Name: Yang Zan Title: Associate Professor

Email: yangzan@ncu.edu.cn

Supervisor Type: Master's Supervisor

Main Courses: Machine Learning

Personal Website: https://www.x-mol.com/groups/yang_zan

Research Directions

The team has long been engaged in the research and application of accurate performance modeling and optimization for complex mechanical products. It is committed to achieving precise prediction and optimization of product performance through mathematical methods, artificial intelligence, and other approaches. Its applied research focuses on such directions as intelligent optimal design for complex equipment and intelligent recognition for complex targets, while its theoretical research includes the design of efficient AI-driven intelligent evolutionary architectures and highly robust & lightweight deep learning models. The specific research directions are as follows:

1. Theory of Machine Learning-based Optimal Design for Complex and Computationally Expensive Problems: Aiming at the complex characteristics in engineering, such as time-consuming objective simulation, high-dimensional design space, strong coupling of complex constraints, and difficulty in balancing multiple objectives, this direction focuses on designing an optimal design architecture that integrates the predictive capability of machine learning, the reasoning capability of large models, and the global search capability of intelligent algorithms, so as to realize efficient and high-precision optimization.

2. Intelligent Design of Complex Equipment: Targeting the design requirements of complex equipment tailored to engineering problem characteristics—including maximizing the stiffness-to-weight ratio of airfoil composite structures for aerospace equipment, lightweight design of key chassis components (e.g., vehicle frames) for automotive equipment, maximizing the load-bearing performance of core precision transmission components for humanoid robots, and noise reduction, vibration damping, and infrared stealth coating optimization for stealth power stations in military equipment—this direction is dedicated to developing adaptive intelligent design architectures, thereby achieving the ultimate improvement of key performance of complex equipment.

3. Intelligent Recognition of Complex Targets: For complex scenarios such as industrial inspection and special monitoring, this direction focuses on designing highly robust and lightweight deep learning detection models to address the challenges of micro/small target detection and multi-form recognition. In view of the common characteristics of medical images (e.g., tiny pathogens like Cryptococcus spp. and Plasmodium, as well as CT/MRI/PET imaging, etc.), including modality diversity, data dimensional complexity, and special quality disturbance, it conducts research on highly lightweight & high-precision multi-modal fusion, segmentation and detection architectures, so as to realize intelligent auxiliary diagnosis of medical images.

4. Integration of AI-driven Intelligent Design Platform: Centering on the integration of AI-driven intelligent design platforms, this direction focuses on deeply integrating multi-modal AI capabilities with design tools such as CAD and CAE, breaking down the data and knowledge barriers throughout the entire design process, establishing a human-machine collaborative design paradigm, and building a flexible and scalable platform architecture. It aims to achieve full-link intelligent collaboration in design from requirement analysis to simulation verification, ultimately enhancing design efficiency, lowering the threshold for innovation, and driving the transformation of the design field towards an intelligent collaborative paradigm.

Education Background

Sep 2016 – Jun 2022, Huazhong University of Science and Technology, Mechanical Engineering, Ph.D.

Mar 2021 – Mar 2022, National University of Singapore, Mechanical Engineering, CSC Jointly-Trained PhD

Sep 2012 – Jun 2016, Shandong University of Science and Technology, Industrial Engineering, Ph.D.

Work Experience

Jul 2022 – Present, Nanchang University, School of Advanced Manufacturing, Associate Professor

Academic Appointments

1, Youth Editorial Board Member: CJME: Additive Manufacturing Frontiers (AMF)

2, Youth Editorial Board Member:《机械设计》、《智能制造》

3, Topic Editors: Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems (https://www.mdpi.com/topics/5H23414FC9), 2023-2024

4, Journal Reviewer: IEEE T EVOLUT COMPUT；IEEE T CYBERNETICS；SWARM EVOL COMPUT；Engineering；ENG APPL ARTIF INTEL；PATTERN RECOGN；COMPUT METHOD APPL M；etc.

Personal Honors

[1] The project achievement "R&D and Industrialization of Flexible Intelligent Production Lines for Complex Electronic Product Packaging" was awarded the Second Prize of the Innovation Achievement Award (Science and Technology Innovation Award Category) of the China Industry-University-Research Collaboration Association, 2024.

[2] The project achievement "R&D and Industrialization of Key Technologies for Personalized Intelligent Manufacturing of IC Cards" was awarded the Second Prize of Jiangxi Provincial Science and Technology Progress Award.

[3] Honored as the 2024 Outstanding Reviewer of ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems (an ASME journal). Only two scholars worldwide received this award in 2024.

