Shijie Wang | Robotics | Best Researcher Award

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Dr. Shijie Wang | Robotics | Best Researcher Award

Dr. Shijie Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, China

Dr. Shijie Wang is a PhD Candidate at Hebei University of Technology and a joint researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. With a background in engineering (B.Eng, M.Eng), Dr. Wang’s research focuses on robotics and mechanical systems. He has authored impactful publications in Applied Mathematical Modelling and contributed to several patents on construction robotics and high-load manipulators. Dr. Wang has won multiple awards, including the China “Challenge Cup” and 3D Digital Innovative Design Competition. His work on construction robotics has earned substantial funding, highlighting his innovative contributions. 🤖🔧📚📑

 

Publication Profile

Scopus

Orcid

Academic & Professional Qualifications

Dr. Shijie Wang is a PhD candidate at Hebei University of Technology and a joint training researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He holds a B.Eng and M.Eng from Hebei University of Technology. His academic journey has been complemented by professional experience, including his role as a research assistant at the Shenzhen Institutes and as a chief process engineer at Beijing Jingdiao Group. Dr. Wang has also worked as an R&D engineer at Shinetek Instruments Research Institute. His diverse expertise contributes significantly to advancements in engineering and robotics. 🎓🔬🤖

 

Representative Works and Awards

Dr. Shijie Wang has made significant contributions to the fields of robotics and mechanical engineering. His recent publication, Unified Recursive Kinematics and Statics Modeling (2024) in Applied Mathematical Modelling (IF 4.4), presents innovative work on high-load manipulators. Other notable publications include work on flapping-wing micro air vehicles (2023) and kinematic analysis of parallel manipulators (2022). He holds several patents, including inventions in construction robots and hydraulic manipulators. Dr. Wang has received prestigious awards, including the second prize in the 2015 China “Challenge Cup” and first prize in the 2014 3D Digital Innovative Design Competition. 🏆📚🤖

 

Research Focus

Dr. Shijie Wang’s research focuses on robotics, automation, and advanced manufacturing technologies. He explores kinematics and statics modeling of manipulators, with applications in redundantly actuated systems and functionally graded materials (FGMs). His work includes the optimization of robotic systems for construction and manufacturing, path planning strategies for 3D printing, and the modeling of dynamic mixing processes for materials fabrication. Dr. Wang is also deeply involved in machine learning applications in design and fabrication. His research has significant implications in construction robotics, material science, and robotic systems design. 🤖🔧📐📊

 

Publication Top Notes

  • Unified recursive kinematics and statics modeling of a redundantly actuated series-parallel manipulator with high load/mass ratio (2024) 🛠️
  • Process parameter modeling for the fabrication of functionally graded materials via direct ink writing (2024) 🖨️
  • Optimization of Pin Type Single Screw Mixer for Fabrication of Functionally Graded Materials (2024) 🔧
  • Numerical Simulation of a Dynamic Mixing Process of Ceramic-Grade Materials for Extruded 3D Printing (2023) 🏗️
  • Path planning strategy of functionally graded materials printed by material extrusion process (2023) 🌐
  • A Review: Applications of Machine Learning in Design-Fabrication of Functionally Graded Materials (2023) 🤖
  • Attitude Control of Flapping-Wing Micro Air Vehicles Based on Hyperbolic Tangent Function Sliding Mode Control (2023) ✈️
  • Digital prediction method for delay information for preparing FGMs parts by direct write forming (2023) ⏱️
  • Functionally graded materials model is constructed by B-spline surface and point gradient source (2022) 🧱
  • Kinematic Performance Analysis of Spatial 2-DOF Redundantly Actuated Parallel Manipulator (2022) 🔄

K ASHWINI | Computer Science | Best Researcher Award

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K ASHWINI | Computer Science | Best Researcher Award

K ASHWINI, National Institute of Technology Rourkela, India

K. Ashwini is a dedicated Ph.D. candidate in Computer Science and Engineering at NIT Rourkela, specializing in deep learning applications for grading diabetic retinopathy. She holds an M.Tech. from VSSUT Burla and a B.Tech. from Synergy Institute of Engineering & Technology, Dhenkanal. Her research includes notable publications, such as her work on CNN-based diabetic retinopathy grading in Biomedical Signal Processing and Control. Skilled in Python, MATLAB, and LaTeX, she has actively participated in workshops on machine learning and signal processing. Ashwini is fluent in Hindi, Telugu, and English.

Publication profile

google scholar

Academic Background

Ms. K. Ashwini is a Research Scholar in Computer Science and Engineering (CSE) at NIT Rourkela, currently pursuing her Ph.D., with her research focused on diabetic retinopathy grading using deep learning techniques. Her advanced studies in deep learning, combined with an M.Tech. in CSE from VSSUT Burla, highlight her dedication to exploring complex topics within biomedical and computational research. She has maintained a strong academic record throughout her studies, underscoring her commitment and expertise in her field.

Research Focus and Publications

Ashwini’s primary research area is in biomedical signal processing, specifically targeting diabetic retinopathy grading using CNNs and soft attention mechanisms. She has contributed a journal article to Biomedical Signal Processing and Control and presented multiple conference papers at reputable IEEE and Springer conferences, indicating her active participation in disseminating her research findings. Notably, her publications demonstrate her capacity to employ and innovate with advanced computational methods for impactful health-related applications, a relevant focus for this award.

