Dr. Junjie Yang | Engineering | Best Researcher Award

Engineer at China Three Gorges Corporation, China

Dr. Junjie Yang is an accomplished researcher specializing in fault diagnosis, anomaly detection, and machine learning applications in complex systems. With a Ph.D. from the University of Paris-Saclay, his groundbreaking work has introduced methodologies such as the Local Mahalanobis Distance (LMD) for incipient fault diagnosis, earning recognition through high-impact publications. He has contributed to diverse domains, from renewable energy systems to multivariate statistical analysis, showcasing his ability to blend theoretical innovation with practical applications. Dr. Yang’s global research experience spans institutions like CNRS Singapore and China Three Gorges Corporation, where he developed hybrid AI frameworks and advanced diagnostic tools. Proficient in Python, Matlab, and AI libraries, he bridges traditional engineering and modern computational techniques. His commitment to interdisciplinary research, strong publication record, and collaboration with renowned experts position him as a leading figure in his field. Dr. Yang exemplifies excellence in leveraging AI for impactful real-world solutions.

Professional Profile

• Google Scholar
• Scopus Profile

Education

Dr. Junjie Yang has a robust educational foundation that underpins his expertise in fault diagnosis and machine learning. He earned his Ph.D. from the University of Paris-Saclay in 2023, focusing on fault diagnosis and prognosis in multivariate complex systems. His doctoral research introduced innovative methodologies, such as the Local Mahalanobis Distance (LMD), for detecting and isolating faults in complex environments. Prior to this, he completed his M.Sc. in Control Science and Engineering at Guangdong University of Technology, China, in 2019, where he developed a novel method for estimating the volume under a three-class ROC surface using kNN classifiers. His academic journey began with a B.Sc. in Automation from the same university in 2016, during which he worked on open-circuit fault diagnosis for interleaved DC-DC converters. Additionally, Dr. Yang enriched his academic portfolio as a visiting student at Polytech Nantes, France, specializing in wireless embedded technology.

Professional Experience

Dr. Junjie Yang possesses extensive professional experience in the fields of fault diagnosis, renewable energy systems, and machine learning. Currently, he serves as an Engineer at China Three Gorges Corporation, where he focuses on leveraging Large Language Models (LLMs) for fault diagnosis in renewable energy systems. Prior to this role, he was a Research Fellow at CNRS @ CREATE in Singapore, where he developed hybrid models integrating Convolutional Auto-Encoders with traditional physical characteristics for unsupervised high-impedance fault detection. Dr. Yang’s professional journey includes impactful research roles addressing complex problems in power systems and automation, underscored by his innovative contributions to incipient fault detection using AI-driven methodologies. His ability to transition seamlessly between academia and industry highlights his adaptability and focus on real-world applications. Through his work, Dr. Yang demonstrates a unique ability to bridge the gap between theoretical advancements and practical engineering solutions.

Research Interest

Dr. Junjie Yang’s research interests lie at the intersection of machine learning, statistical analysis, and engineering, with a particular focus on fault diagnosis and anomaly detection in complex systems. He is deeply engaged in developing advanced methodologies for one-class classification, semi-supervised learning, and multivariate statistical analysis to tackle challenges in identifying and isolating incipient faults. His work emphasizes the integration of AI techniques, such as Convolutional Auto-Encoders and Local Mahalanobis Distance (LMD), with traditional engineering models to enhance fault detection and prognosis in renewable energy systems and other industrial applications. Dr. Yang is also interested in applying data-driven and hybrid approaches to improve system reliability and performance in multivariate and high-dimensional environments. His research aims to address practical challenges in automation, energy systems, and beyond, making his contributions valuable for advancing both theoretical knowledge and real-world applications in intelligent fault diagnosis and system monitoring.