[4] The teaching achievement "Construction and Practice of a Three-Level Progressive, Four-Industry Linked and Five-Dimension Integrated Innovation Capability Training System for Postgraduates in Mechanical Engineering under the Manufacturing Power Strategy" was awarded the Special Prize of the Teaching Achievement Award of Nanchang University (Postgraduate Level), 2023.

[5] The teaching achievement "Exploration and Practice of an Emerging Engineering Talent Training Mode Based on the 'Four-Stage Integration, Three-Combination and Two-Merger' Framework" was awarded the First Prize of the Teaching Achievement Award of Nanchang University (Youth Project Category), 2024.

[6] Guided undergraduate students to win the National Third Prize in the Artificial Intelligence Plus Special Competition of the 19th "Challenge Cup" National College Students' Extracurricular Academic and Technological Works Competition.

[7] Guided undergraduate students to win the National First Prize in the 19th China Good Idea & National Digital Art Design Competition, and was awarded the National Excellent Instructor Award of the competition.

[8] Guided undergraduate students to win the National Third Prize in the National Finals of the 17th National 3D Digital Innovation Design Competition.

Major Achievements

In recent years, Prof. Yang has independently presided over nearly 10 projects, including national/provincial natural science foundations, provincial intelligent manufacturing planning and challenge-based projects, municipal doctoral research innovation center projects, and university-level talent cultivation funds, with the funds under independent charge exceeding 6 million yuan (total funds of hosted and participated projects exceeding 15 million yuan). He has won 1 national-level innovation achievement award and 1 provincial and ministerial-level science and technology progress award, as well as 2 university-level teaching achievement awards. The author has guided undergraduate students to win more than 10 national and provincial awards in Grade B disciplinary competitions such as the Challenge Cup, National 3D Digital Innovation Design Competition, and China Good Idea & National Digital Art Design Competition. Moreover, over 60 SCI papers have been published in prestigious SCI/EI indexed journals including Swarm and Evolutionary Computation, Computer Methods in Applied Mechanics and Engineering, and China Mechanical Engineering (over 30 as the first/corresponding author, including 2 ESI Highly Cited Papers), with more than 1,200 WOS citations and an H-index of 20. In addition, the author has been granted more than 30 national invention patents.

Representative Projects are as follows:

[1] National Natural Science Foundation of China (NSFC) Project: Research on Expensive Constrained Optimization Design Method for Complex Mechanical Products Based on Hierarchical Reconstruction and Adaptive Evolution, Funding: 320,000 yuan, Principal Investigator (PI)

[2] Natural Science Foundation of Jiangxi Province Project: Research on Expensive Constrained Optimization Design Method for Complex Equipment Based on State Recognition and Adaptive Search, Funding: 100,000 yuan, Principal Investigator (PI)

[3] Challenge-based Project of Nanchang County (Xiaolan Economic and Technological Development Zone): Research on Key Technologies of Intelligent Navigation for Vision-guided Wheeled Inspection Robots, Funding: 4,000,000 yuan, Principal Investigator (PI)

[4] Doctoral Research Innovation Center Project of Nanchang City: Topology Optimization Design of Multi-Scale Composite Porous Metamaterial Structures, Funding: 600,000 yuan, Principal Investigator (PI)

[5] Challenge-based Project of Ji'an City: Research on Key Technologies and Equipment Development for Intelligent, High-efficiency and Low-cost Thorn Removal, Funding: 500,000 yuan, Principal Investigator (PI)

[6] Provincial and Ministerial-level Intelligent Manufacturing Planning Project: Consulting and Coaching Project for National-level Intelligent Manufacturing Enterprises Application, Jiangxi Academy of Industry and Information Technology, Funding: 217,000 yuan, Principal Investigator (PI)

[7] Young Talent Cultivation and Innovation Fund Project of Nanchang University: Research on Expensive Constrained Optimization Design Method for Complex Mechanical Products Based on Precise Modeling and Intelligent Evolution, Funding: 200,000 yuan, Principal Investigator (PI)

[8] Sub-project of National Key Science and Technology Project, Ministry of Industry and Information Technology (MIIT) (Ministerial-level National Project): Worker Operation State Recognition and Adaptive Collaboration for Human-machine Efficient Operation, Funding: 830,000 yuan, Participant

[9] Enterprise Demand-oriented Challenge-based Project of Key Science and Technology R&D Program of Jiangxi Province: Development of Intelligent Control, High-efficiency Thermal Management, Noise Reduction, Vibration Damping and Infrared Stealth Technologies for Multi-Source Micro grids, Funding: 7,000,000 yuan, Participant

