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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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A quinolinyl analog of resveratrol improves neuronal damage after ischemic stroke by promoting Parkin-mediated mitophagy

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Chinese Journal of Natural Medicines 2025年第2期23卷 214-224页

作者： Qingqi Meng Yan Mi Libin Xu Yeshu Liu Dong Liang Yongping Wang Yan Wang Yueyang Liu Guoliang Chen Yue Hou Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110167China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Shenyang Key Laboratory of Vascular Biology Science and Research CenterDepartment of PharmacologyShenyang Medical CollegeShenyang 110034China Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical UniversityShenyang 110016China

Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or ***,extracted from Polygonum cuspidatum *** Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective ***,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately *** study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in *** research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic *** findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy ***,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in *** results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.

关键词： (E)-4-(3 5-dimethoxystyryl)quinoline Resveratrol Ischemic stroke Mitophagy Parkin

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Structure-Based Robust Fractal Graph Neural Network with Molecular Fingerprint BERT for Molecular Property Prediction

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Dong, Yaguo Xu, Meiling Tang, Lixin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

Accurate molecular property prediction is of great importance for AI-based drug design and bioinformatics. Despite recent promising progress, existing methods face challenges due to insufficient data labeling and imbalanced label distribution. Moreover, many current molecular property prediction models are based on 2D molecular graphs, neglecting the 3D characteristics of molecules. Therefore, in this paper, we propose the structure-based robust fractal graph neural network with Molecular Fingerprint Bidirectional Encoder Representations from Transformers (MFBERT) model for molecular property prediction. We construct the pre-trained MFBERT model to explore the use of unlabeled data and obtain representations from molecular extended connectivity fingerprints as the node features of molecular graphs. Then we design the fractal graph neural network that incorporates molecular 3D conformations while ensuring equivariance to molecular rotations and translations. A 3D feature extraction module and a coordinate update module are proposed to enhance the model's ability to capture molecular structural features. Additionally, weighted-gradient distributionally robust optimization is combined with the gradient descent algorithm to update model parameters, which can mitigate issues stemming from imbalanced label distribution and improve the robustness of our model. We conducted experiments on seven datasets from MoleculeNet, comprising three regression tasks and four classification tasks. Our method achieves the state-of-the-art performance compared to the existing models on all these datasets, which demonstrates its effectiveness for molecular property prediction. Additionally, we conducted ablation experiments on our designed modules, further validating the necessity of each component in our method. © 2025 IEEE.

关键词： Graph neural networks

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Voter model on adaptive networks

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Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

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Dynamic multiobjective operation optimization of blast furnace ironmaking process

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Advanced engineering Informatics 2025年 66卷

作者： Yumeng Zhao Xianpeng Wang Xiangman Song National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 Liaoning China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 Liaoning China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 Liaoning China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang 110819 Liaoning China

As the largest energy-consuming and carbon-emitting facility in the iron and steel industry, blast furnace needs to dynamically adjust its operating parameters according to the production conditions to minimize energy consumption and carbon emissions. Most of the previous studies focus only on the static operation optimization of blast furnace, which does not allow for the timely updating of operating parameters and the effective reduction of carbon emissions and production costs. Therefore, in this paper, a dynamic multiobjective operation optimization model for blast furnace, which integrates the identification approach of production condition variations, is developed and a tensor-based dynamic multiobjective evolutionary algorithm (T-DCMOEA/D) is devised to achieve cleaner production and lower cost of ironmaking process. The computational results based on real-world blast furnace operation optimization problems demonstrate that the proposed model and algorithm can effectively address the dynamic variations in the actual blast furnace production, which ensures that the blast furnace always operates in an optimized state. Furthermore, it is shown that our T-DCMOEA/D significantly outperforms some of the state-of-the-art dynamic multiobjective evolutionary algorithms in the literature, with regard to faster convergence, higher adaptivity, and better performance.

