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第41届中国控制会议

作者： Chang Liu Yang Liu Xu Zhao Qikun Dai Qingyu Meng Hongyan Guo China FAW Group Corporation R&D Center Department of Control Science and Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Jilin university

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper uses a nonlinear longitudinal control method for distributed drive electric vehicles（DDEV） to solve the safety problem of DDEV when driving on snow-covered ***,an optimal slip control scheme based on tire force prediction is ***,use the Burckhardt tire model to draw μ-κ,curve,use the magic formula to draw F-κcurve,get the maximum longitudinal tire force that can be provided under different road types *** calculate the tire force required for stable driving of the vehicle,predict the state of the vehicle in *** paper aims at the situation that tire force is not saturated during the driving process of the vehicle,the lower layer uses the nonlinear model predictive control（NMPC）algorithm to design the optimal slip *** main purpose is to control the torque of the four wheels simultaneously so that the slip rate can follow the optimal value,thereby effectively preventing the wheels from over-slipping during driving or locking during braking,enhancing the maneuverability and stability of the vehicle in extreme ***,the dynamic simulation software CarSim is used for simulation verification under various working *** results show that the controller designed in this paper can effectively track the optimal slip value of each wheel,play a driving assistance function,and realize the multi-objective requirements of "safety-power-comfort".

关键词： Distributed Drive Electric Vehicle Tire Force Prediction Slip Control Nonlinear Model Predictive Control

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High-Order Interior Penalty Finite Element Methods for Fourth-Order Phase-Field Models in Fracture Analysis

arXiv

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arXiv 2025年

作者： Tian, Tian Chunyu, Chen Wei, Huayi School of Mathematics and Statistics Xiangtan University Hunan Xiangtan411105 China National Center of Applied Mathematics in Hunan Hunan Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Hunan Xiangtan411105 China

This paper presents a novel approach for solving fourth-order phase-field models in brittle fracture mechanics using the Interior Penalty Finite Element Method (IP-FEM). The fourth-order model improves numerical stability and accuracy compared to traditional second-order phase-field models, particularly when simulating complex crack paths. The IP-FEM provides an efficient framework for discretizing these models, effectively handling nonconforming trial functions and complex boundary conditions. In this study, we leverage the FEALPy framework to implement a flexible computational tool that supports high-order IP-FEM discretizations. Our results show that as the polynomial order increases, the mesh dependence of the phase-field model decreases, offering improved accuracy and faster convergence. Additionally, we explore the trade-offs between computational cost and accuracy with varying polynomial orders and mesh sizes. The findings offer valuable insights for optimizing numerical simulations of brittle fracture in practical engineering applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Brittle fracture

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Ontology-Based BERT Model for Automated Information Extraction from Geological Hazard Reports

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Journal of Earth science 2023年第5期34卷 1390-1405页

作者： Kai Ma Miao Tian Yongjian Tan Qinjun Qiu Zhong Xie Rong Huang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Engineering Technology Research Center for Farmland Environment Monitoring China Three Gorges UniversityYichang 443002China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China College of Economics and Management China Three Gorges UniversityYichang 443002China

Geological knowledge can provide support for knowledge discovery, knowledge inference and mineralization predictions of geological big data. Entity identification and relationship extraction from geological data description text are the key links for constructing knowledge graphs. Given the lack of publicly annotated datasets in the geology domain, this paper illustrates the construction process of geological entity datasets, defines the types of entities and interconceptual relationships by using the geological entity concept system, and completes the construction of the geological corpus. To address the shortcomings of existing language models(such as Word2vec and Glove) that cannot solve polysemous words and have a poor ability to fuse contexts, we propose a geological named entity recognition and relationship extraction model jointly with Bidirectional Encoder Representation from Transformers(BERT) pretrained language model. To effectively represent the text features, we construct a BERT-bidirectional gated recurrent unit network(BiGRU)-conditional random field(CRF)-based architecture to extract the named entities and the BERT-BiGRU-Attention-based architecture to extract the entity relations. The results show that the F1-score of the BERT-BiGRU-CRF named entity recognition model is 0.91 and the F1-score of the BERT-BiGRU-Attention relationship extraction model is 0.84, which are significant performance improvements when compared to classic language models(e.g., word2vec and Embedding from Language Models(ELMo)).

