Dr. Xiaodan Huang | Photonic Sensors Award | Best Researcher Award

Posted on by ScienceFather

Dr. Xiaodan Huang | Photonic Sensors Award | Best Researcher Award 

Dr. Xiaodan Huang, Changzhou Vocational Institute of Mechatronic Technology, China

Xiaodan Huang is an Associate Professor at the Changzhou Vocational Institute of Mechatronic Technology. She earned her Bachelor’s degree in Physics from Soochow University in 2002, followed by a Master’s degree in Materials from Guilin University of Electronic Technology in 2009, and a Ph.D. in Physics from Southeast University in 2018. With a strong academic foundation, Dr. Huang has authored over 20 journal papers and contributed a book chapter to her field. Her current research interests lie in optical sensors, nanophotonics, anti-reflection coatings, and solar cell technologies, where she continues to contribute to advancements in these cutting-edge areas.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: Associate Professor Xiaodan Huang

Research Contributions:
Associate Professor Xiaodan Huang has demonstrated significant contributions to the field of nanophotonics and optical sensors. With a solid academic foundation, including a Ph.D. in Physics, she has authored over 20 journal papers and a book chapter, establishing herself as a prominent researcher in her field.

Education

  • Ph.D. in Physics
    Southeast University, 2018
  • M.S. in Materials
    Guilin University of Electronic Technology, 2009
  • B.S. in Physics
    Soochow University, 2002

Work Experience

  • Associate Professor
    Changzhou Vocational Institute of Mechatronic Technology
    (Date of employment not specified, but she is currently in this role)
  • Research Contributions
    • Author of over 20 journal papers and one book chapter.

Research Interests

  • Optical sensors
  • Nanophotonics
  • Anti-reflection technologies

Publication top Notes:

Broadband and highly efficient asymmetric optical transmission through periodic Si cylinder arrays on the dielectric substrates

Asymmetric optical transmission through periodic metallic hemisphere arrays on the transparent substrates

Broadband anti-reflection coating for Si solar cell applications based on periodic Si nanopillar dimer arrays & Si3N4 layer

Dual-spectral narrow bandwidth surface lattice resonance sensors

Detailed formation mechanism of sharp plasmonic lattice modes on Au hemi-ellipsoid arrays in inhomogeneous environment

Plasmonic Lattice Modes Formed Under Both Forward and Backward Illuminations in Inhomogeneous Environment

 

Prof Wanrun Li | Vision Sensing | Best Researcher Award

Posted on by ScienceFather

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

Posted on by ScienceFather

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.

 

 

Best Machine Learning for Sensing

Posted on by ScienceFather

Introduction Best Machine Learning for Sensing

Welcome to the ‘Best Machine Learning for Sensing‘ award, honoring innovative solutions that leverage machine learning to advance sensing technologies. This award recognizes outstanding contributions in developing algorithms, models, and systems that enhance sensing capabilities across various domains.

About the Award:
  • Eligibility: Open to individuals, teams, academic institutions, and organizations worldwide.
  • Age Limits: None.
  • Qualification: Projects or research work showcasing the application of machine learning in sensing technologies.
  • Publications: Relevant publications or patents are encouraged but not required.
  • Requirements: Submissions must demonstrate innovative use of machine learning in sensing, with clear impact and potential for advancement in the field.
Evaluation Criteria:
  • Innovation: Uniqueness and originality of the approach.
  • Impact: Significance and relevance of the work in advancing sensing technologies.
  • Technical Merit: Soundness of the methodology and technical rigor.
  • Applicability: Potential for practical application and scalability.
  • Presentation: Clarity, organization, and effectiveness of the submission.
Submission Guidelines:
  • Submissions should include a detailed description of the project or research work.
  • Supplementary materials such as videos, code repositories, and datasets are welcome.
  • All submissions must be in English.
Recognition:
  • The winner will receive a prestigious award and recognition at a special ceremony.
  • Winners and finalists will be featured on our website and in press releases.
Community Impact:
  • Projects with demonstrated positive impact on society or the environment will be highly regarded.
  • Community engagement and collaboration will be considered favorably.
Biography:
  • Provide a brief biography highlighting the key contributors and their roles in the project.
Abstract:
  • A concise summary of the project, highlighting its significance and key findings.
Supporting Files:
  • Upload any relevant files such as research papers, presentations, or supplementary materials.

 

Best Overall Sensing Technology

Posted on by ScienceFather

Introduction Best Overall Sensing Technology

Welcome to the Best Overall Sensing Technology Award, celebrating innovation and excellence in sensing technology. This award recognizes groundbreaking contributions that have the potential to revolutionize industries and improve the quality of life.

About the Award: The Best Overall Sensing Technology Award is open to individuals and teams who have developed cutting-edge sensing technologies. There are no age limits for applicants, and both academic and industry professionals are eligible to apply. Publications related to the sensing technology are encouraged but not required.

Eligibility:

  • Open to individuals and teams
  • No age limits
  • Academic and industry professionals eligible
  • Publications encouraged but not required

Qualifications: Applicants should have a strong background in sensing technology, demonstrated through academic achievements, professional experience, and innovative contributions to the field.

Submission Guidelines:

  • Submit a detailed description of the sensing technology
  • Include supporting documents such as publications, patents, and technical specifications
  • Provide a biography highlighting relevant experience and achievements
  • Include an abstract summarizing the technology and its potential impact
  • Submit supporting files, such as videos, images, or prototypes, if available

Evaluation Criteria: Submissions will be evaluated based on the following criteria:

  • Innovation and creativity
  • Technical merit
  • Potential impact on industry or society
  • Feasibility and scalability

Recognition: Winners of the Best Overall Sensing Technology Award will receive a cash prize, a certificate of achievement, and recognition on our website and social media platforms. They will also have the opportunity to present their technology at a special event.

Community Impact: The award aims to promote collaboration and knowledge sharing within the sensing technology community, fostering a culture of innovation and advancement in the field.

Biography: Applicants should provide a brief biography highlighting their relevant experience, qualifications, and achievements in the field of sensing technology.

Abstract and Supporting Files: Applicants should include an abstract summarizing their sensing technology and its potential impact. Supporting files such as videos, images, or prototypes can also be submitted to supplement the application.