Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Dr. Sangyeop Lee, LG Electronics, South Korea

Sangyeop Lee, Ph.D., is a seasoned Senior Researcher and Data Scientist at LG Electronics, currently based at the Life Data Fusion Laboratory within the B2B Advanced Technology Center in Seoul, Republic of Korea. With a robust academic background, including a Ph.D. in Computer Science from Yonsei University, Sangyeop has been actively involved in both research and academia. His research interests span various domains, notably including LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness in the clinical domain using machine learning techniques. Throughout his career, he has contributed significantly to cutting-edge projects such as Smartcare in Kindergarten and neptuNE, addressing critical issues like child behavior monitoring and home healthcare. Sangyeop’s expertise extends to teaching and mentoring, evident from his engagements as a lecturer and teaching assistant at Yonsei University. His dedication to advancing technology and solving real-world problems underscores his commitment to innovation in the fields of data science and healthcare.

Orcid

Affiliation:

Sangyeop is currently affiliated with the LEAD technology task at the Life Data Fusion Laboratory within the B2B Advanced Technology Center at LG Electronics, located in Seocho R&D Campus, Seoul, Republic of Korea.

Research Interests:

His research interests include LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness using machine learning techniques in clinical settings.

Teaching Experience:

Sangyeop has contributed to education as a lecturer and teaching assistant at Yonsei University, covering subjects like AI for Medical Problems and Engineering Information Processing, where he taught Python practice.

Projects:

Smartcare in Kindergarten: Collaborated with DNX Kidsnote and Severance Hospital to utilize AI technology in studying children’s behavior and location in kindergartens using wearables/radars.
neptuNE: Developed sensors and mobile devices for home monitoring, addressing nocturnal enuresis in children, in collaboration with Samsung Electronics and Severance Hospital.
Ready-Made Implant: Conducted a confidential study on mass production with pre-made implants and recommending customized implant models through dental data analysis, in collaboration with Ostem Implant and Yonsei University.

Publications:

Sangyeop has several publications in prestigious conferences and journals, including IEEE Radar Conference and Sensors, focusing on topics like artificial intelligence, biomedical engineering, and healthcare.

Application:

Sangyeop has contributed to the development of in-home monitoring with wearables and NE Diary Application, enhancing healthcare solutions through technology.

Sangyeop’s dedication to advancing data-driven solutions in healthcare underscores his commitment to innovation and improving patient outcomes. 🌟

Publications Notes:📄

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Continuous body impedance measurement to detect bladder volume changes during urodynamic study: A prospective study in pediatric patients

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