Bio: Yu Zhao is a Professor and Ph.D. Supervisor at Northwestern Polytechnical University (NPU). He is a recipient of the National Science Fund for Distinguished Young Scholars, a High-Level Talent of Shaanxi Province, and an "Aoxiang Scholar" of NPU. He is also a core member of the Aoxiang Team of NPU. His research primarily focuses on swarm intelligent decision-making, planning, and control, as well as their applications in the aerospace and marine sectors. Professor Zhao has presided over and participated in more than 20 research projects, including the National Natural Science Foundation of China (NSFC), the National Key Research and Development Program, JKW projects, Open Funds of State Key Laboratories, and the Shaanxi Provincial Natural Science Foundation. He has published over 100 academic papers, including 20 papers in top-tier control journals such as IEEE Transactions on Automatic Control and Automatica (10 of which are Full Paper). Additionally, he has over 10 ESI Highly Cited Papers. His research findings have been actively cited and positively evaluated by more than 30 academicians from both domestic and international institutions, and he has been consecutively included in the "World's Top 2% Scientists List" published by Stanford University.

His outstanding contributions have been recognized with numerous awards, including the Second Prize of the National Defense Science and Technology Progress Award, the First Prize of the Shaanxi Provincial Graduate Education Achievement Award, and the Best Paper Award at the National Complex Networks Conference. He has also received the Shaanxi Provincial Excellent Natural Science Academic Paper Award twice, the Best Paper Award at the IEEE ICUS International Conference on Unmanned Systems twice, the First Prize of the Best Paper Award at the 2025 Greater Bay Area Conference, the Best Paper Award at the IEEE YAC Conference, and the Best Paper Award at the 1st System Engineering and Electronics Conference. Furthermore, he served as the General Chair of the 16th IFAC Workshop on Large Scale Complex Systems (IFAC-LSS-2022).

Abstract:To address the targeting inaccuracies caused by severe encirclement center offsets in space flying net systems for capturing non-cooperative maneuvering spacecraft, this study investigates the key scientific problem of dynamic average consensus over asymmetric networks. First, a novel "dynamic surplus" cooperative compensation mechanism is discovered. Then, two new dynamic average consensus algorithms—the "integral surplus" and "non-smooth surplus" algorithms—are proposed. Through rigorous theoretical analysis, the parameter dependencies among the dynamic surplus, network topology, and cooperative algorithms are clarified. Ultimately, this research solves the problem of distributed precise estimation of the cooperative encirclement center. The effectiveness of the algorithms is verified via simulations and experiments on the high-precision, time-space-coordinated cooperative encirclement of dynamic non-cooperative swarm targets.

Bio: Chu Ning, a professor level senior engineer, a senior member of the International Society of Electrical and Electronic Engineering, a national expert in the wind turbine industry, the chief researcher of Shangfeng High tech, an industry professor of Shaoxing University, and an outstanding teacher of the Brunei Class of the Belt and Road of Zhejiang University. Engaged in Bayesian in-depth learning, multi physical field measurement and information fusion, green intelligent Internet plus and health diagnosis for ventilation equipment, and made the first special report in the half hour of the second economic center. In the past 5 years, I have published 41 high-level SCI papers as the first and corresponding author, authorized 35 invention patents as the first author, 4 international patents, 14 software works, undertaken 4 national level projects, and 5 provincial and ministerial level projects. Long term close cooperation with universities in France, Switzerland, Belgium, Australia, New Zealand, and Japan.

Abstract: In smart agriculture, Unmanned Aerial Vehicle (UAV) imagery frequently suffers from severe nonuniform rotational blur, critically impairing downstream visual tasks. Existing methods strugglewith prohibitive computational overhead for edge deployment and inadequate fitting for complex non-linear trajectories, inevitably over-smoothing texture details. To address the dual challenges ofhigh computational costs and complex trajectory adaptation, we propose AeroMamba, an efficientfrequency-domain modeling approach for blind motion deblurring tailored to UAV scenarios. Specifically, we construct a Synergistic Frequency Mamba (SFM) module that decouples high- and lowfrequency features via the Discrete Wavelet Transform (DWT), and utilizes low-frequency topologicalstructures to guide the synergistic restoration of high-frequency details, effectively mitigating textureloss. Furthermore, a Low-frequency Guided Adaptive Texture Restoration (LGATR) module is introduced to enhance the model’s perceptual capacity for non-linear rotation and complex motion blur. Additionally, addressing the complex spatially-variant degradations in real agricultural scenarios—such as high-frequency rotor micro-vibrations and large-scale displacements—we construct a highquality UAV motion blur dataset comprising 3,208 image pairs. This dataset bridges the gap in existing benchmarks regarding bird’s-eye views and non-linear degradation trajectories. Experimentalresults demonstrate that, compared to state-of-the-art methods, the proposed approach achieves highlycompetitive image reconstruction accuracy and visual fidelity with extremely low computationalcomplexity in both synthetic and real-world degradation scenarios, showcasing significant potential for practical engineering deployment.