Award and honor

Dr. Junjie Yang has earned recognition for his innovative contributions to the fields of fault diagnosis and machine learning, receiving accolades that highlight his research excellence. His groundbreaking methodologies, such as the Local Mahalanobis Distance (LMD) and hybrid AI models for fault detection, have garnered widespread acclaim within the academic and industrial communities. Dr. Yang has been invited to present his work at prestigious international conferences, including IEEE IECON and ICASSP, underscoring his influence in advancing fault detection techniques. His papers, published in high-impact journals like Signal Processing and Electric Power Systems Research, have been highly cited, reflecting their significance in the field. While specific awards and honors may not be explicitly listed in his profile, his consistent publication in leading journals, collaborations with globally renowned researchers, and research positions at esteemed institutions underscore his distinction and impactful contributions to science and engineering.

Conclusion

Junjie Yang is a highly deserving candidate for the Best Researcher Award due to his groundbreaking contributions to fault diagnosis and renewable energy systems using AI models. His work bridges theoretical innovation with practical applications, evidenced by his extensive publication record and global collaborations. Enhancing the breadth of his applications and adopting newer AI paradigms could further cement his standing as a leader in the field. He embodies the qualities of a researcher who significantly advances the frontiers of science and engineering.

Publications Top Noted

• Title: An incipient fault diagnosis methodology using local Mahalanobis distance: Detection process based on empirical probability density estimation
  Authors: J. Yang, C. Delpha
  Year: 2022
  Citations: 38
• Title: Change point detection with mean shift based on AUC from symmetric sliding windows
  Authors: Y. Wang, G. Huang, J. Yang, H. Lai, S. Liu, C. Chen, W. Xu
  Year: 2020
  Citations: 10
• Title: An incipient fault diagnosis methodology using local Mahalanobis distance: Fault isolation and fault severity estimation
  Authors: J. Yang, C. Delpha
  Year: 2022
  Citations: 9
• Title: Open-circuit fault diagnosis for interleaved DC-DC converters
  Authors: Y. Junjie, C. Delpha
  Year: 2020
  Citations: 7
• Title: A local Mahalanobis distance analysis based methodology for incipient fault diagnosis
  Authors: J. Yang, C. Delpha
  Year: 2021
  Citations: 6
• Title: Local Mahalanobis distance envelope using a robust healthy domain approximation for incipient fault diagnosis
  Authors: J. Yang, C. Delpha
  Year: 2021
  Citations: 5
• Title: An efficient and user-friendly software tool for ordered multi-class receiver operating characteristic analysis based on Python
  Authors: S. Liu, J. Yang, X. Zeng, H. Song, J. Cen, W. Xu
  Year: 2022
  Citations: 2
• Title: Empirical probability density cumulative sum for incipient fault detection
  Authors: J. Yang, C. Delpha
  Year: 2020
  Citations: 2
• Title: A new reconstruction-based method using local Mahalanobis distance for incipient fault isolation and amplitude estimation
  Authors: J. Yang, C. Delpha
  Year: 2023
  Citations: 1
• Title: Bearing Faults Detection Using Statistical Feature Extraction and Probability Based Distance: A Comparative Study
  Authors: J. Yang, C. Delpha
  Year: 2022
  Citations: 1
• Title: IEEE 34 Nodes Test Feeder Simulation Data for High Impedance Fault Detection and Localization
  Authors: J. Yang, D. Benoit
  Year: 2024
  Citations: 0
• Title: Incipient Fault Severity Estimation Using Local Mahalanobis Distance
  Authors: J. Yang, C. Delpha
  Year: 2022
  Citations: 0

Prof. Rana Maya | Engineering | Best Researcher Award

Chair of construction engineering and management department at Tishreen university, Syria

Prof. Rana Maya is an accomplished academic and professional in construction engineering and management, with extensive experience in research, teaching, and quality management. She holds a Ph.D. in Construction Engineering and Management, awarded jointly by Tishreen University, Syria, and Kassel University, Germany. Prof. Maya has led numerous projects for international organizations like UNESCO, UNDP, and TEMPUS, contributing to the design, evaluation, and implementation of quality management systems. She has overseen more than 120 onsite audits and evaluated over 350 projects. Her leadership has earned her the Exceptional Professors Award and recognition for boosting Tishreen University’s global ranking. Prof. Maya has published widely, co-authoring a book on women in engineering leadership, and she supervises research at the graduate level. Fluent in English, Arabic, and Russian, she combines her academic expertise with global experience in both teaching and consulting roles. She is dedicated to advancing sustainable practices and innovative solutions in construction management.