关键词： ontology BERT model name entity recognition relation extraction knowledge graph

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Measuring drug similarity using drug–drug interactions

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Quantitative Biology 2024年第2期12卷 164-172页

作者： Ji Lv Guixia Liu Yuan Ju Houhou Huang Ying Sun College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina Sichuan University Library Sichuan UniversityChengduChina College of Chemistry Jilin UniversityChangchunChina Department of Respiratory Medicine The First Hospital of Jilin UniversityChangchunChina

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug *** existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of *** this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction *** explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning *** unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group *** addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug *** method exceeds existing approaches in terms of ***,our experiments demonstrate the effectiveness and practicability of the proposed similarity *** the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown *** the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

关键词： drug similarity drug–drug interactions drug combinations synergy effect clustering semi-supervised learning

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Performance Evaluation of Evolutionary Multi-Objective Algorithms Using Real-World Problems with an Additional Total Constraint Violation Objective

Performance Evaluation of Evolutionary Multi-Objective Algor...

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Congress on Evolutionary computation

作者： Yang Nan Hisao Ishibuchi Tianye Shu Department of Computer Science and Engineering Guangdong Provinial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the community of evolutionary multi-objective optimization (EMO), one important issue is the choice of test problems for performance evaluations of EMO algorithms. This is because performance evaluation results of EMO algorithms totally depend on the choice of test problems. This means that the research on new EMO algorithm design is also influenced by the choice of test problems. Recently, researchers have started to use real-world problems for performance evaluation of EMO algorithms. Among them, a real-world problem suite RE has attracted much attention and has been used in many studies. However, most RE problems have been created from real-world constrained problems by using the total constraint violation as an additional objective. That is, the original versions of most RE problems are not unconstrained multi-objective problems. Thus, even when a good solution set is obtained by an EMO algorithm for an RE problem, it can be a poor solution set for its original constrained problem. This is because many well-distributed solutions over the entire Pareto front of the transformed unconstrained problem are usually infeasible solutions of the original constrained problem with some positive total constraint violation values. In this paper, we examine whether good solution sets obtained by EMO algorithms for RE problems are also good solution sets for their original constrained problems. Our experimental results show that good solutions sets for most RE problems include good feasible solution sets for their original constrained problems. However, for a few RE problems, good solution sets obtained by some high performance EMO algorithms do not include good feasible solution sets. Our results show that high-performance EMO algorithms on most RE problems generate good feasible solution sets for their original constrained versions. This observation supports the usefulness of those RE problems as test problems for performance evaluation of EMO algorithms.

关键词： Performance evaluation Constraint handling Sociology Evolutionary computation Search problems Statistics Optimization

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Migration: An Efficient Explorer Operator to Guide Swarm Evolution

Migration: An Efficient Explorer Operator to Guide Swarm Evo...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiaosong Han Chengyang Zhou Yanchun Liang Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Zhuhai College of Science and Technology Zhuhai China

Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks. The Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar to the reduction and recovery of dimension. Therefore, we design the Migration operator where a pretrained Wasserstein Autoencoders (WAE) is applied to simulate the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to produce better generation. In comparison with famous baseline methods in some benchmark functions, MPSO converges faster and more accurately, which shows the great potential of migration operations.

