Prof Dr. Han-Taw Chen | Inverse CFD method | Best Researcher Award

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Prof Dr. Han-Taw Chen | Inverse CFD method | Best Researcher Award

Prof Dr. Han-Taw Chen, National Cheng Kung University, Taiwan

Professor Han-Taw Chen is a distinguished figure in the field of mechanical engineering, with a career spanning over four decades. Graduating from the esteemed Department of Mechanical Engineering at National Cheng Kung University, Taiwan in 1979, he went on to obtain his Ph.D. from the same institution in 1987. Since then, his academic journey has been marked by significant achievements and contributions to the field. Rising through the ranks, Professor Chen was promoted to the position of Professor in 1991, recognizing his expertise and dedication to the field. His exceptional scholarly contributions led to further accolades, including being honored as a Distinguished Professor in 2008.

 

šŸŒ Professional Profile:

SCOPUS

šŸ« Education:

Graduated from the Department of Mechanical Engineering, National Cheng Kung University, Taiwan in 1979. Received Ph.D. degree from the same department in 1987.

šŸ‘Øā€šŸ« Career Highlights:

  • Promoted to Professor in 1991.
  • Became Distinguished Professor in 2008.
  • Currently still active in the Department of Mechanical Engineering at National Cheng Kung University.

šŸ† Awards and Recognitions:

  • Distinguished Engineering Professor Award in 2016 by the Chinese Mechanical Engineering Society, Taiwan.
  • Awarded Fellow in 2020 by the Chinese Mechanical Engineering Society, Taiwan.

šŸ“ Research and Publications:

  • Published 92 papers over the years.
  • h-index: 26
  • Number of citations: 2032
  • Average citations per paper: 22.1

šŸ” Areas of Expertise:

  • Pioneering work in heat transfer on microscopic time scales.
  • Expertise in inverse non-Fourier and non-Fick heat conduction problems.
  • Recent focus on inverse CFD methods.

šŸ“š Notable Contributions:

  • Investigated the 3D inverse turbulent natural convection-conduction conjugate heat transfer problem using excessive experimental data.
  • Foundings from recent inverse CFD studies include predicting heat transfer coefficients on solid surfaces, unknown heat transfer rates, appropriate flow models, and near-wall treatments.

Publication Top Notes:

  • Temperature control in warehouse with internal and external heat rejections
    • Citations: 4
  • Experimental and numerical study of inverse natural convection-conduction problem in a fully partitioned cavity
    • Citations: 5
  • Experimental and numerical study of inverse natural convection-conduction heat transfer in a cavity with a fin
    • Citations: 8
  • Study of inverse natural convection-conduction heat transfer for in-line tube heat exchanger in a hot box with experimental data
    • Citations: 6
  • Prediction of 3D natural convection heat transfer characteristics in a shallow enclosure with experimental data
    • Citations: 8

 

 

 

 

Ms. Gayathri Shanmugam | Anomaly detection | Best Researcher Award-2798

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Ms. Gayathri Shanmugam | Anomaly detection | Best Researcher Award

Ms. Gayathri Shanmugam ,Bannari Amman Institute Of Technology, India

Dr. Gayathri Shanmugam, an accomplished educator and researcher, was born on August 23, 1992, in Namakkal, India. With an unwavering dedication to academia, [Name] has amassed over eight years of experience in college-level teaching, specializing in computer science and engineering. Currently serving as an Assistant Professor at Karpagam College of Engineering in Coimbatore since June 2019, [Name] has demonstrated remarkable prowess in both teaching and research. Gayathri ShanmugamĀ has left an indelible mark in the academic sphere, having published several papers in esteemed international journals indexed in Scopus. Their contributions extend beyond conventional research, as evidenced by the publication of a book chapter on artificial intelligence, showcasing a multidisciplinary approach to education and scholarship.

 

šŸŒ Professional Profile:

ORCID

šŸ‘Øā€šŸ’¼ Employment History:

  • Assistant Professor, Karpagam College of Engineering, Coimbatore
    June 2019 ā€” Present

    • Published papers in major international journals indexed in Scopus.
    • Authored a book chapter on artificial intelligence.
    • Developed and taught computer science courses, resulting in a 100% increase in student pass rates.
    • 3 years of experience in the Placement Department.
    • 2 years of experience in the Admission Department.
    • Mentored and advised students on academic and professional development, achieving a 90% success rate.

šŸŽ“ Education:

  • Ph.D., Anna University, Chennai
    July 2021 ā€” Present

    • Pursuing a degree part-time.
  • M.E Computer Science and Engineering, J K K Munirajah College of Technology, Erode
    June 2015 ā€” April 2018

    • Graduated with a 7.97 CGPA.

šŸ“š Core Knowledge:

  • W Programming
  • Java Programming
  • Operating Systems
  • Computer Networks
  • Database Management and Systems
  • Data Warehousing and Mining
  • Image Processing

šŸ’¼ Skills:

  • Computer Skills
  • Effective Time Management
  • Effective Teaching
  • Communication Skills
  • Leadership
  • Classroom Management
  • Ability to Work in a Team

šŸ–„ļø Project Experience:

  • Third-Party Emergency Alert System over Cloud Messaging Service, B.E Project (April 2013)
  • Entire Day Essential Accuracy for Darkness Discovery and Elimination, M.E Project (April 2018)

This comprehensive background highlights Dr. [Name]’s dedication, expertise, and continuous pursuit of knowledge in the field of computer science and engineering.

Publication Top Notes:

1.Ā  Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

2.Ā  Handbook of Research on Advancements in AI and IoT Convergence Technologies

3.Ā  Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises

4.Ā  A Review of Mobile Cloud Applications, Offloading Methods, and Technologies in the Smartphone’s

5.Ā  Retire away essential accuracy for darkness discovery and elimination