Dr. Xueqiao Xu | Benchmark Datasets | Best Researcher Award Posted on 25/01/202525/01/2025 by ScienceFather Dr. Xueqiao Xu | Benchmark Datasets | Best Researcher Award Doctorate at Lawrence Livermore National Laboratory, United States Profiles Scopus Orcid Google Scholar Summary Dr. Xueqiao Xu is a distinguished theoretical and computational plasma physicist specializing in boundary plasma physics, large-scale computing, and AI/ML applications. Dr. Xu is the original developer of the BOUT code and co-developer of the BOUT++ code, leading international collaborations on edge turbulence and divertor heat flux simulations. Education M.S. in Plasma Physics – Institute of Plasma Physics, Chinese Academy of Science, 1985 Ph.D. in Plasma Physics – University of Texas, Austin, 1990 Professional Experience Staff Scientist (1996–Present) – Fusion Energy Sciences Program, Lawrence Livermore National Laboratory Principal Investigator (2023–2027) – U.S. DOE Scientific Discovery through Advanced Computing Adjunct Professor (2012–Present) – School of Physics, Peking University, China Postdoctoral Researcher (1993–1996) – Lawrence Livermore National Laboratory Postdoctoral Researcher (1990–1993) – University of California, Berkeley Research Interests Dr. Xu focuses on boundary plasma physics, edge turbulence, ELMs, and divertor heat flux simulations. He leads the development of the BOUT++ code. Publications How turbulence spreading improves power handling in quiescent high confinement fusion plasmas Author: Li, Z., Chen, X., Diamond, P.H., Khabanov, F., McKee, G.R. Journal: Communications Physics Year: 2024 Overview of recent experimental results on the EAST Tokamak Author: Song, Y., Wan, B., Li, J., Salewski, M., Schuster, E. Journal: Nuclear Fusion Year: 2024 DIII-D research to provide solutions for ITER and fusion energy Author: Holcomb, C.T., Abbate, J., Abe, A., Zimmerman, J., Zuniga, C. Journal: Nuclear Fusion Year: 2024 Turbulence simulations with BOUT++ by using SOLPS grids for SOLPS/BOUT++ coupling Author: Zhang, D.R., Ding, R., Si, H., Xu, X.Q., Xia, T.Y. Journal: Contributions to Plasma Physics Year: 2024 Theoretical and global simulation analysis of collisional microtearing modes Author: Zhu, M., Ma, L. Journal: Physics of Plasmas Year: 2024
Dr. Shiliang Wang | Benchmark Datasets | Best Scholar Award Posted on 26/09/202427/09/2024 by ScienceFather Dr. Shiliang Wang | Benchmark Datasets | Best Scholar Award Doctorate at Xi`an University of Architecture and Technology, China Profile Orcid Summary Dr. Wang Shiliang is an accomplished researcher and educator specializing in Performance-Driven Intelligent Generative Design for buildings. His work integrates machine learning, big data, and urban health, focusing on creating innovative solutions for architectural design. Dr. Wang has published impactful research and mentored award-winning student projects, earning recognition for his contributions to the architectural field. Education Bachelor’s Degree: Jinan University, Jinan (2009-2014) Master’s Degree: Xi’an University of Architecture and Technology, Xi’an (2014-2017) PhD Degree: Xi’an University of Architecture and Technology, Xi’an (2018-Present) Professional Experience Published SCI and conference papers on climate-adaptive shading systems, thermal comfort, and urban-scale assessments Authored three utility model patents, including innovations in ventilation systems and adjustable shading windows Guided award-winning student architectural design projects in prestigious national and international competitions Teaching Experience and Achievements “Ancient City Pacemaker”: First Prize, UIA Hope Cup 2020 International Student Architectural Design Competition “Desert Pearl”: First Prize, 2022 Tianhua Cup ART&TECH National Student Architectural Design Competition “Inside and Outside the City Wall”: Excellent Award, 2022 10th “Tianzuo Award” International Student Architectural Design Competition “Bypass Surgery: Heart Bridge”: Finalist, 2022 eVolo Skyscraper Competition Research Interests Machine Learning for architectural design Intelligent Generation and performance-driven solutions Big Data applications in urban health Enhancing thermal comfort and cognitive performance in built environments Publication Coupled Impact of Points of Interest and Thermal Environment on Outdoor Human Behavior Using Visual Intelligence Authors: Shiliang Wang, Qun Zhang, Peng Gao, Chenglin Wang, Jiang An, Lan Wang Journal: Buildings Year: 2024
