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data-driven modeling and prediction on hysteresis behavior of flexure RC columns using deep learning networks

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2023年第11-12期32卷 e2039-e2039页

作者： Guo, Jiangmeng Wang, Luji Shan, Jiazeng Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China Stanford Univ Dept Civil & Environm Engn Stanford CA USA Shanghai Engn Res Ctr Resilient Cities & Intellige Shanghai Peoples R China

Hysteresis behavior of structural components has been one of the research focus for the structural engineering community for decades, comprehensively determines the structural performance and safety, and plays an important role in structural disaster mitigation. It is of great significance to continuously monitor structural responses and accurately characterize structural hysteresis. Currently, the nonlinear properties of real-world structural components cannot be obtained before its failure. Thus, a historical database is collected firstly. Then, a data-driven analysis method is proposed for predicting hysteresis behaviors of reinforced concrete (RC) columns. A bidirectional LSTM (BLSTM) network is developed to model and predict hysteresis curves. The data with unfixed lengths are specially processed to simultaneously guarantee a uniform size and avoid data loss, and the clipping layers are inserted in the model to clip off inferior predictions and improve the accuracy. This methodology is systematically studied and validated by employing a sythetic database generated by numerical simulation and the full-scale experiment database named PEER database. Result shows that proposed BLSTM can predict the hysteresis curves of the RC components with acceptable accuracy and robustness. Moreover, the interpretability analysis is performed on identifying the learning and prediction principle of the BLSTM model on hysteresis prediction and its accuracy variation under different model architectures.

关键词： data-driven modeling hysteresis behavior LSTM model prediction

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data-driven modeling of General Fluid Density Under Subcritical and Supercritical Conditions

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AIAA JOURNAL 2023年第4期61卷 1519-1531页

作者： Zhou, Mingshuo Chen, Wei Su, Xingyu Sung, Chih-Li Wang, Xingjian Ren, Zhuyin Tsinghua Univ Dept Energy & Power Engn Beijing 100084 Peoples R China Florida Inst Technol Dept Mech & Civil Engn Melbourne FL 32901 USA Tsinghua Univ Ctr Combust Energy Beijing 100084 Peoples R China Michigan State Univ Dept Stat & Probabil E Lansing MI 48824 USA Tsinghua Univ Inst Aero Engine Beijing 100084 Peoples R China

Advanced propulsion and power-generation systems often operate under extreme conditions, where thermophysical properties of the working fluids undergo complex variations in a wide range of fluid states, where empirical cubic equations of state could yield substantial errors in density prediction. The present work develops data-driven models for accurate density estimation of general fluids across all thermodynamic regimes. The model starts with the cubic equation of state, whose alpha function is calibrated in a data-driven manner with statistical correction accounting for inherent correlations among training data samples. The developed models are examined for the representative pure substances in aerospace propulsion systems, including oxygen, nitrogen, carbon dioxide, and hydrocarbon fuels. Results show that the model with pressure and temperature as input variables provides consistently superior accuracy over wide ranges of temperatures and pressures, especially in the compressed-liquid region, where the Peng-Robinson equation of state significantly underperforms. The corresponding absolute average relative deviation for the studied substances is below 0.65% at different pressures, compared to 7.16% by the Peng-Robinson equation of state. The model is also extended to examine the density calculations of the selected binary and ternary mixtures, and the consistent result is obtained. The data-driven approach can be adopted to evaluate other thermodynamic properties of fluids and fluid mixtures and characteristics of vapor-liquid equilibrium, and further incorporated into large-scale multiphysics simulations where nonideal gas behavior occurs in the future.

关键词： Thermodynamic Properties Kernel Functions supercritical fluid transcritical fluid data-driven modeling Peng-Robinson equation of state Guassian process regression liquid propellants Near-critical behavior high-pressure properties

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data-driven modeling for Gait Phase Recognition in a Wearable Exoskeleton Using Estimated Forces

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IEEE TRANSACTIONS ON ROBOTICS 2023年第4期39卷 3072-3086页

作者： Park, Kyeong-Won Choi, Jungsu Kong, Kyoungchul Republ Korea AF Acad Dept Mech Engn Cheongju 28187 South Korea Yeungnam Univ Dept Robot Engn Gyongsan 712749 South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea