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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Magnetic modulation of keyhole instability during laser welding and additive manufacturing

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science (New York, N.Y.) 2025年第6736期387卷 864-869页

作者： Fan, Xianqiang Fleming, Tristan G. Clark, Samuel J. Fezzaa, Kamel Getley, Anna C M Marussi, Sebastian Wang, Hongze Leung, Chu Lun Alex Kao, Andrew Lee, Peter D. Mechanical Engineering University College London London United Kingdom Research Complex at Harwell Harwell Campus Didcot United Kingdom Department of Physics Engineering Physics & Astronomy Queen's University Kingston ON Canada X-ray Science Division Argonne National Laboratory Lemont IL United States State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong University Shanghai China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Computational Science and Engineering Group Centre for Advanced Simulation and Modelling University of Greenwich London United Kingdom

keyhole instability during laser welding and laser powder bed fusion (LPBF) can cause keyhole collapse and pore formation. Using high-speed x-ray imaging, we demonstrate that the flow vortex-induced protrusion on the rear keyhole wall is crucial in initiating keyhole instability. Applying a transverse magnetic field suppresses the keyhole instability by driving a secondary thermoelectric magnetohydrodynamics (TEMHD) flow that alters the net flow vortex. This minimizes protrusions and large-amplitude keyhole oscillations. The suppression effectiveness depends on the laser scanning direction relative to the magnetic field orientation because this controls the Seebeck effect-induced Lorentz force's direction. We show that at LPBF length scales, electromagnetic damping is weak, and for alloys with a large Seebeck coefficient, TEMHD becomes the dominant mechanism controlling flow behind the keyhole.

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Insight into the efficiency-stability-cost balanced organic solar cell based on a polymerized nonfused-ring electron acceptor

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Aggregate 2023年第6期4卷 172-179页

作者： Xiaobin Gu Yanan Wei Guanyu Lu Ziyang Han Di Zheng Guanghao Lu Jianqi Zhang Zhixiang Wei Yunhao Cai Xin Zhang Hui Huang College of Materials Science and Opto-Electronic Technology Center of Materials Science and Optoelectronics EngineeringCAS Center for Excellence in Topological Quantum ComputationCAS Key Laboratory of Vacuum PhysicsUniversity of Chinese Academy of SciencesBeijingChina Frontier Institute of Science and Technology Xi’an Jiaotong UniversityXi’anChina CAS Key Laboratory of Nanosystem and Hierarchical Fabrication CAS Center for Excellence in NanoscienceNational Center for Nanoscience and TechnologyBeijingChina

Organic solar cells(OSCs)have attracted extensive attention from both academia and industry in recent years due to their remarkable improvement in power conversion efficiency(PCE).However,the Golden Triangle(the balance of efficiency-stability-cost)required for large-scale industrialization of OSCs still remains a great ***,a new nonfused-ring electron acceptor(NFREA)BF and its polymerized counterpart PBF were designed and synthesized,and their photovoltaic performance,storage stability and material cost were systematically *** blended with a widely-used polymer donor PBDB-T,the PBFbased all-polymer solar cell(all-PSC)displayed a record high PCE of 12.61%for polymerized NFREAs(PNFREAs)with an excellent stability(95.2%of initial PCE after 800 h storage),superior to the BF ***,PBF-based allPSC possesses the highest industrial figure-of-merit(i-FOM)value of 0.309 based on an efficiency-stability-cost evaluation,in comparison to several representative OSC systems(such as PM6:Y6 and PBDB-T:PZ1).This work provides an insight into the balance of efficiency,stability,and cost,and also indicates that the PNFREAs are promising materials toward the commercial application of OSCs.

关键词： all-polymer solar cells efﬁciency-stability-cost balance noncovalently conformational locks organic solar cell polymerized nonfused-ring electron acceptors

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A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

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Green Energy and Intelligent Transportation 2024年第2期3卷 50-59页

作者： Yuan Chen Wenxian Duan Yigang He Shunli Wang Carlos Fernandez School of Artificial Intelligence Anhui UniversityHefei 230009China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430000China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Pharmacy and Life Sciences Robert Gordon UniversityAberdeenAB10-7GJUK

Battery life prediction is of great significance to the safe operation,and reduces the maintenance *** paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the *** feature extraction,eight features are obtained to fed into the life prediction *** hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,*** parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search *** algorithm can improve the local escape ability and convergence performance and find the global *** comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction *** with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.

关键词： State of heath Improved sparrow search algorithm Remaining useful life Variational mode decomposition Multi-kernel support vector regression Feature extraction

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