[10] Key R&D Program of Jiangxi Province Project: Application of Complete Set Technology for Long-stroke and High-reliability Linear Guide Rails and R&D of High-precision Equipment, Funding: 1,000,000 yuan, Participant

[11] Academic and Disciplinary Leader Project of Jiangxi Province: Research on Key Technologies of Ultra-Thin Metal Bipolar Plates for High-power Hydrogen Fuel Cells Oriented to Multi-Source Energy Equipment, Funding: 500,000 yuan, Participant

Representative Papers are as follows:

[1] J. Liu, H. Hu, Z. Liu, Z. Yang*, L. Chen, X. Cai, Expensive constrained multi-objective optimization via adaptive surrogate-assisted dense weight multi-objective evolutionary algorithm, Swarm Evol. Comput. 97 (2025) 102033. https://doi.org/10.1016/j.swevo.2025.102033.

[2] H. Lu, Z. Yang*, J. Huang, L. Chen, X. Cai, Angle information enhanced kriging-assisted adaptive evolutionary algorithm for computationally highly expensive high-dimensional problems, Swarm Evol. Comput. 96 (2025) 101992. https://doi.org/10.1016/j.swevo.2025.101992.

[3] H. Tao, Z. Yang*, J. Liu, H. Qiu, X. Li, L. Gao, Iterative model pruning with sparsity learning for infrared rotary-wing UAV detection, Expert Syst. Appl. 295 (2026) 128755. https://doi.org/10.1016/j.eswa.2025.128755.

[4] J. Liu, J. Chen, Z. Yang*, Y. Liu, H. Qiu, L. Gao, Two-layer surrogate-assisted collaborative framework for expensive constrained optimization problems involving mixed integer variables, Inf. Sci. (Ny). 690 (2025) 121522. https://doi.org/10.1016/j.ins.2024.121522.

[5] H. Tao, J. Liu, Z. Yang*, G. Wang, J. Shang, H. Qiu, L. Gao, Revolutionizing flame detection: Novelization in flame detection through transferring distillation for knowledge to pruned model, Expert Syst. Appl. 249 (2024) 123787. https://doi.org/10.1016/j.eswa.2024.123787.

[6] Z. Yang, S. Chu, J. Liu, H. Qiu, M. Xiao, L. Gao, Incorporating gradient information into dimension perturbation mutation for high-dimensional expensive optimization, Swarm Evol. Comput. 84 (2024) 101446. https://doi.org/10.1016/j.swevo.2023.101446.

[7] Z. Yang, H. Qiu, L. Gao, L. Chen, J. Liu, Surrogate-assisted MOEA/D for expensive constrained multi-objective optimization, Inf. Sci. (Ny). 639 (2023) 119016. https://doi.org/10.1016/j.ins.2023.119016.

[8] X. Du, S. Lu, R. Tang, X. Li, J. Miao, L. Wu, Z. Yang*, B. Chen, An efficient method for designing high-performance planetary roller screw mechanism with low contact stress, Tribol. Int. 187 (2023) 108709. https://doi.org/10.1016/j.triboint.2023.108709.

[9] Z. Yang, H. Qiu, L. Gao, D. Xu, Y. Liu, A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems, Inf. Sci. (Ny). 619 (2023) 491–508. https://doi.org/10.1016/j.ins.2022.11.021.

[10] Z. Yang, H. Qiu, L. Gao, X. Cai, C. Jiang, L. Chen, Surrogate-assisted classification-collaboration differential evolution for expensive constrained optimization problems, Inf. Sci. (Ny). 508 (2020) 50–63. https://doi.org/10.1016/j.ins.2019.08.054.

[11] S. Chu, Z. Yang*, M. Xiao, H. Qiu, K. Gao, L. Gao, Explicit topology optimization of novel polyline-based core sandwich structures using surrogate-assisted evolutionary algorithm, Comput. Methods Appl. Mech. Eng. 369 (2020) 113215. https://doi.org/10.1016/jNaNa.2020.113215.

Representative Patents are as follows:

[1] 杨赞, 罗云行, 谢传楠, 等. 注意力信息增强的径向基函数优化设计方法: 中国, ZL202410502586.5[P]. 2024-06-25.

[2] 杨赞, 罗云行, 谢传楠, 等. 一种隐身电站设计的优化方法及系统: 中国, ZL202410480282.3[P]. 2024-07-12.

