Sedighe Mirbolouk | Engineering | Editorial Board Member

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Dr. Sedighe Mirbolouk | Engineering | Editorial Board Member 

Iran National Science Foundation | Iran

Dr. Sedighe Mirbolouk is a dedicated postdoctoral researcher and advanced machine learning specialist with strong expertise in communication systems, data science, and artificial intelligence. She is affiliated with the Iran National Science Foundation and has built a diverse research portfolio spanning deep learning, wireless communication optimization, image processing, and intelligent sensing systems. Her technical proficiency covers a wide spectrum of tools and programming environments, including Python, MATLAB, LATEX, and advanced libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, SciPy, Pandas, and Matplotlib. With a strong theoretical foundation in data telecommunication networks, convex optimization, communication theory, and signal and image processing, she integrates computational intelligence with modern communication challenges. In her role as a Postdoctoral Researcher (2024–2025) at the Iran National Science Foundation, Dr. Mirbolouk focuses on cutting-edge topics in graph learning and federated learning, particularly designing machine learning approaches for predictive beamforming in Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems. Her work aims to improve efficiency, adaptability, and intelligence in next-generation wireless communication networks. Previously, she served as a Visiting Researcher (2022) at the University of Oulu in Finland, where she explored advanced deep reinforcement learning methods to enhance ISAC designs. These research experiences have positioned her at the frontier of combining AI with communication technologies. During her doctoral studies at the University of Urmia (2018–2021), Dr. Mirbolouk contributed significantly to satellite–UAV cooperative network optimization. She developed innovative solutions involving UAV selection and power allocation for CoMP-NOMA transmissions, introducing both Lagrangian and heuristic algorithms that advanced energy-efficient communication frameworks. Alongside communications research, she proposed image processing solutions such as fuzzy histogram weighting methods and contrast enhancement techniques. Her academic involvement includes teaching core engineering subjects—Digital Communication, Probability and Statistics, and Signals and Systems—and assisting courses on Stochastic Processes and Digital Signal Processing. Her work at the National Elite Foundation (2020–2022) expanded her portfolio into biomedical machine learning applications, where she designed systems for automatic breast cancer detection using histopathology images and cardiac arrhythmia recognition using ECG signals through deep learning approaches. Dr. Mirbolouk holds a Ph.D. in Electrical Engineering, with earlier B.Sc. and M.Sc. degrees from the University of Guilan, where she studied SAR radar Doppler ambiguity for moving targets. Her scholarly contributions include high-impact publications in journals such as IEEE Transactions on Vehicular Technology, Physical Communication, and Multimedia Tools and Applications. Collectively, her research reflects an outstanding integration of machine learning, optimization, sensing, and communication technologies.

Profile: Google Scholar

Featured Publications

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Ali, S. (2022). Relay selection and power allocation for energy efficiency maximization in hybrid satellite-UAV networks with CoMP-NOMA transmission. IEEE Transactions on Vehicular Technology, 71(5), 5087–5100.

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Choukali, M. A. (2021). A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications, 80(2), 2221–2241.

Mirbolouk, S., Choukali, M. A., Valizadeh, M., & Amirani, M. C. (2020). Relay selection for CoMP-NOMA transmission in satellite and UAV cooperative networks. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5.

Choukali, M. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2023). A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancement. Multimedia Tools and Applications, 82(18), 28345–28365.

Hussein, A. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2025). Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework. Scientific Reports, 15(1), 25071.

Choukali, M. A., Mirbolouk, S., Valizadeh, M., & Amirani, M. C. (2024). Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication. Physical Communication, 68, 102576.

Soujanya Reddy Annapareddy | Engineering | Women Researcher Award

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Mrs. Soujanyareddy Annapareddy | Engineering | Women Researcher Award

