Dr. Longbin Jin | Signal Processing Awards | Best Researcher Award

Posted on by ScienceFather

Dr. Longbin Jin | Signal Processing Awards | Best Researcher Award 

Dr. Longbin Jin, Konkuk University, South Korea

Longbin Jin, is a Ph.D. candidate in Computer Science at Konkuk University, Korea, with an expected graduation in February 2025. His research focuses on adaptive visual prompting for video action recognition in vision-language models under the guidance of Professor Eun Yi Kim. He holds a Master’s degree in Smart ICT Convergence and a Bachelor’s degree in Mechanical Engineering & Automation from Shanghai University, China. Throughout his academic career, Longbin has received numerous accolades, including winning the ICASSP 2023 SPGC Challenge and multiple Excellence and Encouragement Prizes at the Korea Software Congress. Currently, he serves as an AI Researcher at Voinosis in Seoul, where he develops AI models for early detection of hearing loss and cognitive impairment in the elderly. He is also an instructor at Konkuk University, teaching courses on Artificial Intelligence, Computer Vision, and Machine Learning. His project experience includes collaborations on medical imaging and virtual reality, demonstrating his expertise in applying AI technologies across diverse fields. Longbin is proficient in English, Chinese, and Korean, reflecting his international background and commitment to advancing technology in healthcare and education.

Professional Profile:

GOOGLE SCHOLAR

Research for Community Impact Award: Longbin Jin’s Suitability

Longbin Jin is a highly qualified candidate for the Research for Community Impact Award due to his significant contributions in the fields of artificial intelligence and healthcare, particularly in projects that directly benefit the community.

📚 Education

  • Ph.D. in Computer Science
    Konkuk University, Korea
    Expected: February 2025
    Thesis: Adaptive Visual Prompting for Video Action Recognition in Vision-Language Models
    Advisor: Prof. Eun Yi Kim
  • M.S. in Smart ICT Convergence
    Konkuk University, Korea
    Graduated: August 2020
    Thesis: E-EmoticonNet: EEG-based Emotion Recognition with Context Information
    Advisor: Prof. Eun Yi Kim
  • B.S. in Mechanical Engineering & Automation
    Shanghai University, China
    Graduated: August 2018

💼 Work Experience

  • AI Researcher
    Voinosis, Seoul, Korea
    December 2022 – Present

    • Researcher on AI models for early detection of hearing loss and cognitive impairment based on voice analysis for the elderly (VoiceCheck & BrainGuardDoctor Apps).
  • Instructor
    Konkuk University, Seoul, Korea
    March 2022 – Present

    • Teaching courses on Computer Vision, Artificial Intelligence, and Machine Learning.
  • AI Engineer
    Lulla, Seoul, Korea
    October 2022 – November 2022

    • Main researcher for an AI model for a child face-matching system to assist kindergarten teachers (Lulla App).

🏆 Achievements, Awards, and Honors

  • Winner of ICASSP 2023 SPGC Challenge: Multilingual Alzheimer’s Dementia Recognition through Spontaneous Speech (First Author) 🥇
  • Excellence Prize, Korea Software Congress 2023 🥇
  • Encouragement Prize, ACM Student Research Competition, Computer Human Interaction 2020 (First Author) 🎖️
  • Excellence Prize, Korea Software Congress 2019 (First Author) 🏅
  • Encouragement Prize, Korea Software Congress 2019 (First Author) 🎖️
  • Excellent Presentation, International Conference on Culture Technology 2018 🌟

Publication Top Notes:

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features†

CITED:29

Consen: Complementary and simultaneous ensemble for alzheimer’s disease detection and mmse score prediction

CITED:15

Eeg-based user identification using channel-wise features

CITED:7

E-EmotiConNet: EEG-based emotion recognition with context information

CITED:2

Emotion Recognition based BCI using Channel-wise Features

CITED:1

 

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Posted on by ScienceFather

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Assist Prof Dr. Hwa-Dong Liu, Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taiwan