[3] 杨赞, 刘建胜, 黄纪绘, 等. 面向混合整数昂贵优化问题的代理模型辅助差分进化方法: 中国, ZL202311499733.X[P]. 2024-10-11.

[4] 杨赞, 陈宇航, 陈艳慧, 等. 一种机器学习优化设计方法及系统: 中国, ZL202411223433.3[P]. 2024-11-22.

[5] 杨赞, 刘建胜, 黄纪绘, 等. 面向高维昂贵优化问题的代理模型辅助的并行协同方法: 中国, ZL202311500236.7[P]. 2024-12-27.

[6] 杨赞, 朱紫华, 鲁翠媛, 等. 基于机器学习的IN718镍基合金成形缺失数据生成与预测方法: 中国, ZL202411237627.9[P]. 2024-12-27.

[7] 杨赞, 刘建胜, 黄纪绘, 等. 面向高维昂贵优化问题的梯度信息驱动维度扰动变异方法: 中国, ZL202311482150.6[P]. 2025-02-14.

[8] 杨赞, 刘旭涛, 刘建胜, 等. 一种汽车后副车架轻量化设计方法及系统: 中国, ZL202411280855.4[P]. 2025-03-04.

[9] 杨赞, 欧阳宏超, 杜兴, 等. 一种基于禁忌空间划定的机器学习协助的差分进化方法: 中国, ZL202411303019.3[P]. 2025-03-18.

[10] 杨赞, 朱紫华, 柯星, 等. 一种基于信息融合的机翼升阻比机器学习优化设计方法: 中国, ZL202411906948.3[P]. 2025-05-02.

[11] 杨赞, 朱紫华, 刘辉鸿, 等. 一种基于机器学习的翼形结构轻量化设计方法: 中国, ZL202411854261.X[P]. 2025-05-02.

[12] 杨赞, 卢航旭, 杜兴, 等. 一种机器学习引导的动态种群优化设计方法: 中国, ZL202411534758.3[P]. 2025-05-09.

[13] 杨赞, 欧阳宏超, 黄纪绘, 等. 一种基于ML模型的红外隐身材料膜层多目标优化设计方法: 中国, ZL202510328975.5[P]. 2025-06-06.

[14] 杨赞, 卢航旭, 黄纪绘, 等. 一种基于机器学习的红外隐身材料膜层优化设计方法: 中国, ZL202510413042.6[P]. 2025-06-06.

[15] 杨赞, 陈宇航, 黄纪绘, 等. 基于ML的后副车架结构轻量化与模态优化的目标设计方法: 中国, ZL202510421424.3[P]. 2025-06-10.

[16] 杨赞, 陈宇航, 胡子奇, 等. 基于ML的全回转推进器驱动轴服役寿命与轻量化设计方法: 中国, ZL202411974852.0[P]. 2025-11-18.

[17] 杨赞, 周子恒, 李西安, 等. 基于神经网络模型的优化方法、设备、介质和程序产品: 中国, ZL202511249010.3[P]. 2025-11-21.

[18] 陈艳慧, 杨赞, 胡龙华, 等. 一种基于深度卷积神经网络的隐球菌图像识别方法: 中国, ZL202410666582.0[P]. 2024-08-06.

[19] 陈艳慧, 杨赞, 胡龙华, 等. 一种基于数据增强DETR的隐球菌识别方法及系统: 中国, ZL202410963272.5[P]. 2024-09-20.

[20] 肖承地, 杜兴, 杨赞, 等. 一种双向自锁行星滚柱丝杠副设计方法及系统: 中国, ZL202411339333.7[P]. 2025-05-16.

[21] 刘建胜, 陈晋, 杨赞, 等. 一种基于全局与局部代理模型辅助的双区域协同优化方法: 中国, ZL202411814959.9[P]. 2025-05-30.

[22] 罗怡雯, 陈艳慧, 杨赞, 等. 基于超图计算的YOLOv8多形态隐球菌检测与计数方法及其系统: 中国, ZL202510757179.3[P]. 2025-08-15.

[23] 刘旭波, 曹达辉, 杨赞, 等. 汽车后副车架刚度及NVH的两目标设计方法、设备、介质: 中国, ZL202511053844.7[P]. 2025-10-14.

[24] 熊君星, 罗怡雯, 杨赞, 等. 一种考虑模态约束的汽车前副车架轻量化设计方法: 中国, ZL202511148781.3[P]. 2025-11-07.

[25] 陈艳慧, 王小中, 熊建球, 杨赞, 等. 一种疟原虫图像检测方法及系统: 中国, ZL202411204201.3[P]. 2024-11-22.