Benchmark Datasets and Evaluation Methods Posted on 10/10/202314/10/2023 by ScienceFather Introduction of Benchmark Datasets and Evaluation Methods Benchmark Datasets and Evaluation Methods research is an essential component of the computer vision and machine learning fields. It focuses on the development of standardized datasets and evaluation protocols to objectively assess the performance of algorithms and models. This research plays a pivotal role in advancing the state-of-the-art in various computer vision tasks, enabling fair comparisons and driving innovation. Subtopics in Benchmark Datasets and Evaluation Methods: Object Detection Datasets: Researchers create benchmark datasets containing images with annotated objects of interest, facilitating the evaluation of object detection algorithms in terms of accuracy, speed, and robustness. Image Segmentation Benchmarks: This subfield focuses on datasets and evaluation metrics for image segmentation tasks, enabling the assessment of algorithms that partition images into meaningful regions or objects. Visual Recognition Challenges: Research teams organize challenges and competitions around specific computer vision tasks, providing a platform for evaluating and comparing the performance of algorithms from various research groups. Evaluation Metrics: Developing novel evaluation metrics that go beyond traditional measures to assess the quality of results, especially in cases where subjective human judgment is involved, such as image quality assessment. Large-Scale Image Retrieval: Researchers create benchmark datasets for evaluating image retrieval algorithms, allowing for the assessment of search accuracy and efficiency in large-scale image databases. Benchmark Datasets and Evaluation Methods research ensures that computer vision and machine learning algorithms are rigorously tested and compared, fostering advancements in the field and enabling the development of more accurate and efficient models. These subtopics represent the critical aspects of this research area. Object Detection and Recognition Introduction Object Detection and Recognition: Object Detection and Recognition is a vibrant and evolving field of computer vision and artificial intelligence, dedicated to the automated identification and localization of objects Image Processing and Enhancement Introduction Image Processing and Enhancement: Image Processing and Enhancement is a pivotal domain within the realm of computer vision and digital imaging. This field is dedicated to the development of Computer Vision for Robotics and Autonomous Systems Introduction of Computer Vision for Robotics and Autonomous Introduction: Computer Vision for Robotics and Autonomous Systems is a multidisciplinary field at the intersection of computer vision, robotics, and artificial intelligence. 3D Computer Vision Introduction of 3D Computer Vision 3D Computer Vision is a dynamic and interdisciplinary field that aims to enable machines to perceive and understand the three-dimensional structure of the world from Medical Image Analysis Introduction of Medical Image Analysis Medical Image Analysis is a critical and rapidly evolving field that harnesses the power of computer vision and machine learning to extract valuable insights from Video Analysis Introduction of Video Analysis Video Analysis and Understanding is a dynamic and interdisciplinary field that aims to develop algorithms and techniques for extracting meaningful information from video data. It plays Deep Learning for Computer Vision Introduction of Deep Learning for Computer Vision Deep Learning for Computer Vision is at the forefront of modern artificial intelligence, revolutionizing the way machines perceive and interpret visual information. It Applications of Computer Vision Introduction of Applications of Computer Vision Applications of Computer Vision represent a diverse and ever-expanding landscape of practical uses for visual data analysis and interpretation. Computer vision technology has transitioned Human-Computer Interaction Introduction of Human-Computer Interaction Introduction: Human-Computer Interaction (HCI) research is a multidisciplinary field that focuses on understanding and improving the interaction between humans and technology. It explores how users interact Biometrics and Security Introduction of Biometrics and Security Biometrics and Security research is dedicated to the development of cutting-edge technologies that leverage unique physiological or behavioral characteristics of individuals for identity verification and 1 2 3 … 5 Next »