TAE Power Solutions | United States

Mrs. Soujanya Reddy Annapareddy is a seasoned Firmware Automation and Software Test Engineer with over 7.5 years of professional experience in embedded systems testing, automation frameworks, and data-driven validation methodologies. Her research and professional interests lie at the intersection of firmware validation, automation engineering, and intelligent system testing, focusing on how advanced test automation techniques enhance the performance, reliability, and scalability of embedded and IoT systems. At TAE Power Solutions, she has contributed to the automation and validation of Battery Energy Storage System (BESS) control platforms, integrating hardware-in-the-loop (HIL) environments and open-source frameworks such as PyTest, pandas, and matplotlib to improve regression coverage and testing efficiency. Her work explores the application of data analytics, fault-injection methods, and CI/CD pipeline integration in firmware testing to ensure real-world performance and fault tolerance. Her prior experience at Google Inc. involved automation testing for Android devices, wearable technologies, and data center systems, where she developed automation scripts in Python, Go, and C++, applied object-oriented design principles, and leveraged tools such as Mobly, Blueberry, and Buganizer for large-scale system validation. Soujanya’s analytical research focuses on automated testing frameworks, system-level reliability modeling, and signal strength optimization in wireless and connectivity domains. Methodologically, she employs Python-based automation, statistical analysis, and cloud-integrated validation frameworks, with hands-on experience in Linux environments, GCP cloud infrastructure, and RF system automation. Her interdisciplinary expertise bridges firmware engineering, test analytics, and computer science, offering insights into how automation accelerates innovation in embedded systems. Soujanya holds a Master of Science in Computer Technology from Eastern Illinois University and a Bachelor of Technology in Electronics and Communication Engineering from Jawaharlal Nehru Technological University Hyderabad (JNTUH), where she graduated with distinction. Her academic projects and industrial research underscore her commitment to advancing intelligent automation, embedded testing, and data-driven system optimization in modern technology ecosystems.

Profile: Google Scholar

Featured Publications

Annapareddy, S. R. (2025). Edge AI for real-time fault detection in embedded systems. International Journal of Emerging Trends in Computer Science and Information Systems.

Annapareddy, S. R. (2024). Managing power flows and energy efficiency in embedded systems for BESS. IJAIDR – Journal of Advances in Developmental Research, 15(2), 1–5.

Annapareddy, S. R. (2024). Advanced fault detection and diagnostics in embedded control units for BESS. IJSAT – International Journal on Science and Technology, 15(4).

Annapareddy, S. R. (2024). Firmware architecture and safety standards in battery energy storage systems. International Journal of Innovative Research in Engineering.

Annapareddy, S. R. (2024). Optimizing Android device testing with automation frameworks. International Journal of Innovative Research and Creative Technology, 10(4), 1–7.

Annapareddy, S. R. (2024). Real-world applications of Python in firmware and software automation. International Journal of Innovative Research and Creative Technology, 10(2), 1–6.

Annapareddy, S. R. (2024). Advancements in firmware testing and validation techniques. ESP Journal of Engineering & Technology Advancements, 4(3).

Abu Farzan Mitul | Engineering | Best Researcher Award

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Dr. Abu Farzan Mitul | Engineering | Best Researcher Award

Leidos | United States

Dr. Abu Farzan Mitul is an accomplished researcher and educator specializing in opto-electronic device fabrication, fiber optic sensing technologies, and nanostructured thin-film materials. His research bridges the intersection of photonics, materials science, and advanced sensing systems — contributing to innovations that enhance environmental monitoring, industrial automation, and biomedical diagnostics. Dr. Mitul earned his Ph.D. in Electrical and Computer Engineering from the University of Texas at El Paso (UTEP), USA, where he designed and developed advanced fiber Bragg grating sensors and thin-film photonic devices for multi-parameter sensing applications. His earlier academic training includes a B.Sc. and M.Sc. in Applied Physics, Electronics, and Communication Engineering from the University of Dhaka, Bangladesh. Throughout his career, Dr. Mitul has collaborated with leading U.S. research institutions and agencies, including the Department of Energy (DOE), Department of Defense (DoD), and NASA, focusing on next-generation optoelectronic and energy-efficient sensing systems. His extensive publication record spans high-impact journals and international conferences in photonics, sensor technology, and materials characterization. In addition to his research, Dr. Mitul has served as a faculty member and laboratory instructor, mentoring undergraduate and graduate students in electronics, photonics, and experimental physics. He is passionate about advancing interdisciplinary research in fiber optic sensing, MEMS/NEMS devices, photonic integrated systems, and nanotechnology-driven device engineering. Dr. Mitul continues to explore innovative pathways toward miniaturized, high-sensitivity photonic systems with applications across environmental, aerospace, and biomedical fields — aligning cutting-edge materials research with sustainable technological development.

