DongHee Park | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

DongHee Park
DAVISS, South Korea

DongHee Park
Affiliation DAVISS
Country South Korea
Documents 8
Subject Area Data Science and Analytics
Event International Soil Scientist Awards
ORCID 0009-0008-0010-1756

DongHee Park is a researcher affiliated with DAVISS in South Korea whose documented scholarly work applies data science and machine-learning methods to industrial fault diagnosis. The available record contains eight publications addressing condition-index representations, diagnostic automation, convolutional neural networks and feature classification for machinery faults. [1]

Abstract

Park’s research focuses on computational approaches for identifying and classifying faults in industrial machinery. Recent work compares conventional feature-based representations with physics-guided condition-index representations for motor fault diagnosis, while related studies investigate automated elevator diagnosis, rotor-system classification and machine-learning methods for blade-rubbing defects. [1] [2] The research demonstrates a consistent interest in translating operational data into diagnostic information.

Keywords

Data science; machine learning; fault diagnosis; condition monitoring; industrial motors; diagnostic automation; convolutional neural networks; rotor systems; blade rubbing; feature classification; predictive maintenance.

Introduction

Modern industrial systems generate large quantities of operational data that can be analysed to detect abnormal behaviour. Machine-learning algorithms and engineered diagnostic features provide methods for distinguishing normal operation from specific fault conditions. Park’s publication record addresses these challenges across several machinery categories, linking data analytics with practical condition-monitoring problems. [3]

Research Profile

The supplied profile records eight documents in the subject area of Data Science and Analytics. Citation and h-index values were not provided and therefore are not inferred. The publication sequence from 2024 to 2026 indicates sustained investigation of machine-learning feature development, classification methods, automated diagnosis and physics-guided representations.

Research Contributions

  • Evaluation of conventional and physics-guided representations for industrial motor fault diagnosis. [1]
  • Development of rule-based automation technology for elevator fault diagnosis. [2]
  • Use of combination images of feature vectors with convolutional neural networks for rotor fault classification. [3]
  • Development and classification of machine-learning features for early blade-rubbing diagnosis. [4] [5]

Publications

Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis (2026), Machines. [1]

Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis (2025), Sensors. [2]

CNN-based fault classification using combination image of feature vectors in rotor systems (2024), Journal of Mechanical Science and Technology. [3]

A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing (2024), Sensors.[4]

Development of features for blade rubbing defect classification in machine learning (2024), Journal of Mechanical Science and Technology.[5]

Research Impact

Because citation and h-index values were not supplied, quantitative scholarly impact cannot be assessed from the available profile alone. The publication record nevertheless demonstrates application of data-driven techniques to multiple industrial systems, including motors, elevators, rotor systems and machinery affected by blade rubbing. Publication across Machines, Sensors and the Journal of Mechanical Science and Technology provides a documented scholarly context for these contributions. [1] [3]

Award Suitability

The Innovative Research Award profile is supported by a coherent body of research applying machine learning and analytical representations to industrial fault diagnosis. The progression from feature development and classification toward automated diagnostic systems and physics-guided condition indices indicates methodological continuity. [2] [5] Final recognition should be determined using the complete scholarly record, originality, methodological quality, practical relevance, reproducibility and broader research contributions.

Conclusion

DongHee Park’s documented research is centred on data science and analytics for industrial condition monitoring and fault diagnosis. Publications from 2024 through 2026 cover machine-learning features, convolutional neural networks, rule-based automation and physics-guided diagnostic representations. This focused research trajectory provides a substantive basis for consideration for the Innovative Research Award within the International Soil Scientist Awards framework.