Hwa-Dong Liu is an Assistant Professor at National Taiwan Normal University (NTNU) in Taipei, Taiwan, specializing in power electronics, microcontrollers, rail vehicle power systems, and solar power systems. He holds a Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST). His research interests include the development of advanced power converters, control strategies for renewable energy systems, and innovative solutions for electric vehicle charging. Dr. Liu has authored numerous papers in reputable journals, with a focus on improving the efficiency and performance of power electronic systems and renewable energy technologies. His recent work includes contributions to energy management systems, high-gain boost converters, and novel MPPT algorithms for solar power generation.

Professional Profile:

Summary of Suitability for Best Researcher Award 

Hwa-Dong Liu has expertise in several cutting-edge fields including power electronics, microcontrollers, rail vehicle power systems, and solar power systems. This diversity indicates a broad impact on multiple important areas of research.

Education

  • Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST).

Work Experience

  • Assistant Professor at National Taiwan Normal University (NTNU).

Expertise

  1. Power Electronics
  2. Microcontroller
  3. Rail Vehicle Power Systems
  4. Solar Power Systems

Publication top Notes:

An improved solar step-up power converter for next-generation electric vehicle charging

Hybrid Management Strategy for Outsourcing Electromechanical Maintenance and Selecting Contractors in Taipei MRT

An Improved High Gain Continuous Input Current Quadratic Boost Converter for Next-Generation Sustainable Energy Application

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

High-Voltage Autonomous Current-Fed Push-Pull Converter with Wireless Communication Applied to X-Ray Generation

 

 

 

Fengshou Gu | Signal Processing Award | Best Researcher Award

Posted on by ScienceFather

Prof Dr. Fengshou Gu | Signal Processing Award | Best Researcher Award

Professor at University of Huddersfield – The Institute of Railway Research (IRR) – Huddersfield, United Kingdom

Professor Fengshou Gu is a highly accomplished researcher and academic with a distinguished career in the field of condition monitoring and diagnostics. With over 30 years of experience, he has made significant contributions to developing advanced monitoring and diagnostic techniques, numerical simulation methods, and signal processing techniques. His research has focused on various areas, including machine modeling, fault diagnosis, energy harvesting, and wireless sensor networks. Professor Gu’s work has been published in numerous prestigious journals, and he has presented his research at international conferences. He has also supervised over 100 PhD students and examined many more worldwide. Overall, Professor Gu’s expertise, innovative research, and dedication to advancing the field of condition monitoring and diagnostics make him a highly respected figure in the academic and research community.

Professional Profile

Education:

Professor Fengshou Gu’s academic journey began at Taiyuan University of Technology in Shanxi, China, where he earned his Bachelor of Science (B.S.) in Mechanical Engineering, graduating in September 1979. He continued his studies at the same institution, completing his Master of Science (M.Sc.) in the Mechanical Department from January 1981 to March 1985. Professor Gu pursued his doctoral studies at the University of Manchester, United Kingdom, where he obtained his Doctorate (Dr.) from the School of Mechanical Engineering from August 2004 to September 2008.

Work Experiences:

Professor Fengshou Gu has accumulated a wealth of experience throughout his career, starting with his tenure as a Lecturer in Vibration and Acoustics at Taiyuan University of Technology, China, from January 1985 to June 1991. Following this, he served as a Research Engineer at the University of Manchester, U.K., from July 1991 to October 1996. His role evolved to Senior Research Engineer at the same institution, where he continued his impactful work until September 2007. Since then, Professor Gu has held the positions of Principal Research Fellow, Professor, Head of MDARG (Machine Diagnostics, Dynamics, and Artificial Intelligence Research Group), and Deputy Director of CEPE (Centre of Excellence for Precision Engineering), solidifying his reputation as a leading expert in condition monitoring and diagnostics.