Profiles: Orcid | Google Scholar | Linkedin

Featured Publications

Adhikari, N., Dubey, A., Khatiwada, D., Mitul, A. F., Wang, Q., Venkatesan, S., & Qiao, Q. (2015). Interfacial study to suppress charge carrier recombination for high efficiency perovskite solar cells. ACS Applied Materials & Interfaces, 7(48), 26445–26454. https://doi.org/10.1021/acsami.5b08343

Rana, G. M. S. M., Khan, A. A. M., Hoque, M. N., & Mitul, A. F. (2013, December). Design and implementation of a GSM based remote home security and appliance control system. In 2013 2nd International Conference on Advances in Electrical Engineering (ICAEE) (pp. 291–295). IEEE. https://doi.org/10.1109/ICAEE.2013.6750340

Khatiwada, D., Venkatesan, S., Adhikari, N., Dubey, A., Mitul, A. F., Mohammad, L., … & Qiao, Q. (2015). Efficient perovskite solar cells by temperature control in single and mixed halide precursor solutions and films. The Journal of Physical Chemistry C, 119(46), 25747–25753. https://doi.org/10.1021/acs.jpcc.5b08667

Mitul, A. F., Mohammad, L., Venkatesan, S., Adhikari, N., Sigdel, S., Wang, Q., … & Qiao, Q. (2015). Low temperature efficient interconnecting layer for tandem polymer solar cells. Nano Energy, 11, 56–63. https://doi.org/10.1016/j.nanoen.2014.10.030

Venkatesan, S., Ngo, E. C., Chen, Q., Dubey, A., Mohammad, L., Adhikari, N., … & Qiao, Q. (2014). Benzothiadiazole-based polymer for single and double junction solar cells with high open circuit voltage. Nanoscale, 6(12), 7093–7100. https://doi.org/10.1039/C4NR00606H

Islam, M. M., Rafi, F. H. M., Mitul, A. F., Ahmad, M., Rashid, M. A., & Malek, M. F. B. A. (2012, May). Development of a noninvasive continuous blood pressure measurement and monitoring system. In 2012 International Conference on Informatics, Electronics & Vision (ICIEV) (pp. 695–699). IEEE. https://doi.org/10.1109/ICIEV.2012.6317425

 

William Gardner | Engineering | Best Researcher Award

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Prof. William Gardner | Engineering | Best Researcher Award 

University of California, Davis | United States

Dr. William A. Gardner is an esteemed scholar and pioneer in statistical signal processing, particularly renowned for his foundational contributions to cyclostationary signal processing theory and methods. His postsecondary education began with a Certificate in Aircraft Radio Repair (1961) at Keesler Air Force Base, followed by coursework in electronics and electrical engineering at Foothill College and Stanford University, where he earned his M.S. in Electrical Engineering (1967). He pursued further graduate studies at MIT and Bell Labs, and earned his Ph.D. in Electrical Engineering from the University of Massachusetts Amherst (1972). Dr. Gardner joined the University of California, Davis in 1972, where he advanced to Professor VII before becoming Professor Emeritus in 2001. Over his career, he supervised numerous M.S. and Ph.D. theses focused on statistical signal processing, especially the exploitation of cyclostationarity in communications and signals intelligence. In 1986, Dr. Gardner founded Statistical Signal Processing, Inc. (SSPI), a private research firm dedicated to advanced algorithm development for radio reconnaissance, signals intelligence, and cellular communications. The firm, which operated for 25 years, licensed its technologies to major corporations including Apple Inc. and Lockheed Martin. Post-retirement, he continued research collaborations—most notably with Prof. Antonio Napolitano—on advanced statistical cyclicity and nonstationary signal behavior. His recent work has expanded into electromagnetic modeling of cosmic plasma and laboratory-confined plasma, supporting paradigm-challenging efforts such as the Plasma Universe, Thunderbolts Project, and the SAFIRE Project, all aimed at redefining astrophysical theory and clean energy generation. Dr. Gardner is the author of four influential books, including Introduction to Random Processes and Statistical Spectral Analysis, and editor of Cyclostationarity in Communications and Signal Processing. He has contributed chapters to five other books, authored or co-authored over 110 peer-reviewed journal papers, and holds 15 U.S. patents. His academic impact is reflected in a citation count exceeding 7489, an h-index of 33, and continued recognition for shaping the theoretical underpinnings of modern signal processing. He has delivered invited lectures globally and remains a thought leader across academia, industry, and emerging scientific paradigms.

