Prof. Xiaoxia Wan | Sensing Technology | Excellence in Innovation

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Prof. Xiaoxia Wan | Sensing Technology | Excellence in Innovation 

Prof. Xiaoxia Wan, Wuhan University, China

Wan Xiaoxia, is a distinguished professor and doctoral supervisor specializing in color science and technology. She is currently a faculty member in the Department of Printing and Packaging at Wuhan University, where she has served since 2018. Wan holds a Ph.D. in Cartography and Geographic Information Systems from Wuhan University, and her educational background includes a Master’s in Geographic Information Systems and a Bachelor’s in Cartography from the same institution. Her academic journey was enriched by her experience as a senior visiting scholar at the Munsell Color Science Laboratory, California State University, and Rochester Institute of Technology. In addition to her teaching role, she holds several significant positions, including Deputy Director of the Teaching Guidance Committee for Light Industry Majors of the Ministry of Education and member of both the China Printing and Color Standardization Technical Committees. Wan has received numerous awards for her teaching and research, including the National Excellent Course designation for her course “Introduction to Printing” and recognition as a leading talent in the news and publishing industry. Her research has led to successful projects funded by the National Natural Science Foundation of China, focusing on color reproduction methods for cultural relics. Wan’s contributions to her field have been acknowledged through several prestigious awards, including the Hubei Science and Technology Progress Award and the China National Textile and Apparel Council Science and Technology Award.

Professional Profile:

SCOPUS

Summary of Suitability for Excellence in Innovation: Wan Xiaoxia

Dr. Wan Xiaoxia is a highly qualified candidate for the Excellence in Innovation award, distinguished by her extensive contributions to color science and technology, particularly in the realm of printing and packaging. Her pioneering research and leadership roles in various educational and professional organizations reflect her commitment to advancing innovation in her field.

Education 🎓

  • Senior Visiting Scholar
    Munsell Color Science Laboratory, California State University, Los Angeles and Rochester Institute of Technology (2004-2006)
  • Ph.D. in Engineering
    Cartography and Geographic Information Systems, Wuhan University (1995-2002)
  • Master of Engineering
    Geographic Information Systems, Wuhan University of Surveying and Mapping (1992-1995)
  • Bachelor of Engineering
    Cartography, Wuhan University of Surveying and Mapping (1982-1986)

Work Experience 💼

  • Professor & Doctoral Supervisor
    Department of Printing and Packaging, Wuhan University (2018-present)
  • Vice Dean
    School of Journalism and Communication, Wuhan University (2000-present)
  • Lecturer
    School of Printing Engineering, Wuhan University of Surveying and Mapping (1996-2000)
  • Teaching Assistant
    Department of Cartography, Wuhan University of Surveying and Mapping (1986-1992)
  • Doctoral Supervisor
    School of Printing and Packaging, Wuhan University (2004-present)

Achievements 📚

  • National Excellent Course
    “Introduction to Printing” (2008)
  • National Excellent Shared Course
    “Introduction to Printing” (2010)
  • National Bi Sheng Newcomer Award (2009)
  • Suzhou Industrial Park Leading Talent (2012)
  • Hubei Province News and Publishing Figures (2012)
  • National News and Publishing Industry Leading Talent (2013)
  • Wuhan City Huanghe Talent (2014)
  • Wuhan University Excellent Teaching and Research Achievement First Prize (2008)
  • Hubei Province Higher Education Teaching Achievement First Prize (2008, 2018)

Awards & Honors 🏆

  • Second Prize of Hubei Science and Technology Progress Award
    Key Technology and Application of Color Reproduction Based on Spectrum (2020)
  • Dunhuang Academy “Excellent Academic Achievement Award”
    Protection Technology Category, Second Prize (2017)
  • China National Textile and Apparel Council Science and Technology Award
    Key Technology and Industrialization of Full-Color Yarn Manufacturing, Second Place (2016)

Publication Top Notes:

A Color Reproduction Method for Exploring the Laser-Induced Color Gamut on Stainless Steel Surfaces Based on a Genetic Algorithm

Enhancement of laser-induced surface coloring through laser double-scan method

Prediction model for laser marking colors based on color mixing

A Novel Correction Method of Kubelka–Munk Model for Color Prediction of Pre-colored Fiber Blends