References

  1. Park, DongHee. (2026). Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis. Machines.
    https://doi.org/10.3390/machines14091024
  2. Park, DongHee. (2025). Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis. Sensors.
    https://doi.org/10.3390/s26010223
  3. Park, DongHee. (2024). CNN-based fault classification using combination image of feature vectors in rotor systems. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-024-1006-z
  4. Park, DongHee. (2024). A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing. Sensors.
    https://doi.org/10.3390/s24186013
  5. Park, DongHee. (2024). Development of features for blade rubbing defect classification in machine learning. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-023-1201-3

Aqsa Zafar Abbasi | Data Science | Women Researcher Award

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Ms. Aqsa Zafar Abbasi | Data Science | Women Researcher Award

Ms. Aqsa Zafar Abbasi, Institute of Space Technology, Pakistan

Ms. Aqsa Zafar Abbasi is a PhD candidate in Mathematics at the Institute of Space Technology, Pakistan, specializing in group theory, cryptography, and mathematical modeling. Her research focuses on the parametric exploration of homomorphisms of modular groups for finite generalized triangle groups, with applications in image encryption, substitution box design, and neural network-based cryptographic security. With six years of teaching experience, she has served as a visiting lecturer at multiple institutions, teaching courses in differential equations, discrete mathematics, graph theory, and engineering mathematics. She has published extensively in high-impact journals, contributing to research in cryptography, fuzzy algebra, and numerical simulations. Aqsa is proficient in MATLAB, Python, R, and computational intelligence techniques, making her a dedicated researcher in the fields of mathematics and data security.

Profile

Scopus

Summary:

Ms. Aqsa Zafar Abbasi is an exceptional researcher in mathematical cryptography, data security, and computational mathematics. Her significant contributions to encryption techniques, strong publication record, and interdisciplinary expertise make her a strong candidate for the Research for Women Researcher Award. While she already has an impressive academic and research background, expanding collaborations, engaging in industry applications, and securing research funding could further elevate her contributions.

 

🎓 Education

Aqsa Zafar Abbasi is currently pursuing a PhD in Mathematics at the Institute of Space Technology, Islamabad, Pakistan, with an expected completion date in February 2025. Her research focuses on the parametric exploration of homomorphisms of modular groups for finite generalized triangle groups, with applications in cryptography and neural network-based encryption. She holds an MS in Mathematics from the same institution, completed in 2021, where she explored algebraic characteristics of bipolar-valued fuzzy subgroups and their applications in decision-making and S-box analysis. She also earned an MSc in Mathematics from PMAS Arid Agriculture University, Rawalpindi, with an excellent academic record.

 

💼Experience

With six years of teaching experience, Aqsa has served as a visiting lecturer at prestigious institutions, including Pir Mehr Ali Shah Arid Agriculture University, National Defence University, Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (SZABIST), and the Institute of Space Technology, Islamabad. She has taught differential equations, discrete mathematics, graph theory, real analysis, linear algebra, and engineering mathematics to students in mathematics, software engineering, and computer science programs.

 

🔬Research Focus

Aqsa’s research expertise includes group theory, cryptography, algebraic geometry, numerical solutions of PDEs, and machine learning applications in mathematical modeling. She specializes in highly secure substitution box construction for encryption, particularly for image encryption and watermarking, using parameterized homomorphisms of modular groups. Additionally, she integrates neural networks with encryption schemes to enhance data security. Her research also extends to bipolar-valued fuzzy set theory, focusing on its applications in decision-making and cryptographic analysis.

Awards

Aqsa has gained recognition for her contributions to cryptography, algebraic structures, and numerical analysis, with publications in renowned scientific journals. Her research on mathematical modeling and encryption techniques has positioned her as a dedicated scholar in her field.

Skills

Aqsa possesses a diverse skill set, including mathematical modeling, computational intelligence, programming (C++, Python, MATLAB, R, Wolfram Mathematica), research writing, editing, and proofreading. She is also skilled in seminar organization, event management, leadership, and teamwork, making her a well-rounded academic professional.

 

Publications

  • Publication: A digital audio data protection method using parametric action of generalised triangle group on GF(28)
    Authors: A.Z. Abbasi (Aqsa Zafar Abbasi), A. Rafiq (Ayesha Rafiq), B.M. Alshammari (Badr Mesned)
    Journal: Ain Shams Engineering Journal
    Year: 2025

 

Conclusion:

Ms. Aqsa Zafar Abbasi is a highly qualified and deserving candidate for the Research for Women Researcher Award. Her expertise in mathematical cryptography, interdisciplinary research, and teaching makes her a valuable asset to the academic community. With continued research, international collaborations, and industry engagement, she has the potential to make even greater contributions to her field.