Profiles:  Scopus | Orcid | Google Scholar

Featured Publications

Gardner, W. A. (2002). Exploitation of spectral redundancy in cyclostationary signals. IEEE Signal Processing Magazine, 8(2), 14–36.

Gardner, W. A. (1990). Introduction to random processes: With applications to signals and systems. McGraw-Hill.

Gardner, W. A., Napolitano, A., & Paura, L. (2006). Cyclostationarity: Half a century of research. Signal Processing, 86(4), 639–697.

Gardner, W. A., & Robinson, E. A. (1989). Statistical spectral analysis—A nonprobabilistic theory. Prentice-Hall.

Gardner, W. A. (1994). Cyclostationarity in communications and signal processing. IEEE Press.

Gardner, W. A. (2002). Signal interception: A unifying theoretical framework for feature detection. IEEE Transactions on Communications, 36(8), 897–906.

Gardner, W. A., Brown, W., & Chen, C. K. (1987). Spectral correlation of modulated signals: Part II—Digital modulation. IEEE Transactions on Communications, 35(6), 595–601.

Gardner, W. A., & Franks, L. E. (1975). Characterization of cyclostationary random signal processes. IEEE Transactions on Information Theory, 21(1), 4–14.

Gardner, W. A., & Spooner, C. M. (1992). Signal interception: Performance advantages of cyclic-feature detectors. IEEE Transactions on Communications, 40(1), 149–159.

Ning Chen | Engineering | Best Researcher Award | 13558

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Mr. Ning Chen | Engineering | Best Researcher Award

Mr. Ning Chen, Shandong University of Science and Technology, China

Mr. Ning Chen, Lecturer at Shandong University of Science and Technology, China, is an emerging researcher in high-precision mechatronic systems. With a Ph.D. in mechanical engineering and prior industry experience, he has developed innovative piezoelectric galvanometers, stiffness-adjustable servo systems, and micro-nano motion platforms. His work is shaping the future of laser positioning, scanning, and ultra-precision control technologies. Backed by prestigious national and provincial research grants, Mr. Chen exemplifies academic excellence and practical innovation in mechanical and precision engineering.

Author Profile

Orcid

Education

Dr. Weijian Wang embarked on his academic journey with a solid foundation in chemical sciences. He earned his Bachelor’s degree in Chemical Engineering and Technology from the China University of Petroleum (East China)—an institution known for producing talent in energy and chemical sectors. His academic excellence and growing passion for applied chemical research led him to pursue a Master’s degree in Chemical Engineering at the China University of Mining and Technology, where he deepened his understanding of reaction engineering, process modeling, and advanced materials.

Eager to contribute to cutting-edge innovation in the energy sector, Dr. Wang pursued his Ph.D. in Chemical Technology at the Research Institute of Petroleum Processing (RIPP), Sinopec, one of China’s leading industrial research institutions. His doctoral training provided him with hands-on experience in industrial-scale research, advanced materials development, and interdisciplinary collaboration. To further strengthen his academic and research profile, Dr. Wang completed a postdoctoral fellowship at Zhejiang University, where he explored emerging materials and device applications, preparing him for a career at the intersection of academia and applied science.

Experience

In 2022, Dr. Wang joined Beibu Gulf University as an Associate Professor, where he has since led a promising research group focusing on halide perovskite materials. As a faculty member, he has embraced both teaching and research, mentoring students while pursuing innovative solutions to modern energy and optoelectronic challenges.

One of his key professional milestones includes leading the Guangxi Science and Technology Major Program (GuikeAA23062016). This ambitious research initiative demonstrates his leadership and technical capability in managing multi-disciplinary projects aligned with regional and national scientific goals. With no industry consultancies yet, Dr. Wang remains fully invested in academic research, pushing boundaries in materials science through both simulations and experimental designs.