Dynamic Projection Method of Electronic Navigational Charts for Polar Navigation

Spectral missing color correction based on an adaptive parameter fitting model

 

Prof Wanrun Li | Vision Sensing | Best Researcher Award

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Prof Wanrun Li | Vision Sensing | Best Researcher Award

Prof Wanrun Li, Lanzhou University of Technology, China 

Professor Wanrun Li is a distinguished academic and researcher in Structural Health Monitoring, currently serving as the Vice Dean of the School of Civil Engineering at Lanzhou University of Technology, China. With a focus on fatigue analysis, wind turbine vibration control, and structural health monitoring, he has led numerous research projects funded by national and regional foundations. His work has significantly contributed to understanding the seismic performance of super-tall buildings, damage identification, and fatigue life prediction of wind turbines. He has held multiple leadership roles, including Associate Professor and department head, and has been a visiting scholar at prestigious institutions.

Professional Profile:

Suitability for the Best Researcher Award: 

Professor Wanrun Li’s extensive research portfolio, leadership in high-stakes projects, and contributions to structural engineering make him a strong candidate for the Best Researcher Award. His work in wind turbine vibration control and fatigue analysis is critical for the advancement of sustainable energy infrastructure. However, to strengthen his candidacy, broadening the impact of his research on industry standards and further enhancing global outreach would be valuable steps forward. Overall, his dedication to innovative research and significant contributions to civil engineering position him as a deserving nominee for this prestigious award.

Education

Professor Wanrun Li earned his Ph.D. in Structural Engineering from Lanzhou University of Technology in 2013, specializing in Structural Health Monitoring under the guidance of Prof. Yongfeng Du and Prof. Y.Q. Ni. He was also a joint-supervised Ph.D. candidate at Hong Kong Polytechnic University from 2011-2012. His academic background includes an M.S. in Disaster Prevention and Mitigation from Lanzhou University of Technology (2010) and a B.S. in Civil Engineering from the same institution (2008). His education laid the foundation for his expertise in monitoring structural health and analyzing the fatigue of civil structures.

Work Experience

Wanrun Li has over a decade of academic and research experience. Currently, he is a Professor and Vice Dean at Lanzhou University of Technology. He previously served as an Associate Professor and Head of the Department of Building Engineering. His international experience includes being a visiting scholar at the University of Illinois Urbana-Champaign and Southeast University, China. Over the years, he has led research initiatives focused on vibration control, fatigue life prediction, and damage identification in large structures such as wind turbines and high-rise buildings.

Skills

Professor Li’s technical expertise includes advanced knowledge in Structural Health Monitoring, Wind Turbine Vibration Control, and Weld Fatigue Analysis. His skills extend to predictive modeling, statistical pattern recognition, and seismic data analysis. He is proficient in developing new devices for vibration reduction, using machine vision technology for turbine blade detection, and applying experimental and multi-scale simulation techniques for assessing fatigue in steel structures. His proficiency with numerical modeling, experimental research, and structural design underpins his research and teaching efforts.

Awards and Honors

Wanrun Li has been recognized with multiple prestigious awards, including the Hongling Outstanding Young Scholar Award at Lanzhou University of Technology (2019-2021) and the Science Fund for Distinguished Young Scholars of Gansu Province (2021-2024). His work has earned several grants from the National Natural Science Foundation of China (NSFC), totaling over ¥1.9 million for projects related to wind turbine structures and vibration control. These honors affirm his significant contributions to structural engineering and disaster prevention.

Membership

Professor Li is actively engaged in several professional organizations, contributing to the advancement of structural health monitoring and civil engineering. He collaborates closely with renowned research groups and institutions, including the University of Illinois Urbana-Champaign and Southeast University. His membership in national scientific communities has enabled him to secure significant research funding and present his findings in both domestic and international conferences.

Teaching Experience

With a passion for mentoring, Professor Li has been an academic instructor at Lanzhou University of Technology since 2013, progressing from instructor to professor. As a faculty member, he has taught courses related to Structural Engineering, Civil Engineering, and Structural Health Monitoring. His role as the Chief Duty Professor for the Hongliu Top-class Major in Civil Engineering reflects his commitment to academic excellence. He also supervises graduate students, guiding them through research projects and fostering a collaborative learning environment.