Professional Profile

• Google Scholar
• Scopus Profile
• ORCID Profile

Education

Professional Experience

Prof. Rana Maya has extensive professional experience in construction engineering, management, and quality assurance. She has held various academic positions, including Professor and Department Chair at Tishreen University, Syria, where she taught courses in construction project management, quality management, and systems analysis. She also serves as an Associate Professor at the Syrian Virtual University, specializing in Building Information Modeling (BIMM) and quality management at the Master’s level. Beyond academia, Prof. Maya has worked as a consultant, leading quality management and accreditation projects for several organizations, including UNDP and UNESCO. She has supervised over 120 onsite audits and evaluated more than 350 projects in Syria and Germany. Her leadership in academic accreditation has helped institutions like Tishreen University and Al-Sham Private University achieve key certifications. Additionally, Prof. Maya has contributed as a senior management consultant for multiple organizations, improving organizational excellence and project management capabilities across various sectors.

Research Interest

Prof. Rana Maya’s research interests primarily focus on construction engineering, project management, and quality management systems, with an emphasis on improving performance and sustainability in construction projects. She explores innovative approaches to enhancing project management practices, quality assurance, and organizational excellence, particularly in challenging environments. Her work includes the development and evaluation of quality management frameworks and systems in construction projects, aiming to optimize performance and ensure compliance with international standards. Prof. Maya is also interested in the integration of Building Information Modeling (BIMM) in project management, particularly its role in improving efficiency and decision-making processes. Additionally, her research extends to the fields of strategic management, sustainability in construction, and the application of digital tools to support project planning, monitoring, and evaluation. She has contributed to studies on organizational resilience and the implementation of quality standards in both public and private sector construction projects.

Award and Honor

Conclusion

Rana Maya’s exemplary career, marked by significant academic, research, and managerial achievements, positions her as a strong candidate for the Best Researcher Award. Her ability to bridge academic excellence with practical applications, coupled with a proven track record in quality management and education, highlights her suitability. Strategic enhancements in international collaboration and cutting-edge research areas would further solidify her standing as a leader in her field.

Publications Top Noted

• Performance management for Syrian construction projects
  Authors: R. A. Maya
  Year: 2016
  Cited by: 47
• BIM Implementation Maturity Level and Proposed Approach for the Upgrade in Lithuania
  Authors: N. Lepkova, R. Maya, S. Ahmed, V. Šarka
  Year: 2019
  Cited by: 31
• Develop an artificial neural network (ANN) model to predict construction projects performance in Syria
  Authors: R. Maya, B. Hassan, A. Hassan
  Year: 2023
  Cited by: 30
• Incorporating BIM into the Academic Curricula of Faculties of Architecture within the Framework of Standards for Engineering Education
  Authors: L. Raad, R. Maya, P. Dlask
  Year: 2023
  Cited by: 10
• Defining the Areas and Priorities of Performance Improvement in Construction Companies Case Study for General Company for Construction and Building
  Authors: B. Hassan, J. Omran, R. Maya
  Year: 2015
  Cited by: 9
• Methodology of Project Management Assessment and the Financial Effects of Its Practices
  Authors: H. Bassam, O. Jamal, M. Rana
  Year: 2008
  Cited by: 6
• Quality Assurance of Construction Design and Contractual Phases in Syria Within BIM Environment: A Case study
  Authors: D. Y. Rudwan, R. Maya, N. Lepkova
  Year: 2023
  Cited by: 5
• The Role of BIM in Managing Risks in Sustainability of Bridge Projects: A Systematic Review with Meta-Analysis
  Authors: D. M. Ahmad, L. Gáspár, Z. Bencze, R. A. Maya
  Year: 2024
  Cited by: 3
• Determining the Most Appropriate Performance Indicators for Improving the Performance of Construction in Syria
  Authors: L. Maya, R. Ahmad
  Year: 2014
  Cited by: 3
• Optimal Government Strategies for BIM Implementation in Low-Income Economies: A Case Study in Syria
  Authors: M. S. Al-Mohammad, A. T. Haron, R. Maya, R. A. Rahman
  Year: 2024
  Cited by: 2
• The importance of regional planning in the processes of development and modernization in Syria: the challenges and the scopes of priority for working
  Author: R. Maya
  Year: 2008
  Cited by: 2
• Measuring the performance of construction firms, using data envelopment analysis
  Authors: B. Hassan, J. Omran, R. Maya
  Year: 2008
  Cited by: 2
• An Applied Study to Improve the Sustainability of Buildings by Reducing Energy Consumption Costs Using Building Information Modeling (BIM)
  Author: R. Maya
  Year: 2023
  Cited by: 1
• Suggesting a Model for Applying Digital Engineering to Lean Project Construction
  Author: R. Maya
  Year: 2022
  Cited by: 1
• BSC Designer to Manage Construction Project Performance Information through Visual Analysis
  Authors: M. Rana, O. Jamal, H. Bassam, A. Layal, P. Ghodous, F. Khosrowshahi
  Year: 2014
  Cited by: 1