Technical Skills and Training

Her technical skill set, including Python, MATLAB, and LaTeX, complements her research competencies. Ashwini’s training in SQL and experience with clustering and fraud detection in mobile networks contribute to a robust and versatile research portfolio. Her academic research skills and fluency in programming languages further solidify her qualifications as a proficient researcher in her domain.

Workshops and Professional Development

Ms. Ashwini has participated in several workshops and short-term training programs across India, including those focused on biomedical signal processing, machine learning, and image processing applications. Her engagement in diverse professional development initiatives, such as faculty development programs and national seminars, showcases her continuous effort to enhance her knowledge base and technical skills.

Publication top notes

Grading diabetic retinopathy using multiresolution based CNN

Soft attention with convolutional neural network for grading diabetic retinopathy

Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks

Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

Check for updates Modified Inception V3 Using Soft Attention for the Grading of Diabetic Retinopathy

Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

Grading of Diabetic Retinopathy using iterative Attentional Feature Fusion (iAFF)

Conclusion

Ms. K. Ashwini exemplifies a suitable candidate for the Research for Best Researcher Award. Her specialized research in diabetic retinopathy grading, supported by a solid academic and technical background, positions her as a promising researcher. Her publications and active participation in workshops further validate her dedication and contributions to biomedical signal processing and computer vision applications, aligning well with the award’s criteria for excellence in research and innovation.

Inam Ullah | Robotics Award | Young Scientist Award

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Prof Dr. Inam Ullah | Robotics Award | Young Scientist Award

Prof Dr. Inam Ullah, Shenzhen University, China

Based on the details provided, the candidate seems well-suited for the Research for Young Scientist Award. Here’s an evaluation of their profile, formatted with headings and conclusions for each section:

Publication profile

Professional Experience

The candidate has a robust professional background in research and academia. Their current role as an Assistant Professor at Gachon University, combined with recent postdoctoral research and consulting experience, showcases a strong blend of teaching, research, and industry application. This varied experience aligns well with the criteria for a Young Scientist Award, highlighting their capability in contributing to cutting-edge research and practical solutions.

Education

With a PhD in Information & Communication Engineering from Hohai University, the candidate has demonstrated excellence in their academic pursuits. Their research on mobile robot localization and underwater localization algorithms reflects a high level of expertise in advanced technological areas relevant to the award.

Research Interests

The candidate’s diverse research interests, including IoT, robotics, and AI, align with current trends in science and technology. Their focus on cutting-edge areas such as autonomous vehicles, network security, and machine learning showcases their commitment to advancing knowledge in these fields.

Citations and Impact

With an impressive cumulative impact factor of 268.20 and substantial Google Scholar citations, the candidate’s research output has significantly influenced their field. Their h-index of 28 and i10-index of 51 further attest to the high quality and impact of their work.

Awards, Funding, and Honors

The candidate has received multiple prestigious awards, including the Top-10 Outstanding Students Award and the Jiangsu Province Distinguish International Students Award. These recognitions, along with their consistent academic performance, highlight their exceptional achievements.

Other Experience and Projects

The candidate’s experience in supervising projects, teaching, and developing instructional methodologies showcases their commitment to education and research. Their involvement in student supervision and research activities further highlights their capability to contribute to both academic and practical advancements.

Publication Top Notes

  • A Review of Underwater Localization Techniques, Algorithms, and Challenges – S Xin, U Inam, L Xiaofeng, C Dongmin, Journal of Sensors 2020, 24 – Cited by 162 📘 (2020)
  • A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms – I Ullah, Y Shen, X Su, C Esposito, C Choi, IEEE Access 8, 2233-2246 – Cited by 147 🔍 (2019)
  • Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces – MT Sadiq, X Yu, Z Yuan, F Zeming, AU Rehman, I Ullah, G Li, G Xiao, IEEE Access 7, 171431-171451 – Cited by 145 🧠 (2019)
  • Localization and Detection of Targets in Underwater Wireless Sensor Using Distance and Angle Based Algorithms – I Ullah, J Chen, X Su, C Esposito, C Choi, IEEE Access 7, 45693-45704 – Cited by 132 🌊 (2019)
  • Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions – T Ahsan B, M Y-K, K M K. A, M F, J AR, U Inam, K Rahim, Computational and Mathematical Methods in Medicine 2021, 28 – Cited by 99 📈 (2021)
  • Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network – BK Yousafzai, SA Khan, T Rahman, I Khan, I Ullah, A Ur Rehman, M Baz, Sustainability 13 (17), 9775 – Cited by 86 🎓 (2021)
  • A Multi-Layer Cluster Based Energy Efficient Routing Scheme for UWSNs – W Khan, H Wang, MS Anwar, M Ayaz, S Ahmad, I Ullah, IEEE Access 7, 77398-77410 – Cited by 79 🔋 (2019)
  • Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review – M Tehseen, I Hafiz Muhammad, H Inayatul, U Inam, A Madiha, Electronics 12 (1), 26 – Cited by 74 💡 (2023)
  • Efficient and Accurate Target Localization in Underwater Environment – I Ullah, Y Liu, X Su, P Kim, IEEE Access 7, 101415-101426 – Cited by 72 🛰️ (2019)
  • Analysis of Cyber Security Attacks and Its Solutions for the Smart Grid Using Machine Learning and Blockchain Methods – M Tehseen, I Hafiz Muhammad, K Sunawar, H Inayatul, U Inam, Future Internet 15 (83), 1-38 – Cited by 67 🔐 (2023)

Conclusion

Overall, the candidate’s comprehensive experience, strong educational background, significant research contributions, and recognition in their field make them a highly suitable candidate for the Research for Young Scientist Award.