Research Focus

Dr. Weijian Wang’s research is centered on the green synthesis and application of halide perovskite materials, a rapidly evolving class of compounds celebrated for their extraordinary optoelectronic properties. These materials are particularly promising in fields such as solar energy conversion, light-emitting diodes (LEDs), and medical bioimaging. At the heart of Dr. Wang’s innovation is the drive for sustainability. He has developed eco-friendly synthesis techniques that minimize environmental harm while maintaining material performance, advancing the goal of sustainable science. 🌱

In the field of perovskite solar cells, Dr. Wang employs simulation-assisted design methodologies to enhance energy conversion efficiency. His designs have led to devices with superior performance characteristics, addressing one of the key challenges in renewable energy technology. 🌞 Beyond energy, his research also extends to optoelectronic devices, including perovskite-based LEDs and imaging systems with applications in healthcare diagnostics and bioimaging. 💡

Dr. Wang’s robust scientific output includes 11 peer-reviewed publications in internationally recognized SCI-indexed journals, with eight authored as first or corresponding author. Additionally, he has secured 15 authorized invention patents as the primary inventor, underscoring his capacity to translate theoretical research into tangible technological innovations.

Award and Recognition

Despite being in the early stages of his academic journey, Dr. Wang has already built a strong research profile distinguished by originality, technical rigor, and innovation. His contributions have earned him 11 published articles in high-impact SCI-indexed journals, demonstrating both quality and consistency in scientific communication. 📚

Dr. Wang also holds 15 authorized invention patents, a notable achievement that reflects his focus on applied research and technology transfer. 🧾 These patents not only reinforce his expertise in halide perovskite materials but also highlight his dedication to practical solutions for global energy and environmental challenges.

Further elevating his academic standing, Dr. Wang currently leads a major government-funded research program, indicating trust in his leadership and vision at the national level. 💼 His H-index of 5 signifies an increasing impact within the scholarly community, with a trajectory that suggests sustained and growing influence in the years to come.

Although he does not yet hold editorial roles or memberships in professional societies, his impressive publication and patent record mark him as a promising figure in materials science. His career is on a path toward broader recognition, leadership roles, and continued contributions to the scientific community.

Publications

📖 A Semi-analytical Method for Vibro-Acoustic Properties of Functionally Graded Porous Piezoelectric Annular Plates with Cavity – Journal of Vibration Engineering and Technologies (2025).
📖 Enhancing the motion performance of 3-DOF micro/nano-manipulators facing thermo-piezoelectric-mechanical coupling effects – Sensors and Actuators A Physical (2025)
📖 Robust control of uncertain asymmetric hysteretic nonlinear systems with adaptive neural network disturbance observer – Applied Soft Computing (2024)
📖 Low thermal budget lead zirconate titanate thick films integrated on Si for piezo-MEMS applications – Microelectronic Engineering (2020)

 

 

 

Dandan Zhu | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Dandan Zhu | Engineering | Best Researcher Award

Assoc. Prof. Dr. Dandan Zhu,China University of Petroleum, Beijing,China

Dr. Dandan Zhu, Associate Professor at China University of Petroleum, Beijing, is a leading researcher in integrating artificial intelligence with petroleum engineering. Her work on intelligent drilling technologies and real-time trajectory control has advanced automation in complex subsurface environments. With over 40 research projects, 39 journal publications, and multiple patents, she bridges theory and field application. Her innovative learning frameworks and strong industry collaborations have significantly contributed to the development of smart drilling systems, reinforcing her candidacy for the Best Researcher Award.

Author Profile

Google  Scholar

🎓 Early Academic Pursuits

Dr. Dandan Zhu’s academic journey reflects a deep-rooted passion for engineering and innovation. Her pursuit of excellence began at Beihang University, one of China’s leading institutions in aerospace and engineering, where she earned her Master’s degree in Aircraft Design. This foundational training laid the groundwork for her precision-oriented approach and problem-solving mindset. Driven by a keen interest in cutting-edge technologies and global research exposure, she went on to pursue a Ph.D. in Precision Engineering at the University of Tokyo, Japan. Her doctoral research refined her expertise in high-accuracy systems and complex mechanical processes—skills that would later fuel her contributions in artificial intelligence (AI) and petroleum engineering.