Mr. Bin Rao | Engineering | Best Researcher Award

Research Assistant at Univercity of Macao, China

Mr. Bin Rao is a highly accomplished researcher specializing in traffic engineering and intelligent transportation systems. He holds a Bachelor’s degree in Traffic Engineering and a Master’s in Transportation Engineering, both from South China University of Technology, with outstanding academic achievements. Currently a Research Assistant at the University of Macau, Mr. Rao has made significant contributions to data-driven transportation research, focusing on travel time prediction, trajectory visualization, and outlier detection. He has co-authored multiple peer-reviewed publications, filed patents, and developed innovative algorithms leveraging machine learning and spatiotemporal reconstruction. His work has been recognized with prestigious scholarships and awards in national and provincial innovation competitions. Proficient in programming and data analysis tools like Python and MATLAB, Mr. Rao demonstrates a rare combination of technical expertise and problem-solving skills. With a proven research track record and a commitment to innovation, he is an emerging leader in the field of transportation engineering.

Professional Profile:

• ORCID Profile

Education

Mr. Bin Rao has a strong academic foundation in traffic and transportation engineering, supported by consistent excellence in his studies. He earned a Bachelor’s degree in Traffic Engineering with a minor in Finance from South China University of Technology (2017-2021), graduating with a remarkable GPA of 3.80/4.00 and ranking 3rd among 35 students. His coursework included Probability & Mathematical Statistics, Linear Algebra, Traffic Control, and Intelligent Transportation Systems, providing him with a comprehensive understanding of his field. Continuing his academic journey, Mr. Rao pursued a Master’s degree in Transportation Engineering (2021-2024) at the same institution through a postgraduate recommendation, achieving a GPA of 3.81/4.00 and ranking 10th among 60 peers. His advanced coursework encompassed subjects like Operational Research and Data Processing, further sharpening his analytical and technical skills. Currently, as a Research Assistant at the University of Macau, Mr. Rao continues to integrate academic knowledge with impactful research.

Professioanl Experience

Mr. Bin Rao’s professional experience showcases his expertise in traffic engineering and his ability to apply research to real-world problems. He began his career with innovative projects during his academic tenure, such as developing a real-time tunnel vehicle accident detection system based on RSSI technology. This device demonstrated high-precision positioning capabilities, enhancing safety in tunnel environments. As a Research Assistant at the University of Macau since 2024, Mr. Rao has focused on advanced data-driven transportation solutions under the mentorship of Prof. Zhengning Li. He has contributed to groundbreaking projects like urban road network travel time prediction using a WGCN-BiLSTM model and outlier detection algorithms for travel time data. These projects leveraged license plate recognition data to improve traffic management and prediction accuracy. Mr. Rao’s work is distinguished by a combination of technical proficiency, innovative algorithms, and collaborative research, solidifying his role as a rising expert in intelligent transportation systems.