Research Focus

Professor Li’s research is centered on Structural Health Monitoring, Weld Fatigue Analysis, and Vibration Control of Wind Turbines. He is particularly interested in seismic data analysis of tall structures, wind-induced fatigue of turbines, and developing new devices for reducing tower vibrations. His projects include studies on vibration control using tuned liquid column dampers and fatigue life prediction of wind turbines in harsh environments. His innovative work integrates machine vision technology and UAVs for turbine blade detection, and he has contributed significantly to enhancing structural safety and durability.

Publication top Notes:

“Wind turbine blade defect detection and measurement technology based on improved SegFormer and pixel matching”

    • Year: 2024
    • Journal: Optics & Laser Technology
    • DOI: 10.1016/j.optlastec.2024.111381

“Mitigation of In-Plane Vibrations in Large-Scale Wind Turbine Blades with a Track Tuned Mass Damper”

    • Year: 2023
    • Journal: Structural Control and Health Monitoring
    • DOI: 10.1155/2023/8645831

“Dynamic Characteristic Monitoring of Wind Turbine Structure Using Smartphone and Optical Flow Method”

    • Year: 2022
    • Journal: Buildings
    • DOI: 10.3390/buildings12112021

“Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV”

    • Year: 2022
    • Journal: Remote Sensing
    • DOI: 10.3390/rs14133113

“Seismic Vibration Mitigation of Wind Turbine Tower Using Bi-Directional Tuned Mass Dampers”

    • Year: 2020
    • Journal: Mathematical Problems in Engineering
    • DOI: 10.1155/2020/8822611

“Low-cycle fatigue test and life assessment of carbon structural steel GB Q235B butt joints and cruciform joints”

    • Year: 2019
    • Journal: Advances in Structural Engineering
    • DOI: 10.1177/1369433218795292

“Seismic Performance of a New Precast Concrete Shear Wall with Bolt Connection”

    • Year: 2019
    • Journal: Gongcheng Kexue Yu Jishu/Advanced Engineering Science
    • DOI: 10.15961/j.jsuese.201801163

“Time-Varying Nonlinear Parametric Identification of Isolated Structure Based on Wavelet Multiresolution Analysis”

    • Year: 2019
    • Journal: Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis
    • DOI: 10.16450/j.cnki.issn.1004-6801.2019.03.024

“Welding Residual Stress Simulation and Experimental Verification in Beam-to-Column Joints of Q345B Steel”

    • Year: 2019
    • Journal: Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science)
    • DOI: 10.12141/j.issn.1000-565X.190044

 

Best Industrial Sensing Technology

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Introductio Best Industrial Sensing Technology

Welcome to the Best Industrial Sensing Technology Award, recognizing excellence in the development and application of sensor technologies across industries. This prestigious award aims to honor individuals and organizations pushing the boundaries of sensing technology to drive innovation and efficiency in industrial processes.

About the Award:

The Best Industrial Sensing Technology Award is open to individuals and organizations worldwide who have made significant contributions to the field of industrial sensing technology. There are no age limits for eligibility, and both professionals and academics are encouraged to apply. Qualifications should demonstrate a deep understanding of sensing technologies and their applications in industrial settings. Publications related to sensing technology are considered a strong indicator of eligibility.

Requirements:

Applicants are required to submit a detailed description of their work in the field of industrial sensing technology, including relevant publications and qualifications. Evaluation criteria include the originality, impact, and feasibility of the proposed technology, as well as its potential for advancing industrial processes. Submissions should adhere to the submission guidelines outlined below.

Submission Guidelines:

Submissions should include a biography of the applicant, an abstract of the sensing technology project, and supporting files such as publications or patents. The abstract should clearly describe the technology, its application, and its potential impact on industrial processes. Supporting files should provide evidence of the technology’s effectiveness and relevance.

Evaluation Criteria:

Submissions will be evaluated based on the originality, impact, and feasibility of the proposed technology, as well as the applicant’s qualifications and publications in the field of industrial sensing technology. Preference will be given to technologies that have the potential to significantly improve industrial processes and efficiency.

Recognition:

Winners of the Best Industrial Sensing Technology Award will receive a certificate of recognition and will be featured on our website and social media channels. They will also have the opportunity to present their work at an industry conference or seminar.

Community Impact:

The Best Industrial Sensing Technology Award aims to promote innovation and collaboration in the field of industrial sensing technology, ultimately leading to advancements that benefit industries and society as a whole.