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Visualization and simulation of steel metallurgy processes

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1387-1396页

作者： Te Xu Guang Song Yang Yang Pei-xin Ge Li-xin Tang Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang 110819China Institute of Industrial&Systems Engineering Northeastern UniversityShenyang 110819China

Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.

关键词： virtual reality steel metallurgy plant-wide visualization

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Dynamic multi-objective optimization algorithm based on weighted differential prediction model 4

Dynamic multi-objective optimization algorithm based on weig...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Zhao, Yumeng Wang, Xianpeng Dong, Zhiming Wang, Yao Lou, Hangyu Hu, Tenghui Fu, Kai Tang, Lixin Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Shenyang110819 China Northeastern University Liaoning Engineering Laboratory of Operation Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this paper, a new algorithm for solving dynamic multi-objective optimization problems(DMOPs) is proposed. Most of the traditional dynamic multi-objective optimization algorithms will make predictions based on the overall average evolutionary direction of the population, which is hardly applicable to problems where the solution set and frontier do not vary with the environmental rules. In this paper, a dynamic multi-objective optimization algorithm based on weight difference prediction model is designed to solve such problems. The algorithm contains a weighted differential prediction strategy, and a differential model is built for each individual using the weights to predict the initial population after environmental changes. With this approach, each individual in the population can be made to respond quickly to environmental changes. We used three classical comparison algorithms to conduct experiments on a series of test problems. The experimental results show that the WD-MOEA/D algorithm can significantly improve the dynamic optimization performance and is effective in solving different types of dynamic problems. © 2022 IEEE.

关键词： Evolutionary algorithms

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Dynamic Locomotion of Quadruped Robot in Sloping Terrain 6

Dynamic Locomotion of Quadruped Robot in Sloping Terrain

引用

6th International Symposium on Autonomous Systems, ISAS 2023

作者： Chen, Donglin Cong, Dehong Song, Xiangman Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (纸本)9798350316155

In order to improve the locomotion capability of quadruped robot in the sloping terrain and enhance whose adaptability to the environment, this paper proposes a method to realize dynamic locomotion of quadruped robot in sloping terrain completely based on model predictive control (MPC) and proprioception. According to the foot contact signal and the robot body posture information measured by inertial measurement unit (IMU), quadruped robot actively changes whose body posture to adapt to different sloping terrains. This paper builds quadruped robot virtual prototype and test terrain in Webots simulation environment. The experimental results show that with this method, the roll angle fluctuation range of the quadruped robot is less than 0.05rad and the yaw angle fluctuation range is not more than 0.003rad. At the same time, when the angle of slope changes, the terrain adaptation time of the quadruped robot is less than 1.8s. The above results show that quadruped robot has excellent movement ability in sloping terrain and environmental adaptability under the condition of ensuring stability with the method proposed in this paper. © 2023 IEEE.

关键词： Model predictive control

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Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method

arXiv

引用

arXiv 2025年

作者： Yang, Yang Zhang, Tong Wu, Jian Su, Lijie National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China China Criminal Police University Shenyang China Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA United States Beijing Institute for General Artificial Intelligence Beijing China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University ChinaShenyang Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic analysis framework that incorporates convex optimization to improve topic consistency, sparsity, and interpretability. In Stage 1, a two-layer non-negative matrix factorization (NMF) model is employed to extract annual topics and identify key terms. In Stage 2, a convex optimization algorithm refines the dynamic topic structure using the convex NMF (cNMF) model, further enhancing topic integration and stability. Applying the proposed method to IEEE journal abstracts from 2004 to 2022 effectively identifies and quantifies emerging research topics, such as COVID-19 and digital twins. By optimizing sparsity differences in the clustering feature space between traditional and emerging research topics, the framework provides deeper insights into topic evolution and ranking analysis. Moreover, the NMF-cNMF model demonstrates superior stability in topic consistency. At sparsity levels of 0.4, 0.6, and 0.9, the proposed approach improves topic ranking stability by 24.51%, 56.60%, and 36.93%, respectively. The source code is available at https://***/meetyangyang/CDNMF. © 2025, CC BY.

关键词： Non-negative matrix factorization

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