Accurate identification of gait phases is critical in effectively assessing the assistance provided by lower limb exoskeletons. In this study, we propose a novel gait phase recognition system called ObsNet to analyze the gait of individuals with spinal cord injuries (SCI). To ensure the reliable use of exoskeletons, it is essential to maintain practicality and avoid exposing the system to unnecessary risks of fatigue, inaccuracy, or incompatibility with human-centered devices. Therefore, we propose a new approach to characterize exoskeletal-assisted gait by estimating forces on exoskeletal joints during walking. Although these estimated forces are potentially useful for detecting gait phases, their nonlinearities make it challenging for existing algorithms to generalize accurately. To address this challenge, we introduce a data-driven model that simultaneously captures both feature extraction and order dependencies, and enhance its performance through a threshold-based compensational method to filter out momentary errors. We evaluated the effectiveness of ObsNet through robotic walking experiments with two practical users with complete paraplegia. Our results indicate that ObsNet outperformed state-of-the-art methods that use joint information and other recurrent networks in identifying the gait phases of individuals with SCI (p< 0.05). We also observed reliable imitation of ground truth after compensation. Overall, our research highlights the potential of wearable technology to improve the daily lives of individuals with disabilities through accurate and stable state assessment.

关键词： Robots Legged locomotion Exoskeletons Robot sensing systems Trajectory Force Dynamics data-driven modeling gait phase recognition sensorless force estimation wearable robotics

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data-driven modeling and Distributed Predictive Control of Mixed Vehicle Platoons

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 572-582页

作者： Zhan, Jingyuan Ma, Zibo Zhang, Liguo Beijing Univ Technol Fac Informat Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China

With the development of automatic driving technology and the internet of vehicles, platooning based on control of connected autonomous vehicles has become one of the most promising approaches to improve traffic efficiency. This paper studies the control problem of mixed vehicle platoons consisting of human-driven vehicles and connected autonomous vehicles. Firstly, we propose a data-driven method to model mixed vehicle platoons based on Koopman operator theory. This method gives a way to represent the mixed vehicle platoon by a linear model in a high-dimensional space, the approximation of which is obtained by a neural network framework. Secondly, we employ model predictive control (MPC) to address the platoon control problem of mixed vehicle platoons, where both centralized MPC and distributed MPC algorithms are designed. Finally, the effectiveness of the data-driven modeling method and the centralized/distributed MPC algorithms is verified by numerical simulations. It is revealed that the proposed data-driven DMPC algorithm exhibits comparable control performance with less computation cost compared with the centralized MPC algorithm, and it shows faster convergence speed than the nonlinear model based DMPC algorithm.

关键词： Aerospace electronics Computational modeling data-driven modeling Space vehicles Predictive models Analytical models Vehicle dynamics distributed control Koopman operator theory model predictive control mixed vehicle platoon

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data-driven modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第3期20卷 1551-1564页

作者： Zhang, Tong Lu, Yiruo Guan, Yongpei Zhong, Xiang Hogan, Thanh Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Univ Florida Hlth Jacksonville Clin Operat Jacksonville FL 32209 USA

The COVID-19 pandemic presents unprecedented challenges for the US healthcare system, and the critical care settings are heavily impacted by the pressures of caring for COVID-19 patients. However, hospital pandemic preparedness has been hampered by a lack of disease specific planning guidelines. In this paper, we proposed a holistic modeling and analysis approach, with a system dynamics model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization, to support the hospital planning decisions. Our model was trained using the public data from the JHU Coronavirus Resource Center and was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL and public data from the Florida Department of Health (FDOH). Various experiments were conducted to investigate different control measures and the variants of the virus and their impact on the disease transmission, and subsequently, the hospital planning needs. Our proposed approach can be tailored to a given hospital setting of interest and is also generalizable to other hospitals to tackle the pandemic planning challenge. Note to Practitioners-We proposed a holistic modeling and analysis approach to support hospital preparedness and resource planning during the COVID-19 pandemic. To capture the highly dynamic pandemic environment, we developed a numerical method to estimate R-0, the effective basic reproductive rate, and used the most recent estimated data series of daily R-0 to project the change in R-0 in a short-term forecast window. The prediction of the daily confirmed cases in that forecast window were then obtained based on recursively solving the system dynamics model, and was validated to be very close to the real confirmed cases from the public record. This data-driven approach allows us to gain a systematic understanding of the common trends across different states and regions, and to evaluate the effect of the control mea

关键词： COVID-19 hospital pandemic planning data-driven modeling computer simulation

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data-driven modeling of Comprehensive Academic Level and Physical/Moral Education for Students in Control Discipline 36

Data-driven Modeling of Comprehensive Academic Level and Phy...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhao, Ziyan Li, Shuangru Li, Shipeng Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Physical education and moral education, as two important components, have a profound impact on the overall development of students. This work deeply studies the relationship between physical/moral education and students' comprehensive academic level through data-driven modeling. Based on students' grades in physical/moral-related courses, three machine learning models, logistic regression, support vector machine and random forest, are constructed to realize the high-accuracy classification of the students having different comprehensive academic level. Experimental results show that the constructed data-driven models can achieve significant accuracy and stability in predicting students' comprehensive academic level. It proves that physical education and moral education have obvious positive correlation with students' comprehensive academic level, and thus highlights the importance of physical/moral education in students' all-round development.