Research Interest

Mr. Bin Rao’s research interests lie at the intersection of intelligent transportation systems, data-driven modeling, and advanced traffic engineering. He is particularly focused on leveraging machine learning, spatiotemporal analysis, and big data technologies to address complex transportation challenges. His work centers on developing predictive models for travel time estimation, such as the innovative WGCN-BiLSTM model, which enhances the accuracy and robustness of urban traffic predictions. He is also passionate about trajectory visualization and anomaly detection, utilizing license plate recognition data and advanced algorithms to refine traffic flow analysis and improve operational efficiency. Mr. Rao is keen on exploring new frontiers in autonomous traffic management, ethical trajectory planning, and long-tail trajectory prediction to better adapt transportation systems to real-world uncertainties. With a commitment to integrating theoretical insights with practical applications, his research aims to revolutionize urban mobility and contribute to sustainable and intelligent transportation networks.

Award and Honor

Mr. Bin Rao has earned numerous awards and honors in recognition of his academic excellence and innovative contributions to transportation engineering. During his undergraduate and postgraduate studies, he consistently received prestigious scholarships, including the National Encouragement Scholarship and the South China University of Technology School Scholarship. These accolades underscore his outstanding academic performance and dedication to his field. Mr. Rao has also excelled in national and provincial competitions, securing top prizes in events like the Fifth National University Intelligent Transportation Innovation and Entrepreneurship Competition, where he earned a National First Prize. His achievements in the Guangzhou Universities “Internet + Transportation” Competition and other innovation contests highlight his ability to translate theoretical knowledge into practical, impactful solutions. These honors reflect Mr. Rao’s commitment to advancing transportation engineering through innovative approaches and his potential as a leader in intelligent transportation systems. His accomplishments exemplify excellence, creativity, and a forward-thinking mindset.

Conclusion

Bin Rao demonstrates an exceptional blend of academic rigor, technical innovation, and collaborative research expertise, making a strong case for the Best Researcher Award. His achievements in publication, patents, and competitive accolades reflect both depth and impact in traffic engineering. With further diversification and enhanced independent contributions, he has the potential to emerge as a leading figure in his field. Based on the current profile, Bin Rao is highly suitable for the Best Researcher Award.

Publications Top Noted

• Xu, Minggui, Rao, Bin, Li, Yue, and Qi, Weiwei (2023)
  Visualization method of urban motor vehicle trajectory based on license plate recognition data.
  Published in: Smart Transportation Systems 2023.
• Qi, Weiwei, Rao, Bin, and Fu, Chuanyun (2023)
  A novel filtering method of travel time outliers extracted from large-scale traffic checkpoint data.
  Published in: Journal of Transportation Engineering, Part A: Systems.
• Qi, Weiwei, Rao, Bin (2024)
  Urban road network travel time prediction method based on “node-link-network” spatiotemporal reconstruction: a license plate data-driven WGCN-BiLSTM model.
  Status: Invited for presentation at CICTP 2024, submitted to TITS.
• Rao, Bin, Ye, Zhihong, Lin, Yongjie, et al. (2021)
  Tunnel vehicle accident detection and early warning device based on RSSI.
  Patent: CN216110866U (Issued March 22, 2022).
• Qi, Weiwei, Rao, Bin (2022)
  A Visualization Method for Motor Vehicle Trajectories Based on License Plate Recognition Data.
  Patent: ZL 2022 1 1021382.7 (Issued April 12, 2024).
• Qi, Weiwei, Rao, Bin (2023)
  A Vehicle Convoy Travel Time Abnormal Value Filtering System and Filtering Method.
  Patent: ZL 2023 1 1002516.5 (Issued August 23, 2024).