🧑‍💼 Professional Endeavors

Since 2015, Dr. Zhu has served as an Associate Professor at the College of Artificial Intelligence, China University of Petroleum, Beijing (CUPB). In this role, she has emerged as a thought leader and mentor in the field of intelligent energy systems. Her work involves teaching, supervising postgraduate students, and leading several high-impact research initiatives. Dr. Zhu has also built a bridge between academia and industry by actively participating in national-level science and technology programs, NSFC–enterprise joint funding projects, and technical consultations with leading energy companies. Her professional portfolio boasts 40 completed and ongoing research projects and 27 consultancy or industry-driven assignments. These efforts are deeply rooted in real-world challenges, ensuring that her research not only advances academic knowledge but also meets the evolving demands of energy exploration and production sectors.

🧠 Contributions and Research Focus

Dr. Zhu’s core research area lies at the intersection of artificial intelligence and petroleum engineering. Her pioneering work focuses on intelligent drilling systems, real-time wellbore trajectory control, reinforcement learning, and geological modeling. She has developed a robust learning framework that combines offline training, real-time geosteering decision-making, and post-drilling strategy optimization. By leveraging reinforcement learning algorithms and generative simulation environments, Dr. Zhu’s research enhances the adaptability and robustness of drilling operations in geologically uncertain environments. Her research contributions extend beyond theory. Integrated software platforms developed under her leadership have been field-tested in collaboration with major Chinese oil and gas companies, such as CNPC, Sinopec, and CNOOC. These platforms facilitate intelligent automation in subsurface operations, ensuring improved safety, efficiency, and cost-effectiveness.

🏅 Accolades and Recognition

Although Dr. Zhu maintains a modest public profile, her work has earned substantial recognition within academic and professional circles. She has authored 39 papers in reputed journals indexed by SCI and Scopus, and her publications have collectively received over 60 citations since 2020—a testament to their relevance and influence. Her book, published under ISBN: 978-7-3025-3524-9, further underscores her authority in the domain of intelligent drilling technologies. She holds five patents, reflecting her commitment to innovation and practical impact. While she has not yet served on editorial boards, her active participation in international conferences and professional associations such as IEEE, ACM, and SPE demonstrates her ongoing contribution to the global scientific community through peer review and scholarly discourse.

🌍 Impact and Influence

Dr. Zhu’s interdisciplinary collaborations have significantly influenced both academia and industry. Her work has helped develop more intelligent, data-driven petroleum engineering systems, contributing to the broader push for digital transformation in energy exploration. Through partnerships with research institutions and enterprises, she has been instrumental in advancing the application of AI in areas such as hydraulic fracturing, electromagnetic exploration, and 3D geological visualization. Beyond technical outcomes, her projects have delivered impactful results such as enhanced resource recovery, reduced environmental impact, and optimized operational costs—outcomes highly valued by industrial stakeholders. Furthermore, her mentorship of students and young researchers ensures the continuity of innovation and excellence in the field.

🔮 Legacy and Future Contributions

Looking forward, Dr. Zhu is poised to further advance the integration of AI with traditional engineering practices. Her vision includes the development of autonomous drilling systems that can self-optimize and self-correct in real time, even in highly unpredictable geological conditions. She also plans to expand research into simulation-based control frameworks and digital twins, providing a virtual testing ground for future subsurface technologies. With her continued dedication, Dr. Zhu is expected to leave a lasting legacy as a trailblazer in intelligent energy systems. She not only represents the new era of AI-driven engineering but also serves as an inspiration for the next generation of researchers aiming to solve the world’s most pressing energy challenges.

✍️Publication Top Notes

📘End-to-end multiplayer violence detection based on deep 3D CNN

Author: C Li, L Zhu, D Zhu, J Chen, Z Pan, X Li, B Wang

Journal: international conference on network …

Year: 2018

📘PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

Author: K Zhang, DD Zhu, Q Xu, H Zhou, C Zheng

Journal: international conference on network …arXiv preprint arXiv:2403.02635

Year:  2024

📘An intelligent drilling guide algorithm design framework based on high interactive learning mechanism

Author: Y Zhao, DD Zhu, F Wang, XP Dai, HS Jiao, ZJ Zhou

Journal: Petroleum Science

Year:  2025