Skills:

Professor Fengshou Gu possesses a diverse range of skills that have been instrumental in his research and academic endeavors. He is proficient in numerical analysis, particularly in the context of friction stir welding, as evidenced by his review publications in this area. His expertise also extends to predictive modeling for biodiesel properties and their impact on engine performance, highlighting his strong background in engineering analysis and modeling. Additionally, Professor Gu has a deep understanding of machine condition monitoring, demonstrated by his work on energy harvesting technologies for self-powered wireless sensor networks and his research on diesel engine combustion characteristics. His skills also encompass signal processing techniques, including acoustic measurements and independent component analysis for fault diagnosis in mechanical equipment. Professor Gu’s proficiency in thermal imaging enhancement and modal analysis further underlines his expertise in machinery fault diagnosis. Overall, his skills in numerical analysis, predictive modeling, condition monitoring, and signal processing have contributed significantly to his impactful research contributions.

Achievements:

Professor Fengshou Gu has achieved numerous milestones in the field of condition monitoring and diagnostics, showcasing his exceptional expertise and innovative contributions. He has developed groundbreaking techniques such as single-channel Blind Source Separation (BSS) for acoustic source separation and the MSB-SE nonlinear modulation analysis theory, which have significantly advanced the field. His pioneering work on On-Rotor Sensing (ORS) based dynamic measurement and analysis theory has revolutionized dynamic measurement approaches. Professor Gu’s research has also led to the establishment of vibro-acoustic models (AAC, FAS) for tribological systems and diagnostic approaches, enhancing the understanding and diagnosis of complex machinery. Additionally, he has made significant contributions to online Operational Modal Analysis (OMA) with his Correlation Signal Cluster-based Stochastic Subspace Identification (CSC-SSI) method, applicable to both linear and nonlinear systems. Professor Gu’s innovative work extends to the development of instantaneous electric signature analysis for motor-driven system monitoring, nonlinear dynamic-based energy harvesting concepts, and thermal energy-based self-powered wireless sensor networks, showcasing his commitment to advancing sustainable and efficient monitoring technologies. His research on the nonlinear temperature field distribution of infrared thermal images for machine condition and performance monitoring has further demonstrated his pioneering approach to condition monitoring. Furthermore, Professor Gu has developed remote modal identification techniques based on photogrammetry analysis, highlighting his multidisciplinary and innovative research efforts.

Publications:

A review of numerical analysis of friction stir welding

Authors: X He, F Gu, A Ball

Citations: 542

Year: 2014

Prediction models for density and viscosity of biodiesel and their effects on fuel supply system in CI engines

Authors: B Tesfa, R Mishra, F Gu, N Powles

Citations: 278

Year: 2010

The measurement of instantaneous angular speed

Authors: Y Li, F Gu, G Harris, A Ball, N Bennett, K Travis

Citations: 230

Year: 2005

Energy harvesting technologies for achieving self-powered wireless sensor networks in machine condition monitoring: A review

Authors: X Tang, X Wang, R Cattley, F Gu, AD Ball

Citations: 216

Year: 2018

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

Authors: P Charles, JK Sinha, F Gu, L Lidstone, AD Ball

Citations: 205

Year: 2009

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Authors: Z Wang, G Feng, D Zhen, F Gu, A Ball

Citations: 197

Year: 2021

Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring

Authors: M Elhaj, F Gu, AD Ball, A Albarbar, M Al-Qattan, A Naid

Citations: 196

Year: 2008

Combustion and performance characteristics of CI (compression ignition) engine running with biodiesel

Authors: B Tesfa, R Mishra, C Zhang, F Gu, AD Ball

Citations: 185

Year: 2013

Water injection effects on the performance and emission characteristics of a CI engine operating with biodiesel

Authors: B Tesfa, R Mishra, F Gu, AD Ball

Citations: 185

Year: 2012

A study of the noise from diesel engines using the independent component analysis

Authors: W Li, F Gu, AD Ball, AYT Leung, CE Phipps

Citations: 183

Year: 2001