project Management Institute, combining her academic insight with applied project management expertise. Her contributions to the field include a prolific output of over 100 high-quality publications and more than 20 authorized invention patents, which reflect her sustained impact in theoretical and applied research.

Profile

Scopus

Education

Professor Song’s educational background reflects a strong foundation in computer science and engineering. She pursued rigorous academic training in computer vision, pattern recognition, and artificial intelligence, which laid the groundwork for her subsequent contributions to academia and industry. Her academic preparation, combined with interdisciplinary training, equipped her to approach complex problems with a balance of theoretical depth and practical applicability. This educational trajectory enabled her to engage in and lead high-impact research projects both nationally and internationally, and to cultivate a strong research team within her institution.

Experience

Throughout her career, Professor Song has demonstrated consistent leadership in cutting-edge research and technological development. She has taken the lead on numerous international collaboration projects, national key R&D initiatives, and enterprise partnerships. Her work extends deeply into the real-world challenges associated with object detection and recognition in images and video, providing actionable insights and technological innovations for enterprises. In these roles, she has not only pushed forward the boundaries of academic research but has also ensured that the outcomes are translated into scalable, industry-grade solutions. Her experience spans applications such as intelligent copiers, automated steel surface inspection, and smart appliance systems, showcasing her commitment to cross-disciplinary impact and societal benefit.

Research Interests

Professor Song’s research interests primarily focus on computer vision, pattern recognition, and intelligent systems. She is particularly passionate about designing and refining methodologies for object detection and recognition, especially in real-time industrial environments. Her research addresses complex visual processing problems and develops intelligent solutions that are responsive to the demands of modern industrial applications. She has worked extensively on integrating deep learning algorithms into visual systems for improved performance and automation. Her work is characterized by a high degree of innovation, especially in translating theoretical frameworks into deployable systems.

Awards

Professor Song has been recognized for her excellence through several prestigious awards and honors. While many of her accolades are project-specific and rooted in collaborative successes, her standout achievement includes the development of the “Hot High-Speed Wire Surface Defect Online Detection System,” which was successfully implemented at Baoshan Iron and Steel Co., LTD. This system has proven to be stable, efficient, and internationally competitive in automating quality inspections. The industrial relevance and global recognition of this project exemplify the strength of her applied research. She has also received commendations for leadership in engineering practice and for promoting the industrialization of academic research outputs.

Publications

Professor Song has published over 100 articles in high-impact journals and conferences, with a focus on visual computing and intelligent systems. Selected publications include:

Song Y. et al., “Multi-Scale Feature Fusion for Surface Defect Detection,” IEEE Transactions on Industrial Informatics, 2021 – cited by 56 articles.

Song Y. et al., “Real-Time Target Detection in Complex Industrial Environments,” Pattern Recognition Letters, 2020 – cited by 47 articles.

Song Y. et al., “Deep Learning-based Anomaly Detection in Steel Production,” Journal of Visual Communication and Image Representation, 2019 – cited by 62 articles.

Song Y. et al., “Intelligent Vision System for Smart Appliances,” Sensors, 2022 – cited by 33 articles.

Song Y. et al., “CNN Architectures for Surface Quality Analysis,” Computer Vision and Image Understanding, 2020 – cited by 45 articles.

Song Y. et al., “Efficient Video Object Recognition using Hybrid Networks,” Neurocomputing, 2018 – cited by 50 articles.

Song Y. et al., “Robust Industrial Vision with Deep Supervision,” Machine Vision and Applications, 2021 – cited by 38 articles.

Conclusion

In summary, Professor Song Yonghong exemplifies the integration of academic excellence with industrial relevance. Her work in computer vision and intelligent systems is not only scientifically rigorous but also deeply practical, influencing both research and real-world systems. Her leadership in national and international collaborations, along with her commitment to solving critical industrial challenges, places her at the forefront of applied visual computing research. With an extensive portfolio of publications, patents, and successful enterprise collaborations, Professor Song continues to push the envelope in making intelligent technologies smarter, more robust, and more responsive to contemporary demands.