Bio: Prof. Yajun Liu was born on September 20, 1974 in Jiangxi, China. Native speaker of Chinese, fluent in English. His Education and Academic Research Experiences is as follows: December, 2016- Now Professor in South China University of Technology School of Mechanical and Automotive Engineering. December, 2009- December, 2010. Visiting Professor in Fluid Power Research Center (FPRC) Purdue University at West Lafayette, USA. Feb, 2005 – July, 2016. Post-doctoral Research Fellow, Tokheim JV company in China. June, 2002 Ph. D. in Mechanical Engineering. South China University of Technology, Guangzhou, China. His research interests include Digital signal processing technology and its application in mechanical systems (such as hydraulic System for Energy Saving.); Intelligence control and Manufacturing Engineering. Moreover, Prof. Yajun Liu has published more than 300+ papers in Journals and proceedings of international conferences. 60+ patents on Mechanical System design and manufacturing.

Abstract: Biological materials such as fruits and vegetables possess complex fibrous structures and multiscale cellular architectures. Their disintegration behavior is influenced by the coupled effects of material properties, flow-field dynamics, and processing parameters. Conventional optimization methods relying on empirical knowledge and repeated experiments often require substantial development efforts and provide limited insight into the underlying processing mechanisms. To improve the homogeneous processing of biological materials, a computational optimization framework integrating computational fluid dynamics (CFD) analysis and intelligent control is developed. Based on the shear-induced disintegration mechanism of biological materials, a flow-field model is established to investigate the effects of blade geometry and rotational speed on velocity distribution, turbulence characteristics, and material circulation behavior. Key flow features associated with material disintegration performance are identified through numerical simulations. A fuzzy PID control strategy is introduced to achieve dynamic speed regulation and enhance operational stability under varying load conditions. Particle-size distribution experiments are conducted to validate the numerical results, and a material identification approach based on motor dynamic response signals is preliminarily explored. The results demonstrate that blade geometry and rotational speed significantly affect material circulation patterns and local shear intensity. The optimized processing scheme improves particle-size uniformity while reducing reliance on extensive trial-and-error experiments during system design. The proposed framework provides a computational strategy for the digital design, parameter optimization, and intelligent operation of biological material processing systems.

Bio: Prof. Yufei Gao serves as Party Branch Secretary and Deputy Director. He is a doctoral supervisor in the first-level discipline of Mechanical Engineering and the second-level disciplines of Mechanical Design and Theory, and Intelligent Manufacturing Engineering. His research interests include digitalization and simulation, precision processing technology and equipment for diamond wire saws, scientific and engineering issues in photovoltaic crystalline silicon solar cell and module manufacturing, preparation and performance evaluation of electroplated diamond saw wires, intelligent manufacturing and equipment, and machining condition monitoring.Prof. Gao holds key academic roles as a reviewer for the National Natural Science Foundation of China, Ministry of Education graduate thesis evaluator, and various provincial scientific program assessments. He serves on the editorial board of the journal Diamond & Abrasives Engineering and is a guest editor for the international journals Materials and Applied Sciences. He is also a professional member of the International Solar Energy Society (ISES) and a reviewer for over 30 international and domestic journals. He is recognized as a key urgently needed talent supported by Shandong Province.

Abstract:Electroplated diamond wire (EDW) is the primary tool for cutting hard and brittle materials. The distribution of the abrasives on its surface directly affects the processing efficiency and quality. This presentation proposes an abrasives distribution detection model YOLO-EDW and a semantic segmentation model EDW-UNet based on improved UNet.YOLO-EDW model effectively enhances the ability to detect small targets and occluded abrasives and achieves precise identification of abrasives distribution characteristics. EDW-Unet achieves accurate segmentation of individual and aggregated abrasive particles on the EDW surface, and successfully calculates key feature parameters such as the density, maximum size, and number of abrasive particles contained in the aggregated abrasives. This method provides a high-precision and efficient solution for EDW manufacturing quality assessment, filling the gap in quantitative detection of aggregated abrasive particle characteristics and providing a solid foundation for EDW product quality inspection.