Dr. Saul Solorio Fernández | Computational Intelligence | Best Researcher Award | 2048

Dr. Saul Solorio Fernández | Computational Intelligence | Best researcher Award

Dr. Saul Solorio Fernández is a distinguished academic and researcher in the field of renewable energy, holds a PhD in Biosystems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

Professional Profiles:

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Professional Goals

As a specialist in developing pattern recognition and data mining algorithms, I bring over 10 years of experience to the field. Collaborating with various Mexican educational and government institutions like UNAM, INNEL, SEMAR, and CFE, I’m recognized for teamwork and adaptability in diverse projects.

Computer Skills

Operating Systems: Linux, MacOS, Windows Programming Languages: Java, Python, C Mathematical Software: Matlab, Octave, Mathematica Data Mining Libraries and Frameworks: Weka, RapidMiner, Orange, SMILE, Rattle, Encog, scikit feature, Scikit Learn Office Suites and Document Preparation Systems: Latex, Microsoft Office, iWork Productivity Suite, LibreOffice Reference Management Software: Mendeley, Zotero, JabRef, KBibteX

Academic Background

Ph.D. in Computer Science from INAOE | January 2016-June 2020 M.Sc. in Computer Science from INAOE | August 2008-November 2010 BSc. in Mathematics (Computing area) from Universidad Autónoma de Guerrero (UAGRO) | August 2002-February 2006

Work Experience

Advisor and collaborator in the Smart Electric Networks and Micro Grids project | 2021 Scientific advisor and developer in the system proposal for Volkswagen Puebla | 2020 Data scientist and developer in the project for preventing atmospheric electric discharges | 2015-2016 Scientific advisor, data analyst, and programmer in various projects for institutions like SEMAR, Mexican Navy, and CFE | 2012-2015

Courses and Accomplishments

Completed various courses on computational thinking, web scraping, OOP, data engineering, and data science. Proficient in Spanish (Native) and English (TOEFL score: 556)

Hobbies

Passionate about chess Plays guitar and piano, enjoys singing Enjoys swimming, athletics, cycling, exploring cultures, and traveling

Additional Information

Date of Birth: November 08, 1983 Nationality: Mexican Availability for travel or changing residence: Yes Academic activities include teaching experience, online course instruction, and presentations at seminars.

Academic Activities and Professional Services

Full-time Professor/Researcher at Universidad Tecnológica de la Mixteca (UTM) Instructor in online courses related to machine learning and data analysis Engaged in teaching, research, and contributions to international journals as a reviewer.

Other Professional/Academic Activities and Awards

Member of the National System of Researchers (SNI) from 2021-2025 Honorable Mention for academic achievements Supervised Bachelor Theses and participated in academic exchanges at various international universities.

Publications Top Note :

Dr. Zepeng Liu | Fault Diagnosis

Dr. Zepeng Liu | Fault Diagnosis | Best researcher Award

Dr. Zepeng Liu is a distinguished academic and researcher in the field of renewable energy, holds a PhD in Biosystems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

Professional Profiles:

google scholar

orcid 

Dr. Zepeng Liu

Institute School of Engineering, Newcastle University, UK Position  Lecturer in Electrification

Working Experience:

2023/09-Present  Lecturer in Electrification at Newcastle University, UK 2020/12-2023/09 Research Associate in Advanced Manufacturing Systems Condition Monitoring, University of Sheffield, UK 2020/5-2020/8 Research Associate, University of Manchester, UK

Education Background:

2017/1-2021/2: Ph.D. in Electrical & Electronic Engineering, University of Manchester, UK 2015/9–2016/9 MSc in Power Systems Engineering, University College London, UK 2013/9–2015/6  BEng in Electrical Engineering & Electronics, University of Liverpool, UK

Research Interests:

Data-Driven Modelling:

Nonlinear system modelling in the frequency domain

Machine and statistical learning, Neural networks

Sparse representation and nonlinear filtering

Modelling and analysis for complex systems:

Advanced manufacturing

Condition monitoring and fault detection of wind turbine systems and components

Structural health monitoring

Smart structures and systems

Application of machine learning to machinery fault diagnosis

Research Experience:

Dr. Liu has extensive experience in research projects involving advanced manufacturing, condition monitoring, fault diagnosis, and machine learning applications. Notable achievements include developing online monitoring systems for cutting tool condition monitoring and fault diagnosis, and creating novel algorithms for real-time CMFD in large slow-speed pitch systems.

Teaching Experience:

Dr. Liu has been a Teaching Assistant for various MSc courses at the University of Manchester since 2016. Additionally, he supervises PhD students and undergraduate/postgraduate projects.

Professional Activities and Recognitions:

 

Review Editor of Frontiers in Robotics and AI Reviewer for learned journals in various science and engineering subject areas

Grant Application Experience:

Participation in grant applications for projects related to wind turbine condition assessment and machine-tool condition monitoring.

Selected Publications:

Dr. Liu has contributed to several high-impact journal papers and conference papers focusing on fault detection, condition monitoring, and machine learning applications in various fields including wind turbine systems, cutting tools, and vibration analysis.

This summary highlights Dr. Liu’s extensive academic background, research contributions, teaching experience, and involvement in professional activities and grant applications.

📊 Citation Metrics (Google Scholar):

  • Citations by: All – 721, Since 2018 – 716
  • h-index: All – 9, Since 2018 – 8
  • i10 index: All – 8, Since 2018 – 7

Publications Top Note :

paper published in 2020 cite by 37

Naturally damaged wind turbine blade bearing fault detection using novel iterative nonlinear filter and morphological analysis  paper published in 2019 cite by 36

paper published in 2022 cite by 9

Wavelet Package Energy Transmissibility Function and Its Application to Wind Turbine Blade Fault Detection

paper published in 2022 cite by 8