Selvakumaran | EEE | Best Researcher Award

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Dr. Selvakumaran | EEE | Best Researcher Award

Dr. S. Selvakumaran is an accomplished academician with 15 years of experience in engineering education, specializing in power electronics, smart grids, and renewable energy systems. He has held key institutional roles, including NBA, NAAC, Exam Cell, and ISO Coordinator. With a strong research focus on green energy applications, he has published in reputed journals and conferences. His expertise spans control systems, electrical machines, and metaheuristic optimization techniques for power converters. He has guided over 80 UG and 15 PG students, contributing significantly to academia.

Profile

Education 🎓

Dr. Selvakumaran earned his Ph.D. in Electrical Engineering from Anna University in 2024, focusing on optimization-based converters for green energy. He completed his M.E. in Power Electronics and Drives from Government College of Engineering, Tirunelveli (2009) with 73% and his B.E. in Electrical and Electronics Engineering from Dhanalakshmi Srinivasan Engineering College, Perambalur (2007) with 72%. His academic journey reflects his deep commitment to power electronics and renewable energy research.

Experience 👨‍🏫

Dr. Selvakumaran served as an Assistant Professor at Dhanalakshmi Srinivasan Engineering College, Perambalur (2009-2020), mentoring students in electrical engineering. From 2021 to 2024, he was a full-time research scholar at Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, focusing on optimization algorithms for energy applications. His expertise includes NBA and NAAC accreditation, exam cell coordination, and institutional quality management.

Research Interests 🔬

Dr. Selvakumaran’s research focuses on metaheuristic optimization algorithms for power converters, renewable energy integration, and smart grids. He has worked extensively on hybrid energy systems, solar PV optimization, and intelligent power management. His studies include grid-connected electric vehicle systems, power quality improvement, and AI-driven energy optimization, contributing to sustainable energy solutions.

Dr. Selvakumaran has been recognized for his contributions to electrical engineering through best paper awards at national and international conferences. His publications in high-impact journals like the Journal of Energy Storage (IF: 8.9) and IETE Journal of Research (IF: 1.59) highlight his research excellence. He has received appreciation for mentoring students and serving in key academic roles, enhancing institutional accreditation standards.

Publications 📚

  • Optimal planning of photovoltaic, wind turbine and battery to mitigate flicker and power loss in distribution network

    Journal of Energy Storage
    2025-04 | Journal article
    DOI: 10.1016/j.est.2025.116034
    Part ofISSN: 2352-152X
    CONTRIBUTORS: G. Muralikrishnan; K. Preetha; S. Selvakumaran; P. Hariramakrishnan

  • A hybrid approach for PV based grid tied intelligent controlled water pump system

    International Journal of Adaptive Control and Signal Processing
    2024 | Journal article
    DOI: 10.1002/acs.3748
    EID:

    2-s2.0-85184388143

    Part ofISSN: 10991115 08906327
    CONTRIBUTORS: Selvakumaran, S.; Baskaran, K.

  • A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

    IETE Journal of Research
    2024 | Journal article
    DOI: 10.1080/03772063.2024.2352644
    EID:

    2-s2.0-85198697794

    Part ofISSN: 0974780X 03772063
    CONTRIBUTORS: Selvakumaran, S.; Baskaran, K.

  • Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

    Journal of Renewable and Sustainable Energy
    2024 | Journal article
    DOI: 10.1063/5.0220051
    EID:

    2-s2.0-85208946652

    Part of ISSN: 19417012
    CONTRIBUTORS: Muralikrishnan, G.; Preetha, K.; Selvakumaran, S.; Nagendran, J.

Kaveri Hatti | Engineering| Women Researcher Award

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Mrs. Kaveri Hatti | Engineering| Women Researcher Award

 

 

Profile

Education

averi Hatti is a dedicated researcher and educator in the field of VLSI Design, Embedded Systems, and Hardware Security. She is currently pursuing a Ph.D. at Amrita School of Engineering, Bangalore, focusing on FPGA-based security architectures. With a strong academic background, she holds an M.Tech in VLSI Design and Embedded Systems from VTU Regional Office, Gulbarga, and a B.Tech in Electronics and Communication Engineering from SLN College of Engineering, Raichur.

Kaveri has extensive teaching experience, having served as a Lecturer at Tagore Memorial Polytechnic College and Government Polytechnic College in Raichur before joining Amrita School of Engineering, Bangalore, as a Teaching Assistant in 2022. Her expertise lies in FPGA design, Verilog, RTL design, and hardware security implementations, utilizing tools like Xilinx ISE, VIVADO, ModelSim, and Cadence.

 

Work experience

Kaveri Hatti has a strong background in academia, with extensive teaching experience spanning several years. She began her career as a Lecturer at Tagore Memorial Polytechnic College, Raichur, from August 2009 to December 2012, where she played a key role in instructing and mentoring students in electronics and communication engineering. Simultaneously, she also served as a Lecturer at Government Polytechnic College, Raichur, from June 2009 to June 2011. Currently, she is working as a Teaching Assistant at Amrita School of Engineering, Bangalore, since February 2022, contributing to research and assisting in the academic development of students in the field of VLSI Design and Embedded Systems.

Publication

Dingming Wu | Computer Science | Best Researcher Award

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Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.