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data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

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Defence Technology（防务技术） 2024年第3期33卷 55-65页

作者： Zhiyuan Tang Yu Wang Khalil I.Elkhodary Zefeng Yu Shan Tang Dan Peng State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of Technology116023DalianPR China The Department of Mechanical Engineering The American University in CairoNew Cairo11835Egypt Department of Neurology The Second Hospital of Dalian Medical UniversityDalian116023PR China

Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and

关键词： data driven Constitutive law Anisotropy Brain tissue Internal pressure

Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method

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Advances in Manufacturing 2024年第1期12卷 185-206页

作者： Lin Ling Zhe-Ming Song Xi Zhang Peng-Zhou Cao Xiao-Qiao Wang Cong-Hu Liu Ming-Zhou Liu School of Mechanical Engineering Hefei University of TechnologyHefei230009People’s Republic of China Guobo Electronics Co.Ltd. Nanjing211111People’s Republic of China School of Mechanical and Electronic Engineering Suzhou UniversitySuzhou234000AnhuiPeople’s Republic of China Sino-US Global Logistics Institute Shanghai Jiao Tong UniversityShanghai200030People’s Republic of China

Production logistics(PL)is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems.To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits,this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain(MTDC).First,the manufacturing task chain(MTC)is defined to characterize the discrete production process of a product.To handle manufacturing big data,the MTC data paradigm is designed,and the MTDC is established.Then,the logistics trajectory model is presented,where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC.Based on this,a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL.Finally,a case study is applied to verify the proposed method,and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment,which can assist managers in implementing the optimization decisions.

关键词： Production logistics(PL) Logistics trajectory analysis Logistics optimization data driven Manufacturing task data chain(MTDC)

data-driven Based Uncertainty Set Modeling Method for Microgrid Robust Optimization with Correlated Wind Power

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 420-432页

作者： Xinchen Li Yixin Liu Li Guo Xialin Li Chengshan Wang Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin300072China

Microgrid is considered an important part of the future zero carbon energy systems.However,the uncertainty caused by renewable energy source brings huge challenges to the scheduling of MG and restricts its ability of carbon emission reduction.In this paper,a novel improved multi-ellipsoidal uncertainty set modeling method is proposed to better depict the uncertainty of wind power and reduce the conservativeness of traditional robust optimization.Probabilistic information from historical data is utilized to capture the temporal correlation of forecast error of wind power,as well as the conditional correlation of forecast error with forecast value,making the uncertainty set more data-adaptive to variation of forecast results and more accurate for uncertainty description.A two-stage robust optimization model of a grid-connected microgrid is established based on the proposed uncertainty set and solved by column and constraint generation algorithm.Simulation results based on actual data illustrate the average unbalanced power of microgrid between day-ahead trading and real-time power exchange with utility grid is dropped by nearly 11.16%compared with a deterministic optimization method,11.86%with traditional box uncertainty set-based robust optimization method,and 2.89%with stochastic optimization method.

关键词： Correlation data driven improved multiellipsoidal uncertainty set microgrid robust optimization

data-driven Power Flow Method Based on Exact Linear Regression Equations

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 800-804页

作者： Yanbo Chen Chao Wu Junjian Qi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

关键词： data driven exact linear regression equation Fast-decoupled power flow Newton-Raphson method

data-driven multimodal fusion:approaches and applications in psychiatric research

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Psychoradiology 2023年第1期3卷 135-153页

作者： Jing Sui Dongmei Zhi Vince D Calhoun State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Tni-institutional Center for Translational Research in Neuroimaging and data Science(TReNDS) Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA 30303United States

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

关键词： multimodal fusion approach data driven functional magnetic resonance imaging(fMRI) structural MRI diffusion mag-netic resonance imaging independent component analysis canonical correlation analysis psychiatric disorder

A data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving TCUC.However,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation burden.The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances online.On/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes.Numerical tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

data-driven Product Development Transformation:A Case Study from the Perspective of Adaptive Change in Organizational Routines

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Frontiers of Business Research in China 2022年第2期16卷 151-186页

作者： LIU Yi XIE Kang DENG Honglin School of Business Sun Yat-sen UniversityGuangzhou 510275China

Extant research mostly focuses on the impact of big data on product development(PD)process and performance,and yet there is a lack of theoretical exploration of the transformation path,realization mechanism and transformation paradigm while moving from experience-based PD(EBPD)to data-driven PD(DDPD).This paper,from the integrated perspective of adaptive change and organizational routines,and based on an in-depth case study of HSTYLE,proposes a two-stage transformation model for moving from EBPD to DDPD,dissects the adaptive change mechanism of organizational routines for DDPD,puts forward a theoretical model for the transformation paradigm for DDPD,and provides an innovative interpretation of the connotation of data-driven.This paper constructs a theory for DDPD transformation that comprises three aspects:transformation path,realization mechanism and transformation paradigm.Conclusions of this paper have policy implications for enterprises in their practice on data-driven product innovation in the digital economy era.

关键词： product development transformation data driven organizational routine change adaptive change case study

An online data driven actor-critic-disturbance guidance law for missile-target interception with input constraints

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Chinese Journal of Aeronautics 2022年第7期35卷 144-156页

作者： Chi PENG Jianjun MA Xiaoma LIU College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China

In this article,we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability.Firstly,the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem.The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI)equation,which is generally intractable due to the nonlinearity of the problem.Then,based on the universal approximation capability of Neural Networks(NNs),we construct the critic NN,the actor NN and the disturbance NN,respectively.The Bellman error is adjusted by the normalized-least square method.The proposed scheme is proved to be Uniformly Ultimately Bounded(UUB)stable by Lyapunov method.Finally,the effectiveness and robustness of the developed method are illustrated through numerical simulations against different types of non-stationary targets and initial conditions.

关键词： Actor-critic-disturbance structure data driven Differential game Guidance systems Input constraints

Vision for energy material design:A roadmap for integrated data-driven modeling

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Journal of Energy Chemistry 2022年第8期31卷 56-62,I0003页

作者： Zhilong Wang Yanqiang Han Junfei Cai An Chen Jinjin Li Key Laboratory of Thin Film and Microfabrication of Ministry of Education Department of Micro/Nano ElectronicsShanghai Jiao Tong UniversityShanghai 200240China

The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.

关键词： Energy materials Material attributes Machine learning data driven