关键词： five education simultaneously machine learning data-driven modeling academic level prediction

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A novel data-driven modeling and efficient model predictive control framework for non-autonomous nonlinear systems based on the Invertible Koopman Network

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NONLINEAR DYNAMICS 2025年 1-27页

作者： Jin, Yuhong Hou, Lei Ge, Xiangdong Gao, Qiang Yi, Haiming Li, Zhonggang Feng, Yongzhi Zhong, Shun Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China AECC Shengyang Engine Res Inst Shenyang 110000 Peoples R China Harbin Elect Co Ltd Harbin 150001 Peoples R China Tianjin Univ Dept Mech Tianjin 300072 Peoples R China

The Koopman operator theory is a widely recognized approach for data-driven modeling because it provides a global linearization representation of nonlinear dynamical systems. Designing the observable function and its inverse is the key to obtaining the finite-dimensional approximation of the Koopman operator. However, most reported works exhibit potential shortcomings, such as introducing unnecessary assumptions regarding the form of the observable and the fact that its inverses can only be obtained approximately. To address these issues, we propose a novel data-driven modeling approach for nonlinear, non-autonomous systems, known as the Invertible Koopman Network (IKN). This approach utilizes the Invertible Neural Network (INN) to simultaneously find the optimal observable function and its explicit inverse, thereby facilitating more accurate finite-dimensional approximations of the Koopman operator and achieving lossless reconstruction. Furthermore, we explore integrating the IKN with Model Predictive Control (MPC) to address the optimal control problem for unknown nonlinear systems. By leveraging the linear dynamics in the observable space, the nonconvex optimization problem in classical MPC can be transformed into a simple quadratic programming problem, ensuring high computational efficiency. The method is evaluated on various nonlinear systems and demonstrates superior performance to other Koopman operator-based approaches.

关键词： Deep learning Invertible neural network Koopman operator data-driven modeling Non-autonomous system

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data-driven modeling of Commercial Off-the-shelf Photovoltaic Inverters Using Neuromancer

Data-Driven Modeling of Commercial Off-the-shelf Photovoltai...

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27th International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

作者： Ghimire, Pallavi Poudel, Samip Bhujel, Niranjan Dhiman, Vikas Hummels, Donald Tonkoski, Reinaldo Univ Maine Dept Elect & Comp Engn Orono ME 04469 USA Tech Univ Munich Elect Power Transmiss & Distribut Munich Germany

ISBN: (纸本)9798350387605;9798350387599

With the high penetration of renewable energy sources into the grid, using inverters with advanced grid support functions has become essential to maintain power quality and reliability. However, it is necessary to have accurate and computationally tractable models of the inverter system to assess the dynamics. Detailed models have been shown to be computationally intractable and require sensitive information from manufacturers to be accurate. Thus, methods that rely on data collection of the inverter system dynamics to analyze the main dynamic behavior of systems under various operating conditions and disturbances are important. In addition, some data-driven models might not be able to achieve high accuracy to assess system dynamics. This paper models a photovoltaic inverter based on the data collected while perturbing the voltage at the point of common coupling and observing the corresponding output current injected into the grid. The model is trained using the PyTorch-based library Neuromancer. A different dataset is used to assess model accuracy and computational time under different Volt-Var support modes. The normalized root mean square error (NRMSE) for each Volt-Var support mode was calculated and compared with other data-driven models in terms of accuracy and computational time. Using the Neuromancer library led to higher accuracy;however, it increased the computational time

关键词： data-driven modeling Inverter Models Neuromancer System identification Pytorch Grid Support Function

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data-driven modeling of a four-dimensional stochastic projectile system

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Chinese Physics B 2022年第7期31卷 157-162页

作者： Yong Huang Yang Li School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various *** the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from *** this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal *** specifically,the four-dimensional projectile system is assumed as an It?stochastic differential *** the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real *** effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.

关键词： data-driven modeling machine learning projectile systems Kramers–Moyal formulas

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data-driven modeling of power system dynamics:Challenges,state of the art,and future work

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iEnergy 2023年第3期2卷 200-221页

作者： Heqing Huang Yuzhang Lin Yifan Zhou Yue Zhao Peng Zhang Lingling Fan Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01852USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Interdisciplinary Science Department Brookhaven National LaboratoryUptonNY 11973USA Department of Electrical and Computer Engineering University of South FloridaTampaFL 33620USA

With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented ***-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating *** paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and *** also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.

关键词： data-driven modeling machine learning model construction parameter identification power system dynamics system identification

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